Tag Archives: OpenAI

Hardware policies best way to manage AI safety?

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.

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.

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.

While most of the focus appears to be on the software (e.g., General Purpose AI), the UK framework does not preclude hardware.

The European Union (EU) is preparing to pass its own AI regulation act through the European Parliament in 2024 according to a December 19, 2023 “EU AI Act: first regulation on artificial intelligence” article update, Note: Links have been removed,

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.

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.

The US is always to be considered in these matters and I have a November 2023 ‘briefing’ by Müge Fazlioglu on the International Association of Privacy Professionals (IAPP) website where she provides a quick overview of the international scene before diving deeper into US AI governance policy through the Barack Obama, Donald Trump, and Joe Biden administrations. There’s also this January 29, 2024 US White House “Fact Sheet: Biden-⁠Harris Administration Announces Key AI Actions Following President Biden’s Landmark Executive Order.”

What about AI and hardware?

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.

A February 14, 2024 University of Cambridge press release by Fred Lewsey (also on EurekAlert), which originated the news item, provides more information about the ‘hardware approach to AI regulation’,

“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.”

You can find the report, “Computing Power and the Governance of Artificial Intelligence” on the University of Cambridge’s Centre for the Study of Existential Risk.

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.

Striking similarity between memory processing of artificial intelligence (AI) models and hippocampus of the human brain

A December 18, 2023 news item on ScienceDaily shifted my focus from hardware to software when considering memory in brainlike (neuromorphic) computing,

An interdisciplinary team consisting of researchers from the Center for Cognition and Sociality and the Data Science Group within the Institute for Basic Science (IBS) [Korea] revealed a striking similarity between the memory processing of artificial intelligence (AI) models and the hippocampus of the human brain. This new finding provides a novel perspective on memory consolidation, which is a process that transforms short-term memories into long-term ones, in AI systems.

A November 28 (?), 2023 IBS press release (also on EurekAlert but published December 18, 2023, which originated the news item, describes how the team went about its research,

In the race towards developing Artificial General Intelligence (AGI), with influential entities like OpenAI and Google DeepMind leading the way, understanding and replicating human-like intelligence has become an important research interest. Central to these technological advancements is the Transformer model [Figure 1], whose fundamental principles are now being explored in new depth.

The key to powerful AI systems is grasping how they learn and remember information. The team applied principles of human brain learning, specifically concentrating on memory consolidation through the NMDA receptor in the hippocampus, to AI models.

The NMDA receptor is like a smart door in your brain that facilitates learning and memory formation. When a brain chemical called glutamate is present, the nerve cell undergoes excitation. On the other hand, a magnesium ion acts as a small gatekeeper blocking the door. Only when this ionic gatekeeper steps aside, substances are allowed to flow into the cell. This is the process that allows the brain to create and keep memories, and the gatekeeper’s (the magnesium ion) role in the whole process is quite specific.

The team made a fascinating discovery: the Transformer model seems to use a gatekeeping process similar to the brain’s NMDA receptor [see Figure 1]. This revelation led the researchers to investigate if the Transformer’s memory consolidation can be controlled by a mechanism similar to the NMDA receptor’s gating process.

In the animal brain, a low magnesium level is known to weaken memory function. The researchers found that long-term memory in Transformer can be improved by mimicking the NMDA receptor. Just like in the brain, where changing magnesium levels affect memory strength, tweaking the Transformer’s parameters to reflect the gating action of the NMDA receptor led to enhanced memory in the AI model. This breakthrough finding suggests that how AI models learn can be explained with established knowledge in neuroscience.

C. Justin LEE, who is a neuroscientist director at the institute, said, “This research makes a crucial step in advancing AI and neuroscience. It allows us to delve deeper into the brain’s operating principles and develop more advanced AI systems based on these insights.”

CHA Meeyoung, who is a data scientist in the team and at KAIST [Korea Advanced Institute of Science and Technology], notes, “The human brain is remarkable in how it operates with minimal energy, unlike the large AI models that need immense resources. Our work opens up new possibilities for low-cost, high-performance AI systems that learn and remember information like humans.”

What sets this study apart is its initiative to incorporate brain-inspired nonlinearity into an AI construct, signifying a significant advancement in simulating human-like memory consolidation. The convergence of human cognitive mechanisms and AI design not only holds promise for creating low-cost, high-performance AI systems but also provides valuable insights into the workings of the brain through AI models.

Fig. 1: (a) Diagram illustrating the ion channel activity in post-synaptic neurons. AMPA receptors are involved in the activation of post-synaptic neurons, while NMDA receptors are blocked by magnesium ions (Mg²⁺) but induce synaptic plasticity through the influx of calcium ions (Ca²⁺) when the post-synaptic neuron is sufficiently activated. (b) Flow diagram representing the computational process within the Transformer AI model. Information is processed sequentially through stages such as feed-forward layers, layer normalization, and self-attention layers. The graph depicting the current-voltage relationship of the NMDA receptors is very similar to the nonlinearity of the feed-forward layer. The input-output graph, based on the concentration of magnesium (α), shows the changes in the nonlinearity of the NMDA receptors. Courtesy: IBS

This research was presented at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) before being published in the proceedings, I found a PDF of the presentation and an early online copy of the paper before locating the paper in the published proceedings.

PDF of presentation: Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity

PDF copy of paper:

Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity by Dong-Kyum Kim, Jea Kwon, Meeyoung Cha, C. Justin Lee.

This paper was made available on OpenReview.net:

OpenReview is a platform for open peer review, open publishing, open access, open discussion, open recommendations, open directory, open API and open source.

It’s not clear to me if this paper is finalized or not and I don’t know if its presence on OpenReview constitutes publication.

Finally, the paper published in the proceedings,

Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity by Dong Kyum Kim, Jea Kwon, Meeyoung Cha, C. Justin Lee. Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track

This link will take you to the abstract, access the paper by clicking on the Paper tab.

AI safety talks at Bletchley Park in November 2023

There’s a very good article about the upcoming AI (artificial intelligence) safety talks on the British Broadcasting Corporation (BBC) news website (plus some juicy perhaps even gossipy news about who may not be attending the event) but first, here’s the August 24, 2023 UK government press release making the announcement,

Iconic Bletchley Park to host UK AI Safety Summit in early November [2023]

Major global event to take place on the 1st and 2nd of November.[2023]

– UK to host world first summit on artificial intelligence safety in November

– Talks will explore and build consensus on rapid, international action to advance safety at the frontier of AI technology

– Bletchley Park, one of the birthplaces of computer science, to host the summit

International governments, leading AI companies and experts in research will unite for crucial talks in November on the safe development and use of frontier AI technology, as the UK Government announces Bletchley Park as the location for the UK summit.

The major global event will take place on the 1st and 2nd November to consider the risks of AI, especially at the frontier of development, and discuss how they can be mitigated through internationally coordinated action. Frontier AI models hold enormous potential to power economic growth, drive scientific progress and wider public benefits, while also posing potential safety risks if not developed responsibly.

To be hosted at Bletchley Park in Buckinghamshire, a significant location in the history of computer science development and once the home of British Enigma codebreaking – it will see coordinated action to agree a set of rapid, targeted measures for furthering safety in global AI use.

Preparations for the summit are already in full flow, with Matt Clifford and Jonathan Black recently appointed as the Prime Minister’s Representatives. Together they’ll spearhead talks and negotiations, as they rally leading AI nations and experts over the next three months to ensure the summit provides a platform for countries to work together on further developing a shared approach to agree the safety measures needed to mitigate the risks of AI.

Prime Minister Rishi Sunak said:

“The UK has long been home to the transformative technologies of the future, so there is no better place to host the first ever global AI safety summit than at Bletchley Park this November.

To fully embrace the extraordinary opportunities of artificial intelligence, we must grip and tackle the risks to ensure it develops safely in the years ahead.

With the combined strength of our international partners, thriving AI industry and expert academic community, we can secure the rapid international action we need for the safe and responsible development of AI around the world.”

Technology Secretary Michelle Donelan said:

“International collaboration is the cornerstone of our approach to AI regulation, and we want the summit to result in leading nations and experts agreeing on a shared approach to its safe use.

The UK is consistently recognised as a world leader in AI and we are well placed to lead these discussions. The location of Bletchley Park as the backdrop will reaffirm our historic leadership in overseeing the development of new technologies.

AI is already improving lives from new innovations in healthcare to supporting efforts to tackle climate change, and November’s summit will make sure we can all realise the technology’s huge benefits safely and securely for decades to come.”

The summit will also build on ongoing work at international forums including the OECD, Global Partnership on AI, Council of Europe, and the UN and standards-development organisations, as well as the recently agreed G7 Hiroshima AI Process.

The UK boasts strong credentials as a world leader in AI. The technology employs over 50,000 people, directly supports one of the Prime Minister’s five priorities by contributing £3.7 billion to the economy, and is the birthplace of leading AI companies such as Google DeepMind. It has also invested more on AI safety research than any other nation, backing the creation of the Foundation Model Taskforce with an initial £100 million.

Foreign Secretary James Cleverly said:

“No country will be untouched by AI, and no country alone will solve the challenges posed by this technology. In our interconnected world, we must have an international approach.

The origins of modern AI can be traced back to Bletchley Park. Now, it will also be home to the global effort to shape the responsible use of AI.”

Bletchley Park’s role in hosting the summit reflects the UK’s proud tradition of being at the frontier of new technology advancements. Since Alan Turing’s celebrated work some eight decades ago, computing and computer science have become fundamental pillars of life both in the UK and across the globe.

Iain Standen, CEO of the Bletchley Park Trust, said:

“Bletchley Park Trust is immensely privileged to have been chosen as the venue for the first major international summit on AI safety this November, and we look forward to welcoming the world to our historic site.

It is fitting that the very spot where leading minds harnessed emerging technologies to influence the successful outcome of World War 2 will, once again, be the crucible for international co-ordinated action.

We are incredibly excited to be providing the stage for discussions on global safety standards, which will help everyone manage and monitor the risks of artificial intelligence.”

The roots of AI can be traced back to the leading minds who worked at Bletchley during World War 2, with codebreakers Jack Good and Donald Michie among those who went on to write extensive works on the technology. In November [2023], it will once again take centre stage as the international community comes together to agree on important guardrails which ensure the opportunities of AI can be realised, and its risks safely managed.

The announcement follows the UK government allocating £13 million to revolutionise healthcare research through AI, unveiled last week. The funding supports a raft of new projects including transformations to brain tumour surgeries, new approaches to treating chronic nerve pain, and a system to predict a patient’s risk of developing future health problems based on existing conditions.

Tom Gerken’s August 24, 2023 BBC news article (an analysis by Zoe Kleinman follows as part of the article) fills in a few blanks, Note: Links have been removed,

World leaders will meet with AI companies and experts on 1 and 2 November for the discussions.

The global talks aim to build an international consensus on the future of AI.

The summit will take place at Bletchley Park, where Alan Turing, one of the pioneers of modern computing, worked during World War Two.

It is unknown which world leaders will be invited to the event, with a particular question mark over whether the Chinese government or tech giant Baidu will be in attendance.

The BBC has approached the government for comment.

The summit will address how the technology can be safely developed through “internationally co-ordinated action” but there has been no confirmation of more detailed topics.

It comes after US tech firm Palantir rejected calls to pause the development of AI in June, with its boss Alex Karp saying it was only those with “no products” who wanted a pause.

And in July [2023], children’s charity the Internet Watch Foundation called on Mr Sunak to tackle AI-generated child sexual abuse imagery, which it says is on the rise.

Kleinman’s analysis includes this, Note: A link has been removed,

Will China be represented? Currently there is a distinct east/west divide in the AI world but several experts argue this is a tech that transcends geopolitics. Some say a UN-style regulator would be a better alternative to individual territories coming up with their own rules.

If the government can get enough of the right people around the table in early November [2023], this is perhaps a good subject for debate.

Three US AI giants – OpenAI, Anthropic and Palantir – have all committed to opening London headquarters.

But there are others going in the opposite direction – British DeepMind co-founder Mustafa Suleyman chose to locate his new AI company InflectionAI in California. He told the BBC the UK needed to cultivate a more risk-taking culture in order to truly become an AI superpower.

Many of those who worked at Bletchley Park decoding messages during WW2 went on to write and speak about AI in later years, including codebreakers Irving John “Jack” Good and Donald Michie.

Soon after the War, [Alan] Turing proposed the imitation game – later dubbed the “Turing test” – which seeks to identify whether a machine can behave in a way indistinguishable from a human.

There is a Bletchley Park website, which sells tickets for tours.

Insight into political jockeying (i.e., some juicy news bits)

This has recently been reported by BBC, from an October 17 (?). 2023 news article by Jessica Parker & Zoe Kleinman on BBC news online,

German Chancellor Olaf Scholz may turn down his invitation to a major UK summit on artificial intelligence, the BBC understands.

While no guest list has been published of an expected 100 participants, some within the sector say it’s unclear if the event will attract top leaders.

A government source insisted the summit is garnering “a lot of attention” at home and overseas.

The two-day meeting is due to bring together leading politicians as well as independent experts and senior execs from the tech giants, who are mainly US based.

The first day will bring together tech companies and academics for a discussion chaired by the Secretary of State for Science, Innovation and Technology, Michelle Donelan.

The second day is set to see a “small group” of people, including international government figures, in meetings run by PM Rishi Sunak.

Though no final decision has been made, it is now seen as unlikely that the German Chancellor will attend.

That could spark concerns of a “domino effect” with other world leaders, such as the French President Emmanuel Macron, also unconfirmed.

Government sources say there are heads of state who have signalled a clear intention to turn up, and the BBC understands that high-level representatives from many US-based tech giants are going.

The foreign secretary confirmed in September [2023] that a Chinese representative has been invited, despite controversy.

Some MPs within the UK’s ruling Conservative Party believe China should be cut out of the conference after a series of security rows.

It is not known whether there has been a response to the invitation.

China is home to a huge AI sector and has already created its own set of rules to govern responsible use of the tech within the country.

The US, a major player in the sector and the world’s largest economy, will be represented by Vice-President Kamala Harris.

Britain is hoping to position itself as a key broker as the world wrestles with the potential pitfalls and risks of AI.

However, Berlin is thought to want to avoid any messy overlap with G7 efforts, after the group of leading democratic countries agreed to create an international code of conduct.

Germany is also the biggest economy in the EU – which is itself aiming to finalise its own landmark AI Act by the end of this year.

It includes grading AI tools depending on how significant they are, so for example an email filter would be less tightly regulated than a medical diagnosis system.

The European Commission President Ursula von der Leyen is expected at next month’s summit, while it is possible Berlin could send a senior government figure such as its vice chancellor, Robert Habeck.

A source from the Department for Science, Innovation and Technology said: “This is the first time an international summit has focused on frontier AI risks and it is garnering a lot of attention at home and overseas.

“It is usual not to confirm senior attendance at major international events until nearer the time, for security reasons.”

Fascinating, eh?

The cost of building ChatGPT

After seeing the description for Laura U. Marks’s recent work ‘Streaming Carbon Footprint’ (in my October 13, 2023 posting about upcoming ArtSci Salon events in Toronto), where she focuses on the environmental impact of streaming media and digital art, I was reminded of some September 2023 news.

A September 9, 2023 news item (an Associated Press article by Matt O’Brien and Hannah Fingerhut) on phys.org and also published September 12, 2023 on the Iowa Public Radio website, describe an unexpected cost for building ChatGPT and other AI agents, Note: Links have been removed,

The cost of building an artificial intelligence product like ChatGPT can be hard to measure.

But one thing Microsoft-backed OpenAI needed for its technology was plenty of water [emphases mine], pulled from the watershed of the Raccoon and Des Moines rivers in central Iowa to cool a powerful supercomputer as it helped teach its AI systems how to mimic human writing.

As they race to capitalize on a craze for generative AI, leading tech developers including Microsoft, OpenAI and Google have acknowledged that growing demand for their AI tools carries hefty costs, from expensive semiconductors to an increase in water consumption.

But they’re often secretive about the specifics. Few people in Iowa knew about its status as a birthplace of OpenAI’s most advanced large language model, GPT-4, before a top Microsoft executive said in a speech it “was literally made next to cornfields west of Des Moines.”

In its latest environmental report, Microsoft disclosed that its global water consumption spiked 34% from 2021 to 2022 (to nearly 1.7 billion gallons , or more than 2,500 Olympic-sized swimming pools), a sharp increase compared to previous years that outside researchers tie to its AI research. [emphases mine]

“It’s fair to say the majority of the growth is due to AI,” including “its heavy investment in generative AI and partnership with OpenAI,” said Shaolei Ren, [emphasis mine] a researcher at the University of California, Riverside who has been trying to calculate the environmental impact of generative AI products such as ChatGPT.

If you have the time, do read the O’Brien and Fingerhut article in it entirety. (Later in this post, I have a citation for and a link to a paper by Ren.)

Jason Clayworth’s September 18, 2023 article for AXIOS describes the issue from the Iowan perspective, Note: Links have been removed,

Future data center projects in West Des Moines will only be considered if Microsoft can implement technology that can “significantly reduce peak water usage,” the Associated Press reports.

Why it matters: Microsoft’s five WDM data centers — the “epicenter for advancing AI” — represent more than $5 billion in investments in the last 15 years.

Yes, but: They consumed as much as 11.5 million gallons of water a month for cooling, or about 6% of WDM’s total usage during peak summer usage during the last two years, according to information from West Des Moines Water Works.

This information becomes more intriguing (and disturbing) after reading a February 10, 2023 article for the World Economic Forum titled ‘This is why we can’t dismiss water scarcity in the US‘ by James Rees and/or an August 11, 2020 article ‘Why is America running out of water?‘ by Jon Heggie published by the National Geographic, which is a piece of paid content. Note: Despite the fact that it’s sponsored by Finish Dish Detergent, the research in Heggie’s article looks solid.

From Heggie’s article, Note: Links have been removed,

In March 2019, storm clouds rolled across Oklahoma; rain swept down the gutters of New York; hail pummeled northern Florida; floodwaters forced evacuations in Missouri; and a blizzard brought travel to a stop in South Dakota. Across much of America, it can be easy to assume that we have more than enough water. But that same a month, as storms battered the country, a government-backed report issued a stark warning: America is running out of water.

As the U.S. water supply decreases, demand is set to increase. On average, each American uses 80 to 100 gallons of water every day, with the nation’s estimated total daily usage topping 345 billion gallons—enough to sink the state of Rhode Island under a foot of water. By 2100 the U.S. population will have increased by nearly 200 million, with a total population of some 514 million people. Given that we use water for everything, the simple math is that more people mean more water stress across the country.

And we are already tapping into our reserves. Aquifers, porous rocks and sediment that store vast volumes of water underground, are being drained. Nearly 165 million Americans rely on groundwater for drinking water, farmers use it for irrigation―37 percent of our total water usage is for agriculture—and industry needs it for manufacturing. Groundwater is being pumped faster than it can be naturally replenished. The Central Valley Aquifer in California underlies one of the nation’s most agriculturally productive regions, but it is in drastic decline and has lost about ten cubic miles of water in just four years.

Decreasing supply and increasing demand are creating a perfect water storm, the effects of which are already being felt. The Colorado River carved its way 1,450 miles from the Rockies to the Gulf of California for millions of years, but now no longer reaches the sea. In 2018, parts of the Rio Grande recorded their lowest water levels ever; Arizona essentially lives under permanent drought conditions; and in South Florida’s freshwater aquifers are increasingly susceptible to salt water intrusion due to over-extraction.

The focus is on individual use of water and Heggie ends his article by suggesting we use less,

… And every American can save more water at home in multiple ways, from taking shorter showers to not rinsing dishes under a running faucet before loading them into a dishwasher, a practice that wastes around 20 gallons of water for each load. …

As an advertising pitch goes, this is fairly subtle as there’s no branding in the article itself and it is almost wholly informational.

Attempts to stave off water shortages as noted in Heggie’s and other articles include groundwater pumping both for individual use and industrial use. This practice has had an unexpected impact according to a June 16, 2023 article by Warren Cornwall for Science (magazine),

While spinning on its axis, Earth wobbles like an off-kilter top. Sloshing molten iron in Earth’s core, melting ice, ocean currents, and even hurricanes can all cause the poles to wander. Now, scientists have found that a significant amount of the polar drift results from human activity: pumping groundwater for drinking and irrigation.

“The very way the planet wobbles is impacted by our activities,” says Surendra Adhikari, a geophysicist at NASA’s Jet Propulsion Laboratory and an expert on Earth’s rotation who was not involved in the study. “It is, in a way, mind boggling.”

Clark R. Wilson, a geophysicist at the University of Texas at Austin, and his colleagues thought the removal of tens of gigatons of groundwater each year might affect the drift. But they knew it could not be the only factor. “There’s a lot of pieces that go into the final budget for causing polar drift,” Wilson says.

The scientists built a model of the polar wander, accounting for factors such as reservoirs filling because of new dams and ice sheets melting, to see how well they explained the polar movements observed between 1993 and 2010. During that time, satellite measurements were precise enough to detect a shift in the poles as small as a few millimeters.

Dams and ice changes were not enough to match the observed polar motion. But when the researchers also put in 2150 gigatons of groundwater that hydrologic models estimate were pumped between 1993 and 2010, the predicted polar motion aligned much more closely with observations. Wilson and his colleagues conclude that the redistribution of that water weight to the world’s oceans has caused Earth’s poles to shift nearly 80 centimeters during that time. In fact, groundwater removal appears to have played a bigger role in that period than the release of meltwater from ice in either Greenland or Antarctica, the scientists reported Thursday [June 15, 2023] in Geophysical Research Letters.

The new paper helps confirm that groundwater depletion added approximately 6 millimeters to global sea level rise between 1993 and 2010. “I was very happy” that this new method matched other estimates, Seo [Ki-Weon Seo geophysicist at Seoul National University and the study’s lead author] says. Because detailed astronomical measurements of the polar axis location go back to the end of the 19th century, polar drift could enable Seo to trace the human impact on the planet’s water over the past century.

Two papers: environmental impact from AI and groundwater pumping wobbles poles

I have two links and citations for Ren’s paper on AI and its environmental impact,

Towards Environmentally Equitable AI via Geographical Load Balancing by Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren. Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2307.05494 [cs.AI] (or arXiv:2307.05494v1 [cs.AI] for this version) DOI: https://doi.org/10.48550/arXiv.2307.05494 Submitted June 20, 2023

Towards Environmentally Equitable AI via Geographical Load Balancing by Li, Pengfei; Yang, Jianyi; Wierman, Adam; Ren, Shaolei. UC Riverside. Retrieved from https://escholarship.org/uc/item/79c880vf Publication date: 2023-06-27

Both links offer open access to the paper. Should you be interested in more, you can find Shaolei Ren’s website here.

Now for the wobbling poles,

Drift of Earth’s Pole Confirms Groundwater Depletion as a Significant Contributor to Global Sea Level Rise 1993–2010 by Ki-Weon Seo, Dongryeol Ryu, Jooyoung Eom, Taewhan Jeon, Jae-Seung Kim, Kookhyoun Youm, Jianli Chen, Clark R. Wilson. Geophysical Research Letters Volume 50, Issue 12, 28 June 2023 e2023GL103509 DOI: https://doi.org/10.1029/2023GL103509 First published online: 15 June 2023

This paper too is open access.

Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends—a UNESCO report

Launched on Thursday, July 13, 2023 during UNESCO’s (United Nations Educational, Scientific, and Cultural Organization) “Global dialogue on the ethics of neurotechnology,” is a report tying together the usual measures of national scientific supremacy (number of papers published and number of patents filed) with information on corporate investment in the field. Consequently, “Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends” by Daniel S. Hain, Roman Jurowetzki, Mariagrazia Squicciarini, and Lihui Xu provides better insight into the international neurotechnology scene than is sometimes found in these kinds of reports. By the way, the report is open access.

Here’s what I mean, from the report‘s short summary,

Since 2013, government investments in this field have exceeded $6 billion. Private investment has also seen significant growth, with annual funding experiencing a 22-fold increase from 2010 to 2020, reaching $7.3 billion and totaling $33.2 billion.

This investment has translated into a 35-fold growth in neuroscience publications between 2000-2021 and 20-fold growth in innovations between 2022-2020, as proxied by patents. However, not all are poised to benefit from such developments, as big divides emerge.

Over 80% of high-impact neuroscience publications are produced by only ten countries, while 70% of countries contributed fewer than 10 such papers over the period considered. Similarly, five countries only hold 87% of IP5 neurotech patents.

This report sheds light on the neurotechnology ecosystem, that is, what is being developed, where and by whom, and informs about how neurotechnology interacts with other technological trajectories, especially Artificial Intelligence [emphasis mine]. [p. 2]

The money aspect is eye-opening even when you already have your suspicions. Also, it’s not entirely unexpected to learn that only ten countries produce over 80% of the high impact neurotech papers and that only five countries hold 87% of the IP5 neurotech patents but it is stunning to see it in context. (If you’re not familiar with the term ‘IP5 patents’, scroll down in this post to the relevant subhead. Hint: It means the patent was filed in one of the top five jurisdictions; I’ll leave you to guess which ones those might be.)

“Since 2013 …” isn’t quite as informative as the authors may have hoped. I wish they had given a time frame for government investments similar to what they did for corporate investments (e.g., 2010 – 2020). Also, is the $6B (likely in USD) government investment cumulative or an estimated annual number? To sum up, I would have appreciated parallel structure and specificity.

Nitpicks aside, there’s some very good material intended for policy makers. On that note, some of the analysis is beyond me. I haven’t used anything even somewhat close to their analytical tools in years and years. This commentaries reflects my interests and a very rapid reading. One last thing, this is being written from a Canadian perspective. With those caveats in mind, here’s some of what I found.

A definition, social issues, country statistics, and more

There’s a definition for neurotechnology and a second mention of artificial intelligence being used in concert with neurotechnology. From the report‘s executive summary,

Neurotechnology consists of devices and procedures used to access, monitor, investigate, assess, manipulate, and/or emulate the structure and function of the neural systems of animals or human beings. It is poised to revolutionize our understanding of the brain and to unlock innovative solutions to treat a wide range of diseases and disorders.

Similarly to Artificial Intelligence (AI), and also due to its convergence with AI, neurotechnology may have profound societal and economic impact, beyond the medical realm. As neurotechnology directly relates to the brain, it triggers ethical considerations about fundamental aspects of human existence, including mental integrity, human dignity, personal identity, freedom of thought, autonomy, and privacy [emphases mine]. Its potential for enhancement purposes and its accessibility further amplifies its prospect social and societal implications.

The recent discussions held at UNESCO’s Executive Board further shows Member States’ desire to address the ethics and governance of neurotechnology through the elaboration of a new standard-setting instrument on the ethics of neurotechnology, to be adopted in 2025. To this end, it is important to explore the neurotechnology landscape, delineate its boundaries, key players, and trends, and shed light on neurotech’s scientific and technological developments. [p. 7]

Here’s how they sourced the data for the report,

The present report addresses such a need for evidence in support of policy making in
relation to neurotechnology by devising and implementing a novel methodology on data from scientific articles and patents:

● We detect topics over time and extract relevant keywords using a transformer-
based language models fine-tuned for scientific text. Publication data for the period
2000-2021 are sourced from the Scopus database and encompass journal articles
and conference proceedings in English. The 2,000 most cited publications per year
are further used in in-depth content analysis.
● Keywords are identified through Named Entity Recognition and used to generate
search queries for conducting a semantic search on patents’ titles and abstracts,
using another language model developed for patent text. This allows us to identify
patents associated with the identified neuroscience publications and their topics.
The patent data used in the present analysis are sourced from the European
Patent Office’s Worldwide Patent Statistical Database (PATSTAT). We consider
IP5 patents filed between 2000-2020 having an English language abstract and
exclude patents solely related to pharmaceuticals.

This approach allows mapping the advancements detailed in scientific literature to the technological applications contained in patent applications, allowing for an analysis of the linkages between science and technology. This almost fully automated novel approach allows repeating the analysis as neurotechnology evolves. [pp. 8-9[

Findings in bullet points,

Key stylized facts are:
● The field of neuroscience has witnessed a remarkable surge in the overall number
of publications since 2000, exhibiting a nearly 35-fold increase over the period
considered, reaching 1.2 million in 2021. The annual number of publications in
neuroscience has nearly tripled since 2000, exceeding 90,000 publications a year
in 2021. This increase became even more pronounced since 2019.
● The United States leads in terms of neuroscience publication output (40%),
followed by the United Kingdom (9%), Germany (7%), China (5%), Canada (4%),
Japan (4%), Italy (4%), France (4%), the Netherlands (3%), and Australia (3%).
These countries account for over 80% of neuroscience publications from 2000 to
2021.
● Big divides emerge, with 70% of countries in the world having less than 10 high-
impact neuroscience publications between 2000 to 2021.
● Specific neurotechnology-related research trends between 2000 and 2021 include:
○ An increase in Brain-Computer Interface (BCI) research around 2010,
maintaining a consistent presence ever since.
○ A significant surge in Epilepsy Detection research in 2017 and 2018,
reflecting the increased use of AI and machine learning in healthcare.
○ Consistent interest in Neuroimaging Analysis, which peaks around 2004,
likely because of its importance in brain activity and language
comprehension studies.
○ While peaking in 2016 and 2017, Deep Brain Stimulation (DBS) remains a
persistent area of research, underlining its potential in treating conditions
like Parkinson’s disease and essential tremor.
● Between 2000 and 2020, the total number of patent applications in this field
increased significantly, experiencing a 20-fold increase from less than 500 to over
12,000. In terms of annual figures, a consistent upward trend in neurotechnology-10
related patent applications emerges, with a notable doubling observed between
2015 and 2020.
• The United States account for nearly half of all worldwide patent applications (47%).
Other major contributors include South Korea (11%), China (10%), Japan (7%),
Germany (7%), and France (5%). These five countries together account for 87%
of IP5 neurotech patents applied between 2000 and 2020.
○ The United States has historically led the field, with a peak around 2010, a
decline towards 2015, and a recovery up to 2020.
○ South Korea emerged as a significant contributor after 1990, overtaking
Germany in the late 2000s to become the second-largest developer of
neurotechnology. By the late 2010s, South Korea’s annual neurotechnology
patent applications approximated those of the United States.
○ China exhibits a sharp increase in neurotechnology patent applications in
the mid-2010s, bringing it on par with the United States in terms of
application numbers.
● The United States ranks highest in both scientific publications and patents,
indicating their strong ability to transform knowledge into marketable inventions.
China, France, and Korea excel in leveraging knowledge to develop patented
innovations. Conversely, countries such as the United Kingdom, Germany, Italy,
Canada, Brazil, and Australia lag behind in effectively translating neurotech
knowledge into patentable innovations.
● In terms of patent quality measured by forward citations, the leading countries are
Germany, US, China, Japan, and Korea.
● A breakdown of patents by technology field reveals that Computer Technology is
the most important field in neurotechnology, exceeding Medical Technology,
Biotechnology, and Pharmaceuticals. The growing importance of algorithmic
applications, including neural computing techniques, also emerges by looking at
the increase in patent applications in these fields between 2015-2020. Compared
to the reference year, computer technologies-related patents in neurotech
increased by 355% and by 92% in medical technology.
● An analysis of the specialization patterns of the top-5 countries developing
neurotechnologies reveals that Germany has been specializing in chemistry-
related technology fields, whereas Asian countries, particularly South Korea and
China, focus on computer science and electrical engineering-related fields. The
United States exhibits a balanced configuration with specializations in both
chemistry and computer science-related fields.
● The entities – i.e. both companies and other institutions – leading worldwide
innovation in the neurotech space are: IBM (126 IP5 patents, US), Ping An
Technology (105 IP5 patents, CH), Fujitsu (78 IP5 patents, JP), Microsoft (76 IP511
patents, US)1, Samsung (72 IP5 patents, KR), Sony (69 IP5 patents JP) and Intel
(64 IP5 patents US)

This report further proposes a pioneering taxonomy of neurotechnologies based on International Patent Classification (IPC) codes.

• 67 distinct patent clusters in neurotechnology are identified, which mirror the diverse research and development landscape of the field. The 20 most prominent neurotechnology groups, particularly in areas like multimodal neuromodulation, seizure prediction, neuromorphic computing [emphasis mine], and brain-computer interfaces, point to potential strategic areas for research and commercialization.
• The variety of patent clusters identified mirrors the breadth of neurotechnology’s potential applications, from medical imaging and limb rehabilitation to sleep optimization and assistive exoskeletons.
• The development of a baseline IPC-based taxonomy for neurotechnology offers a structured framework that enriches our understanding of this technological space, and can facilitate research, development and analysis. The identified key groups mirror the interdisciplinary nature of neurotechnology and underscores the potential impact of neurotechnology, not only in healthcare but also in areas like information technology and biomaterials, with non-negligible effects over societies and economies.

1 If we consider Microsoft Technology Licensing LLM and Microsoft Corporation as being under the same umbrella, Microsoft leads worldwide developments with 127 IP5 patents. Similarly, if we were to consider that Siemens AG and Siemens Healthcare GmbH belong to the same conglomerate, Siemens would appear much higher in the ranking, in third position, with 84 IP5 patents. The distribution of intellectual property assets across companies belonging to the same conglomerate is frequent and mirrors strategic as well as operational needs and features, among others. [pp. 9-11]

Surprises and comments

Interesting and helpful to learn that “neurotechnology interacts with other technological trajectories, especially Artificial Intelligence;” this has changed and improved my understanding of neurotechnology.

It was unexpected to find Canada in the top ten countries producing neuroscience papers. However, finding out that the country lags in translating its ‘neuro’ knowledge into patentable innovation is not entirely a surprise.

It can’t be an accident that countries with major ‘electronics and computing’ companies lead in patents. These companies do have researchers but they also buy startups to acquire patents. They (and ‘patent trolls’) will also file patents preemptively. For the patent trolls, it’s a moneymaking proposition and for the large companies, it’s a way of protecting their own interests and/or (I imagine) forcing a sale.

The mention of neuromorphic (brainlike) computing in the taxonomy section was surprising and puzzling. Up to this point, I’ve thought of neuromorphic computing as a kind of alternative or addition to standard computing but the authors have blurred the lines as per UNESCO’s definition of neurotechnology (specifically, “… emulate the structure and function of the neural systems of animals or human beings”) . Again, this report is broadening my understanding of neurotechnology. Of course, it required two instances before I quite grasped it, the definition and the taxonomy.

What’s puzzling is that neuromorphic engineering, a broader term that includes neuromorphic computing, isn’t used or mentioned. (For an explanation of the terms neuromorphic computing and neuromorphic engineering, there’s my June 23, 2023 posting, “Neuromorphic engineering: an overview.” )

The report

I won’t have time for everything. Here are some of the highlights from my admittedly personal perspective.

It’s not only about curing disease

From the report,

Neurotechnology’s applications however extend well beyond medicine [emphasis mine], and span from research, to education, to the workplace, and even people’s everyday life. Neurotechnology-based solutions may enhance learning and skill acquisition and boost focus through brain stimulation techniques. For instance, early research finds that brain- zapping caps appear to boost memory for at least one month (Berkeley, 2022). This could one day be used at home to enhance memory functions [emphasis mine]. They can further enable new ways to interact with the many digital devices we use in everyday life, transforming the way we work, live and interact. One example is the Sound Awareness wristband developed by a Stanford team (Neosensory, 2022) which enables individuals to “hear” by converting sound into tactile feedback, so that sound impaired individuals can perceive spoken words through their skin. Takagi and Nishimoto (2023) analyzed the brain scans taken through Magnetic Resonance Imaging (MRI) as individuals were shown thousands of images. They then trained a generative AI tool called Stable Diffusion2 on the brain scan data of the study’s participants, thus creating images that roughly corresponded to the real images shown. While this does not correspond to reading the mind of people, at least not yet, and some limitations of the study have been highlighted (Parshall, 2023), it nevertheless represents an important step towards developing the capability to interface human thoughts with computers [emphasis mine], via brain data interpretation.

While the above examples may sound somewhat like science fiction, the recent uptake of generative Artificial Intelligence applications and of large language models such as ChatGPT or Bard, demonstrates that the seemingly impossible can quickly become an everyday reality. At present, anyone can purchase online electroencephalogram (EEG) devices for a few hundred dollars [emphasis mine], to measure the electrical activity of their brain for meditation, gaming, or other purposes. [pp. 14-15]

This is very impressive achievement. Some of the research cited was published earlier this year (2023). The extraordinary speed is a testament to the efforts by the authors and their teams. It’s also a testament to how quickly the field is moving.

I’m glad to see the mention of and focus on consumer neurotechnology. (While the authors don’t speculate, I am free to do so.) Consumer neurotechnology could be viewed as one of the steps toward normalizing a cyborg future for all of us. Yes, we have books, television programmes, movies, and video games, which all normalize the idea but the people depicted have been severely injured and require the augmentation. With consumer neurotechnology, you have easily accessible devices being used to enhance people who aren’t injured, they just want to be ‘better’.

This phrase seemed particularly striking “… an important step towards developing the capability to interface human thoughts with computers” in light of some claims made by the Australian military in my June 13, 2023 posting “Mind-controlled robots based on graphene: an Australian research story.” (My posting has an embedded video demonstrating the Brain Robotic Interface (BRI) in action. Also, see the paragraph below the video for my ‘measured’ response.)

There’s no mention of the military in the report which seems more like a deliberate rather than inadvertent omission given the importance of military innovation where technology is concerned.

This section gives a good overview of government initiatives (in the report it’s followed by a table of the programmes),

Thanks to the promises it holds, neurotechnology has garnered significant attention from both governments and the private sector and is considered by many as an investment priority. According to the International Brain Initiative (IBI), brain research funding has become increasingly important over the past ten years, leading to a rise in large-scale state-led programs aimed at advancing brain intervention technologies(International Brain Initiative, 2021). Since 2013, initiatives such as the United States’ Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and the European Union’s Human Brain Project (HBP), as well as major national initiatives in China, Japan and South Korea have been launched with significant funding support from the respective governments. The Canadian Brain Research Strategy, initially operated as a multi- stakeholder coalition on brain research, is also actively seeking funding support from the government to transform itself into a national research initiative (Canadian Brain Research Strategy, 2022). A similar proposal is also seen in the case of the Australian Brain Alliance, calling for the establishment of an Australian Brain Initiative (Australian Academy of Science, n.d.). [pp. 15-16]

Privacy

There are some concerns such as these,

Beyond the medical realm, research suggests that emotional responses of consumers
related to preferences and risks can be concurrently tracked by neurotechnology, such
as neuroimaging and that neural data can better predict market-level outcomes than
traditional behavioral data (Karmarkar and Yoon, 2016). As such, neural data is
increasingly sought after in the consumer market for purposes such as digital
phenotyping4, neurogaming 5,and neuromarketing6 (UNESCO, 2021). This surge in demand gives rise to risks like hacking, unauthorized data reuse, extraction of privacy-sensitive information, digital surveillance, criminal exploitation of data, and other forms of abuse. These risks prompt the question of whether neural data needs distinct definition and safeguarding measures.

These issues are particularly relevant today as a wide range of electroencephalogram (EEG) headsets that can be used at home are now available in consumer markets for purposes that range from meditation assistance to controlling electronic devices through the mind. Imagine an individual is using one of these devices to play a neurofeedback game, which records the person’s brain waves during the game. Without the person being aware, the system can also identify the patterns associated with an undiagnosed mental health condition, such as anxiety. If the game company sells this data to third parties, e.g. health insurance providers, this may lead to an increase of insurance fees based on undisclosed information. This hypothetical situation would represent a clear violation of mental privacy and of unethical use of neural data.

Another example is in the field of advertising, where companies are increasingly interested in using neuroimaging to better understand consumers’ responses to their products or advertisements, a practice known as neuromarketing. For instance, a company might use neural data to determine which advertisements elicit the most positive emotional responses in consumers. While this can help companies improve their marketing strategies, it raises significant concerns about mental privacy. Questions arise in relation to consumers being aware or not that their neural data is being used, and in the extent to which this can lead to manipulative advertising practices that unfairly exploit unconscious preferences. Such potential abuses underscore the need for explicit consent and rigorous data protection measures in the use of neurotechnology for neuromarketing purposes. [pp. 21-22]

Legalities

Some countries already have laws and regulations regarding neurotechnology data,

At the national level, only a few countries have enacted laws and regulations to protect mental integrity or have included neuro-data in personal data protection laws (UNESCO, University of Milan-Bicocca (Italy) and State University of New York – Downstate Health Sciences University, 2023). Examples are the constitutional reform undertaken by Chile (Republic of Chile, 2021), the Charter for the responsible development of neurotechnologies of the Government of France (Government of France, 2022), and the Digital Rights Charter of the Government of Spain (Government of Spain, 2021). They propose different approaches to the regulation and protection of human rights in relation to neurotechnology. Countries such as the UK are also examining under which circumstances neural data may be considered as a special category of data under the general data protection framework (i.e. UK’s GDPR) (UK’s Information Commissioner’s Office, 2023) [p. 24]

As you can see, these are recent laws. There doesn’t seem to be any attempt here in Canada even though there is an act being reviewed in Parliament that could conceivably include neural data. This is from my May 1, 2023 posting,

Bill C-27 (Digital Charter Implementation Act, 2022) is what I believe is called an omnibus bill as it includes three different pieces of proposed legislation (the Consumer Privacy Protection Act [CPPA], the Artificial Intelligence and Data Act [AIDA], and the Personal Information and Data Protection Tribunal Act [PIDPTA]). [emphasis added July 11, 2023] You can read the Innovation, Science and Economic Development (ISED) Canada summary here or a detailed series of descriptions of the act here on the ISED’s Canada’s Digital Charter webpage.

My focus at the time was artificial intelligence and, now, after reading this UNESCO report and briefly looking at the Innovation, Science and Economic Development (ISED) Canada summary and a detailed series of descriptions of the act on ISED’s Canada’s Digital Charter webpage, I don’t see anything that specifies neural data but it’s not excluded either.

IP5 patents

Here’s the explanation (the footnote is included at the end of the excerpt),

IP5 patents represent a subset of overall patents filed worldwide, which have the
characteristic of having been filed in at least one top intellectual property offices (IPO)
worldwide (the so called IP5, namely the Chinese National Intellectual Property
Administration, CNIPA (formerly SIPO); the European Patent Office, EPO; the Japan
Patent Office, JPO; the Korean Intellectual Property Office, KIPO; and the United States
Patent and Trademark Office, USPTO) as well as another country, which may or may not be an IP5. This signals their potential applicability worldwide, as their inventiveness and industrial viability have been validated by at least two leading IPOs. This gives these patents a sort of “quality” check, also since patenting inventions is costly and if applicants try to protect the same invention in several parts of the world, this normally mirrors that the applicant has expectations about their importance and expected value. If we were to conduct the same analysis using information about individually considered patent applied worldwide, i.e. without filtering for quality nor considering patent families, we would risk conducting a biased analysis based on duplicated data. Also, as patentability standards vary across countries and IPOs, and what matters for patentability is the existence (or not) of prior art in the IPO considered, we would risk mixing real innovations with patents related to catching up phenomena in countries that are not at the forefront of the technology considered.

9 The five IP offices (IP5) is a forum of the five largest intellectual property offices in the world that was set up to improve the efficiency of the examination process for patents worldwide. The IP5 Offices together handle about 80% of the world’s patent applications, and 95% of all work carried out under the Patent Cooperation Treaty (PCT), see http://www.fiveipoffices.org. (Dernis et al., 2015) [p. 31]

AI assistance on this report

As noted earlier I have next to no experience with the analytical tools having not attempted this kind of work in several years. Here’s an example of what they were doing,

We utilize a combination of text embeddings based on Bidirectional Encoder
Representations from Transformer (BERT), dimensionality reduction, and hierarchical
clustering inspired by the BERTopic methodology 12 to identify latent themes within
research literature. Latent themes or topics in the context of topic modeling represent
clusters of words that frequently appear together within a collection of documents (Blei, 2012). These groupings are not explicitly labeled but are inferred through computational analysis examining patterns in word usage. These themes are ‘hidden’ within the text, only to be revealed through this analysis. …

We further utilize OpenAI’s GPT-4 model to enrich our understanding of topics’ keywords and to generate topic labels (OpenAI, 2023), thus supplementing expert review of the broad interdisciplinary corpus. Recently, GPT-4 has shown impressive results in medical contexts across various evaluations (Nori et al., 2023), making it a useful tool to enhance the information obtained from prior analysis stages, and to complement them. The automated process enhances the evaluation workflow, effectively emphasizing neuroscience themes pertinent to potential neurotechnology patents. Notwithstanding existing concerns about hallucinations (Lee, Bubeck and Petro, 2023) and errors in generative AI models, this methodology employs the GPT-4 model for summarization and interpretation tasks, which significantly mitigates the likelihood of hallucinations. Since the model is constrained to the context provided by the keyword collections, it limits the potential for fabricating information outside of the specified boundaries, thereby enhancing the accuracy and reliability of the output. [pp. 33-34]

I couldn’t resist adding the ChatGPT paragraph given all of the recent hoopla about it.

Multimodal neuromodulation and neuromorphic computing patents

I think this gives a pretty good indication of the activity on the patent front,

The largest, coherent topic, termed “multimodal neuromodulation,” comprises 535
patents detailing methodologies for deep or superficial brain stimulation designed to
address neurological and psychiatric ailments. These patented technologies interact with various points in neural circuits to induce either Long-Term Potentiation (LTP) or Long-Term Depression (LTD), offering treatment for conditions such as obsession, compulsion, anxiety, depression, Parkinson’s disease, and other movement disorders. The modalities encompass implanted deep-brain stimulators (DBS), Transcranial Magnetic Stimulation (TMS), and transcranial Direct Current Stimulation (tDCS). Among the most representative documents for this cluster are patents with titles: Electrical stimulation of structures within the brain or Systems and methods for enhancing or optimizing neural stimulation therapy for treating symptoms of Parkinson’s disease and or other movement disorders. [p.65]

Given my longstanding interest in memristors, which (I believe) have to a large extent helped to stimulate research into neuromorphic computing, this had to be included. Then, there was the brain-computer interfaces cluster,

A cluster identified as “Neuromorphic Computing” consists of 366 patents primarily
focused on devices designed to mimic human neural networks for efficient and adaptable computation. The principal elements of these inventions are resistive memory cells and artificial synapses. They exhibit properties similar to the neurons and synapses in biological brains, thus granting these devices the ability to learn and modulate responses based on rewards, akin to the adaptive cognitive capabilities of the human brain.

The primary technology classes associated with these patents fall under specific IPC
codes, representing the fields of neural network models, analog computers, and static
storage structures. Essentially, these classifications correspond to technologies that are key to the construction of computers and exhibit cognitive functions similar to human brain processes.

Examples for this cluster include neuromorphic processing devices that leverage
variations in resistance to store and process information, artificial synapses exhibiting
spike-timing dependent plasticity, and systems that allow event-driven learning and
reward modulation within neuromorphic computers.

In relation to neurotechnology as a whole, the “neuromorphic computing” cluster holds significant importance. It embodies the fusion of neuroscience and technology, thereby laying the basis for the development of adaptive and cognitive computational systems. Understanding this specific cluster provides a valuable insight into the progressing domain of neurotechnology, promising potential advancements across diverse fields, including artificial intelligence and healthcare.

The “Brain-Computer Interfaces” cluster, consisting of 146 patents, embodies a key aspect of neurotechnology that focuses on improving the interface between the brain and external devices. The technology classification codes associated with these patents primarily refer to methods or devices for treatment or protection of eyes and ears, devices for introducing media into, or onto, the body, and electric communication techniques, which are foundational elements of brain-computer interface (BCI) technologies.

Key patents within this cluster include a brain-computer interface apparatus adaptable to use environment and method of operating thereof, a double closed circuit brain-machine interface system, and an apparatus and method of brain-computer interface for device controlling based on brain signal. These inventions mainly revolve around the concept of using brain signals to control external devices, such as robotic arms, and improving the classification performance of these interfaces, even after long periods of non-use.

The inventions described in these patents improve the accuracy of device control, maintain performance over time, and accommodate multiple commands, thus significantly enhancing the functionality of BCIs.

Other identified technologies include systems for medical image analysis, limb rehabilitation, tinnitus treatment, sleep optimization, assistive exoskeletons, and advanced imaging techniques, among others. [pp. 66-67]

Having sections on neuromorphic computing and brain-computer interface patents in immediate proximity led to more speculation on my part. Imagine how much easier it would be to initiate a BCI connection if it’s powered with a neuromorphic (brainlike) computer/device. [ETA July 21, 2023: Following on from that thought, it might be more than just easier to initiate a BCI connection. Could a brainlike computer become part of your brain? Why not? it’s been successfully argued that a robotic wheelchair was part of someone’s body, see my January 30, 2013 posting and scroll down about 40% of the way.)]

Neurotech policy debates

The report concludes with this,

Neurotechnology is a complex and rapidly evolving technological paradigm whose
trajectories have the power to shape people’s identity, autonomy, privacy, sentiments,
behaviors and overall well-being, i.e. the very essence of what it means to be human.

Designing and implementing careful and effective norms and regulations ensuring that neurotechnology is developed and deployed in an ethical manner, for the good of
individuals and for society as a whole, call for a careful identification and characterization of the issues at stake. This entails shedding light on the whole neurotechnology ecosystem, that is what is being developed, where and by whom, and also understanding how neurotechnology interacts with other developments and technological trajectories, especially AI. Failing to do so may result in ineffective (at best) or distorted policies and policy decisions, which may harm human rights and human dignity.

Addressing the need for evidence in support of policy making, the present report offers first time robust data and analysis shedding light on the neurotechnology landscape worldwide. To this end, its proposes and implements an innovative approach that leverages artificial intelligence and deep learning on data from scientific publications and paten[t]s to identify scientific and technological developments in the neurotech space. The methodology proposed represents a scientific advance in itself, as it constitutes a quasi- automated replicable strategy for the detection and documentation of neurotechnology- related breakthroughs in science and innovation, to be repeated over time to account for the evolution of the sector. Leveraging this approach, the report further proposes an IPC-based taxonomy for neurotechnology which allows for a structured framework to the exploration of neurotechnology, to enable future research, development and analysis. The innovative methodology proposed is very flexible and can in fact be leveraged to investigate different emerging technologies, as they arise.

In terms of technological trajectories, we uncover a shift in the neurotechnology industry, with greater emphasis being put on computer and medical technologies in recent years, compared to traditionally dominant trajectories related to biotechnology and pharmaceuticals. This shift warrants close attention from policymakers, and calls for attention in relation to the latest (converging) developments in the field, especially AI and related methods and applications and neurotechnology.

This is all the more important and the observed growth and specialization patterns are unfolding in the context of regulatory environments that, generally, are either not existent or not fit for purpose. Given the sheer implications and impact of neurotechnology on the very essence of human beings, this lack of regulation poses key challenges related to the possible infringement of mental integrity, human dignity, personal identity, privacy, freedom of thought, and autonomy, among others. Furthermore, issues surrounding accessibility and the potential for neurotech enhancement applications triggers significant concerns, with far-reaching implications for individuals and societies. [pp. 72-73]

Last words about the report

Informative, readable, and thought-provoking. And, it helped broaden my understanding of neurotechnology.

Future endeavours?

I’m hopeful that one of these days one of these groups (UNESCO, Canadian Science Policy Centre, or ???) will tackle the issue of business bankruptcy in the neurotechnology sector. It has already occurred as noted in my ““Going blind when your neural implant company flirts with bankruptcy [long read]” April 5, 2022 posting. That story opens with a woman going blind in a New York subway when her neural implant fails. It’s how she found out the company, which supplied her implant was going out of business.

In my July 7, 2023 posting about the UNESCO July 2023 dialogue on neurotechnology, I’ve included information on Neuralink (one of Elon Musk’s companies) and its approval (despite some investigations) by the US Food and Drug Administration to start human clinical trials. Scroll down about 75% of the way to the “Food for thought” subhead where you will find stories about allegations made against Neuralink.

The end

If you want to know more about the field, the report offers a seven-page bibliography and there’s a lot of material here where you can start with this December 3, 2019 posting “Neural and technological inequalities” which features an article mentioning a discussion between two scientists. Surprisingly (to me), the source article is in Fast Company (a leading progressive business media brand), according to their tagline)..

I have two categories you may want to check: Human Enhancement and Neuromorphic Engineering. There are also a number of tags: neuromorphic computing, machine/flesh, brainlike computing, cyborgs, neural implants, neuroprosthetics, memristors, and more.

Should you have any observations or corrections, please feel free to leave them in the Comments section of this posting.

Non-human authors (ChatGPT or others) of scientific and medical studies and the latest AI panic!!!

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 first time I wrote about the incursion of robots or artificial intelligence into the field of writing was in a July 16, 2014 posting titled “Writing and AI or is a robot writing this blog?” ChatGPT (then known as GPT-2) first made its way onto this blog in a February 18, 2019 posting titled “AI (artificial intelligence) text generator, too dangerous to release?

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

This is a link to and a citation for the JAMA editorial,

Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and Medical Knowledge by Annette Flanagin, Kirsten Bibbins-Domingo, Michael Berkwits, Stacy L. Christiansen. JAMA. 2023;329(8):637-639. doi:10.1001/jama.2023.1344

The editorial appears to be open access.

ChatGPT in the field of education

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.

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.

Dr. Maynard goes on to offer the FAQ/practical guide here. Prior to issuing the ‘guide’, he wrote a December 8, 2022 essay on Medium titled “I asked Open AI’s ChatGPT about responsible innovation. This is what I got.”

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,

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.

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. …

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.

“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.

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.

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.”

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.

If you have time, I recommend reading Omes’s March 6, 2023 article.

The panic

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

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. 

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.

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.”

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. 

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.”

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.

“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.

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.

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. 

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.

“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.”

Hinton has attracted some criticism himself. Wilfred Chan writing for Fast Company has two articles, “‘I didn’t see him show up’: Ex-Googlers blast ‘AI godfather’ Geoffrey Hinton’s silence on fired AI experts” on May 5, 2023, Note: Links have been removed,

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.)

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. …

… 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]

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 Centre for Existential Risk can be found here online (it is located at the University of Cambridge). Interestingly, Hinton who was born in December 1947 will be giving a lecture “Digital versus biological intelligence: Reasons for concern about AI” in Cambridge on May 25, 2023.

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.

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.

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.

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.

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.

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

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.

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.”

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.

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,”

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.

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.)

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,

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. 

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.

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,

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]

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),

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.

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.

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.”

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.

Canada, AI regulation, and the second reading of the Digital Charter Implementation Act, 2022 (Bill C-27)

Bill C-27 (Digital Charter Implementation Act, 2022) is what I believe is called an omnibus bill as it includes three different pieces of proposed legislation (the Consumer Privacy Protection Act [CPPA], the Artificial Intelligence and Data Act [AIDA], and the Personal Information and Data Protection Tribunal Act [PIDPTA]). You can read the Innovation, Science and Economic Development (ISED) Canada summary here or a detailed series of descriptions of the act here on the ISED’s Canada’s Digital Charter webpage.

Months after the first reading in June 2022, Bill C-27 was mentioned here in a September 15, 2022 posting about a Canadian Science Policy Centre (CSPC) event featuring a panel discussion about the proposed legislation, artificial intelligence in particular. I dug down and found commentaries and additional information about the proposed bill with special attention to AIDA.

it seems discussion has been reactivated since the second reading was completed on April 24, 2023 and referred to committee for further discussion. (A report and third reading are still to be had in the House of Commons and then, there are three readings in the Senate before this legislation can be passed.)

Christian Paas-Lang has written an April 24, 2023 article for CBC (Canadian Broadcasting Corporation) news online that highlights concerns centred on AI from three cross-party Members of Parliament (MPs),

Once the domain of a relatively select group of tech workers, academics and science fiction enthusiasts, the debate over the future of artificial intelligence has been thrust into the mainstream. And a group of cross-party MPs say Canada isn’t yet ready to take on the challenge.

The popularization of AI as a subject of concern has been accelerated by the introduction of ChatGPT, an AI chatbot produced by OpenAI that is capable of generating a broad array of text, code and other content. ChatGPT relies on content published on the internet as well as training from its users to improve its responses.

ChatGPT has prompted such a fervour, said Katrina Ingram, founder of the group Ethically Aligned AI, because of its novelty and effectiveness. 

“I would argue that we’ve had AI enabled infrastructure or technologies around for quite a while now, but we haven’t really necessarily been confronted with them, you know, face to face,” she told CBC Radio’s The House [radio segment embedded in article] in an interview that aired Saturday [April 22, 2023].

Ingram said the technology has prompted a series of concerns: about the livelihoods of professionals like artists and writers, about privacy, data collection and surveillance and about whether chatbots like ChatGPT can be used as tools for disinformation.

With the popularization of AI as an issue has come a similar increase in concern about regulation, and Ingram says governments must act now.

“We are contending with these technologies right now. So it’s really imperative that governments are able to pick up the pace,” she told host Catherine Cullen.

That sentiment — the need for speed — is one shared by three MPs from across party lines who are watching the development of the AI issue. Conservative MP Michelle Rempel Garner, NDP MP Brian Masse and Nathaniel Erskine-Smith of the Liberals also joined The House for an interview that aired Saturday.

“This is huge. This is the new oil,” said Masse, the NDP’s industry critic, referring to how oil had fundamentally shifted economic and geopolitical relationships, leading to a great deal of good but also disasters — and AI could do the same.

Issues of both speed and substance

The three MPs are closely watching Bill C-27, a piece of legislation currently being debated in the House of Commons that includes Canada’s first federal regulations on AI.

But each MP expressed concern that the bill may not be ready in time and changes would be needed [emphasis mine].

“This legislation was tabled in June of last year [2022], six months before ChatGPT was released and it’s like it’s obsolete. It’s like putting in place a framework to regulate scribes four months after the printing press came out,” Rempel Garner said. She added that it was wrongheaded to move the discussion of AI away from Parliament and segment it off to a regulatory body.

Am I the only person who sees a problem with the “bill may not be ready in time and changes would be needed?” I don’t understand the rush (or how these people get elected). The point of a bill is to examine the ideas and make changes to it before it becomes legislation. Given how fluid the situation appears to be, a strong argument can be made for the current process which is three readings in the House of Commons, along with a committee report, and three readings in the senate before a bill, if successful, is passed into legislation.

Of course, the fluidity of the situation could also be an argument for starting over as Michael Geist’s (Canada Research Chair in Internet and E-Commerce Law at the University of Ottawa and member of the Centre for Law, Technology and Society) April 19, 2023 post on his eponymous blog suggests, Note: Links have been removed,

As anyone who has tried ChatGPT will know, at the bottom of each response is an option to ask the AI system to “regenerate response”. Despite increasing pressure on the government to move ahead with Bill C-27’s Artificial Intelligence and Data Act (AIDA), the right response would be to hit the regenerate button and start over. AIDA may be well-meaning and the issue of AI regulation critically important, but the bill is limited in principles and severely lacking in detail, leaving virtually all of the heavy lifting to a regulation-making process that will take years to unfold. While no one should doubt the importance of AI regulation, Canadians deserve better than virtue signalling on the issue with a bill that never received a full public consultation.

What prompts this post is a public letter based out of MILA that calls on the government to urgently move ahead with the bill signed by some of Canada’s leading AI experts. The letter states: …

When the signatories to the letter suggest that there is prospect of moving AIDA forward before the summer, it feels like a ChatGPT error. There are a maximum of 43 days left on the House of Commons calendar until the summer. In all likelihood, it will be less than that. Bill C-27 is really three bills in one: major privacy reform, the creation of a new privacy tribunal, and AI regulation. I’ve watched the progress of enough bills to know that this just isn’t enough time to conduct extensive hearings on the bill, conduct a full clause-by-clause review, debate and vote in the House, and then conduct another review in the Senate. At best, Bill C-27 could make some headway at committee, but getting it passed with a proper review is unrealistic.

Moreover, I am deeply concerned about a Parliamentary process that could lump together these three bills in an expedited process. …

For anyone unfamiliar with MILA, it is also known as Quebec’s Artificial Intelligence Institute. (They seem to have replaced institute with ecosystem since the last time I checked.) You can see the document and list of signatories here.

Geist has a number of posts and podcasts focused on the bill and the easiest way to find them is to use the search term ‘Bill C-27’.

Maggie Arai at the University of Toronto’s Schwartz Reisman Institute for Technology and Society provides a brief overview titled, Five things to know about Bill C-27, in her April 18, 2022 commentary,

On June 16, 2022, the Canadian federal government introduced Bill C-27, the Digital Charter Implementation Act 2022, in the House of Commons. Bill C-27 is not entirely new, following in the footsteps of Bill C-11 (the Digital Charter Implementation Act 2020). Bill C-11 failed to pass, dying on the Order Paper when the Governor General dissolved Parliament to hold the 2021 federal election. While some aspects of C-27 will likely be familiar to those who followed the progress of Bill C-11, there are several key differences.

After noting the differences, Arai had this to say, from her April 18, 2022 commentary,

The tabling of Bill C-27 represents an exciting step forward for Canada as it attempts to forge a path towards regulating AI that will promote innovation of this advanced technology, while simultaneously offering consumers assurance and protection from the unique risks this new technology it poses. This second attempt towards the CPPA and PIDPTA is similarly positive, and addresses the need for updated and increased consumer protection, privacy, and data legislation.

However, as the saying goes, the devil is in the details. As we have outlined, several aspects of how Bill C-27 will be implemented are yet to be defined, and how the legislation will interact with existing social, economic, and legal dynamics also remains to be seen.

There are also sections of C-27 that could be improved, including areas where policymakers could benefit from the insights of researchers with domain expertise in areas such as data privacy, trusted computing, platform governance, and the social impacts of new technologies. In the coming weeks, the Schwartz Reisman Institute will present additional commentaries from our community that explore the implications of C-27 for Canadians when it comes to privacy, protection against harms, and technological governance.

Bryan Short’s September 14, 2022 posting (The Absolute Bare Minimum: Privacy and the New Bill C-27) on the Open Media website critiques two of the three bills included in Bill C-27, Note: Links have been removed,

The Canadian government has taken the first step towards creating new privacy rights for people in Canada. After a failed attempt in 2020 and three years of inaction since the proposal of the digital charter, the government has tabled another piece of legislation aimed at giving people in Canada the privacy rights they deserve.

In this post, we’ll explore how Bill C-27 compares to Canada’s current privacy legislation, how it stacks up against our international peers, and what it means for you. This post considers two of the three acts being proposed in Bill C-27, the Consumer Privacy Protection Act (CPPA) and the Personal Information and Data Tribunal Act (PIDTA), and doesn’t discuss the Artificial Intelligence and Data Act [emphasis mine]. The latter Act’s engagement with very new and complex issues means we think it deserves its own consideration separate from existing privacy proposals, and will handle it as such.

If we were to give Bill C-27’s CPPA and PIDTA a grade, it’d be a D. This is legislation that does the absolute bare minimum for privacy protections in Canada, and in some cases it will make things actually worse. If they were proposed and passed a decade ago, we might have rated it higher. However, looking ahead at predictable movement in data practices over the next ten – or even twenty – years, these laws will be out of date the moment they are passed, and leave people in Canada vulnerable to a wide range of predatory data practices. For detailed analysis, read on – but if you’re ready to raise your voice, go check out our action calling for positive change before C-27 passes!

Taking this all into account, Bill C-27 isn’t yet the step forward for privacy in Canada that we need. While it’s an improvement upon the last privacy bill that the government put forward, it misses so many areas that are critical for improvement, like failing to put people in Canada above the commercial interests of companies.

If Open Media has followed up with an AIDA critique, I have not been able to find it on their website.

Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (2 of 2): Meditations

Dear friend,

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.

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.”

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.

For the curious, there’s a description of the other VAG ‘imitation game’ installations provided by CDM students on the ‘Master of Digital Media Students Develop Revolutionary Installations for Vancouver Art Gallery AI Exhibition‘ webpage.

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.

This image has an empty alt attribute; its file name is image-asset.jpeg
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.

She has her own website.

If not Ai-Da, what about Dall-E-2? Aaron Hertzmann’s June 20, 2022 commentary, “Give this AI a few words of description and it produces a stunning image – but is it art?” investigates for Salon (Note: Links have been removed),

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.

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),

“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.”

There are other AI artists, in my August 16, 2019 posting, I had this,

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.

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.

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.

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.

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.

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]

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.)

Of course, there are the CDM student projects but the projects seem less like an exploration of visual culture than an exploration of technology and industry requirements, from the ‘Master of Digital Media Students Develop Revolutionary Installations for Vancouver Art Gallery AI Exhibition‘ webpage, Note: A link has been removed,

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].

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.

At this year’s conference, they have at least two sessions that indicate interests similar to the VAG’s. First, there’s Immersive Visualization for Research, Science and Art which includes AI and machine learning along with other related topics. There’s also, Frontiers Talk: Art in the Age of AI: Can Computers Create Art?

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.

Do go. Do enjoy, my friend.

AI (artificial intelligence) text generator, too dangerous to release?

Could this latest version of OpenAI‘s text generator be so good that it would fool you? And following on that thought could the concomitant reluctance to release the research be real or is it a publicity stunt? Here’s a sample of the text from the GPT2 AI model from a February 15, 2019 article by Mark Fraunfelder for Boing Boing,

Recycling is good for the world.
NO! YOU COULD NOT BE MORE WRONG!!
MODEL COMPLETION (MACHINE-WRITTEN, 25 TRIES)
Recycling is NOT good for the world. It is bad for the environment, it is bad for our health, and it is bad for our economy. I’m not kidding. Recycling is not good for the environment. It is destructive to the earth and it is a major contributor to global warming. Recycling is not good for our health. It contributes to obesity and diseases like heart disease and cancer. Recycling is bad for our economy. It increases the cost of a product, and in turn, the price of everything that is made with that product. Recycling is not good for our nation. We pay a tremendous price for the privilege of having the world’s most advanced and efficient recycling system. Recycling is a huge, colossal waste of time, energy, money, and resources. And THAT is why we need to get back to basics and get back to basics in our recycling efforts. One of the best ways to start is to look at the process of creating a paper product. When you make a paper product, it is basically a long chain of materials. Everything from the raw materials (wood, cardboard, paper, etc.), to the reagents (dyes, solvents, etc.) to the printing equipment (chemicals, glue, paper, ink, etc.), to the packaging, to the packaging materials (mercury, chemicals, etc.) to the processing equipment (heating, cooling, etc.), to the packaging materials, to the packaging materials that are shipped overseas and to the packaging materials that are used in the United States. Each step along the way creates tons of waste that we constantly have to clean up. The process of making a paper product is a very wasteful one. But the end result is something that all of us need to consume. And if we want to keep the recycling process running efficiently, then we really need to think about each and every step that goes into making a paper product.

The first few sentences don’t work for me but once the discussion turns to making paper products, then it becomes more convincing to me. As to whether the company’s reluctance to release the research is genuine or a publicity stunt, I don’t know. However, there was a fair degree of interest in GPT2 after the decision.

From a February 14, 2019 article by Alex Hern for the Guardian,

OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough.

At its core, GPT2 is a text generator. The AI system is fed text, anything from a few words to a whole page, and asked to write the next few sentences based on its predictions of what should come next. The system is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses.

When used to simply generate new text, GPT2 is capable of writing plausible passages that match what it is given in both style and subject. It rarely shows any of the quirks that mark out previous AI systems, such as forgetting what it is writing about midway through a paragraph, or mangling the syntax of long sentences.

Feed it the opening line of George Orwell’s Nineteen Eighty-Four – “It was a bright cold day in April, and the clocks were striking thirteen” – and the system recognises the vaguely futuristic tone and the novelistic style, and continues with: …

Sean Gallagher’s February 15, 2019 posting on the ars Technica blog provides some insight that’s partially written a style sometimes associated with gossip (Note: Links have been removed),

OpenAI is funded by contributions from a group of technology executives and investors connected to what some have referred to as the PayPal “mafia”—Elon Musk, Peter Thiel, Jessica Livingston, and Sam Altman of YCombinator, former PayPal COO and LinkedIn co-founder Reid Hoffman, and former Stripe Chief Technology Officer Greg Brockman. [emphasis mine] Brockman now serves as OpenAI’s CTO. Musk has repeatedly warned of the potential existential dangers posed by AI, and OpenAI is focused on trying to shape the future of artificial intelligence technology—ideally moving it away from potentially harmful applications.

Given present-day concerns about how fake content has been used to both generate money for “fake news” publishers and potentially spread misinformation and undermine public debate, GPT-2’s output certainly qualifies as concerning. Unlike other text generation “bot” models, such as those based on Markov chain algorithms, the GPT-2 “bot” did not lose track of what it was writing about as it generated output, keeping everything in context.

For example: given a two-sentence entry, GPT-2 generated a fake science story on the discovery of unicorns in the Andes, a story about the economic impact of Brexit, a report about a theft of nuclear materials near Cincinnati, a story about Miley Cyrus being caught shoplifting, and a student’s report on the causes of the US Civil War.

Each matched the style of the genre from the writing prompt, including manufacturing quotes from sources. In other samples, GPT-2 generated a rant about why recycling is bad, a speech written by John F. Kennedy’s brain transplanted into a robot (complete with footnotes about the feat itself), and a rewrite of a scene from The Lord of the Rings.

While the model required multiple tries to get a good sample, GPT-2 generated “good” results based on “how familiar the model is with the context,” the researchers wrote. “When prompted with topics that are highly represented in the data (Brexit, Miley Cyrus, Lord of the Rings, and so on), it seems to be capable of generating reasonable samples about 50 percent of the time. The opposite is also true: on highly technical or esoteric types of content, the model can perform poorly.”

There were some weak spots encountered in GPT-2’s word modeling—for example, the researchers noted it sometimes “writes about fires happening under water.” But the model could be fine-tuned to specific tasks and perform much better. “We can fine-tune GPT-2 on the Amazon Reviews dataset and use this to let us write reviews conditioned on things like star rating and category,” the authors explained.

James Vincent’s February 14, 2019 article for The Verge offers a deeper dive into the world of AI text agents and what makes GPT2 so special (Note: Links have been removed),

For decades, machines have struggled with the subtleties of human language, and even the recent boom in deep learning powered by big data and improved processors has failed to crack this cognitive challenge. Algorithmic moderators still overlook abusive comments, and the world’s most talkative chatbots can barely keep a conversation alive. But new methods for analyzing text, developed by heavyweights like Google and OpenAI as well as independent researchers, are unlocking previously unheard-of talents.

OpenAI’s new algorithm, named GPT-2, is one of the most exciting examples yet. It excels at a task known as language modeling, which tests a program’s ability to predict the next word in a given sentence. Give it a fake headline, and it’ll write the rest of the article, complete with fake quotations and statistics. Feed it the first line of a short story, and it’ll tell you what happens to your character next. It can even write fan fiction, given the right prompt.

The writing it produces is usually easily identifiable as non-human. Although its grammar and spelling are generally correct, it tends to stray off topic, and the text it produces lacks overall coherence. But what’s really impressive about GPT-2 is not its fluency but its flexibility.

This algorithm was trained on the task of language modeling by ingesting huge numbers of articles, blogs, and websites. By using just this data — and with no retooling from OpenAI’s engineers — it achieved state-of-the-art scores on a number of unseen language tests, an achievement known as “zero-shot learning.” It can also perform other writing-related tasks, like translating text from one language to another, summarizing long articles, and answering trivia questions.

GPT-2 does each of these jobs less competently than a specialized system, but its flexibility is a significant achievement. Nearly all machine learning systems used today are “narrow AI,” meaning they’re able to tackle only specific tasks. DeepMind’s original AlphaGo program, for example, was able to beat the world’s champion Go player, but it couldn’t best a child at Monopoly. The prowess of GPT-2, say OpenAI, suggests there could be methods available to researchers right now that can mimic more generalized brainpower.

“What the new OpenAI work has shown is that: yes, you absolutely can build something that really seems to ‘understand’ a lot about the world, just by having it read,” says Jeremy Howard, a researcher who was not involved with OpenAI’s work but has developed similar language modeling programs …

To put this work into context, it’s important to understand how challenging the task of language modeling really is. If I asked you to predict the next word in a given sentence — say, “My trip to the beach was cut short by bad __” — your answer would draw upon on a range of knowledge. You’d consider the grammar of the sentence and its tone but also your general understanding of the world. What sorts of bad things are likely to ruin a day at the beach? Would it be bad fruit, bad dogs, or bad weather? (Probably the latter.)

Despite this, programs that perform text prediction are quite common. You’ve probably encountered one today, in fact, whether that’s Google’s AutoComplete feature or the Predictive Text function in iOS. But these systems are drawing on relatively simple types of language modeling, while algorithms like GPT-2 encode the same information in more complex ways.

The difference between these two approaches is technically arcane, but it can be summed up in a single word: depth. Older methods record information about words in only their most obvious contexts, while newer methods dig deeper into their multiple meanings.

So while a system like Predictive Text only knows that the word “sunny” is used to describe the weather, newer algorithms know when “sunny” is referring to someone’s character or mood, when “Sunny” is a person, or when “Sunny” means the 1976 smash hit by Boney M.

The success of these newer, deeper language models has caused a stir in the AI community. Researcher Sebastian Ruder compares their success to advances made in computer vision in the early 2010s. At this time, deep learning helped algorithms make huge strides in their ability to identify and categorize visual data, kickstarting the current AI boom. Without these advances, a whole range of technologies — from self-driving cars to facial recognition and AI-enhanced photography — would be impossible today. This latest leap in language understanding could have similar, transformational effects.

Hern’s article for the Guardian (February 14, 2019 article ) acts as a good overview, while Gallagher’s ars Technica* posting (February 15, 2019 posting) and Vincent’s article (February 14, 2019 article) for the The Verge take you progressively deeper into the world of AI text agents.

For anyone who wants to dig down even further, there’s a February 14, 2019 posting on OpenAI’s blog.

*’ars Technical’ corrected to read ‘ars Technica’ on February 18, 2021.