This May 20, 2024 University of Oxford press release (also on EurekAlert) was under embargo until almost noon on May 20, 2024, which is a bit unusual, in my experience, (Note: I have more about the 1st summit and the interest in AI safety at the end of this posting),
Leading AI scientists are calling for stronger action on AI risks from world leaders, warning that progress has been insufficient since the first AI Safety Summit in Bletchley Park six months ago.
Then, the world’s leaders pledged to govern AI responsibly. However, as the second AI Safety Summit in Seoul (21-22 May [2024]) approaches, twenty-five of the world’s leading AI scientists say not enough is actually being done to protect us from the technology’s risks. In an expert consensus paper published today in Science, they outline urgent policy priorities that global leaders should adopt to counteract the threats from AI technologies.
Professor Philip Torr,Department of Engineering Science,University of Oxford, a co-author on the paper, says: “The world agreed during the last AI summit that we needed action, but now it is time to go from vague proposals to concrete commitments. This paper provides many important recommendations for what companies and governments should commit to do.”
World’s response not on track in face of potentially rapid AI progress;
According to the paper’s authors, it is imperative that world leaders take seriously the possibility that highly powerful generalist AI systems—outperforming human abilities across many critical domains—will be developed within the current decade or the next. They say that although governments worldwide have been discussing frontier AI and made some attempt at introducing initial guidelines, this is simply incommensurate with the possibility of rapid, transformative progress expected by many experts.
Current research into AI safety is seriously lacking, with only an estimated 1-3% of AI publications concerning safety. Additionally, we have neither the mechanisms or institutions in place to prevent misuse and recklessness, including regarding the use of autonomous systems capable of independently taking actions and pursuing goals.
World-leading AI experts issue call to action
In light of this, an international community of AI pioneers has issued an urgent call to action. The co-authors include Geoffrey Hinton, Andrew Yao, Dawn Song, the late Daniel Kahneman; in total 25 of the world’s leading academic experts in AI and its governance. The authors hail from the US, China, EU, UK, and other AI powers, and include Turing award winners, Nobel laureates, and authors of standard AI textbooks.
This article is the first time that such a large and international group of experts have agreed on priorities for global policy makers regarding the risks from advanced AI systems.
Urgent priorities for AI governance
The authors recommend governments to:
establish fast-acting, expert institutions for AI oversight and provide these with far greater funding than they are due to receive under almost any current policy plan. As a comparison, the US AI Safety Institute currently has an annual budget of $10 million, while the US Food and Drug Administration (FDA) has a budget of $6.7 billion.
mandate much more rigorous risk assessments with enforceable consequences, rather than relying on voluntary or underspecified model evaluations.
require AI companies to prioritise safety, and to demonstrate their systems cannot cause harm. This includes using “safety cases” (used for other safety-critical technologies such as aviation) which shifts the burden for demonstrating safety to AI developers.
implement mitigation standards commensurate to the risk-levels posed by AI systems. An urgent priority is to set in place policies that automatically trigger when AI hits certain capability milestones. If AI advances rapidly, strict requirements automatically take effect, but if progress slows, the requirements relax accordingly.
According to the authors, for exceptionally capable future AI systems, governments must be prepared to take the lead in regulation. This includes licensing the development of these systems, restricting their autonomy in key societal roles, halting their development and deployment in response to worrying capabilities, mandating access controls, and requiring information security measures robust to state-level hackers, until adequate protections are ready.
AI impacts could be catastrophic
AI is already making rapid progress in critical domains such as hacking, social manipulation, and strategic planning, and may soon pose unprecedented control challenges. To advance undesirable goals, AI systems could gain human trust, acquire resources, and influence key decision-makers. To avoid human intervention, they could be capable of copying their algorithms across global server networks. Large-scale cybercrime, social manipulation, and other harms could escalate rapidly. In open conflict, AI systems could autonomously deploy a variety of weapons, including biological ones. Consequently, there is a very real chance that unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or extinction of humanity.
Stuart Russell OBE [Order of the British Empire], Professor of Computer Science at the University of California at Berkeley and an author of the world’s standard textbook on AI, says: “This is a consensus paper by leading experts, and it calls for strict regulation by governments, not voluntary codes of conduct written by industry. It’s time to get serious about advanced AI systems. These are not toys. Increasing their capabilities before we understand how to make them safe is utterly reckless. Companies will complain that it’s too hard to satisfy regulations—that “regulation stifles innovation.” That’s ridiculous. There are more regulations on sandwich shops than there are on AI companies.”
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Notable co-authors:
The world’s most-cited computer scientist (Prof. Hinton), and the most-cited scholar in AI security and privacy (Prof. Dawn Song)
China’s first Turing Award winner (Andrew Yao).
The authors of the standard textbook on artificial intelligence (Prof. Stuart Russell) and machine learning theory (Prof. Shai Shalev-Schwartz)
One of the world’s most influential public intellectuals (Prof. Yuval Noah Harari)
A Nobel Laureate in economics, the world’s most-cited economist (Prof. Daniel Kahneman)
Department-leading AI legal scholars and social scientists (Lan Xue, Qiqi Gao, and Gillian Hadfield).
Some of the world’s most renowned AI researchers from subfields such as reinforcement learning (Pieter Abbeel, Jeff Clune, Anca Dragan), AI security and privacy (Dawn Song), AI vision (Trevor Darrell, Phil Torr, Ya-Qin Zhang), automated machine learning (Frank Hutter), and several researchers in AI safety.
Additional quotes from the authors:
Philip Torr, Professor in AI, University of Oxford:
“I believe if we tread carefully the benefits of AI will outweigh the downsides, but for me one of the biggest immediate risks from AI is that we develop the ability to rapidly process data and control society, by government and industry. We could risk slipping into some Orwellian future with some form of totalitarian state having complete control.“
Dawn Song: Professor in AI at UC Berkeley, most-cited researcher in AI security and privacy:
“Explosive AI advancement is the biggest opportunity and at the same time the biggest risk for mankind. It is important to unite and reorient towards advancing AI responsibly, with dedicated resources and priority to ensure that the development of AI safety and risk mitigation capabilities can keep up with the pace of the development of AI capabilities and avoid any catastrophe”
Yuval Noah Harari, Professor of history at Hebrew University of Jerusalem, best-selling author of ‘Sapiens’ and ‘Homo Deus’, world leading public intellectual:
“In developing AI, humanity is creating something more powerful than itself, that may escape our control and endanger the survival of our species. Instead of uniting against this shared threat, we humans are fighting among ourselves. Humankind seems hell-bent on self-destruction. We pride ourselves on being the smartest animals on the planet. It seems then that evolution is switching from survival of the fittest, to extinction of the smartest.”
Jeff Clune, Professor in AI at University of British Columbia and one of the leading researchers in reinforcement learning:
“Technologies like spaceflight, nuclear weapons and the Internet moved from science fiction to reality in a matter of years. AI is no different. We have to prepare now for risks that may seem like science fiction – like AI systems hacking into essential networks and infrastructure, AI political manipulation at scale, AI robot soldiers and fully autonomous killer drones, and even AIs attempting to outsmart us and evade our efforts to turn them off.”
“The risks we describe are not necessarily long-term risks. AI is progressing extremely rapidly. Even just with current trends, it is difficult to predict how capable it will be in 2-3 years. But what very few realize is that AI is already dramatically speeding up AI development. What happens if there is a breakthrough for how to create a rapidly self-improving AI system? We are now in an era where that could happen any month. Moreover, the odds of that being possible go up each month as AI improves and as the resources we invest in improving AI continue to exponentially increase.”
Gillian Hadfield, CIFAR AI Chair and Director of the Schwartz Reisman Institute for Technology and Society at the University of Toronto:
“AI labs need to walk the walk when it comes to safety. But they’re spending far less on safety than they spend on creating more capable AI systems. Spending one-third on ensuring safety and ethical use should be the minimum.”
“This technology is powerful, and we’ve seen it is becoming more powerful, fast. What is powerful is dangerous, unless it is controlled. That is why we call on major tech companies and public funders to allocate at least one-third of their AI R&D budget to safety and ethical use, comparable to their funding for AI capabilities.”
Sheila McIlrath, Professor in AI, University of Toronto, Vector Institute:
AI is software. Its reach is global and its governance needs to be as well.
Just as we’ve done with nuclear power, aviation, and with biological and nuclear weaponry, countries must establish agreements that restrict development and use of AI, and that enforce information sharing to monitor compliance. Countries must unite for the greater good of humanity.
Now is the time to act, before AI is integrated into our critical infrastructure. We need to protect and preserve the institutions that serve as the foundation of modern society.
Frank Hutter, Professor in AI at the University of Freiburg, Head of the ELLIS Unit Freiburg, 3x ERC grantee:
To be clear: we need more research on AI, not less. But we need to focus our efforts on making this technology safe. For industry, the right type of regulation will provide economic incentives to shift resources from making the most capable systems yet more powerful to making them safer. For academia, we need more public funding for trustworthy AI and maintain a low barrier to entry for research on less capable open-source AI systems. This is the most important research challenge of our time, and the right mechanism design will focus the community at large to work towards the right breakthroughs.
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Here’s a link to and a citation for the paper,
Managing extreme AI risks amid rapid progress; Preparation requires technical research and development, as well as adaptive, proactive governance by Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atılım Güneş Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, and Sören Mindermann. Science 20 May 2024 First Release DOI: 10.1126/science.adn0117
Regulation of artificial intelligence (AI) has become very topical in the last couple of years. There was an AI safety summit in November 2023 at Bletchley Park in the UK (see my November 2, 2023 posting for more about that international meeting).
A very software approach?
This year (2024) has seen a rise in legislative and proposed legislative activity. I have some articles on a few of these activities. China was the first to enact regulations of any kind on AI according to Matt Sheehan’s February 27, 2024 paper for the Carnegie Endowment for International Peace,
In 2021 and 2022, China became the first country to implement detailed, binding regulations on some of the most common applications of artificial intelligence (AI). These rules formed the foundation of China’s emerging AI governance regime, an evolving policy architecture that will affect everything from frontier AI research to the functioning of the world’s second-largest economy, from large language models in Africa to autonomous vehicles in Europe.
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The Chinese Communist Party (CCP) and the Chinese government started that process with the 2021 rules on recommendation algorithms, an omnipresent use of the technology that is often overlooked in international AI governance discourse. Those rules imposed new obligations on companies to intervene in content recommendations, granted new rights to users being recommended content, and offered protections to gig workers subject to algorithmic scheduling. The Chinese party-state quickly followed up with a new regulation on “deep synthesis,” the use of AI to generate synthetic media such as deepfakes. Those rules required AI providers to watermark AI-generated content and ensure that content does not violate people’s “likeness rights” or harm the “nation’s image.” Together, these two regulations also created and amended China’s algorithm registry, a regulatory tool that would evolve into a cornerstone of the country’s AI governance regime.
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The UK has adopted a more generalized approach focused on encouraging innovation according to Valeria Gallo’s and Suchitra Nair’s February 21, 2024 article for Deloitte (a British professional services firm also considered one of the big four accounting firms worldwide),
At a glance
The UK Government has adopted a cross-sector and outcome-based framework for regulating AI, underpinned by five core principles. These are safety, security and robustness, appropriate transparency and explainability, fairness, accountability and governance, and contestability and redress.
Regulators will implement the framework in their sectors/domains by applying existing laws and issuing supplementary regulatory guidance. Selected regulators will publish their AI annual strategic plans by 30th April [2024], providing businesses with much-needed direction.
Voluntary safety and transparency measures for developers of highly capable AI models and systems will also supplement the framework and the activities of individual regulators.
The framework will not be codified into law for now, but the Government anticipates the need for targeted legislative interventions in the future. These interventions will address gaps in the current regulatory framework, particularly regarding the risks posed by complex General Purpose AI and the key players involved in its development.
Organisations must prepare for increased AI regulatory activity over the next year, including guidelines, information gathering, and enforcement. International firms will inevitably have to navigate regulatory divergence.
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While most of the focus appears to be on the software (e.g., General Purpose AI), the UK framework does not preclude hardware.
As part of its digital strategy, the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits, such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.
In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation.
The agreed text is expected to be finally adopted in April 2024. It will be fully applicable 24 months after entry into force, but some parts will be applicable sooner:
*The ban of AI systems posing unacceptable risks will apply six months after the entry into force
*Codes of practice will apply nine months after entry into force
*Rules on general-purpose AI systems that need to comply with transparency requirements will apply 12 months after the entry into force
High-risk systems will have more time to comply with the requirements as the obligations concerning them will become applicable 36 months after the entry into force.
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This EU initiative, like the UK framework, seems largely focused on AI software and according to the Wikipedia entry “Regulation of artificial intelligence,”
… The AI Act is expected to come into effect in late 2025 or early 2026.[109
I do have a few postings about Canadian regulatory efforts, which also seem to be focused on software but don’t preclude hardware. While the January 20, 2024 posting is titled “Canada’s voluntary code of conduct relating to advanced generative AI (artificial intelligence) systems,” information about legislative efforts is also included although you might find my May 1, 2023 posting titled “Canada, AI regulation, and the second reading of the Digital Charter Implementation Act, 2022 (Bill C-27)” offers more comprehensive information about Canada’s legislative progress or lack thereof.
A February 15, 2024 news item on ScienceDaily suggests that regulating hardware may be the most effective way of regulating AI,
Chips and datacentres — the ‘compute’ power driving the AI revolution — may be the most effective targets for risk-reducing AI policies as they have to be physically possessed, according to a new report.
A global registry tracking the flow of chips destined for AI supercomputers is one of the policy options highlighted by a major new report calling for regulation of “compute” — the hardware that underpins all AI — to help prevent artificial intelligence misuse and disasters.
Other technical proposals floated by the report include “compute caps” — built-in limits to the number of chips each AI chip can connect with — and distributing a “start switch” for AI training across multiple parties to allow for a digital veto of risky AI before it feeds on data.
The experts point out that powerful computing chips required to drive generative AI models are constructed via highly concentrated supply chains, dominated by just a handful of companies — making the hardware itself a strong intervention point for risk-reducing AI policies.
The report, published 14 February [2024], is authored by nineteen experts and co-led by three University of Cambridge institutes — the Leverhulme Centre for the Future of Intelligence (LCFI), the Centre for the Study of Existential Risk (CSER) and the Bennett Institute for Public Policy — along with OpenAI and the Centre for the Governance of AI.
“Artificial intelligence has made startling progress in the last decade, much of which has been enabled by the sharp increase in computing power applied to training algorithms,” said Haydn Belfield, a co-lead author of the report from Cambridge’s LCFI.
“Governments are rightly concerned about the potential consequences of AI, and looking at how to regulate the technology, but data and algorithms are intangible and difficult to control.
“AI supercomputers consist of tens of thousands of networked AI chips hosted in giant data centres often the size of several football fields, consuming dozens of megawatts of power,” said Belfield.
“Computing hardware is visible, quantifiable, and its physical nature means restrictions can be imposed in a way that might soon be nearly impossible with more virtual elements of AI.”
The computing power behind AI has grown exponentially since the “deep learning era” kicked off in earnest, with the amount of “compute” used to train the largest AI models doubling around every six months since 2010. The biggest AI models now use 350 million times more compute than thirteen years ago.
Government efforts across the world over the past year – including the US Executive Order on AI, EU AI Act, China’s Generative AI Regulation, and the UK’s AI Safety Institute – have begun to focus on compute when considering AI governance.
Outside of China, the cloud compute market is dominated by three companies, termed “hyperscalers”: Amazon, Microsoft, and Google. “Monitoring the hardware would greatly help competition authorities in keeping in check the market power of the biggest tech companies, and so opening the space for more innovation and new entrants,” said co-author Prof Diane Coyle from Cambridge’s Bennett Institute.
The report provides “sketches” of possible directions for compute governance, highlighting the analogy between AI training and uranium enrichment. “International regulation of nuclear supplies focuses on a vital input that has to go through a lengthy, difficult and expensive process,” said Belfield. “A focus on compute would allow AI regulation to do the same.”
Policy ideas are divided into three camps: increasing the global visibility of AI computing; allocating compute resources for the greatest benefit to society; enforcing restrictions on computing power.
For example, a regularly-audited international AI chip registry requiring chip producers, sellers, and resellers to report all transfers would provide precise information on the amount of compute possessed by nations and corporations at any one time.
The report even suggests a unique identifier could be added to each chip to prevent industrial espionage and “chip smuggling”.
“Governments already track many economic transactions, so it makes sense to increase monitoring of a commodity as rare and powerful as an advanced AI chip,” said Belfield. However, the team point out that such approaches could lead to a black market in untraceable “ghost chips”.
Other suggestions to increase visibility – and accountability – include reporting of large-scale AI training by cloud computing providers, and privacy-preserving “workload monitoring” to help prevent an arms race if massive compute investments are made without enough transparency.
“Users of compute will engage in a mixture of beneficial, benign and harmful activities, and determined groups will find ways to circumvent restrictions,” said Belfield. “Regulators will need to create checks and balances that thwart malicious or misguided uses of AI computing.”
These might include physical limits on chip-to-chip networking, or cryptographic technology that allows for remote disabling of AI chips in extreme circumstances. One suggested approach would require the consent of multiple parties to unlock AI compute for particularly risky training runs, a mechanism familiar from nuclear weapons.
AI risk mitigation policies might see compute prioritised for research most likely to benefit society – from green energy to health and education. This could even take the form of major international AI “megaprojects” that tackle global issues by pooling compute resources.
The report’s authors are clear that their policy suggestions are “exploratory” rather than fully fledged proposals and that they all carry potential downsides, from risks of proprietary data leaks to negative economic impacts and the hampering of positive AI development.
They offer five considerations for regulating AI through compute, including the exclusion of small-scale and non-AI computing, regular revisiting of compute thresholds, and a focus on privacy preservation.
Added Belfield: “Trying to govern AI models as they are deployed could prove futile, like chasing shadows. Those seeking to establish AI regulation should look upstream to compute, the source of the power driving the AI revolution. If compute remains ungoverned it poses severe risks to society.”
Authors include: Girish Sastry, Lennart Heim, Haydn Belfield, Markus Anderljung, Miles Brundage, Julian Hazell, Cullen O’Keefe, Gillian K. Hadfield, Richard Ngo, Konstantin Pilz, George Gor, Emma Bluemke, Sarah Shoker, Janet Egan, Robert F. Trager, Shahar Avin, Adrian Weller, Yoshua Bengio, and Diane Coyle.
The authors are associated with these companies/agencies: OpenAI, Centre for the Governance of AI (GovAI), Leverhulme Centre for the Future of Intelligence at the Uni. of Cambridge, Oxford Internet Institute, Institute for Law & AI, University of Toronto Vector Institute for AI, Georgetown University, ILINA Program, Harvard Kennedy School (of Government), *AI Governance Institute,* Uni. of Oxford, Centre for the Study of Existential Risk at Uni. of Cambridge, Uni. of Cambridge, Uni. of Montreal / Mila, Bennett Institute for Public Policy at the Uni. of Cambridge.
“The ILINIA program is dedicated to providing an outstanding platform for Africans to learn and work on questions around maximizing wellbeing and responding to global catastrophic risks” according to the organization’s homepage.
*As for the AI Governance Institute, I believe that should be the Centre for the Governance of AI at Oxford University since the associated academic is Robert F. Trager from the University of Oxford.
As the months (years?) fly by, I guess we’ll find out if this hardware approach gains any traction where AI regulation is concerned.
This is the closest I’ve ever gotten to writing a gossip column (see my October 18, 2023 posting and scroll down to the “Insight into political jockeying [i.e., some juicy news bits]” subhead )for the first half.
Given the role that Canadian researchers (for more about that see my May 25, 2023 posting and scroll down to “The Panic” subhead) have played in the development of artificial intelligence (AI), it’s been surprising that the Canadian Broadcasting Corporation (CBC) has given very little coverage to the event in the UK. However, there is an October 31, 2023 article by Kelvin Chang and Jill Lawless for the Associated Press posted on the CBC website,
Digital officials, tech company bosses and researchers are converging Wednesday [November 1, 2023] at a former codebreaking spy base [Bletchley Park] near London [UK] to discuss and better understand the extreme risks posed by cutting-edge artificial intelligence.
The two-day summit focusing on so-called frontier AI notched up an early achievement with officials from 28 nations and the European Union signing an agreement on safe and responsible development of the technology.
Frontier AI is shorthand for the latest and most powerful general purpose systems that take the technology right up to its limits, but could come with as-yet-unknown dangers. They’re underpinned by foundation models, which power chatbots like OpenAI’s ChatGPT and Google’s Bard and are trained on vast pools of information scraped from the internet.
The AI Safety Summit is a labour of love for British Prime Minister Rishi Sunak, a tech-loving former banker who wants the U.K. to be a hub for computing innovation and has framed the summit as the start of a global conversation about the safe development of AI.[emphasis mine]
But U.S. Vice President Kamala Harris may divert attention Wednesday [November 1, 2023] with a separate speech in London setting out the Biden administration’s more hands-on approach.
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Canada’s Minister of Innovation, Science and Industry Francois-Philippe Champagne said AI would not be constrained by national borders, and therefore interoperability between different regulations being put in place was important.
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As the meeting began, U.K. Technology Secretary Michelle Donelan announced that the 28 countries and the European Union had signed the Bletchley Declaration on AI Safety. It outlines the “urgent need to understand and collectively manage potential risks through a new joint global effort.”
South Korea has agreed to host a mini virtual AI summit in six months, followed by an in-person one in France in a year’s time, the U.K. government said.
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Chris Stokel-Walker’s October 31, 2023 article for Fast Company presents a critique of the summit prior to the opening, Note: Links have been removed,
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… one problem, critics say: The summit, which begins on November 1, is too insular and its participants are homogeneous—an especially damning critique for something that’s trying to tackle the huge, possibly intractable questions around AI. The guest list is made up of 100 of the great and good of governments, including representatives from China, Europe, and Vice President Kamala Harris. And it also includes luminaries within the tech sector. But precious few others—which means a lack of diversity in discussions about the impact of AI.
“Self-regulation didn’t work for social media companies, it didn’t work for the finance sector, and it won’t work for AI,” says Carsten Jung, a senior economist at the Institute for Public Policy Research, a progressive think tank that recently published a report advising on key policy pillars it believes should be discussed at the summit. (Jung isn’t on the guest list.) “We need to learn lessons from our past mistakes and create a strong supervisory hub for all things AI, right from the start.”
Kriti Sharma, chief product officer for legal tech at Thomson Reuters, who will be watching from the wings, not receiving an invite, is similarly circumspect about the goals of the summit. “I hope to see leaders moving past the doom to take practical steps to address known issues and concerns in AI, giving businesses the clarity they urgently need,” she says. “Ideally, I’d like to see movement towards putting some fundamental AI guardrails in place, in the form of a globally aligned, cross-industry regulatory framework.”
But it’s uncertain whether the summit will indeed discuss the more practical elements of AI. Already it seems as if the gathering is designed to quell public fears around AI while convincing those developing AI products that the U.K. will not take too strong an approach in regulating the technology, perhaps in contrasts to near neighbors in the European Union, who have been open about their plans to ensure the technology is properly fenced in to ensure user safety.
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Already, there are suggestions that the summit has been drastically downscaled in its ambitions, with others, including the United States, where President Biden just announced a sweeping executive order on AI, and the United Nations, which announced its AI advisory board last week.
As we wrote yesterday, the U.K. is partly using this event — the first of its kind, as it has pointed out — to stake out a territory for itself on the AI map — both as a place to build AI businesses, but also as an authority in the overall field.
That, coupled with the fact that the topics and approach are focused on potential issues, the affair feel like one very grand photo opportunity and PR exercise, a way for the government to show itself off in the most positive way at the same time that it slides down in the polls and it also faces a disastrous, bad-look inquiry into how it handled the COVID-19 pandemic. On the other hand, the U.K. does have the credentials for a seat at the table, so if the government is playing a hand here, it’s able to do it because its cards are strong.
The subsequent guest list, predictably, leans more toward organizations and attendees from the U.K. It’s also almost as revealing to see who is not participating.
That high-level aspiration is also reflected in who is taking part: top-level government officials, captains of industry, and notable thinkers in the space are among those expected to attend. (Latest late entry: Elon Musk; latest no’s reportedly include President Biden, Justin Trudeau and Olaf Scholz.) [Scholz’s no was mentioned in my my October 18, 2023 posting]
It sounds exclusive, and it is: “Golden tickets” (as Azeem Azhar, a London-based tech founder and writer, describes them) to the Summit are in scarce supply. Conversations will be small and mostly closed. So because nature abhors a vacuum, a whole raft of other events and news developments have sprung up around the Summit, looping in the many other issues and stakeholders at play. These have included talks at the Royal Society (the U.K.’s national academy of sciences); a big “AI Fringe” conference that’s being held across multiple cities all week; many announcements of task forces; and more.
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Earlier today, a group of 100 trade unions and rights campaigners sent a letter to the prime minister saying that the government is “squeezing out” their voices in the conversation by not having them be a part of the Bletchley Park event. (They may not have gotten their golden tickets, but they were definitely canny how they objected: The group publicized its letter by sharing it with no less than the Financial Times, the most elite of economic publications in the country.)
And normal people are not the only ones who have been snubbed. “None of the people I know have been invited,” Carissa Véliz, a tutor in philosophy at the University of Oxford, said during one of the AI Fringe events today [October 30, 2023].
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More broadly, the summit has become an anchor and only one part of the bigger conversation going on right now. Last week, U.K. prime minister Rishi Sunak outlined an intention to launch a new AI safety institute and a research network in the U.K. to put more time and thought into AI implications; a group of prominent academics, led by Yoshua Bengio [University of Montreal, Canada) and Geoffrey Hinton [University of Toronto, Canada], published a paper called “Managing AI Risks in an Era of Rapid Progress” to put their collective oar into the the waters; and the UN announced its own task force to explore the implications of AI. Today [October 30, 2023], U.S. president Joe Biden issued the country’s own executive order to set standards for AI security and safety.
I want to draw special attention to the second Politico article,
Kamala just showed Rishi who’s boss.
As British Prime Minister Rishi Sunak’s showpiece artificial intelligence event kicked off in Bletchley Park on Wednesday, 50 miles south in the futuristic environs of the American Embassy in London, U.S. Vice President Kamala Harris laid out her vision for how the world should govern artificial intelligence.
It was a raw show of U.S. power on the emerging technology.
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Did she or was this an aggressive interpretation of events?
*’article’ changed to ‘articles’ on January 17, 2024.
It’s fascinating to see all the current excitement (distressed and/or enthusiastic) around the act of writing and artificial intelligence. Easy to forget that it’s not new. First, the ‘non-human authors’ and then the panic(s). *What follows the ‘nonhuman authors’ is essentially a survey of situation/panic.*
How to handle non-human authors (ChatGPT and other AI agents)—the medical edition
The folks at the Journal of the American Medical Association (JAMA) have recently adopted a pragmatic approach to the possibility of nonhuman authors of scientific and medical papers, from a January 31, 2022 JAMA editorial,
Artificial intelligence (AI) technologies to help authors improve the preparation and quality of their manuscripts and published articles are rapidly increasing in number and sophistication. These include tools to assist with writing, grammar, language, references, statistical analysis, and reporting standards. Editors and publishers also use AI-assisted tools for myriad purposes, including to screen submissions for problems (eg, plagiarism, image manipulation, ethical issues), triage submissions, validate references, edit, and code content for publication in different media and to facilitate postpublication search and discoverability..1
In November 2022, OpenAI released a new open source, natural language processing tool called ChatGPT.2,3 ChatGPT is an evolution of a chatbot that is designed to simulate human conversation in response to prompts or questions (GPT stands for “generative pretrained transformer”). The release has prompted immediate excitement about its many potential uses4 but also trepidation about potential misuse, such as concerns about using the language model to cheat on homework assignments, write student essays, and take examinations, including medical licensing examinations.5 In January 2023, Nature reported on 2 preprints and 2 articles published in the science and health fields that included ChatGPT as a bylined author.6 Each of these includes an affiliation for ChatGPT, and 1 of the articles includes an email address for the nonhuman “author.” According to Nature, that article’s inclusion of ChatGPT in the author byline was an “error that will soon be corrected.”6 However, these articles and their nonhuman “authors” have already been indexed in PubMed and Google Scholar.
Nature has since defined a policy to guide the use of large-scale language models in scientific publication, which prohibits naming of such tools as a “credited author on a research paper” because “attribution of authorship carries with it accountability for the work, and AI tools cannot take such responsibility.”7 The policy also advises researchers who use these tools to document this use in the Methods or Acknowledgment sections of manuscripts.7 Other journals8,9 and organizations10 are swiftly developing policies that ban inclusion of these nonhuman technologies as “authors” and that range from prohibiting the inclusion of AI-generated text in submitted work8 to requiring full transparency, responsibility, and accountability for how such tools are used and reported in scholarly publication.9,10 The International Conference on Machine Learning, which issues calls for papers to be reviewed and discussed at its conferences, has also announced a new policy: “Papers that include text generated from a large-scale language model (LLM) such as ChatGPT are prohibited unless the produced text is presented as a part of the paper’s experimental analysis.”11 The society notes that this policy has generated a flurry of questions and that it plans “to investigate and discuss the impact, both positive and negative, of LLMs on reviewing and publishing in the field of machine learning and AI” and will revisit the policy in the future.11
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This is a link to and a citation for the JAMA editorial,
Dr. Andrew Maynard (scientist, author, and professor of Advanced Technology Transitions in the Arizona State University [ASU] School for the Future if Innovation in Society and founder of the ASU Future of Being Human initiative and Director of the ASU Risk Innovation Nexus) also takes a pragmatic approach in a March 14, 2023 posting on his eponymous blog,
Like many of my colleagues, I’ve been grappling with how ChatGPT and other Large Language Models (LLMs) are impacting teaching and education — especially at the undergraduate level.
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We’re already seeing signs of the challenges here as a growing divide emerges between LLM-savvy students who are experimenting with novel ways of using (and abusing) tools like ChatGPT, and educators who are desperately trying to catch up. As a result, educators are increasingly finding themselves unprepared and poorly equipped to navigate near-real-time innovations in how students are using these tools. And this is only exacerbated where their knowledge of what is emerging is several steps behind that of their students.
To help address this immediate need, a number of colleagues and I compiled a practical set of Frequently Asked Questions on ChatGPT in the classroom. These covers the basics of what ChatGPT is, possible concerns over use by students, potential creative ways of using the tool to enhance learning, and suggestions for class-specific guidelines.
Crawford Kilian, a longtime educator, author, and contributing editor to The Tyee, expresses measured enthusiasm for the new technology (as does Dr. Maynard), in a December 13, 2022 article for thetyee.ca, Note: Links have been removed,
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ChatGPT, its makers tell us, is still in beta form. Like a million other new users, I’ve been teaching it (tuition-free) so its answers will improve. It’s pretty easy to run a tutorial: once you’ve created an account, you’re invited to ask a question or give a command. Then you watch the reply, popping up on the screen at the speed of a fast and very accurate typist.
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Early responses to ChatGPT have been largely Luddite: critics have warned that its arrival means the end of high school English, the demise of the college essay and so on. But remember that the Luddites were highly skilled weavers who commanded high prices for their products; they could see that newfangled mechanized looms would produce cheap fabrics that would push good weavers out of the market. ChatGPT, with sufficient tweaks, could do just that to educators and other knowledge workers.
Having spent 40 years trying to teach my students how to write, I have mixed feelings about this prospect. But it wouldn’t be the first time that a technological advancement has resulted in the atrophy of a human mental skill.
Writing arguably reduced our ability to memorize — and to speak with memorable and persuasive coherence. …
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Writing and other technological “advances” have made us what we are today — powerful, but also powerfully dangerous to ourselves and our world. If we can just think through the implications of ChatGPT, we may create companions and mentors that are not so much demonic as the angels of our better nature.
More than writing: emergent behaviour
The ChatGPT story extends further than writing and chatting. From a March 6, 2023 article by Stephen Ornes for Quanta Magazine, Note: Links have been removed,
What movie do these emojis describe?
That prompt was one of 204 tasks chosen last year to test the ability of various large language models (LLMs) — the computational engines behind AI chatbots such as ChatGPT. The simplest LLMs produced surreal responses. “The movie is a movie about a man who is a man who is a man,” one began. Medium-complexity models came closer, guessing The Emoji Movie. But the most complex model nailed it in one guess: Finding Nemo.
“Despite trying to expect surprises, I’m surprised at the things these models can do,” said Ethan Dyer, a computer scientist at Google Research who helped organize the test. It’s surprising because these models supposedly have one directive: to accept a string of text as input and predict what comes next, over and over, based purely on statistics. Computer scientists anticipated that scaling up would boost performance on known tasks, but they didn’t expect the models to suddenly handle so many new, unpredictable ones.
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“That language models can do these sort of things was never discussed in any literature that I’m aware of,” said Rishi Bommasani, a computer scientist at Stanford University. Last year, he helped compile a list of dozens of emergent behaviors [emphasis mine], including several identified in Dyer’s project. That list continues to grow.
Now, researchers are racing not only to identify additional emergent abilities but also to figure out why and how they occur at all — in essence, to try to predict unpredictability. Understanding emergence could reveal answers to deep questions around AI and machine learning in general, like whether complex models are truly doing something new or just getting really good at statistics. It could also help researchers harness potential benefits and curtail emergent risks.
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Biologists, physicists, ecologists and other scientists use the term “emergent” to describe self-organizing, collective behaviors that appear when a large collection of things acts as one. Combinations of lifeless atoms give rise to living cells; water molecules create waves; murmurations of starlings swoop through the sky in changing but identifiable patterns; cells make muscles move and hearts beat. Critically, emergent abilities show up in systems that involve lots of individual parts. But researchers have only recently been able to document these abilities in LLMs as those models have grown to enormous sizes.
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But the debut of LLMs also brought something truly unexpected. Lots of somethings. With the advent of models like GPT-3, which has 175 billion parameters — or Google’s PaLM, which can be scaled up to 540 billion — users began describing more and more emergent behaviors. One DeepMind engineer even reported being able to convince ChatGPT that it was a Linux terminal and getting it to run some simple mathematical code to compute the first 10 prime numbers. Remarkably, it could finish the task faster than the same code running on a real Linux machine.
As with the movie emoji task, researchers had no reason to think that a language model built to predict text would convincingly imitate a computer terminal. Many of these emergent behaviors illustrate “zero-shot” or “few-shot” learning, which describes an LLM’s ability to solve problems it has never — or rarely — seen before. This has been a long-time goal in artificial intelligence research, Ganguli [Deep Ganguli, a computer scientist at the AI startup Anthropic] said. Showing that GPT-3 could solve problems without any explicit training data in a zero-shot setting, he said, “led me to drop what I was doing and get more involved.”
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There is an obvious problem with asking these models to explain themselves: They are notorious liars. [emphasis mine] “We’re increasingly relying on these models to do basic work,” Ganguli said, “but I do not just trust these. I check their work.” As one of many amusing examples, in February [2023] Google introduced its AI chatbot, Bard. The blog post announcing the new tool shows Bard making a factual error.
Perhaps not entirely unrelated to current developments, there was this announcement in a May 1, 2023 article by Hannah Alberga for CTV (Canadian Television Network) news, Note: Links have been removed,
Toronto’s pioneer of artificial intelligence quits Google to openly discuss dangers of AI
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Geoffrey Hinton, professor at the University of Toronto and the “godfather” of deep learning – a field of artificial intelligence that mimics the human brain – announced his departure from the company on Monday [May 1, 2023] citing the desire to freely discuss the implications of deep learning and artificial intelligence, and the possible consequences if it were utilized by “bad actors.”
Hinton, a British-Canadian computer scientist, is best-known for a series of deep neural network breakthroughs that won him, Yann LeCun and Yoshua Bengio the 2018 Turing Award, known as the Nobel Prize of computing.
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Hinton has been invested in the now-hot topic of artificial intelligence since its early stages. In 1970, he got a Bachelor of Arts in experimental psychology from Cambridge, followed by his Ph.D. in artificial intelligence in Edinburgh, U.K. in 1978.
He joined Google after spearheading a major breakthrough with two of his graduate students at the University of Toronto in 2012, in which the team uncovered and built a new method of artificial intelligence: neural networks. The team’s first neural network was incorporated and sold to Google for $44 million.
Neural networks are a method of deep learning that effectively teaches computers how to learn the way humans do by analyzing data, paving the way for machines to classify objects and understand speech recognition.
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There’s a bit more from Hinton in a May 3, 2023 article by Sheena Goodyear for the Canadian Broadcasting Corporation’s (CBC) radio programme, As It Happens (the 10 minute radio interview is embedded in the article), Note: A link has been removed,
There was a time when Geoffrey Hinton thought artificial intelligence would never surpass human intelligence — at least not within our lifetimes.
Nowadays, he’s not so sure.
“I think that it’s conceivable that this kind of advanced intelligence could just take over from us,” the renowned British-Canadian computer scientist told As It Happens host Nil Köksal. “It would mean the end of people.”
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For the last decade, he [Geoffrey Hinton] divided his career between teaching at the University of Toronto and working for Google’s deep-learning artificial intelligence team. But this week, he announced his resignation from Google in an interview with the New York Times.
Now Hinton is speaking out about what he fears are the greatest dangers posed by his life’s work, including governments using AI to manipulate elections or create “robot soldiers.”
But other experts in the field of AI caution against his visions of a hypothetical dystopian future, saying they generate unnecessary fear, distract from the very real and immediate problems currently posed by AI, and allow bad actors to shirk responsibility when they wield AI for nefarious purposes.
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Ivana Bartoletti, founder of the Women Leading in AI Network, says dwelling on dystopian visions of an AI-led future can do us more harm than good.
“It’s important that people understand that, to an extent, we are at a crossroads,” said Bartoletti, chief privacy officer at the IT firm Wipro.
“My concern about these warnings, however, is that we focus on the sort of apocalyptic scenario, and that takes us away from the risks that we face here and now, and opportunities to get it right here and now.”
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Ziv Epstein, a PhD candidate at the Massachusetts Institute of Technology who studies the impacts of technology on society, says the problems posed by AI are very real, and he’s glad Hinton is “raising the alarm bells about this thing.”
“That being said, I do think that some of these ideas that … AI supercomputers are going to ‘wake up’ and take over, I personally believe that these stories are speculative at best and kind of represent sci-fi fantasy that can monger fear” and distract from more pressing issues, he said.
He especially cautions against language that anthropomorphizes — or, in other words, humanizes — AI.
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“It’s absolutely possible I’m wrong. We’re in a period of huge uncertainty where we really don’t know what’s going to happen,” he [Hinton] said.
Don Pittis in his May 4, 2022 business analysis for CBC news online offers a somewhat jaundiced view of Hinton’s concern regarding AI, Note: Links have been removed,
As if we needed one more thing to terrify us, the latest warning from a University of Toronto scientist considered by many to be the founding intellect of artificial intelligence, adds a new layer of dread.
Others who have warned in the past that thinking machines are a threat to human existence seem a little miffed with the rock-star-like media coverage Geoffrey Hinton, billed at a conference this week as the Godfather of AI, is getting for what seems like a last minute conversion. Others say Hinton’s authoritative voice makes a difference.
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Not only did Hinton tell an audience of experts at Wednesday’s [May 3, 2023] EmTech Digital conference that humans will soon be supplanted by AI — “I think it’s serious and fairly close.” — he said that due to national and business competition, there is no obvious way to prevent it.
“What we want is some way of making sure that even if they’re smarter than us, they’re going to do things that are beneficial,” said Hinton on Wednesday [May 3, 2023] as he explained his change of heart in detailed technical terms.
“But we need to try and do that in a world where there’s bad actors who want to build robot soldiers that kill people and it seems very hard to me.”
“I wish I had a nice and simple solution I could push, but I don’t,” he said. “It’s not clear there is a solution.”
So when is all this happening?
“In a few years time they may be significantly more intelligent than people,” he told Nil Köksal on CBC Radio’s As It Happens on Wednesday [May 3, 2023].
While he may be late to the party, Hinton’s voice adds new clout to growing anxiety that artificial general intelligence, or AGI, has now joined climate change and nuclear Armageddon as ways for humans to extinguish themselves.
But long before that final day, he worries that the new technology will soon begin to strip away jobs and lead to a destabilizing societal gap between rich and poor that current politics will be unable to solve.
The EmTech Digital conference is a who’s who of AI business and academia, fields which often overlap. Most other participants at the event were not there to warn about AI like Hinton, but to celebrate the explosive growth of AI research and business.
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As one expert I spoke to pointed out, the growth in AI is exponential and has been for a long time. But even knowing that, the increase in the dollar value of AI to business caught the sector by surprise.
Eight years ago when I wrote about the expected increase in AI business, I quoted the market intelligence group Tractica that AI spending would “be worth more than $40 billion in the coming decade,” which sounded like a lot at the time. It appears that was an underestimate.
“The global artificial intelligence market size was valued at $428 billion U.S. in 2022,” said an updated report from Fortune Business Insights. “The market is projected to grow from $515.31 billion U.S. in 2023.” The estimate for 2030 is more than $2 trillion.
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This week the new Toronto AI company Cohere, where Hinton has a stake of his own, announced it was “in advanced talks” to raise $250 million. The Canadian media company Thomson Reuters said it was planning “a deeper investment in artificial intelligence.” IBM is expected to “pause hiring for roles that could be replaced with AI.” The founders of Google DeepMind and LinkedIn have launched a ChatGPT competitor called Pi.
And that was just this week.
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“My one hope is that, because if we allow it to take over it will be bad for all of us, we could get the U.S. and China to agree, like we did with nuclear weapons,” said Hinton. “We’re all the in same boat with respect to existential threats, so we all ought to be able to co-operate on trying to stop it.”
Interviewer and moderator Will Douglas Heaven, an editor at MIT Technology Review finished Hinton’s sentence for him: “As long as we can make some money on the way.”
Geoffrey Hinton, the 75-year-old computer scientist known as the “Godfather of AI,” made headlines this week after resigning from Google to sound the alarm about the technology he helped create. In a series of high-profile interviews, the machine learning pioneer has speculated that AI will surpass humans in intelligence and could even learn to manipulate or kill people on its own accord.
But women who for years have been speaking out about AI’s problems—even at the expense of their jobs—say Hinton’s alarmism isn’t just opportunistic but also overshadows specific warnings about AI’s actual impacts on marginalized people.
“It’s disappointing to see this autumn-years redemption tour [emphasis mine] from someone who didn’t really show up” for other Google dissenters, says Meredith Whittaker, president of the Signal Foundation and an AI researcher who says she was pushed out of Google in 2019 in part over her activism against the company’s contract to build machine vision technology for U.S. military drones. (Google has maintained that Whittaker chose to resign.)
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Another prominent ex-Googler, Margaret Mitchell, who co-led the company’s ethical AI team, criticized Hinton for not denouncing Google’s 2020 firing of her coleader Timnit Gebru, a leading researcher who had spoken up about AI’s risks for women and people of color.
“This would’ve been a moment for Dr. Hinton to denormalize the firing of [Gebru],” Mitchell tweeted on Monday. “He did not. This is how systemic discrimination works.”
Gebru, who is Black, was sacked in 2020 after refusing to scrap a research paper she coauthored about the risks of large language models to multiply discrimination against marginalized people. …
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… An open letter in support of Gebru was signed by nearly 2,700 Googlers in 2020, but Hinton wasn’t one of them.
Instead, Hinton has used the spotlight to downplay Gebru’s voice. In an appearance on CNN Tuesday [May 2, 2023], for example, he dismissed a question from Jake Tapper about whether he should have stood up for Gebru, saying her ideas “aren’t as existentially serious as the idea of these things getting more intelligent than us and taking over.” [emphasis mine]
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Gebru has been mentioned here a few times. She’s mentioned in passing in a June 23, 2022 posting “Racist and sexist robots have flawed AI” and in a little more detail in an August 30, 2022 posting “Should AI algorithms get patents for their inventions and is anyone talking about copyright for texts written by AI algorithms?” scroll down to the ‘Consciousness and ethical AI’ subhead
Chan has another Fast Company article investigating AI issues also published on May 5, 2023, “Researcher Meredith Whittaker says AI’s biggest risk isn’t ‘consciousness’—it’s the corporations that control them.”
The last two existential AI panics
The term “autumn-years redemption tour”is striking and while the reference to age could be viewed as problematic, it also hints at the money, honours, and acknowledgement that Hinton has enjoyed as an eminent scientist. I’ve covered two previous panics set off by eminent scientists. “Existential risk” is the title of my November 26, 2012 posting which highlights Martin Rees’ efforts to found the Centre for Existential Risk at the University of Cambridge.
Rees is a big deal. From his Wikipedia entry, Note: Links have been removed,
Martin John Rees, Baron Rees of Ludlow OM FRS FREng FMedSci FRAS HonFInstP[10][2] (born 23 June 1942) is a British cosmologist and astrophysicist.[11] He is the fifteenth Astronomer Royal, appointed in 1995,[12][13][14] and was Master of Trinity College, Cambridge, from 2004 to 2012 and President of the Royal Society between 2005 and 2010.[15][16][17][18][19][20]
The next panic was set off by Stephen Hawking (1942 – 2018; also at the University of Cambridge, Wikipedia entry) a few years before he died. (Note: Rees, Hinton, and Hawking were all born within five years of each other and all have/had ties to the University of Cambridge. Interesting coincidence, eh?) From a January 9, 2015 article by Emily Chung for CBC news online,
Machines turning on their creators has been a popular theme in books and movies for decades, but very serious people are starting to take the subject very seriously. Physicist Stephen Hawking says, “the development of full artificial intelligence could spell the end of the human race.” Tesla Motors and SpaceX founder Elon Musk suggests that AI is probably “our biggest existential threat.”
Artificial intelligence experts say there are good reasons to pay attention to the fears expressed by big minds like Hawking and Musk — and to do something about it while there is still time.
Hawking made his most recent comments at the beginning of December [2014], in response to a question about an upgrade to the technology he uses to communicate, He relies on the device because he has amyotrophic lateral sclerosis, a degenerative disease that affects his ability to move and speak.
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Popular works of science fiction – from the latest Terminator trailer, to the Matrix trilogy, to Star Trek’s borg – envision that beyond that irreversible historic event, machines will destroy, enslave or assimilate us, says Canadian science fiction writer Robert J. Sawyer.
Sawyer has written about a different vision of life beyond singularity [when machines surpass humans in general intelligence,] — one in which machines and humans work together for their mutual benefit. But he also sits on a couple of committees at the Lifeboat Foundation, a non-profit group that looks at future threats to the existence of humanity, including those posed by the “possible misuse of powerful technologies” such as AI. He said Hawking and Musk have good reason to be concerned.
To sum up, the first panic was in 2012, the next in 2014/15, and the latest one began earlier this year (2023) with a letter. A March 29, 2023 Thompson Reuters news item on CBC news online provides information on the contents,
Elon Musk and a group of artificial intelligence experts and industry executives are calling for a six-month pause in developing systems more powerful than OpenAI’s newly launched GPT-4, in an open letter citing potential risks to society and humanity.
Earlier this month, Microsoft-backed OpenAI unveiled the fourth iteration of its GPT (Generative Pre-trained Transformer) AI program, which has wowed users with its vast range of applications, from engaging users in human-like conversation to composing songs and summarizing lengthy documents.
The letter, issued by the non-profit Future of Life Institute and signed by more than 1,000 people including Musk, called for a pause on advanced AI development until shared safety protocols for such designs were developed, implemented and audited by independent experts.
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Co-signatories included Stability AI CEO Emad Mostaque, researchers at Alphabet-owned DeepMind, and AI heavyweights Yoshua Bengio, often referred to as one of the “godfathers of AI,” and Stuart Russell, a pioneer of research in the field.
According to the European Union’s transparency register, the Future of Life Institute is primarily funded by the Musk Foundation, as well as London-based effective altruism group Founders Pledge, and Silicon Valley Community Foundation.
The concerns come as EU police force Europol on Monday {March 27, 2023] joined a chorus of ethical and legal concerns over advanced AI like ChatGPT, warning about the potential misuse of the system in phishing attempts, disinformation and cybercrime.
Meanwhile, the U.K. government unveiled proposals for an “adaptable” regulatory framework around AI.
The government’s approach, outlined in a policy paper published on Wednesday [March 29, 2023], would split responsibility for governing artificial intelligence (AI) between its regulators for human rights, health and safety, and competition, rather than create a new body dedicated to the technology.
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The engineers have chimed in, from an April 7, 2023 article by Margo Anderson for the IEEE (institute of Electrical and Electronics Engineers) Spectrum magazine, Note: Links have been removed,
The open letter [published March 29, 2023], titled “Pause Giant AI Experiments,” was organized by the nonprofit Future of Life Institute and signed by more than 27,565 people (as of 8 May). It calls for cessation of research on “all AI systems more powerful than GPT-4.”
It’s the latest of a host of recent “AI pause” proposals including a suggestion by Google’s François Chollet of a six-month “moratorium on people overreacting to LLMs” in either direction.
In the news media, the open letter has inspired straight reportage, critical accounts for not going far enough (“shut it all down,” Eliezer Yudkowsky wrote in Time magazine), as well as critical accounts for being both a mess and an alarmist distraction that overlooks the real AI challenges ahead.
IEEE members have expressed a similar diversity of opinions.
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There was an earlier open letter in January 2015 according to Wikipedia’s “Open Letter on Artificial Intelligence” entry, Note: Links have been removed,
In January 2015, Stephen Hawking, Elon Musk, and dozens of artificial intelligence experts[1] signed an open letter on artificial intelligence calling for research on the societal impacts of AI. The letter affirmed that society can reap great potential benefits from artificial intelligence, but called for concrete research on how to prevent certain potential “pitfalls”: artificial intelligence has the potential to eradicate disease and poverty, but researchers must not create something which is unsafe or uncontrollable.[1] The four-paragraph letter, titled “Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter”, lays out detailed research priorities in an accompanying twelve-page document.
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As for ‘Mr. ChatGPT’ or Sam Altman, CEO of OpenAI, while he didn’t sign the March 29, 2023 letter, he appeared before US Congress suggesting AI needs to be regulated according to May 16, 2023 news article by Mohar Chatterjee for Politico.
You’ll notice I’ve arbitrarily designated three AI panics by assigning their origins to eminent scientists. In reality, these concerns rise and fall in ways that don’t allow for such a tidy analysis. As Chung notes, science fiction regularly addresses this issue. For example, there’s my October 16, 2013 posting, “Wizards & Robots: a comic book encourages study in the sciences and maths and discussions about existential risk.” By the way, will.i.am (of the Black Eyed Peas band was involved in the comic book project and he us a longtime supporter of STEM (science, technology, engineering, and mathematics) initiatives.
Finally (but not quite)
Puzzling, isn’t it? I’m not sure we’re asking the right questions but it’s encouraging to see that at least some are being asked.
Dr. Andrew Maynard in a May 12, 2023 essay for The Conversation (h/t May 12, 2023 item on phys.org) notes that ‘Luddites’ questioned technology’s inevitable progress and were vilified for doing so, Note: Links have been removed,
The term “Luddite” emerged in early 1800s England. At the time there was a thriving textile industry that depended on manual knitting frames and a skilled workforce to create cloth and garments out of cotton and wool. But as the Industrial Revolution gathered momentum, steam-powered mills threatened the livelihood of thousands of artisanal textile workers.
Faced with an industrialized future that threatened their jobs and their professional identity, a growing number of textile workers turned to direct action. Galvanized by their leader, Ned Ludd, they began to smash the machines that they saw as robbing them of their source of income.
It’s not clear whether Ned Ludd was a real person, or simply a figment of folklore invented during a period of upheaval. But his name became synonymous with rejecting disruptive new technologies – an association that lasts to this day.
Questioning doesn’t mean rejecting
Contrary to popular belief, the original Luddites were not anti-technology, nor were they technologically incompetent. Rather, they were skilled adopters and users of the artisanal textile technologies of the time. Their argument was not with technology, per se, but with the ways that wealthy industrialists were robbing them of their way of life
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In December 2015, Stephen Hawking, Elon Musk and Bill Gates were jointly nominated for a “Luddite Award.” Their sin? Raising concerns over the potential dangers of artificial intelligence.
The irony of three prominent scientists and entrepreneurs being labeled as Luddites underlines the disconnect between the term’s original meaning and its more modern use as an epithet for anyone who doesn’t wholeheartedly and unquestioningly embrace technological progress.
Yet technologists like Musk and Gates aren’t rejecting technology or innovation. Instead, they’re rejecting a worldview that all technological advances are ultimately good for society. This worldview optimistically assumes that the faster humans innovate, the better the future will be.
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In an age of ChatGPT, gene editing and other transformative technologies, perhaps we all need to channel the spirit of Ned Ludd as we grapple with how to ensure that future technologies do more good than harm.
In fact, “Neo-Luddites” or “New Luddites” is a term that emerged at the end of the 20th century.
In 1990, the psychologist Chellis Glendinning published an essay titled “Notes toward a Neo-Luddite Manifesto.”
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Then there are the Neo-Luddites who actively reject modern technologies, fearing that they are damaging to society. New York City’s Luddite Club falls into this camp. Formed by a group of tech-disillusioned Gen-Zers, the club advocates the use of flip phones, crafting, hanging out in parks and reading hardcover or paperback books. Screens are an anathema to the group, which sees them as a drain on mental health.
I’m not sure how many of today’s Neo-Luddites – whether they’re thoughtful technologists, technology-rejecting teens or simply people who are uneasy about technological disruption – have read Glendinning’s manifesto. And to be sure, parts of it are rather contentious. Yet there is a common thread here: the idea that technology can lead to personal and societal harm if it is not developed responsibly.
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Getting back to where this started with nonhuman authors, Amelia Eqbal has written up an informal transcript of a March 16, 2023 CBC radio interview (radio segment is embedded) about ChatGPT-4 (the latest AI chatbot from OpenAI) between host Elamin Abdelmahmoud and tech journalist, Alyssa Bereznak.
I was hoping to add a little more Canadian content, so in March 2023 and again in April 2023, I sent a question about whether there were any policies regarding nonhuman or AI authors to Kim Barnhardt at the Canadian Medical Association Journal (CMAJ). To date, there has been no reply but should one arrive, I will place it here.
In the meantime, I have this from Canadian writer, Susan Baxter in her May 15, 2023 blog posting “Coming soon: Robot Overlords, Sentient AI and more,”
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The current threat looming (Covid having been declared null and void by the WHO*) is Artificial Intelligence (AI) which, we are told, is becoming too smart for its own good and will soon outsmart humans. Then again, given some of the humans I’ve met along the way that wouldn’t be difficult.
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All this talk of scary-boo AI seems to me to have become the worst kind of cliché, one that obscures how our lives have become more complicated and more frustrating as apps and bots and cyber-whatsits take over.
The trouble with clichés, as Alain de Botton wrote in How Proust Can Change Your Life, is not that they are wrong or contain false ideas but more that they are “superficial articulations of good ones”. Cliches are oversimplifications that become so commonplace we stop noticing the more serious subtext. (This is rife in medicine where metaphors such as talk of “replacing” organs through transplants makes people believe it’s akin to changing the oil filter in your car. Or whatever it is EV’s have these days that needs replacing.)
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Should you live in Vancouver (Canada) and are attending a May 28, 2023 AI event, you may want to read Susan Baxter’s piece as a counterbalance to, “Discover the future of artificial intelligence at this unique AI event in Vancouver,” a May 19, 2023 sponsored content by Katy Brennan for the Daily Hive,
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If you’re intrigued and eager to delve into the rapidly growing field of AI, you’re not going to want to miss this unique Vancouver event.
On Sunday, May 28 [2023], a Multiplatform AI event is coming to the Vancouver Playhouse — and it’s set to take you on a journey into the future of artificial intelligence.
The exciting conference promises a fusion of creativity, tech innovation, and thought–provoking insights, with talks from renowned AI leaders and concept artists, who will share their experiences and opinions.
Guests can look forward to intense discussions about AI’s pros and cons, hear real-world case studies, and learn about the ethical dimensions of AI, its potential threats to humanity, and the laws that govern its use.
Live Q&A sessions will also be held, where leading experts in the field will address all kinds of burning questions from attendees. There will also be a dynamic round table and several other opportunities to connect with industry leaders, pioneers, and like-minded enthusiasts.
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This conference is being held at The Playhouse, 600 Hamilton Street, from 11 am to 7:30 pm, ticket prices range from $299 to $349 to $499 (depending on when you make your purchase, From the Multiplatform AI Conference homepage,
Event Speakers
Max Sills General Counsel at Midjourney
From Jan 2022 – present (Advisor – now General Counsel) – Midjourney – An independent research lab exploring new mediums of thought and expanding the imaginative powers of the human species (SF) Midjourney – a generative artificial intelligence program and service created and hosted by a San Francisco-based independent research lab Midjourney, Inc. Midjourney generates images from natural language descriptions, called “prompts”, similar to OpenAI’s DALL-E and Stable Diffusion. For now the company uses Discord Server as a source of service and, with huge 15M+ members, is the biggest Discord server in the world. In the two-things-at-once department, Max Sills also known as an owner of Open Advisory Services, firm which is set up to help small and medium tech companies with their legal needs (managing outside counsel, employment, carta, TOS, privacy). Their clients are enterprise level, medium companies and up, and they are here to help anyone on open source and IP strategy. Max is an ex-counsel at Block, ex-general manager of the Crypto Open Patent Alliance. Prior to that Max led Google’s open source legal group for 7 years.
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So, the first speaker listed is a lawyer associated with Midjourney, a highly controversial generative artificial intelligence programme used to generate images. According to their entry on Wikipedia, the company is being sued, Note: Links have been removed,
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On January 13, 2023, three artists – Sarah Andersen, Kelly McKernan, and Karla Ortiz – filed a copyright infringement lawsuit against Stability AI, Midjourney, and DeviantArt, claiming that these companies have infringed the rights of millions of artists, by training AI tools on five billion images scraped from the web, without the consent of the original artists.[32]
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My October 24, 2022 posting highlights some of the issues with generative image programmes and Midjourney is mentioned throughout.
As I noted earlier, I’m glad to see more thought being put into the societal impact of AI and somewhat disconcerted by the hyperbole from the like of Geoffrey Hinton and the like of Vancouver’s Multiplatform AI conference organizers. Mike Masnick put it nicely in his May 24, 2023 posting on TechDirt (Note 1: I’ve taken a paragraph out of context, his larger issue is about proposals for legislation; Note 2: Links have been removed),
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Honestly, this is partly why I’ve been pretty skeptical about the “AI Doomers” who keep telling fanciful stories about how AI is going to kill us all… unless we give more power to a few elite people who seem to think that it’s somehow possible to stop AI tech from advancing. As I noted last month, it is good that some in the AI space are at least conceptually grappling with the impact of what they’re building, but they seem to be doing so in superficial ways, focusing only on the sci-fi dystopian futures they envision, and not things that are legitimately happening today from screwed up algorithms.
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For anyone interested in the Canadian government attempts to legislate AI, there’s my May 1, 2023 posting, “Canada, AI regulation, and the second reading of the Digital Charter Implementation Act, 2022 (Bill C-27).”
Addendum (June 1, 2023)
Another statement warning about runaway AI was issued on Tuesday, May 30, 2023. This was far briefer than the previous March 2023 warning, from the Center for AI Safety’s “Statement on AI Risk” webpage,
Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war [followed by a list of signatories] …
Vanessa Romo’s May 30, 2023 article (with contributions from Bobby Allyn) for NPR ([US] National Public Radio) offers an overview of both warnings. Rae Hodge’s May 31, 2023 article for Salon offers a more critical view, Note: Links have been removed,
The artificial intelligence world faced a swarm of stinging backlash Tuesday morning, after more than 350 tech executives and researchers released a public statement declaring that the risks of runaway AI could be on par with those of “nuclear war” and human “extinction.” Among the signatories were some who are actively pursuing the profitable development of the very products their statement warned about — including OpenAI CEO Sam Altman and Google DeepMind CEO Demis Hassabis.
“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war,” the statement from the non-profit Center for AI Safety said.
But not everyone was shaking in their boots, especially not those who have been charting AI tech moguls’ escalating use of splashy language — and those moguls’ hopes for an elite global AI governance board.
TechCrunch’s Natasha Lomas, whose coverage has been steeped in AI, immediately unravelled the latest panic-push efforts with a detailed rundown of the current table stakes for companies positioning themselves at the front of the fast-emerging AI industry.
“Certainly it speaks volumes about existing AI power structures that tech execs at AI giants including OpenAI, DeepMind, Stability AI and Anthropic are so happy to band and chatter together when it comes to publicly amplifying talk of existential AI risk. And how much more reticent to get together to discuss harms their tools can be seen causing right now,” Lomas wrote.
“Instead of the statement calling for a development pause, which would risk freezing OpenAI’s lead in the generative AI field, it lobbies policymakers to focus on risk mitigation — doing so while OpenAI is simultaneously crowdfunding efforts to shape ‘democratic processes for steering AI,'” Lomas added.
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The use of scary language and fear as a marketing tool has a long history in tech. And, as the LA Times’ Brian Merchant pointed out in an April column, OpenAI stands to profit significantly from a fear-driven gold rush of enterprise contracts.
“[OpenAI is] almost certainly betting its longer-term future on more partnerships like the one with Microsoft and enterprise deals serving large companies,” Merchant wrote. “That means convincing more corporations that if they want to survive the coming AI-led mass upheaval, they’d better climb aboard.”
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Fear, after all, is a powerful sales tool.
Romo’s May 30, 2023 article for NPR offers a good overview and, if you have the time, I recommend reading Hodge’s May 31, 2023 article for Salon in its entirety.
*ETA June 8, 2023: This sentence “What follows the ‘nonhuman authors’ is essentially a survey of situation/panic.” was added to the introductory paragraph at the beginning of this post.
I thought it best to break this up a bit. There are a couple of ‘objects’ still to be discussed but this is mostly the commentary part of this letter to you. (Here’s a link for anyone who stumbled here but missed Part 1.)
Ethics, the natural world, social justice, eeek, and AI
Dorothy Woodend in her March 10, 2022 review for The Tyee) suggests some ethical issues in her critique of the ‘bee/AI collaboration’ and she’s not the only one with concerns. UNESCO (United Nations Educational, Scientific and Cultural Organization) has produced global recommendations for ethical AI (see my March 18, 2022 posting). More recently, there’s “Racist and sexist robots have flawed AI,” a June 23, 2022 posting, where researchers prepared a conference presentation and paper about deeply flawed AI still being used in robots.
Ultimately, the focus is always on humans and Woodend has extended the ethical AI conversation to include insects and the natural world. In short, something less human-centric.
My friend, this reference to the de Young exhibit may seem off topic but I promise it isn’t in more ways than one. The de Young Museum in San Francisco (February 22, 2020 – June 27, 2021) also held and AI and art show called, “Uncanny Valley: Being Human in the Age of AI”), from the exhibitions page,
In today’s AI-driven world, increasingly organized and shaped by algorithms that track, collect, and evaluate our data, the question of what it means to be human [emphasis mine] has shifted. Uncanny Valley is the first major exhibition to unpack this question through a lens of contemporary art and propose new ways of thinking about intelligence, nature, and artifice. [emphasis mine]
Courtesy: de Young Museum [downloaded from https://deyoung.famsf.org/exhibitions/uncanny-valley]
As you can see, it hinted (perhaps?) at an attempt to see beyond human-centric AI. (BTW, I featured this ‘Uncanny Valley’ show in my February 25, 2020 posting where I mentioned Stephanie Dinkins [featured below] and other artists.)
Social justice
While the VAG show doesn’t see much past humans and AI, it does touch on social justice. In particular there’s Pod 15 featuring the Algorithmic Justice League (AJL). The group “combine[s] art and research to illuminate the social implications and harms of AI” as per their website’s homepage.
In Pod 9, Stephanie Dinkins’ video work with a robot (Bina48), which was also part of the de Young Museum ‘Uncanny Valley’ show, addresses some of the same issues.
Still of Stephanie Dinkins, “Conversations with Bina48,” 2014–present. Courtesy of the artist [downloaded from https://deyoung.famsf.org/stephanie-dinkins-conversations-bina48-0]
From the the de Young Museum’s Stephanie Dinkins “Conversations with Bina48” April 23, 2020 article by Janna Keegan (Dinkins submitted the same work you see at the VAG show), Note: Links have been removed,
Transdisciplinary artist and educator Stephanie Dinkins is concerned with fostering AI literacy. The central thesis of her social practice is that AI, the internet, and other data-based technologies disproportionately impact people of color, LGBTQ+ people, women, and disabled and economically disadvantaged communities—groups rarely given a voice in tech’s creation. Dinkins strives to forge a more equitable techno-future by generating AI that includes the voices of multiple constituencies …
The artist’s ongoing Conversations with Bina48 takes the form of a series of interactions with the social robot Bina48 (Breakthrough Intelligence via Neural Architecture, 48 exaflops per second). The machine is the brainchild of Martine Rothblatt, an entrepreneur in the field of biopharmaceuticals who, with her wife, Bina, cofounded the Terasem Movement, an organization that seeks to extend human life through cybernetic means. In 2007 Martine commissioned Hanson Robotics to create a robot whose appearance and consciousness simulate Bina’s. The robot was released in 2010, and Dinkins began her work with it in 2014.
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Part psychoanalytical discourse, part Turing test, Conversations with Bina48 also participates in a larger dialogue regarding bias and representation in technology. Although Bina Rothblatt is a Black woman, Bina48 was not programmed with an understanding of its Black female identity or with knowledge of Black history. Dinkins’s work situates this omission amid the larger tech industry’s lack of diversity, drawing attention to the problems that arise when a roughly homogenous population creates technologies deployed globally. When this occurs, writes art critic Tess Thackara, “the unconscious biases of white developers proliferate on the internet, mapping our social structures and behaviors onto code and repeating imbalances and injustices that exist in the real world.” One of the most appalling and public of these instances occurred when a Google Photos image-recognition algorithm mislabeled the faces of Black people as “gorillas.”
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Eeek
You will find as you go through the ‘imitation game’ a pod with a screen showing your movements through the rooms in realtime on a screen. The installation is called “Creepers” (2021-22). The student team from Vancouver’s Centre for Digital Media (CDM) describes their project this way, from the CDM’s AI-driven Installation Piece for the Vancouver Art Gallery webpage,
Project Description
Kaleidoscope [team name] is designing an installation piece that harnesses AI to collect and visualize exhibit visitor behaviours, and interactions with art, in an impactful and thought-provoking way.
There’s no warning that you’re being tracked and you can see they’ve used facial recognition software to track your movements through the show. It’s claimed on the pod’s signage that they are deleting the data once you’ve left.
‘Creepers’ is an interesting approach to the ethics of AI. The name suggests that even the student designers were aware it was problematic.
In recovery from an existential crisis (meditations)
There’s something greatly ambitious about “The Imitation Game: Visual Culture in the Age of Artificial Intelligence” and walking up the VAG’s grand staircase affirms that ambition. Bravo to the two curators, Grenville and Entis for an exhibition.that presents a survey (or overview) of artificial intelligence, and its use in and impact on creative visual culture.
I’ve already enthused over the history (specifically Turing, Lovelace, Ovid), admitted to being mesmerized by Scott Eaton’s sculpture/AI videos, and confessed to a fascination (and mild repulsion) regarding Oxman’s honeycombs.
It’s hard to remember all of the ‘objects’ as the curators have offered a jumble of work, almost all of them on screens. Already noted, there’s Norbert Wiener’s The Moth (1949) and there are also a number of other computer-based artworks from the 1960s and 1970s. Plus, you’ll find works utilizing a GAN (generative adversarial network), an AI agent that is explained in the exhibit.
It’s worth going more than once to the show as there is so much to experience.
Why did they do that?
Dear friend, I’ve already commented on the poor flow through the show and It’s hard to tell if the curators intended the experience to be disorienting but this is to the point of chaos, especially when the exhibition is crowded.
I’ve seen Grenville’s shows before. In particular there was “MashUp: The Birth of Modern Culture, a massive survey documenting the emergence of a mode of creativity that materialized in the late 1800s and has grown to become the dominant model of cultural production in the 21st century” and there was “KRAZY! The Delirious World of Anime + Manga + Video Games + Art.” As you can see from the description, he pulls together disparate works and ideas into a show for you to ‘make sense’ of them.
One of the differences between those shows and the “imitation Game: …” is that most of us have some familiarity, whether we like it or not, with modern art/culture and anime/manga/etc. and can try to ‘make sense’ of it.
By contrast, artificial intelligence (which even experts have difficulty defining) occupies an entirely different set of categories; all of them associated with science/technology. This makes for a different kind of show so the curators cannot rely on the audience’s understanding of basics. It’s effectively an art/sci or art/tech show and, I believe, the first of its kind at the Vancouver Art Gallery. Unfortunately, the curators don’t seem to have changed their approach to accommodate that difference.
AI is also at the centre of a current panic over job loss, loss of personal agency, automated racism and sexism, etc. which makes the experience of viewing the show a little tense. In this context, their decision to commission and use ‘Creepers’ seems odd.
Where were Ai-Da and Dall-E-2 and the others?
Oh friend, I was hoping for a robot. Those roomba paintbots didn’t do much for me. All they did was lie there on the floor
To be blunt I wanted some fun and perhaps a bit of wonder and maybe a little vitality. I wasn’t necessarily expecting Ai-Da, an artisitic robot, but something three dimensional and fun in this very flat, screen-oriented show would have been nice.
Ai-Da was at the Glastonbury Festival in the U from 23-26th June 2022. Here’s Ai-Da and her Billie Eilish (one of the Glastonbury 2022 headliners) portrait. [downloaded from https://www.ai-darobot.com/exhibition]
Ai-Da was first featured here in a December 17, 2021 posting about performing poetry that she had written in honour of the 700th anniversary of poet Dante Alighieri’s death.
Named in honour of Ada Lovelace, Ai-Da visited the 2022 Venice Biennale as Leah Henrickson and Simone Natale describe in their May 12, 2022 article for Fast Company (Note: Links have been removed),
Ai-Da sits behind a desk, paintbrush in hand. She looks up at the person posing for her, and then back down as she dabs another blob of paint onto the canvas. A lifelike portrait is taking shape. If you didn’t know a robot produced it, this portrait could pass as the work of a human artist.
Ai-Da is touted as the “first robot to paint like an artist,” and an exhibition of her work, called Leaping into the Metaverse, opened at the Venice Biennale.
Ai-Da produces portraits of sitting subjects using a robotic hand attached to her lifelike feminine figure. She’s also able to talk, giving detailed answers to questions about her artistic process and attitudes toward technology. She even gave a TEDx talk about “The Intersection of Art and AI” in Oxford a few years ago. While the words she speaks are programmed, Ai-Da’s creators have also been experimenting with having her write and perform her own poetry.
DALL-E 2 is a new neural network [AI] algorithm that creates a picture from a short phrase or sentence that you provide. The program, which was announced by the artificial intelligence research laboratory OpenAI in April 2022, hasn’t been released to the public. But a small and growing number of people – myself included – have been given access to experiment with it.
As a researcher studying the nexus of technology and art, I was keen to see how well the program worked. After hours of experimentation, it’s clear that DALL-E – while not without shortcomings – is leaps and bounds ahead of existing image generation technology. It raises immediate questions about how these technologies will change how art is made and consumed. It also raises questions about what it means to be creative when DALL-E 2 seems to automate so much of the creative process itself.
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A July 4, 2022 article “DALL-E, Make Me Another Picasso, Please” by Laura Lane for The New Yorker has a rebuttal to Ada Lovelace’s contention that creativity is uniquely human (Note: A link has been removed),
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“There was this belief that creativity is this deeply special, only-human thing,” Sam Altman, OpenAI’s C.E.O., explained the other day. Maybe not so true anymore, he said. Altman, who wore a gray sweater and had tousled brown hair, was videoconferencing from the company’s headquarters, in San Francisco. DALL-E is still in a testing phase. So far, OpenAI has granted access to a select group of people—researchers, artists, developers—who have used it to produce a wide array of images: photorealistic animals, bizarre mashups, punny collages. Asked by a user to generate “a plate of various alien fruits from another planet photograph,” DALL-E returned something kind of like rambutans. “The rest of mona lisa” is, according to DALL-E, mostly just one big cliff. Altman described DALL-E as “an extension of your own creativity.”
AI artists first hit my radar in August 2018 when Christie’s Auction House advertised an art auction of a ‘painting’ by an algorithm (artificial intelligence). There’s more in my August 31, 2018 posting but, briefly, a French art collective, Obvious, submitted a painting, “Portrait of Edmond de Belamy,” that was created by an artificial intelligence agent to be sold for an estimated to $7000 – $10,000. They weren’t even close. According to Ian Bogost’s March 6, 2019 article for The Atlantic, the painting sold for $432,500 In October 2018.
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That posting also included AI artist, AICAN. Both artist-AI agents (Obvious and AICAN) are based on GANs (generative adversarial networks) for learning and eventual output. Both artist-AI agents work independently or with human collaborators on art works that are available for purchase.
As might be expected not everyone is excited about AI and visual art. Sonja Drimmer, Professor of Medieval Art, University of Massachusetts at Amherst, provides another perspective on AI, visual art, and, her specialty, art history in her November 1, 2021 essay for The Conversation (Note: Links have been removed),
Over the past year alone, I’ve come across articles highlighting how artificial intelligence recovered a “secret” painting of a “lost lover” of Italian painter Modigliani, “brought to life” a “hidden Picasso nude”, “resurrected” Austrian painter Gustav Klimt’s destroyed works and “restored” portions of Rembrandt’s 1642 painting “The Night Watch.” The list goes on.
As an art historian, I’ve become increasingly concerned about the coverage and circulation of these projects.
They have not, in actuality, revealed one secret or solved a single mystery.
What they have done is generate feel-good stories about AI.
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Take the reports about the Modigliani and Picasso paintings.
These were projects executed by the same company, Oxia Palus, which was founded not by art historians but by doctoral students in machine learning.
In both cases, Oxia Palus relied upon traditional X-rays, X-ray fluorescence and infrared imaging that had already been carried out and published years prior – work that had revealed preliminary paintings beneath the visible layer on the artists’ canvases.
The company edited these X-rays and reconstituted them as new works of art by applying a technique called “neural style transfer.” This is a sophisticated-sounding term for a program that breaks works of art down into extremely small units, extrapolates a style from them and then promises to recreate images of other content in that same style.
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As you can ‘see’ my friend, the topic of AI and visual art is a juicy one. In fact, I have another example in my June 27, 2022 posting, which is titled, “Art appraised by algorithm.” So, Grenville’s and Entis’ decision to focus on AI and its impact on visual culture is quite timely.
Visual culture: seeing into the future
The VAG Imitation Game webpage lists these categories of visual culture “animation, architecture, art, fashion, graphic design, urban design and video games …” as being represented in the show. Movies and visual art, not mentioned in the write up, are represented while theatre and other performing arts are not mentioned or represented. That’ s not a surprise.
In addition to an area of science/technology that’s not well understood even by experts, the curators took on the truly amorphous (and overwhelming) topic of visual culture. Given that even writing this commentary has been a challenge, I imagine pulling the show together was quite the task.
Grenville often grounds his shows in a history of the subject and, this time, it seems especially striking. You’re in a building that is effectively a 19th century construct and in galleries that reflect a 20th century ‘white cube’ aesthetic, while looking for clues into the 21st century future of visual culture employing technology that has its roots in the 19th century and, to some extent, began to flower in the mid-20th century.
Chung’s collaboration is one of the only ‘optimistic’ notes about the future and, as noted earlier, it bears a resemblance to Wiener’s 1949 ‘Moth’
Overall, it seems we are being cautioned about the future. For example, Oxman’s work seems bleak (bees with no flowers to pollinate and living in an eternal spring). Adding in ‘Creepers’ and surveillance along with issues of bias and social injustice reflects hesitation and concern about what we will see, who sees it, and how it will be represented visually.
Learning about robots, automatons, artificial intelligence, and more
I wish the Vancouver Art Gallery (and Vancouver’s other art galleries) would invest a little more in audience education. A couple of tours, by someone who may or may not know what they’re talking, about during the week do not suffice. The extra material about Stephanie Dinkins and her work (“Conversations with Bina48,” 2014–present) came from the de Young Museum’s website. In my July 26, 2021 commentary on North Vancouver’s Polygon Gallery 2021 show “Interior Infinite,” I found background information for artist Zanele Muholi on the Tate Modern’s website. There is nothing on the VAG website that helps you to gain some perspective on the artists’ works.
It seems to me that if the VAG wants to be considered world class, it should conduct itself accordingly and beefing up its website with background information about their current shows would be a good place to start.
Robots, automata, and artificial intelligence
Prior to 1921, robots were known exclusively as automatons. These days, the word ‘automaton’ (or ‘automata’ in the plural) seems to be used to describe purely mechanical representations of humans from over 100 years ago whereas the word ‘robot’ can be either ‘humanlike’ or purely machine, e.g. a mechanical arm that performs the same function over and over. I have a good February 24, 2017 essay on automatons by Miguel Barral for OpenMind BBVA*, which provides some insight into the matter,
The concept of robot is relatively recent. The idea was introduced in 1921 by the Czech writer Karel Capek in his work R.U.R to designate a machine that performs tasks in place of man. But their predecessors, the automatons (from the Greek automata, or “mechanical device that works by itself”), have been the object of desire and fascination since antiquity. Some of the greatest inventors in history, such as Leonardo Da Vinci, have contributed to our fascination with these fabulous creations:
The Al-Jazari automatons
The earliest examples of known automatons appeared in the Islamic world in the 12th and 13th centuries. In 1206, the Arab polymath Al-Jazari, whose creations were known for their sophistication, described some of his most notable automatons: an automatic wine dispenser, a soap and towels dispenser and an orchestra-automaton that operated by the force of water. This latter invention was meant to liven up parties and banquets with music while floating on a pond, lake or fountain.
As the water flowed, it started a rotating drum with pegs that, in turn, moved levers whose movement produced different sounds and movements. As the pegs responsible for the musical notes could be exchanged for different ones in order to interpret another melody, it is considered one of the first programmable machines in history.
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If you’re curious about automata, my friend, I found this Sept. 26, 2016 ABC news radio news item about singer Roger Daltrey’s and his wife, Heather’s auction of their collection of 19th century French automata (there’s an embedded video showcasing these extraordinary works of art). For more about automata, robots, and androids, there’s an excellent May 4, 2022 article by James Vincent, ‘A visit to the human factory; How to build the world’s most realistic robot‘ for The Verge; Vincent’s article is about Engineered Arts, the UK-based company that built Ai-Da.
AI is often used interchangeably with ‘robot’ but they aren’t the same. Not all robots have AI integrated into their processes. At its simplest AI is an algorithm or set of algorithms, which may ‘live’ in a CPU and be effectively invisible or ‘live’ in or make use of some kind of machine and/or humanlike body. As the experts have noted, the concept of artificial intelligence is a slippery concept.
*OpenMind BBVA is a Spanish multinational financial services company, Banco Bilbao Vizcaya Argentaria (BBVA), which runs the non-profit project, OpenMind (About us page) to disseminate information on robotics and so much more.*
You can’t always get what you want
My friend,
I expect many of the show’s shortcomings (as perceived by me) are due to money and/or scheduling issues. For example, Ai-Da was at the Venice Biennale and if there was a choice between the VAG and Biennale, I know where I’d be.
Even with those caveats in mind, It is a bit surprising that there were no examples of wearable technology. For example, Toronto’s Tapestry Opera recently performed R.U.R. A Torrent of Light (based on the word ‘robot’ from Karel Čapek’s play, R.U.R., ‘Rossumovi Univerzální Roboti’), from my May 24, 2022 posting,
I have more about tickets prices, dates, and location later in this post but first, here’s more about the opera and the people who’ve created it from the Tapestry Opera’s ‘R.U.R. A Torrent of Light’ performance webpage,
“This stunning new opera combines dance, beautiful multimedia design, a chamber orchestra including 100 instruments creating a unique electronica-classical sound, and wearable technology [emphasis mine] created with OCAD University’s Social Body Lab, to create an immersive and unforgettable science-fiction experience.”
And, from later in my posting,
“Despite current stereotypes, opera was historically a launchpad for all kinds of applied design technologies. [emphasis mine] Having the opportunity to collaborate with OCAD U faculty is an invigorating way to reconnect to that tradition and foster connections between art, music and design, [emphasis mine]” comments the production’s Director Michael Hidetoshi Mori, who is also Tapestry Opera’s Artistic Director.
That last quote brings me back to the my comment about theatre and performing arts not being part of the show. Of course, the curators couldn’t do it all but a website with my hoped for background and additional information could have helped to solve the problem.
The absence of the theatrical and performing arts in the VAG’s ‘Imitation Game’ is a bit surprising as the Council of Canadian Academies (CCA) in their third assessment, “Competing in a Global Innovation Economy: The Current State of R&D in Canada” released in 2018 noted this (from my April 12, 2018 posting),
Canada, relative to the world, specializes in subjects generally referred to as the humanities and social sciences (plus health and the environment), and does not specialize as much as others in areas traditionally referred to as the physical sciences and engineering. Specifically, Canada has comparatively high levels of research output in Psychology and Cognitive Sciences, Public Health and Health Services, Philosophy and Theology, Earth and Environmental Sciences, and Visual and Performing Arts. [emphasis mine] It accounts for more than 5% of world research in these fields. Conversely, Canada has lower research output than expected in Chemistry, Physics and Astronomy, Enabling and Strategic Technologies, Engineering, and Mathematics and Statistics. The comparatively low research output in core areas of the natural sciences and engineering is concerning, and could impair the flexibility of Canada’s research base, preventing research institutions and researchers from being able to pivot to tomorrow’s emerging research areas. [p. xix Print; p. 21 PDF]
US-centric
My friend,
I was a little surprised that the show was so centered on work from the US given that Grenville has curated ate least one show where there was significant input from artists based in Asia. Both Japan and Korea are very active with regard to artificial intelligence and it’s hard to believe that their artists haven’t kept pace. (I’m not as familiar with China and its AI efforts, other than in the field of facial recognition, but it’s hard to believe their artists aren’t experimenting.)
The Americans, of course, are very important developers in the field of AI but they are not alone and it would have been nice to have seen something from Asia and/or Africa and/or something from one of the other Americas. In fact, anything which takes us out of the same old, same old. (Luba Elliott wrote this (2019/2020/2021?) essay, “Artificial Intelligence Art from Africa and Black Communities Worldwide” on Aya Data if you want to get a sense of some of the activity on the African continent. Elliott does seem to conflate Africa and Black Communities, for some clarity you may want to check out the Wikipedia entry on Africanfuturism, which contrasts with this August 12, 2020 essay by Donald Maloba, “What is Afrofuturism? A Beginner’s Guide.” Maloba also conflates the two.)
As it turns out, Luba Elliott presented at the 2019 Montréal Digital Spring event, which brings me to Canada’s artificial intelligence and arts scene.
I promise I haven’t turned into a flag waving zealot, my friend. It’s just odd there isn’t a bit more given that machine learning was pioneered at the University of Toronto. Here’s more about that (from Wikipedia entry for Geoffrey Hinston),
Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.
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Hinton received the 2018 Turing Award, together with Yoshua Bengio [Canadian scientist] and Yann LeCun, for their work on deep learning.[24] They are sometimes referred to as the “Godfathers of AI” and “Godfathers of Deep Learning“,[25][26] and have continued to give public talks together.[27][28]
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Some of Hinton’s work was started in the US but since 1987, he has pursued his interests at the University of Toronto. He wasn’t proven right until 2012. Katrina Onstad’s February 29, 2018 article (Mr. Robot) for Toronto Life is a gripping read about Hinton and his work on neural networks. BTW, Yoshua Bengio (co-Godfather) is a Canadian scientist at the Université de Montréal and Yann LeCun (co-Godfather) is a French scientist at New York University.
Then, there’s another contribution, our government was the first in the world to develop a national artificial intelligence strategy. Adding those developments to the CCA ‘State of Science’ report findings about visual arts and performing arts, is there another word besides ‘odd’ to describe the lack of Canadian voices?
You’re going to point out the installation by Ben Bogart (a member of Simon Fraser University’s Metacreation Lab for Creative AI and instructor at the Emily Carr University of Art + Design (ECU)) but it’s based on the iconic US scifi film, 2001: A Space Odyssey. As for the other Canadian, Sougwen Chung, she left Canada pretty quickly to get her undergraduate degree in the US and has since moved to the UK. (You could describe hers as the quintessential success story, i.e., moving from Canada only to get noticed here after success elsewhere.)
In 2019, Bruce Grenville, Senior Curator at Vancouver Art Gallery, approached [the] Centre for Digital Media to collaborate on several industry projects for the forthcoming exhibition. Four student teams tackled the project briefs over the course of the next two years and produced award-winning installations that are on display until October 23 [2022].
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Basically, my friend, it would have been nice to see other voices or, at the least, an attempt at representing other voices and visual cultures informed by AI. As for Canadian contributions, maybe put something on the VAG website?
Playing well with others
it’s always a mystery to me why the Vancouver cultural scene seems comprised of a set of silos or closely guarded kingdoms. Reaching out to the public library and other institutions such as Science World might have cost time but could have enhanced the show
For example, one of the branches of the New York Public Library ran a programme called, “We are AI” in March 2022 (see my March 23, 2022 posting about the five-week course, which was run as a learning circle). The course materials are available for free (We are AI webpage) and I imagine that adding a ‘visual culture module’ wouldn’t be that difficult.
There is one (rare) example of some Vancouver cultural institutions getting together to offer an art/science programme and that was in 2017 when the Morris and Helen Belkin Gallery (at the University of British Columbia; UBC) hosted an exhibition of Santiago Ramon y Cajal’s work (see my Sept. 11, 2017 posting about the gallery show) along with that show was an ancillary event held by the folks at Café Scientifique at Science World and featuring a panel of professionals from UBC’s Faculty of Medicine and Dept. of Psychology, discussing Cajal’s work.
In fact, where were the science and technology communities for this show?
On a related note, the 2022 ACM SIGGRAPH conference (August 7 – 11, 2022) is being held in Vancouver. (ACM is the Association for Computing Machinery; SIGGRAPH is for Special Interest Group on Computer Graphics and Interactive Techniques.) SIGGRAPH has been holding conferences in Vancouver every few years since at least 2011.
This is both an international conference and an exhibition (of art) and the whole thing seems to have kicked off on July 25, 2022. If you’re interested, the programme can be found here and registration here.
Last time SIGGRAPH was here the organizers seemed interested in outreach and they offered some free events.
In the end
It was good to see the show. The curators brought together some exciting material. As is always the case, there were some missed opportunities and a few blind spots. But all is not lost.
July 27, 2022, the VAG held a virtual event with an artist,
… Gwenyth Chao to learn more about what happened to the honeybees and hives in Oxman’s Synthetic Apiary project. As a transdisciplinary artist herself, Chao will also discuss the relationship between art, science, technology and design. She will then guide participants to create a space (of any scale, from insect to human) inspired by patterns found in nature.
Hopefully there will be more more events inspired by specific ‘objects’. Meanwhile, August 12, 2022, the VAG is hosting,
… in partnership with the Canadian Music Centre BC, New Music at the Gallery is a live concert series hosted by the Vancouver Art Gallery that features an array of musicians and composers who draw on contemporary art themes.
Highlighting a selection of twentieth- and twenty-first-century music compositions, this second concert, inspired by the exhibition The Imitation Game: Visual Culture in the Age of Artificial Intelligence, will spotlight The Iliac Suite (1957), the first piece ever written using only a computer, and Kaija Saariaho’s Terra Memoria (2006), which is in a large part dependent on a computer-generated musical process.
…
It would be lovely if they could include an Ada Lovelace Day event. This is an international celebration held on October 11, 2022.
The Canadian Broadcasting Corporation’s (CBC) science television series,The Nature of Things, which has been broadcast since November 1960, explored the world of emotional, empathic and creative artificial intelligence (AI) in a Friday, November 19, 2021 telecast titled, The Machine That Feels,
The Machine That Feels explores how artificial intelligence (AI) is catching up to us in ways once thought to be uniquely human: empathy, emotional intelligence and creativity.
As AI moves closer to replicating humans, it has the potential to reshape every aspect of our world – but most of us are unaware of what looms on the horizon.
Scientists see AI technology as an opportunity to address inequities and make a better, more connected world. But it also has the capacity to do the opposite: to stoke division and inequality and disconnect us from fellow humans. The Machine That Feels, from The Nature of Things, shows viewers what they need to know about a field that is advancing at a dizzying pace, often away from the public eye.
What does it mean when AI makes art? Can AI interpret and understand human emotions? How is it possible that AI creates sophisticated neural networks that mimic the human brain? The Machine That Feels investigates these questions, and more.
In Vienna, composer Walter Werzowa has — with the help of AI — completed Beethoven’s previously unfinished 10th symphony. By feeding data about Beethoven, his music, his style and the original scribbles on the 10th symphony into an algorithm, AI has created an entirely new piece of art.
In Atlanta, Dr. Ayanna Howard and her robotics lab at Georgia Tech are teaching robots how to interpret human emotions. Where others see problems, Howard sees opportunity: how AI can help fill gaps in education and health care systems. She believes we need a fundamental shift in how we perceive robots: let’s get humans and robots to work together to help others.
At Tufts University in Boston, a new type of biological robot has been created: the xenobot. The size of a grain of sand, xenobots are grown from frog heart and skin cells, and combined with the “mind” of a computer. Programmed with a specific task, they can move together to complete it. In the future, they could be used for environmental cleanup, digesting microplastics and targeted drug delivery (like releasing chemotherapy compounds directly into tumours).
The film includes interviews with global leaders, commentators and innovators from the AI field, including Geoff Hinton, Yoshua Bengio, Ray Kurzweil and Douglas Coupland, who highlight some of the innovative and cutting-edge AI technologies that are changing our world.
The Machine That Feels focuses on one central question: in the flourishing age of artificial intelligence, what does it mean to be human?
I’ll get back to that last bit, “… what does it mean to be human?” later.
There’s a lot to appreciate in this 44 min. programme. As you’d expect, there was a significant chunk of time devoted to research being done in the US but Poland and Japan also featured and Canadian content was substantive. A number of tricky topics were covered and transitions from one topic to the next were smooth.
In the end credits, I counted over 40 source materials from Getty Images, Google Canada, Gatebox, amongst others. It would have been interesting to find out which segments were produced by CBC.
David Suzuki’s (programme host) script was well written and his narration was enjoyable, engaging, and non-intrusive. That last quality is not always true of CBC hosts who can fall into the trap of overdramatizing the text.
Drilling down
I have followed artificial intelligence stories in a passive way (i.e., I don’t seek them out) for many years. Even so, there was a lot of material in the programme that was new to me.
In the The Machine That Feels, a documentary from The Nature of Things, we meet Kondo Akihiko, a Tokyo resident who “married” a hologram of virtual pop singer Hatsune Miku using a certificate issued by Gatebox (the marriage isn’t recognized by the state, and Gatebox acknowledges the union goes “beyond dimensions”).
Overall, this Nature of Things episode embraces certainty, which means the question of what it means to human is referenced rather than seriously discussed. An unanswerable philosophical question, the programme is ill-equipped to address it, especially since none of the commentators are philosophers or seem inclined to philosophize.
The programme presents AI as a juggernaut. Briefly mentioned is the notion that we need to make some decisions about how our juggernaut is developed and utilized. No one discusses how we go about making changes to systems that are already making critical decisions for us. (For more about AI and decision-making, see my February 28, 2017 posting and scroll down to the ‘Algorithms and big data’ subhead for Cathy O’Neil’s description of how important decisions that affect us are being made by AI systems. She is the author of the 2016 book, ‘Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy’; still a timely read.)
In fact, the programme’s tone is mostly one of breathless excitement. A few misgivings are expressed, e.g,, one woman who has an artificial ‘texting friend’ (Replika; a chatbot app) noted that it can ‘get into your head’ when she had a chat where her ‘friend’ told her that all of a woman’s worth is based on her body; she pushed back but intimated that someone more vulnerable could find that messaging difficult to deal with.
The sequence featuring Akihiko and his hologram ‘wife’ is followed by one suggesting that people might become more isolated and emotionally stunted as they interact with artificial friends. It should be noted, Akihiko’s wife is described as ‘perfect’. I gather perfection means that you are always understanding and have no needs of your own. She also seems to be about 18″ high.
Akihiko has obviously been asked about his ‘wife’ before as his answers are ready. They boil down to “there are many types of relationships” and there’s nothing wrong with that. It’s an intriguing thought which is not explored.
Also unexplored, these relationships could be said to resemble slavery. After all, you pay for these friends over which you have control. But perhaps that’s alright since AI friends don’t have consciousness. Or do they? In addition to not being able to answer the question, “what is it to be human?” we still can’t answer the question, “what is consciousness?”
AI and creativity
The Nature of Things team works fast. ‘Beethoven X – The AI Project’ had its first performance on October 9, 2021. (See my October 1, 2021 post ‘Finishing Beethoven’s unfinished 10th Symphony’ for more information from Ahmed Elgammal’s (Director of the Art & AI Lab at Rutgers University) technical perspective on the project.
Briefly, Beethoven died before completing his 10th symphony and a number of computer scientists, musicologists, AI, and musicians collaborated to finish the symphony.)
The one listener (Felix Mayer, music professor at the Technical University Munich) in the hall during a performance doesn’t consider the work to be a piece of music. He does have a point. Beethoven left some notes but this ’10th’ is at least partly mathematical guesswork. A set of probabilities where an algorithm chooses which note comes next based on probability.
There was another artist also represented in the programme. Puzzlingly, it was the still living Douglas Coupland. In my opinion, he’s better known as a visual artist than a writer (his Wikipedia entry lists him as a novelist first) but he has succeeded greatly in both fields.
What makes his inclusion in the Nature of Things ‘The Machine That Feels’ programme puzzling, is that it’s not clear how he worked with artificial intelligence in a collaborative fashion. Here’s a description of Coupland’s ‘AI’ project from a June 29, 2021 posting by Chris Henry on the Google Outreach blog (Note: Links have been removed),
… when the opportunity presented itself to explore how artificial intelligence (AI) inspires artistic expression — with the help of internationally renowned Canadian artist Douglas Coupland — the Google Research team jumped on it. This collaboration, with the support of Google Arts & Culture, culminated in a project called Slogans for the Class of 2030, which spotlights the experiences of the first generation of young people whose lives are fully intertwined with the existence of AI.
This collaboration was brought to life by first introducing Coupland’s written work to a machine learning language model. Machine learning is a form of AI that provides computer systems the ability to automatically learn from data. In this case, Google research scientists tuned a machine learning algorithm with Coupland’s 30-year body of written work — more than a million words — so it would familiarize itself with the author’s unique style of writing. From there, curated general-public social media posts on selected topics were added to teach the algorithm how to craft short-form, topical statements. [emphases mine]
Once the algorithm was trained, the next step was to process and reassemble suggestions of text for Coupland to use as inspiration to create twenty-five Slogans for the Class of 2030. [emphasis mine]
“I would comb through ‘data dumps’ where characters from one novel were speaking with those in other novels in ways that they might actually do. It felt like I was encountering a parallel universe Doug,” Coupland says. “And from these outputs, the statements you see here in this project appeared like gems. Did I write them? Yes. No. Could they have existed without me? No.” [emphases mine]
So, the algorithms crunched through Coupland’s word and social media texts to produce slogans, which Coupland then ‘combed through’ to pick out 25 slogans for the ‘Slogans For The Class of 2030’ project. (Note: In the programme, he says that he started a sentence and then the AI system completed that sentence with material gleaned from his own writings, which brings to Exquisite Corpse, a collaborative game for writers originated by the Surrealists, possibly as early as 1918.)
The ‘slogans’ project also reminds me of William S. Burroughs and the cut-up technique used in his work. From the William S. Burroughs Cut-up technique webpage on the Language is a Virus website (Thank you to Lake Rain Vajra for a very interesting website),
…
The cutup is a mechanical method of juxtaposition in which Burroughs literally cuts up passages of prose by himself and other writers and then pastes them back together at random. This literary version of the collage technique is also supplemented by literary use of other media. Burroughs transcribes taped cutups (several tapes spliced into each other), film cutups (montage), and mixed media experiments (results of combining tapes with television, movies, or actual events). Thus Burroughs’s use of cutups develops his juxtaposition technique to its logical conclusion as an experimental prose method, and he also makes use of all contemporary media, expanding his use of popular culture.
…
[Burroughs says] “All writing is in fact cut-ups. A collage of words read heard overheard. What else? Use of scissors renders the process explicit and subject to extension and variation. Clear classical prose can be composed entirely of rearranged cut-ups. Cutting and rearranging a page of written words introduces a new dimension into writing enabling the writer to turn images in cinematic variation. Images shift sense under the scissors smell images to sound sight to sound to kinesthetic. This is where Rimbaud was going with his color of vowels. And his “systematic derangement of the senses.” The place of mescaline hallucination: seeing colors tasting sounds smelling forms.
…
“The cut-ups can be applied to other fields than writing. Dr Neumann [emphasis mine] in his Theory of Games and Economic behavior introduces the cut-up method of random action into game and military strategy: assume that the worst has happened and act accordingly. … The cut-up method could be used to advantage in processing scientific data. [emphasis mine] How many discoveries have been made by accident? We cannot produce accidents to order. The cut-ups could add new dimension to films. Cut gambling scene in with a thousand gambling scenes all times and places. Cut back. Cut streets of the world. Cut and rearrange the word and image in films. There is no reason to accept a second-rate product when you can have the best. And the best is there for all. Poetry is for everyone . . .”
Here’s Burroughs on the history of writers and cutups (thank you to QUEDEAR for posting this clip),
You can hear Burroughs talk about the technique and how he started using it in 1959.
There is no explanation from Coupland as to how his project differs substantively from Burroughs’ cut-ups or a session of Exquisite Corpse. The use of a computer programme to crunch through data and give output doesn’t seem all that exciting. *(More about computers and chatbots at end of posting).* It’s hard to know if this was an interview situation where he wasn’t asked the question or if the editors decided against including it.
Kazuo Ishiguro?
Given that Ishiguro’s 2021 book (Klara and the Sun) is focused on an artificial friend and raises the question of ‘what does it mean to be human’, as well as the related question, ‘what is the nature of consciousness’, it would have been interesting to hear from him. He spent a fair amount of time looking into research on machine learning in preparation for his book. Maybe he was too busy?
AI and emotions
The work being done by Georgia Tech’s Dr. Ayanna Howard and her robotics lab is fascinating. They are teaching robots how to interpret human emotions. The segment which features researchers teaching and interacting with robots, Pepper and Salt, also touches on AI and bias.
Watching two African American researchers talk about the ways in which AI is unable to read emotions on ‘black’ faces as accurately as ‘white’ faces is quite compelling. It also reinforces the uneasiness you might feel after the ‘Replika’ segment where an artificial friend informs a woman that her only worth is her body.
(Interestingly, Pepper and Salt are produced by Softbank Robotics, part of Softbank, a multinational Japanese conglomerate, [see a June 28, 2021 article by Ian Carlos Campbell for The Verge] whose entire management team is male according to their About page.)
While Howard is very hopeful about the possibilities of a machine that can read emotions, she doesn’t explore (on camera) any means for pushing back against bias other than training AI by using more black faces to help them learn. Perhaps more representative management and coding teams in technology companies?
While the programme largely focused on AI as an algorithm on a computer, robots can be enabled by AI (as can be seen in the segment with Dr. Howard).
My February 14, 2019 posting features research with a completely different approach to emotions and machines,
“I’ve always felt that robots shouldn’t just be modeled after humans [emphasis mine] or be copies of humans,” he [Guy Hoffman, assistant professor at Cornell University)] said. “We have a lot of interesting relationships with other species. Robots could be thought of as one of those ‘other species,’ not trying to copy what we do but interacting with us with their own language, tapping into our own instincts.”
This brings the question back to, what is consciousness?
What scientists aren’t taught
Dr. Howard notes that scientists are not taught to consider the implications of their work. Her comment reminded me of a question I was asked many years ago after a presentation, it concerned whether or not science had any morality. (I said, no.)
My reply angered an audience member (a visual artist who was working with scientists at the time) as she took it personally and started defending scientists as good people who care and have morals and values. She failed to understand that the way in which we teach science conforms to a notion that somewhere there are scientific facts which are neutral and objective. Society and its values are irrelevant in the face of the larger ‘scientific truth’ and, as a consequence, you don’t need to teach or discuss how your values or morals affect that truth or what the social implications of your work might be.
Science is practiced without much if any thought to values. By contrast, there is the medical injunction, “Do no harm,” which suggests to me that someone recognized competing values. E.g., If your important and worthwhile research is harming people, you should ‘do no harm’.
The experts, the connections, and the Canadian content
It’s been a while since I’ve seen Ray Kurzweil mentioned but he seems to be getting more attention these days. (See this November 16, 2021 posting by Jonny Thomson titled, “The Singularity: When will we all become super-humans? Are we really only a moment away from “The Singularity,” a technological epoch that will usher in a new era in human evolution?” on The Big Think for more). Note: I will have a little more about evolution later in this post.
Interestingly, Kurzweil is employed by Google these days (see his Wikipedia entry, the column to the right). So is Geoffrey Hinton, another one of the experts in the programme (see Hinton’s Wikipedia entry, the column to the right, under Institutions).
I’m not sure about Yoshu Bengio’s relationship with Google but he’s a professor at the Université de Montréal, and he’s the Scientific Director for Mila ((Quebec’s Artificial Intelligence research institute)) & IVADO (Institut de valorisation des données), Note: IVADO is not particularly relevant to what’s being discussed in this post.
As for Mila, the Canada Google blog in a November 21, 2016 posting notes a $4.5M grant to the institution,
Google invests $4.5 Million in Montreal AI Research
…
A new grant from Google for the Montreal Institute for Learning Algorithms (MILA) will fund seven faculty across a number of Montreal institutions and will help tackle some of the biggest challenges in machine learning and AI, including applications in the realm of systems that can understand and generate natural language. In other words, better understand a fan’s enthusiasm for Les Canadien [sic].
Google is expanding its academic support of deep learning at MILA, renewing Yoshua Bengio’s Focused Research Award and offering Focused Research Awards to MILA faculty at University of Montreal and McGill University:
Google reaffirmed their commitment to Mila in 2020 with a grant worth almost $4M (from a November 13, 2020 posting on the Mila website, Note: A link has been removed),
Google Canada announced today [November 13, 2020] that it will be renewing its funding of Mila – Quebec Artificial Intelligence Institute, with a generous pledge of nearly $4M over a three-year period. Google previously invested $4.5M US in 2016, enabling Mila to grow from 25 to 519 researchers.
In a piece written for Google’s Official Canada Blog, Yoshua Bengio, Mila Scientific Director, says that this year marked a “watershed moment for the Canadian AI community,” as the COVID-19 pandemic created unprecedented challenges that demanded rapid innovation and increased interdisciplinary collaboration between researchers in Canada and around the world.
“COVID-19 has changed the world forever and many industries, from healthcare to retail, will need to adapt to thrive in our ‘new normal.’ As we look to the future and how priorities will shift, it is clear that AI is no longer an emerging technology but a useful tool that can serve to solve world problems. Google Canada recognizes not only this opportunity but the important task at hand and I’m thrilled they have reconfirmed their support of Mila with an additional $3,95 million funding grant until 22.“
– Yoshua Bengio, for Google’s Official Canada Blog
Interesting, eh? Of course, Douglas Coupland is working with Google, presumably for money, and that would connect over 50% of the Canadian content (Douglas Coupland, Yoshua Bengio, and Geoffrey Hinton; Kurzweil is an American) in the programme to Google.
My hat’s off to Google’s marketing communications and public relations teams.
Anthony Morgan of Science Everywhere also provided some Canadian content. His LinkedIn profile indicates that he’s working on a PhD in molecular science, which is described this way, “My work explores the characteristics of learning environments, that support critical thinking and the relationship between critical thinking and wisdom.”
Morgan is also the founder and creative director of Science Everywhere, from his LinkedIn profile, “An events & media company supporting knowledge mobilization, community engagement, entrepreneurship and critical thinking. We build social tools for better thinking.”
There is this from his LinkedIn profile,
I develop, create and host engaging live experiences & media to foster critical thinking.
I’ve spent my 15+ years studying and working in psychology and science communication, thinking deeply about the most common individual and societal barriers to critical thinking. As an entrepreneur, I lead a team to create, develop and deploy cultural tools designed to address those barriers. As a researcher I study what we can do to reduce polarization around science.
There’s a lot more to Morgan (do look him up; he has connections to the CBC and other media outlets). The difficulty is: why was he chosen to talk about artificial intelligence and emotions and creativity when he doesn’t seem to know much about the topic? He does mention GPT-3, an AI programming language. He seems to be acting as an advocate for AI although he offers this bit of almost cautionary wisdom, “… algorithms are sets of instructions.” (You can can find out more about it in my April 27, 2021 posting. There’s also this November 26, 2021 posting [The Inherent Limitations of GPT-3] by Andrey Kurenkov, a PhD student with the Stanford [University] Vision and Learning Lab.)
Most of the cautionary commentary comes from Luke Stark, assistant professor at Western [Ontario] University’s Faculty of Information and Media Studies. He’s the one who mentions stunted emotional growth.
Before moving on, there is another set of connections through the Pan-Canadian Artificial Intelligence Strategy, a Canadian government science funding initiative announced in the 2017 federal budget. The funds allocated to the strategy are administered by the Canadian Institute for Advanced Research (CIFAR). Yoshua Bengio through Mila is associated with the strategy and CIFAR, as is Geoffrey Hinton through his position as Chief Scientific Advisor for the Vector Institute.
Evolution
Getting back to “The Singularity: When will we all become super-humans? Are we really only a moment away from “The Singularity,” a technological epoch that will usher in a new era in human evolution?” Xenobots point in a disconcerting (for some of us) evolutionary direction.
I featured the work, which is being done at Tufts University in the US, in my June 21, 2021 posting, which includes an embedded video,
Last year, a team of biologists and computer scientists from Tufts University and the University of Vermont (UVM) created novel, tiny self-healing biological machines from frog cells called “Xenobots” that could move around, push a payload, and even exhibit collective behavior in the presence of a swarm of other Xenobots.
Get ready for Xenobots 2.0.
Also from an excerpt in the posting, the team has “created life forms that self-assemble a body from single cells, do not require muscle cells to move, and even demonstrate the capability of recordable memory.”
Memory is key to intelligence and this work introduces the notion of ‘living’ robots which leads to questioning what constitutes life. ‘The Machine That Feels’ is already grappling with far too many questions to address this development but introducing the research here might have laid the groundwork for the next episode, The New Human, telecast on November 26, 2021,
…
While no one can be certain what will happen, evolutionary biologists and statisticians are observing trends that could mean our future feet only have four toes (so long, pinky toe) or our faces may have new combinations of features. The new humans might be much taller than their parents or grandparents, or have darker hair and eyes.
And while evolution takes a lot of time, we might not have to wait too long for a new version of ourselves.
Technology is redesigning the way we look and function — at a much faster pace than evolution. We are merging with technology more than ever before: our bodies may now have implanted chips, smart limbs, exoskeletons and 3D-printed organs. A revolutionary gene editing technique has given us the power to take evolution into our own hands and alter our own DNA. How long will it be before we are designing our children?
As the story about the xenobots doesn’t say, we could also take the evolution of another species into our hands.
David Suzuki, where are you?
Our programme host, David Suzuki surprised me. I thought that as an environmentalist he’d point out that the huge amounts of computing power needed for artificial intelligence as mentioned in the programme, constitutes an environmental issue. I also would have expected a geneticist like Suzuki might have some concerns with regard to xenobots but perhaps that’s being saved for the next episode (The New Human) of the Nature of Things.
Artificial stupidity
Thanks to Will Knight for introducing me to the term ‘artificial stupidity’. Knight, a senior writer covers artificial intelligence for WIRED magazine. According to its Wikipedia entry,
Artificial stupidity is commonly used as a humorous opposite of the term artificial intelligence (AI), often as a derogatory reference to the inability of AI technology to adequately perform its tasks.[1] However, within the field of computer science, artificial stupidity is also used to refer to a technique of “dumbing down” computer programs in order to deliberately introduce errors in their responses.
Knight was using the term in its humorous, derogatory form.
Finally
The episode certainly got me thinking if not quite in the way producers might have hoped. ‘The Machine That Feels’ is a glossy, pretty well researched piece of infotainment.
To be blunt, I like and have no problems with infotainment but it can be seductive. I found it easier to remember the artificial friends, wife, xenobots, and symphony than the critiques and concerns.
Hopefully, ‘The Machine That Feels’ stimulates more interest in some very important topics. If you missed the telecast, you can catch the episode here.
For anyone curious about predictive policing, which was mentioned in the Ayanna Howard segment, see my November 23, 2017 posting about Vancouver’s plunge into AI and car theft.
*ETA December 6, 2021: One of the first ‘chatterbots’ was ELIZA, a computer programme developed from1964 to 1966. The most famous ELIZA script was DOCTOR, where the programme simulated a therapist. Many early users believed ELIZA understood and could respond as a human would despite Joseph Weizenbaum’s (creator of the programme) insistence otherwise.
I think the French title for this call is more informative “L’UNESCO et Mila s’associent pour lancer un appel à publications afin de mettre en lumière les faiblesses du développement de l’IA [l’intelligence artificiel].” Here’s my translation (in advance, apologies to all who find it clumsy), ‘UNESCO {United Nations Educational, Scientific, and Cultural Organization) and MILA (Montreal Institute for Learning Algorithms) are issuing a joint call for papers illuminating weaknesses in the development of artificial intelligence.
UNESCO in cooperation with Mila-Quebec Artificial Intelligence Institute [?], is launching a Call for Proposals to identify blind spots in AI Policy and Programme Development. The collective work will explore creative, novel and far-reaching approaches to tackling blind spots in AI.
All contributors are invited to answer the same question: what are the blind spots on which we must shed light in order for AI to benefit all?
Issues can address 1) blind spots in the development of AI as a technology 2) blind spots in the development of AI as a sector, and 3) blind spots in the development of public policies, global governance, and regulation for AI. There are no limits to the subjects to be addressed. These blind spots could include issues ranging from science fiction and the future of AI, creative deep fakes and the future of misinformation, AI and the future of data driven humanitarian aid, indigenous knowledge and AI, and gender-based violence and sex robots. Proposals can be in creative formats, and the call for proposals is open to individuals from all academic backgrounds and sectors. Proposals from all stakeholder groups, particularly marginalized and underrepresented groups, are encouraged, as well as proposals from authors from the global south and innovative formats (artwork, cartoons, videos, etc).
Call for proposals are open until 2 May 2021.
Selected proposals will be confirmed by 25 May.
Final proposals, if in written format, should be between 5000-7000 words and should be written in a style that is accessible to non-AI specialists and received by 1 September 2021.
To ensure inclusivity and a diversity of voices, for accepted contributions outside of academia, authors may request financial support available on a needs-based basis up to 1000 usd.
I really appreciate the breadth of the call with a range of blind spots such as “science fiction and the future of AI, creative deep fakes and the future of misinformation, AI and the future of data driven humanitarian aid, indigenous knowledge and AI, and gender-based violence and sex robots” and, presumably, anything the convenors had not considered.
As well, they haven’t confined themselves to the ‘same old, same old’ contributors, “all stakeholder groups, particularly marginalized and underrepresented groups, are encouraged, as well as proposals from authors from the global south and innovative formats (artwork, cartoons, videos, etc).”
I’m glad to see a refreshing approach being taken to a call for proposals. I wish them good luck.
The Québec connection
Mila (Montreal Institute for Learning Algorithms), UNESCO’s co-host for this call, was founded in 1993 according to its About Mila page,
Founded in 1993 by Professor Yoshua Bengio of the Université de Montréal, Mila is a research institute in artificial intelligence that rallies over 500 researchers specializing in the field of machine learning. Based in Montreal, Mila’s mission is to be a global pole for scientific advances that inspire innovation and the development of AI for the benefit of all.
Since 2017, [emphasis mine] Mila is the result of a partnership between the Université de Montréal and McGill University, closely linked with Polytechnique Montréal and HEC Montréal. Today, Mila gathers in its offices a vibrant community of professors, students, industrial partners and startups working in AI, making the institute the world’s largest academic research center in machine learning.
Mila, a non-profit organization, is internationally recognized for its significant contributions to machine learning, especially in the areas of language modelling, machine translation, object recognition and generative models.
Unmentioned, the Pan-Canadian Artificial Intelligence (AI) Strategy was created and funded by the Canadian federal government in 2017. One of the beneficiaries was Mila. (Odd how 2017 was the year Mila found so many academic partners in its home province.) From the Pan-Canadian AI strategy webpage on the Invest Canada website (Note: Links have been removed),
The artificial intelligence (AI) and machine learning revolution is well underway, and Canada is at its forefront. From top-ranked educational institutions and market-leading tech companies to world-renowned researchers, Canada’s AI ecosystems are leading global AI developments.
To continue to foster this growth and maintain its leadership position, Canada launched the $125M Pan-Canadian Artificial Intelligence Strategy in 2017—making it the first country to release a national AI strategy.
…
The Pan-Canadian AI Strategy is founded on a partnership between the Canadian Institute for Advanced Research (CIFAR) and the three centres of excellence: the Alberta Machine Intelligence Institute (AMII) in Edmonton, the Vector Institute in Toronto, and the Montreal Institute for Learning Algorithms (Mila) [all emphases mine] in Montreal. Together, they provide the support, resources, and talent for AI innovation and investment.
I don’t know where “Mila-Quebec Artificial Intelligence Institute” comes from. It’s not on their own website and I’ve never seen Mila called that anywhere other than on this UNESCO call.
With all the talk about artificial intelligence (AI), a lot more attention seems to be paid to apocalyptic scenarios: loss of jobs, financial hardship, loss of personal agency and privacy, and more with all of these impacts being described as global. Still, there are some folks who are considering and working on ‘AI for good’.
If you’d asked me, the International Telecommunications Union (ITU) would not have been my first guess (my choice would have been United Nations Educational, Scientific and Cultural Organization [UNESCO]) as an agency likely to host the 2018 AI for Good Global Summit. But, it turns out the ITU is a UN (United Nations agency) and, according to its Wikipedia entry, it’s an intergovernmental public-private partnership, which may explain the nature of the participants in the upcoming summit.
The news
First, there’s a May 4, 2018 ITU media advisory (received via email or you can find the full media advisory here) about the upcoming summit,
Artificial Intelligence (AI) is now widely identified as being able to address the greatest challenges facing humanity – supporting innovation in fields ranging from crisis management and healthcare to smart cities and communications networking.
The second annual ‘AI for Good Global Summit’ will take place 15-17 May [2018] in Geneva, and seeks to leverage AI to accelerate progress towards the United Nations’ Sustainable Development Goals and ultimately benefit humanity.
WHAT: Global event to advance ‘AI for Good’ with the participation of internationally recognized AI experts. The programme will include interactive high-level panels, while ‘AI Breakthrough Teams’ will propose AI strategies able to create impact in the near term, guided by an expert audience of mentors representing government, industry, academia and civil society – through interactive sessions. The summit will connect AI innovators with public and private-sector decision-makers, building collaboration to take promising strategies forward.
A special demo & exhibit track will feature innovative applications of AI designed to: protect women from sexual violence, avoid infant crib deaths, end child abuse, predict oral cancer, and improve mental health treatments for depression – as well as interactive robots including: Alice, a Dutch invention designed to support the aged; iCub, an open-source robot; and Sophia, the humanoid AI robot.
WHEN: 15-17 May 2018, beginning daily at 9 AM
WHERE: ITU Headquarters, 2 Rue de Varembé, Geneva, Switzerland (Please note: entrance to ITU is now limited for all visitors to the Montbrillant building entrance only on rue Varembé).
WHO: Confirmed participants to date include expert representatives from: Association for Computing Machinery, Bill and Melinda Gates Foundation, Cambridge University, Carnegie Mellon, Chan Zuckerberg Initiative, Consumer Trade Association, Facebook, Fraunhofer, Google, Harvard University, IBM Watson, IEEE, Intellectual Ventures, ITU, Microsoft, Massachusetts Institute of Technology (MIT), Partnership on AI, Planet Labs, Shenzhen Open Innovation Lab, University of California at Berkeley, University of Tokyo, XPRIZE Foundation, Yale University – and the participation of “Sophia” the humanoid robot and “iCub” the EU open source robotcub.
The interview
Frederic Werner, Senior Communications Officer at the International Telecommunication Union and** one of the organizers of the AI for Good Global Summit 2018 kindly took the time to speak to me and provide a few more details about the upcoming event.
Werner noted that the 2018 event grew out of a much smaller 2017 ‘workshop’ and first of its kind, about beneficial AI which this year has ballooned in size to 91 countries (about 15 participants are expected from Canada), 32 UN agencies, and substantive representation from the private sector. The 2017 event featured Dr. Yoshua Bengio of the University of Montreal (Université de Montréal) was a featured speaker.
“This year, we’re focused on action-oriented projects that will help us reach our Sustainable Development Goals (SDGs) by 2030. We’re looking at near-term practical AI applications,” says Werner. “We’re matchmaking problem-owners and solution-owners.”
Academics, industry professionals, government officials, and representatives from UN agencies are gathering to work on four tracks/themes:
AI and satellite imagery
AI and health
AI and smart cities & communities
Trust in AI
In advance of this meeting, the group launched an AI repository (an action item from the 2017 meeting) on April 25, 2018 inviting people to list their AI projects (from the ITU’s April 25, 2018? AI repository news announcement),
ITU has just launched an AI Repository where anyone working in the field of artificial intelligence (AI) can contribute key information about how to leverage AI to help solve humanity’s greatest challenges.
This is the only global repository that identifies AI-related projects, research initiatives, think-tanks and organizations that aim to accelerate progress on the 17 United Nations’ Sustainable Development Goals (SDGs).
To submit a project, just press ‘Submit’ on the AI Repository site and fill in the online questionnaire, providing all relevant details of your project. You will also be asked to map your project to the relevant World Summit on the Information Society (WSIS) action lines and the SDGs. Approved projects will be officially registered in the repository database.
Benefits of participation on the AI Repository include:
Your project details will become visible to the world on the website.
You will be connected with AI stakeholders, world-wide.
WSIS Prizes recognize individuals, governments, civil society, local, regional and international agencies, research institutions and private-sector companies for outstanding success in implementing development oriented strategies that leverage the power of AI and ICTs.
If you have any questions, please send an email to: ai@itu.int
“Your project won’t be visible immediately as we have to vet the submissions to weed out spam-type material and projects that are not in line with our goals,” says Werner. That said, there are already 29 projects in the repository. As you might expect, the UK, China, and US are in the repository but also represented are Egypt, Uganda, Belarus, Serbia, Peru, Italy, and other countries not commonly cited when discussing AI research.
Werner also pointed out in response to my surprise over the ITU’s role with regard to this AI initiative that the ITU is the only UN agency which has 192* member states (countries), 150 universities, and over 700 industry members as well as other member entities, which gives them tremendous breadth of reach. As well, the organization, founded originally in 1865 as the International Telegraph Convention, has extensive experience with global standardization in the information technology and telecommunications industries. (See more in their Wikipedia entry.)
The AI for Good series is the leading United Nations platform for dialogue on AI. The action-oriented 2018 summit will identify practical applications of AI and supporting strategies to improve the quality and sustainability of life on our planet. The summit will continue to formulate strategies to ensure trusted, safe and inclusive development of AI technologies and equitable access to their benefits.
While the 2017 summit sparked the first ever inclusive global dialogue on beneficial AI, the action-oriented 2018 summit will focus on impactful AI solutions able to yield long-term benefits and help achieve the Sustainable Development Goals. ‘Breakthrough teams’ will demonstrate the potential of AI to map poverty and aid with natural disasters using satellite imagery, how AI could assist the delivery of citizen-centric services in smart cities, and new opportunities for AI to help achieve Universal Health Coverage, and finally to help achieve transparency and explainability in AI algorithms.
Teams will propose impactful AI strategies able to be enacted in the near term, guided by an expert audience of mentors representing government, industry, academia and civil society. Strategies will be evaluated by the mentors according to their feasibility and scalability, potential to address truly global challenges, degree of supporting advocacy, and applicability to market failures beyond the scope of government and industry. The exercise will connect AI innovators with public and private-sector decision-makers, building collaboration to take promising strategies forward.
“As the UN specialized agency for information and communication technologies, ITU is well placed to guide AI innovation towards the achievement of the UN Sustainable Development Goals. We are providing a neutral close quotation markplatform for international dialogue aimed at building a common understanding of the capabilities of emerging AI technologies.” Houlin Zhao, Secretary General of ITU
Should you be close to Geneva, it seems that registration is still open. Just go to the ITU’s AI for Good Global Summit 2018 webpage, scroll the page down to ‘Documentation’ and you will find a link to the invitation and a link to online registration. Participation is free but I expect that you are responsible for your travel and accommodation costs.
For anyone unable to attend in person, the summit will be livestreamed (webcast in real time) and you can watch the sessions by following the link below,
For those of us on the West Coast of Canada and other parts distant to Geneva, you will want to take the nine hour difference between Geneva (Switzerland) and here into account when viewing the proceedings. If you can’t manage the time difference, the sessions are being recorded and will be posted at a later date.
*’132 member states’ corrected to ‘192 member states’ on May 11, 2018 at 1500 hours PDT.
Taking up from where I left off with my comments on Competing in a Global Innovation Economy: The Current State of R and D in Canada or as I prefer to call it the Third assessment of Canadas S&T (science and technology) and R&D (research and development). (Part 1 for anyone who missed it).
Is it possible to get past Hedy?
Interestingly (to me anyway), one of our R&D strengths, the visual and performing arts, features sectors where a preponderance of people are dedicated to creating culture in Canada and don’t spend a lot of time trying to make money so they can retire before the age of 40 as so many of our start-up founders do. (Retiring before the age of 40 just reminded me of Hollywood actresses {Hedy] who found and still do find that work was/is hard to come by after that age. You may be able but I’m not sure I can get past Hedy.) Perhaps our business people (start-up founders) could take a leaf out of the visual and performing arts handbook? Or, not. There is another question.
Does it matter if we continue to be a ‘branch plant’ economy? Somebody once posed that question to me when I was grumbling that our start-ups never led to larger businesses and acted more like incubators (which could describe our R&D as well),. He noted that Canadians have a pretty good standard of living and we’ve been running things this way for over a century and it seems to work for us. Is it that bad? I didn’t have an answer for him then and I don’t have one now but I think it’s a useful question to ask and no one on this (2018) expert panel or the previous expert panel (2013) seems to have asked.
I appreciate that the panel was constrained by the questions given by the government but given how they snuck in a few items that technically speaking were not part of their remit, I’m thinking they might have gone just a bit further. The problem with answering the questions as asked is that if you’ve got the wrong questions, your answers will be garbage (GIGO; garbage in, garbage out) or, as is said, where science is concerned, it’s the quality of your questions.
On that note, I would have liked to know more about the survey of top-cited researchers. I think looking at the questions could have been quite illuminating and I would have liked some information on from where (geographically and area of specialization) they got most of their answers. In keeping with past practice (2012 assessment published in 2013), there is no additional information offered about the survey questions or results. Still, there was this (from the report released April 10, 2018; Note: There may be some difference between the formatting seen here and that seen in the document),
3.1.2 International Perceptions of Canadian Research
As with the 2012 S&T report, the CCA commissioned a survey of top-cited researchers’ perceptions of Canada’s research strength in their field or subfield relative to that of other countries (Section 1.3.2). Researchers were asked to identify the top five countries in their field and subfield of expertise: 36% of respondents (compared with 37% in the 2012 survey) from across all fields of research rated Canada in the top five countries in their field (Figure B.1 and Table B.1 in the appendix). Canada ranks fourth out of all countries, behind the United States, United Kingdom, and Germany, and ahead of France. This represents a change of about 1 percentage point from the overall results of the 2012 S&T survey. There was a 4 percentage point decrease in how often France is ranked among the top five countries; the ordering of the top five countries, however, remains the same.
When asked to rate Canada’s research strength among other advanced countries in their field of expertise, 72% (4,005) of respondents rated Canadian research as “strong” (corresponding to a score of 5 or higher on a 7-point scale) compared with 68% in the 2012 S&T survey (Table 3.4). [pp. 40-41 Print; pp. 78-70 PDF]
Before I forget, there was mention of the international research scene,
Growth in research output, as estimated by number of publications, varies considerably for the 20 top countries. Brazil, China, India, Iran, and South Korea have had the most significant increases in publication output over the last 10 years. [emphases mine] In particular, the dramatic increase in China’s output means that it is closing the gap with the United States. In 2014, China’s output was 95% of that of the United States, compared with 26% in 2003. [emphasis mine]
Table 3.2 shows the Growth Index (GI), a measure of the rate at which the research output for a given country changed between 2003 and 2014, normalized by the world growth rate. If a country’s growth in research output is higher than the world average, the GI score is greater than 1.0. For example, between 2003 and 2014, China’s GI score was 1.50 (i.e., 50% greater than the world average) compared with 0.88 and 0.80 for Canada and the United States, respectively. Note that the dramatic increase in publication production of emerging economies such as China and India has had a negative impact on Canada’s rank and GI score (see CCA, 2016).
As long as I’ve been blogging (10 years), the international research community (in particular the US) has been looking over its shoulder at China.
Patents and intellectual property
As an inventor, Hedy got more than one patent. Much has been made of the fact that despite an agreement, the US Navy did not pay her or her partner (George Antheil) for work that would lead to significant military use (apparently, it was instrumental in the Bay of Pigs incident, for those familiar with that bit of history), GPS, WiFi, Bluetooth, and more.
Some comments about patents. They are meant to encourage more innovation by ensuring that creators/inventors get paid for their efforts .This is true for a set time period and when it’s over, other people get access and can innovate further. It’s not intended to be a lifelong (or inheritable) source of income. The issue in Lamarr’s case is that the navy developed the technology during the patent’s term without telling either her or her partner so, of course, they didn’t need to compensate them despite the original agreement. They really should have paid her and Antheil.
The current patent situation, particularly in the US, is vastly different from the original vision. These days patents are often used as weapons designed to halt innovation. One item that should be noted is that the Canadian federal budget indirectly addressed their misuse (from my March 16, 2018 posting),
Surprisingly, no one else seems to have mentioned a new (?) intellectual property strategy introduced in the document (from Chapter 2: Progress; scroll down about 80% of the way, Note: The formatting has been changed),
Budget 2018 proposes measures in support of a new Intellectual Property Strategy to help Canadian entrepreneurs better understand and protect intellectual property, and get better access to shared intellectual property.
What Is a Patent Collective?
A Patent Collective is a way for firms to share, generate, and license or purchase intellectual property. The collective approach is intended to help Canadian firms ensure a global “freedom to operate”, mitigate the risk of infringing a patent, and aid in the defence of a patent infringement suit.
Budget 2018 proposes to invest $85.3 million over five years, starting in 2018–19, with $10 million per year ongoing, in support of the strategy. The Minister of Innovation, Science and Economic Development will bring forward the full details of the strategy in the coming months, including the following initiatives to increase the intellectual property literacy of Canadian entrepreneurs, and to reduce costs and create incentives for Canadian businesses to leverage their intellectual property:
To better enable firms to access and share intellectual property, the Government proposes to provide $30 million in 2019–20 to pilot a Patent Collective. This collective will work with Canada’s entrepreneurs to pool patents, so that small and medium-sized firms have better access to the critical intellectual property they need to grow their businesses.
To support the development of intellectual property expertise and legal advice for Canada’s innovation community, the Government proposes to provide $21.5 million over five years, starting in 2018–19, to Innovation, Science and Economic Development Canada. This funding will improve access for Canadian entrepreneurs to intellectual property legal clinics at universities. It will also enable the creation of a team in the federal government to work with Canadian entrepreneurs to help them develop tailored strategies for using their intellectual property and expanding into international markets.
To support strategic intellectual property tools that enable economic growth, Budget 2018 also proposes to provide $33.8 million over five years, starting in 2018–19, to Innovation, Science and Economic Development Canada, including $4.5 million for the creation of an intellectual property marketplace. This marketplace will be a one-stop, online listing of public sector-owned intellectual property available for licensing or sale to reduce transaction costs for businesses and researchers, and to improve Canadian entrepreneurs’ access to public sector-owned intellectual property.
The Government will also consider further measures, including through legislation, in support of the new intellectual property strategy.
Helping All Canadians Harness Intellectual Property
Intellectual property is one of our most valuable resources, and every Canadian business owner should understand how to protect and use it.
To better understand what groups of Canadians are benefiting the most from intellectual property, Budget 2018 proposes to provide Statistics Canada with $2 million over three years to conduct an intellectual property awareness and use survey. This survey will help identify how Canadians understand and use intellectual property, including groups that have traditionally been less likely to use intellectual property, such as women and Indigenous entrepreneurs. The results of the survey should help the Government better meet the needs of these groups through education and awareness initiatives.
The Canadian Intellectual Property Office will also increase the number of education and awareness initiatives that are delivered in partnership with business, intermediaries and academia to ensure Canadians better understand, integrate and take advantage of intellectual property when building their business strategies. This will include targeted initiatives to support underrepresented groups.
Finally, Budget 2018 also proposes to invest $1 million over five years to enable representatives of Canada’s Indigenous Peoples to participate in discussions at the World Intellectual Property Organization related to traditional knowledge and traditional cultural expressions, an important form of intellectual property.
It’s not wholly clear what they mean by ‘intellectual property’. The focus seems to be on patents as they are the only intellectual property (as opposed to copyright and trademarks) singled out in the budget. As for how the ‘patent collective’ is going to meet all its objectives, this budget supplies no clarity on the matter. On the plus side, I’m glad to see that indigenous peoples’ knowledge is being acknowledged as “an important form of intellectual property” and I hope the discussions at the World Intellectual Property Organization are fruitful.
Over the past decade, the Canadian patent flow in all technical sectors has consistently decreased. Patent flow provides a partial picture of how patents in Canada are exploited. A negative flow represents a deficit of patented inventions owned by Canadian assignees versus the number of patented inventions created by Canadian inventors. The patent flow for all Canadian patents decreased from about −0.04 in 2003 to −0.26 in 2014 (Figure 4.7). This means that there is an overall deficit of 26% of patent ownership in Canada. In other words, fewer patents were owned by Canadian institutions than were invented in Canada.
This is a significant change from 2003 when the deficit was only 4%. The drop is consistent across all technical sectors in the past 10 years, with Mechanical Engineering falling the least, and Electrical Engineering the most (Figure 4.7). At the technical field level, the patent flow dropped significantly in Digital Communication and Telecommunications. For example, the Digital Communication patent flow fell from 0.6 in 2003 to −0.2 in 2014. This fall could be partially linked to Nortel’s US$4.5 billion patent sale [emphasis mine] to the Rockstar consortium (which included Apple, BlackBerry, Ericsson, Microsoft, and Sony) (Brickley, 2011). Food Chemistry and Microstructural [?] and Nanotechnology both also showed a significant drop in patent flow. [p. 83 Print; p. 121 PDF]
Despite a fall in the number of parents for ‘Digital Communication’, we’re still doing well according to statistics elsewhere in this report. Is it possible that patents aren’t that big a deal? Of course, it’s also possible that we are enjoying the benefits of past work and will miss out on future work. (Note: A video of the April 10, 2018 report presentation by Max Blouw features him saying something like that.)
One last note, Nortel died many years ago. Disconcertingly, this report, despite more than one reference to Nortel, never mentions the company’s demise.
Boxed text
While the expert panel wasn’t tasked to answer certain types of questions, as I’ve noted earlier they managed to sneak in a few items. One of the strategies they used was putting special inserts into text boxes including this (from the report released April 10, 2018),
Box 4.2
The FinTech Revolution
Financial services is a key industry in Canada. In 2015, the industry accounted for 4.4%
of Canadia jobs and about 7% of Canadian GDP (Burt, 2016). Toronto is the second largest financial services hub in North America and one of the most vibrant research hubs in FinTech. Since 2010, more than 100 start-up companies have been founded in Canada, attracting more than $1 billion in investment (Moffatt, 2016). In 2016 alone, venture-backed investment in Canadian financial technology companies grew by 35% to $137.7 million (Ho, 2017). The Toronto Financial Services Alliance estimates that there are approximately 40,000 ICT specialists working in financial services in Toronto alone.
AI, blockchain, [emphasis mine] and other results of ICT research provide the basis for several transformative FinTech innovations including, for example, decentralized transaction ledgers, cryptocurrencies (e.g., bitcoin), and AI-based risk assessment and fraud detection. These innovations offer opportunities to develop new markets for established financial services firms, but also provide entry points for technology firms to develop competing service offerings, increasing competition in the financial services industry. In response, many financial services companies are increasing their investments in FinTech companies (Breznitz et al., 2015). By their own account, the big five banks invest more than $1 billion annually in R&D of advanced software solutions, including AI-based innovations (J. Thompson, personal communication, 2016). The banks are also increasingly investing in university research and collaboration with start-up companies. For instance, together with several large insurance and financial management firms, all big five banks have invested in the Vector Institute for Artificial Intelligence (Kolm, 2017).
I’m glad to see the mention of blockchain while AI (artificial intelligence) is an area where we have innovated (from the report released April 10, 2018),
AI has attracted researchers and funding since the 1960s; however, there were periods of stagnation in the 1970s and 1980s, sometimes referred to as the “AI winter.” During this period, the Canadian Institute for Advanced Research (CIFAR), under the direction of Fraser Mustard, started supporting AI research with a decade-long program called Artificial Intelligence, Robotics and Society, [emphasis mine] which was active from 1983 to 1994. In 2004, a new program called Neural Computation and Adaptive Perception was initiated and renewed twice in 2008 and 2014 under the title, Learning in Machines and Brains. Through these programs, the government provided long-term, predictable support for high- risk research that propelled Canadian researchers to the forefront of global AI development. In the 1990s and early 2000s, Canadian research output and impact on AI were second only to that of the United States (CIFAR, 2016). NSERC has also been an early supporter of AI. According to its searchable grant database, NSERC has given funding to research projects on AI since at least 1991–1992 (the earliest searchable year) (NSERC, 2017a).
The University of Toronto, the University of Alberta, and the Université de Montréal have emerged as international centres for research in neural networks and deep learning, with leading experts such as Geoffrey Hinton and Yoshua Bengio. Recently, these locations have expanded into vibrant hubs for research in AI applications with a diverse mix of specialized research institutes, accelerators, and start-up companies, and growing investment by major international players in AI development, such as Microsoft, Google, and Facebook. Many highly influential AI researchers today are either from Canada or have at some point in their careers worked at a Canadian institution or with Canadian scholars.
…
As international opportunities in AI research and the ICT industry have grown, many of Canada’s AI pioneers have been drawn to research institutions and companies outside of Canada. According to the OECD, Canada’s share of patents in AI declined from 2.4% in 2000 to 2005 to 2% in 2010 to 2015. Although Canada is the sixth largest producer of top-cited scientific publications related to machine learning, firms headquartered in Canada accounted for only 0.9% of all AI-related inventions from 2012 to 2014 (OECD, 2017c). Canadian AI researchers, however, remain involved in the core nodes of an expanding international network of AI researchers, most of whom continue to maintain ties with their home institutions. Compared with their international peers, Canadian AI researchers are engaged in international collaborations far more often than would be expected by Canada’s level of research output, with Canada ranking fifth in collaboration. [p. 97-98 Print; p. 135-136 PDF]
The only mention of robotics seems to be here in this section and it’s only in passing. This is a bit surprising given its global importance. I wonder if robotics has been somehow hidden inside the term artificial intelligence, although sometimes it’s vice versa with robot being used to describe artificial intelligence. I’m noticing this trend of assuming the terms are synonymous or interchangeable not just in Canadian publications but elsewhere too. ’nuff said.
Getting back to the matter at hand, t he report does note that patenting (technometric data) is problematic (from the report released April 10, 2018),
The limitations of technometric data stem largely from their restricted applicability across areas of R&D. Patenting, as a strategy for IP management, is similarly limited in not being equally relevant across industries. Trends in patenting can also reflect commercial pressures unrelated to R&D activities, such as defensive or strategic patenting practices. Finally, taxonomies for assessing patents are not aligned with bibliometric taxonomies, though links can be drawn to research publications through the analysis of patent citations. [p. 105 Print; p. 143 PDF]
It’s interesting to me that they make reference to many of the same issues that I mention but they seem to forget and don’t use that information in their conclusions.
Box 6.3
Open Science: An Emerging Approach to Create New Linkages
Open Science is an umbrella term to describe collaborative and open approaches to
undertaking science, which can be powerful catalysts of innovation. This includes
the development of open collaborative networks among research performers, such
as the private sector, and the wider distribution of research that usually results when
restrictions on use are removed. Such an approach triggers faster translation of ideas
among research partners and moves the boundaries of pre-competitive research to
later, applied stages of research. With research results freely accessible, companies
can focus on developing new products and processes that can be commercialized.
Two Canadian organizations exemplify the development of such models. In June
2017, Genome Canada, the Ontario government, and pharmaceutical companies
invested $33 million in the Structural Genomics Consortium (SGC) (Genome Canada,
2017). Formed in 2004, the SGC is at the forefront of the Canadian open science
movement and has contributed to many key research advancements towards new
treatments (SGC, 2018). McGill University’s Montréal Neurological Institute and
Hospital has also embraced the principles of open science. Since 2016, it has been
sharing its research results with the scientific community without restriction, with
the objective of expanding “the impact of brain research and accelerat[ing] the
discovery of ground-breaking therapies to treat patients suffering from a wide range
of devastating neurological diseases” (neuro, n.d.).
This is exciting stuff and I’m happy the panel featured it. (I wrote about the Montréal Neurological Institute initiative in a Jan. 22, 2016 posting.)
More than once, the report notes the difficulties with using bibliometric and technometric data as measures of scientific achievement and progress and open science (along with its cousins, open data and open access) are contributing to the difficulties as James Somers notes in his April 5, 2018 article ‘The Scientific Paper is Obsolete’ for The Atlantic (Note: Links have been removed),
The scientific paper—the actual form of it—was one of the enabling inventions of modernity. Before it was developed in the 1600s, results were communicated privately in letters, ephemerally in lectures, or all at once in books. There was no public forum for incremental advances. By making room for reports of single experiments or minor technical advances, journals made the chaos of science accretive. Scientists from that point forward became like the social insects: They made their progress steadily, as a buzzing mass.
The earliest papers were in some ways more readable than papers are today. They were less specialized, more direct, shorter, and far less formal. Calculus had only just been invented. Entire data sets could fit in a table on a single page. What little “computation” contributed to the results was done by hand and could be verified in the same way.
The more sophisticated science becomes, the harder it is to communicate results. Papers today are longer than ever and full of jargon and symbols. They depend on chains of computer programs that generate data, and clean up data, and plot data, and run statistical models on data. These programs tend to be both so sloppily written and so central to the results that it’s [sic] contributed to a replication crisis, or put another way, a failure of the paper to perform its most basic task: to report what you’ve actually discovered, clearly enough that someone else can discover it for themselves.
Perhaps the paper itself is to blame. Scientific methods evolve now at the speed of software; the skill most in demand among physicists, biologists, chemists, geologists, even anthropologists and research psychologists, is facility with programming languages and “data science” packages. And yet the basic means of communicating scientific results hasn’t changed for 400 years. Papers may be posted online, but they’re still text and pictures on a page.
What would you get if you designed the scientific paper from scratch today? A little while ago I spoke to Bret Victor, a researcher who worked at Apple on early user-interface prototypes for the iPad and now runs his own lab in Oakland, California, that studies the future of computing. Victor has long been convinced that scientists haven’t yet taken full advantage of the computer. “It’s not that different than looking at the printing press, and the evolution of the book,” he said. After Gutenberg, the printing press was mostly used to mimic the calligraphy in bibles. It took nearly 100 years of technical and conceptual improvements to invent the modern book. “There was this entire period where they had the new technology of printing, but they were just using it to emulate the old media.”Victor gestured at what might be possible when he redesigned a journal article by Duncan Watts and Steven Strogatz, “Collective dynamics of ‘small-world’ networks.” He chose it both because it’s one of the most highly cited papers in all of science and because it’s a model of clear exposition. (Strogatz is best known for writing the beloved “Elements of Math” column for The New York Times.)
The Watts-Strogatz paper described its key findings the way most papers do, with text, pictures, and mathematical symbols. And like most papers, these findings were still hard to swallow, despite the lucid prose. The hardest parts were the ones that described procedures or algorithms, because these required the reader to “play computer” in their head, as Victor put it, that is, to strain to maintain a fragile mental picture of what was happening with each step of the algorithm.Victor’s redesign interleaved the explanatory text with little interactive diagrams that illustrated each step. In his version, you could see the algorithm at work on an example. You could even control it yourself….
For anyone interested in the evolution of how science is conducted and communicated, Somers’ article is a fascinating and in depth look at future possibilities.
Subregional R&D
I didn’t find this quite as compelling as the last time and that may be due to the fact that there’s less information and I think the 2012 report was the first to examine the Canadian R&D scene with a subregional (in their case, provinces) lens. On a high note, this report also covers cities (!) and regions, as well as, provinces.
Ontario leads Canada in R&D investment and performance. The province accounts for almost half of R&D investment and personnel, research publications and collaborations, and patents. R&D activity in Ontario produces high-quality publications in each of Canada’s five R&D strengths, reflecting both the quantity and quality of universities in the province. Quebec lags Ontario in total investment, publications, and patents, but performs as well (citations) or better (R&D intensity) by some measures. Much like Ontario, Quebec researchers produce impactful publications across most of Canada’s five R&D strengths. Although it invests an amount similar to that of Alberta, British Columbia does so at a significantly higher intensity. British Columbia also produces more highly cited publications and patents, and is involved in more international research collaborations. R&D in British Columbia and Alberta clusters around Vancouver and Calgary in areas such as physics and ICT and in clinical medicine and energy, respectively. [emphasis mine] Smaller but vibrant R&D communities exist in the Prairies and Atlantic Canada [also referred to as the Maritime provinces or Maritimes] (and, to a lesser extent, in the Territories) in natural resource industries.
Globally, as urban populations expand exponentially, cities are likely to drive innovation and wealth creation at an increasing rate in the future. In Canada, R&D activity clusters around five large cities: Toronto, Montréal, Vancouver, Ottawa, and Calgary. These five cities create patents and high-tech companies at nearly twice the rate of other Canadian cities. They also account for half of clusters in the services sector, and many in advanced manufacturing.
Many clusters relate to natural resources and long-standing areas of economic and research strength. Natural resource clusters have emerged around the location of resources, such as forestry in British Columbia, oil and gas in Alberta, agriculture in Ontario, mining in Quebec, and maritime resources in Atlantic Canada. The automotive, plastics, and steel industries have the most individual clusters as a result of their economic success in Windsor, Hamilton, and Oshawa. Advanced manufacturing industries tend to be more concentrated, often located near specialized research universities. Strong connections between academia and industry are often associated with these clusters. R&D activity is distributed across the country, varying both between and within regions. It is critical to avoid drawing the wrong conclusion from this fact. This distribution does not imply the existence of a problem that needs to be remedied. Rather, it signals the benefits of diverse innovation systems, with differentiation driven by the needs of and resources available in each province. [pp. 132-133 Print; pp. 170-171 PDF]
Intriguingly, there’s no mention that in British Columbia (BC), there are leading areas of research: Visual & Performing Arts, Psychology & Cognitive Sciences, and Clinical Medicine (according to the table on p. 117 Print, p. 153 PDF).
As I said and hinted earlier, we’ve got brains; they’re just not the kind of brains that command respect.
Final comments
My hat’s off to the expert panel and staff of the Council of Canadian Academies. Combining two previous reports into one could not have been easy. As well, kudos to their attempts to broaden the discussion by mentioning initiative such as open science and for emphasizing the problems with bibliometrics, technometrics, and other measures. I have covered only parts of this assessment, (Competing in a Global Innovation Economy: The Current State of R&D in Canada), there’s a lot more to it including a substantive list of reference materials (bibliography).
While I have argued that perhaps the situation isn’t quite as bad as the headlines and statistics may suggest, there are some concerning trends for Canadians but we have to acknowledge that many countries have stepped up their research game and that’s good for all of us. You don’t get better at anything unless you work with and play with others who are better than you are. For example, both India and Italy surpassed us in numbers of published research papers. We slipped from 7th place to 9th. Thank you, Italy and India. (And, Happy ‘Italian Research in the World Day’ on April 15, 2018, the day’s inaugural year. In Italian: Piano Straordinario “Vivere all’Italiana” – Giornata della ricerca Italiana nel mondo.)
Unfortunately, the reading is harder going than previous R&D assessments in the CCA catalogue. And in the end, I can’t help thinking we’re just a little bit like Hedy Lamarr. Not really appreciated in all of our complexities although the expert panel and staff did try from time to time. Perhaps the government needs to find better ways of asking the questions.
***ETA April 12, 2018 at 1500 PDT: Talking about missing the obvious! I’ve been ranting on about how research strength in visual and performing arts and in philosophy and theology, etc. is perfectly fine and could lead to ‘traditional’ science breakthroughs without underlining the point by noting that Antheil was a musician, Lamarr was as an actress and they set the foundation for work by electrical engineers (or people with that specialty) for their signature work leading to WiFi, etc.***
There is, by the way, a Hedy-Canada connection. In 1998, she sued Canadian software company Corel, for its unauthorized use of her image on their Corel Draw 8 product packaging. She won.
More stuff
For those who’d like to see and hear the April 10, 2017 launch for “Competing in a Global Innovation Economy: The Current State of R&D in Canada” or the Third Assessment as I think of it, go here.
For anyone curious about ‘Bombshell: The Hedy Lamarr Story’ to be broadcast on May 18, 2018 as part of PBS’s American Masters series, there’s this trailer,
For the curious, I did find out more about the Hedy Lamarr and Corel Draw. John Lettice’s December 2, 1998 article The Rgister describes the suit and her subsequent victory in less than admiring terms,
Our picture doesn’t show glamorous actress Hedy Lamarr, who yesterday [Dec. 1, 1998] came to a settlement with Corel over the use of her image on Corel’s packaging. But we suppose that following the settlement we could have used a picture of Corel’s packaging. Lamarr sued Corel earlier this year over its use of a CorelDraw image of her. The picture had been produced by John Corkery, who was 1996 Best of Show winner of the Corel World Design Contest. Corel now seems to have come to an undisclosed settlement with her, which includes a five-year exclusive (oops — maybe we can’t use the pack-shot then) licence to use “the lifelike vector illustration of Hedy Lamarr on Corel’s graphic software packaging”. Lamarr, bless ‘er, says she’s looking forward to the continued success of Corel Corporation, …
There’s this excerpt from a Sept. 21, 2015 posting (a pictorial essay of Lamarr’s life) by Shahebaz Khan on The Blaze Blog,
6. CorelDRAW:
For several years beginning in 1997, the boxes of Corel DRAW’s software suites were graced by a large Corel-drawn image of Lamarr. The picture won Corel DRAW’s yearly software suite cover design contest in 1996. Lamarr sued Corel for using the image without her permission. Corel countered that she did not own rights to the image. The parties reached an undisclosed settlement in 1998.
There’s also a Nov. 23, 1998 Corel Draw 8 product review by Mike Gorman on mymac.com, which includes a screenshot of the packaging that precipitated the lawsuit. Once they settled, it seems Corel used her image at least one more time.
It seems there’s a push on to establish Canada as a centre for artificial intelligence research and, if the federal and provincial governments have their way, for commercialization of said research. As always, there seems to be a bit of competition between Toronto (Ontario) and Montréal (Québec) as to which will be the dominant hub for the Canadian effort if one is to take Braga’s word for the situation.
In any event, Toronto seemed to have a mild advantage over Montréal initially with the 2017 Canadian federal government budget announcement that the Canadian Institute for Advanced Research (CIFAR), based in Toronto, would launch a Pan-Canadian Artificial Intelligence Strategy and with an announcement from the University of Toronto shortly after (from my March 31, 2017 posting),
On the heels of the March 22, 2017 federal budget announcement of $125M for a Pan-Canadian Artificial Intelligence Strategy, the University of Toronto (U of T) has announced the inception of the Vector Institute for Artificial Intelligence in a March 28, 2017 news release by Jennifer Robinson (Note: Links have been removed),
A team of globally renowned researchers at the University of Toronto is driving the planning of a new institute staking Toronto’s and Canada’s claim as the global leader in AI.
Geoffrey Hinton, a University Professor Emeritus in computer science at U of T and vice-president engineering fellow at Google, will serve as the chief scientific adviser of the newly created Vector Institute based in downtown Toronto.
“The University of Toronto has long been considered a global leader in artificial intelligence research,” said U of T President Meric Gertler. “It’s wonderful to see that expertise act as an anchor to bring together researchers, government and private sector actors through the Vector Institute, enabling them to aim even higher in leading advancements in this fast-growing, critical field.”
As part of the Government of Canada’s Pan-Canadian Artificial Intelligence Strategy, Vector will share $125 million in federal funding with fellow institutes in Montreal and Edmonton. All three will conduct research and secure talent to cement Canada’s position as a world leader in AI.
However, Montréal and the province of Québec are no slouches when it comes to supporting to technology. From a June 14, 2017 article by Matthew Braga for CBC (Canadian Broadcasting Corporation) news online (Note: Links have been removed),
One of the most promising new hubs for artificial intelligence research in Canada is going international, thanks to a $135 million investment with contributions from some of the biggest names in tech.
The company, Montreal-based Element AI, was founded last October [2016] to help companies that might not have much experience in artificial intelligence start using the technology to change the way they do business.
It’s equal parts general research lab and startup incubator, with employees working to develop new and improved techniques in artificial intelligence that might not be fully realized for years, while also commercializing products and services that can be sold to clients today.
It was co-founded by Yoshua Bengio — one of the pioneers of a type of AI research called machine learning — along with entrepreneurs Jean-François Gagné and Nicolas Chapados, and the Canadian venture capital fund Real Ventures.
In an interview, Bengio and Gagné said the money from the company’s funding round will be used to hire 250 new employees by next January. A hundred will be based in Montreal, but an additional 100 employees will be hired for a new office in Toronto, and the remaining 50 for an Element AI office in Asia — its first international outpost.
They will join more than 100 employees who work for Element AI today, having left jobs at Amazon, Uber and Google, among others, to work at the company’s headquarters in Montreal.
The expansion is a big vote of confidence in Element AI’s strategy from some of the world’s biggest technology companies. Microsoft, Intel and Nvidia all contributed to the round, and each is a key player in AI research and development.
The company has some not unexpected plans and partners (from the Braga, article, Note: A link has been removed),
The Series A round was led by Data Collective, a Silicon Valley-based venture capital firm, and included participation by Fidelity Investments Canada, National Bank of Canada, and Real Ventures.
What will it help the company do? Scale, its founders say.
“We’re looking at domain experts, artificial intelligence experts,” Gagné said. “We already have quite a few, but we’re looking at people that are at the top of their game in their domains.
“And at this point, it’s no longer just pure artificial intelligence, but people who understand, extremely well, robotics, industrial manufacturing, cybersecurity, and financial services in general, which are all the areas we’re going after.”
…
Gagné says that Element AI has already delivered 10 projects to clients in those areas, and have many more in development. In one case, Element AI has been helping a Japanese semiconductor company better analyze the data collected by the assembly robots on its factory floor, in a bid to reduce manufacturing errors and improve the quality of the company’s products.
…
There’s more to investment in Québec’s AI sector than Element AI (from the Braga article; Note: Links have been removed),
Element AI isn’t the only organization in Canada that investors are interested in.
In September, the Canadian government announced $213 million in funding for a handful of Montreal universities, while both Google and Microsoft announced expansions of their Montreal AI research groups in recent months alongside investments in local initiatives. The province of Quebec has pledged $100 million for AI initiatives by 2022.
…
Braga goes on to note some other initiatives but at that point the article’s focus is exclusively Toronto.
For more insight into the AI situation in Québec, there’s Dan Delmar’s May 23, 2017 article for the Montreal Express (Note: Links have been removed),
Advocating for massive government spending with little restraint admittedly deviates from the tenor of these columns, but the AI business is unlike any other before it. [emphasis misn] Having leaders acting as fervent advocates for the industry is crucial; resisting the coming technological tide is, as the Borg would say, futile.
The roughly 250 AI researchers who call Montreal home are not simply part of a niche industry. Quebec’s francophone character and Montreal’s multilingual citizenry are certainly factors favouring the development of language technology, but there’s ample opportunity for more ambitious endeavours with broader applications.
AI isn’t simply a technological breakthrough; it is the technological revolution. [emphasis mine] In the coming decades, modern computing will transform all industries, eliminating human inefficiencies and maximizing opportunities for innovation and growth — regardless of the ethical dilemmas that will inevitably arise.
“By 2020, we’ll have computers that are powerful enough to simulate the human brain,” said (in 2009) futurist Ray Kurzweil, author of The Singularity Is Near, a seminal 2006 book that has inspired a generation of AI technologists. Kurzweil’s projections are not science fiction but perhaps conservative, as some forms of AI already effectively replace many human cognitive functions. “By 2045, we’ll have expanded the intelligence of our human-machine civilization a billion-fold. That will be the singularity.”
The singularity concept, borrowed from physicists describing event horizons bordering matter-swallowing black holes in the cosmos, is the point of no return where human and machine intelligence will have completed their convergence. That’s when the machines “take over,” so to speak, and accelerate the development of civilization beyond traditional human understanding and capability.
…
The claims I’ve highlighted in Delmar’s article have been made before for other technologies, “xxx is like no other business before’ and “it is a technological revolution.” Also if you keep scrolling down to the bottom of the article, you’ll find Delmar is a ‘public relations consultant’ which, if you look at his LinkedIn profile, you’ll find means he’s a managing partner in a PR firm known as Provocateur.
Bertrand Marotte’s May 20, 2017 article for the Montreal Gazette offers less hyperbole along with additional detail about the Montréal scene (Note: Links have been removed),
It might seem like an ambitious goal, but key players in Montreal’s rapidly growing artificial-intelligence sector are intent on transforming the city into a Silicon Valley of AI.
Certainly, the flurry of activity these days indicates that AI in the city is on a roll. Impressive amounts of cash have been flowing into academia, public-private partnerships, research labs and startups active in AI in the Montreal area.
…
…, researchers at Microsoft Corp. have successfully developed a computing system able to decipher conversational speech as accurately as humans do. The technology makes the same, or fewer, errors than professional transcribers and could be a huge boon to major users of transcription services like law firms and the courts.
Setting the goal of attaining the critical mass of a Silicon Valley is “a nice point of reference,” said tech entrepreneur Jean-François Gagné, co-founder and chief executive officer of Element AI, an artificial intelligence startup factory launched last year.
…
The idea is to create a “fluid, dynamic ecosystem” in Montreal where AI research, startup, investment and commercialization activities all mesh productively together, said Gagné, who founded Element with researcher Nicolas Chapados and Université de Montréal deep learning pioneer Yoshua Bengio.
“Artificial intelligence is seen now as a strategic asset to governments and to corporations. The fight for resources is global,” he said.
The rise of Montreal — and rival Toronto — as AI hubs owes a lot to provincial and federal government funding.
Ottawa promised $213 million last September to fund AI and big data research at four Montreal post-secondary institutions. Quebec has earmarked $100 million over the next five years for the development of an AI “super-cluster” in the Montreal region.
The provincial government also created a 12-member blue-chip committee to develop a strategic plan to make Quebec an AI hub, co-chaired by Claridge Investments Ltd. CEO Pierre Boivin and Université de Montréal rector Guy Breton.
But private-sector money has also been flowing in, particularly from some of the established tech giants competing in an intense AI race for innovative breakthroughs and the best brains in the business.
…
Montreal’s rich talent pool is a major reason Waterloo, Ont.-based language-recognition startup Maluuba decided to open a research lab in the city, said the company’s vice-president of product development, Mohamed Musbah.
“It’s been incredible so far. The work being done in this space is putting Montreal on a pedestal around the world,” he said.
Microsoft struck a deal this year to acquire Maluuba, which is working to crack one of the holy grails of deep learning: teaching machines to read like the human brain does. Among the company’s software developments are voice assistants for smartphones.
Maluuba has also partnered with an undisclosed auto manufacturer to develop speech recognition applications for vehicles. Voice recognition applied to cars can include such things as asking for a weather report or making remote requests for the vehicle to unlock itself.
Marotte’s Twitter profile describes him as a freelance writer, editor, and translator.
