Tag Archives: artificial intelligence

Artificial intelligence and metaphors

This is a different approach to artificial intelligence. From a June 27, 2017 news item on ScienceDaily,

Ask Siri to find a math tutor to help you “grasp” calculus and she’s likely to respond that your request is beyond her abilities. That’s because metaphors like “grasp” are difficult for Apple’s voice-controlled personal assistant to, well, grasp.

But new UC Berkeley research suggests that Siri and other digital helpers could someday learn the algorithms that humans have used for centuries to create and understand metaphorical language.

Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary.

The results, published in the journal Cognitive Psychology, demonstrate how throughout history humans have used language that originally described palpable experiences such as “grasping an object” to describe more intangible concepts such as “grasping an idea.”

Unfortunately, this image is not the best quality,

Scientists have created historical maps showing the evolution of metaphoric language. (Image courtesy of Mahesh Srinivasan)

A June 27, 2017 University of California at Berkeley (or UC Berkeley) news release by Yasmin Anwar, which originated the news item,

“The use of concrete language to talk about abstract ideas may unlock mysteries about how we are able to communicate and conceptualize things we can never see or touch,” said study senior author Mahesh Srinivasan, an assistant professor of psychology at UC Berkeley. “Our results may also pave the way for future advances in artificial intelligence.”

The findings provide the first large-scale evidence that the creation of new metaphorical word meanings is systematic, researchers said. They can also inform efforts to design natural language processing systems like Siri to help them understand creativity in human language.

“Although such systems are capable of understanding many words, they are often tripped up by creative uses of words that go beyond their existing, pre-programmed vocabularies,” said study lead author Yang Xu, a postdoctoral researcher in linguistics and cognitive science at UC Berkeley.

“This work brings opportunities toward modeling metaphorical words at a broad scale, ultimately allowing the construction of artificial intelligence systems that are capable of creating and comprehending metaphorical language,” he added.

Srinivasan and Xu conducted the study with Lehigh University psychology professor Barbara Malt.

Using the Metaphor Map of English database, researchers examined more than 5,000 examples from the past millennium in which word meanings from one semantic domain, such as “water,” were extended to another semantic domain, such as “mind.”

Researchers called the original semantic domain the “source domain” and the domain that the metaphorical meaning was extended to, the “target domain.”

More than 1,400 online participants were recruited to rate semantic domains such as “water” or “mind” according to the degree to which they were related to the external world (light, plants), animate things (humans, animals), or intense emotions (excitement, fear).

These ratings were fed into computational models that the researchers had developed to predict which semantic domains had been the sources or targets of metaphorical extension.

In comparing their computational predictions against the actual historical record provided by the Metaphor Map of English, researchers found that their models correctly forecast about 75 percent of recorded metaphorical language mappings over the past millennium.

Furthermore, they found that the degree to which a domain is tied to experience in the external world, such as “grasping a rope,” was the primary predictor of how a word would take on a new metaphorical meaning such as “grasping an idea.”

For example, time and again, researchers found that words associated with textiles, digestive organs, wetness, solidity and plants were more likely to provide sources for metaphorical extension, while mental and emotional states, such as excitement, pride and fear were more likely to be the targets of metaphorical extension.

Scientists have created historical maps showing the evolution of metaphoric language. (Image courtesy of Mahesh Srinivasan)

Here’s a link to and a citation for the paper,

Evolution of word meanings through metaphorical mapping: Systematicity over the past millennium by Yang Xu, Barbara C. Malt, Mahesh Srinivasan. Cognitive Psychology Volume 96, August 2017, Pages 41–53 DOI: https://doi.org/10.1016/j.cogpsych.2017.05.005

The early web version of this paper is behind a paywall.

For anyone interested in the ‘Metaphor Map of English’ database mentioned in the news release, you find it here on the University of Glasgow website. By the way, it also seems to be known as ‘Mapping Metaphor with the Historical Thesaurus‘.

Brain stuff: quantum entanglement and a multi-dimensional universe

I have two brain news bits, one about neural networks and quantum entanglement and another about how the brain operates on more than three dimensions.

Quantum entanglement and neural networks

A June 13, 2017 news item on phys.org describes how machine learning can be used to solve problems in physics (Note: Links have been removed),

Machine learning, the field that’s driving a revolution in artificial intelligence, has cemented its role in modern technology. Its tools and techniques have led to rapid improvements in everything from self-driving cars and speech recognition to the digital mastery of an ancient board game.

Now, physicists are beginning to use machine learning tools to tackle a different kind of problem, one at the heart of quantum physics. In a paper published recently in Physical Review X, researchers from JQI [Joint Quantum Institute] and the Condensed Matter Theory Center (CMTC) at the University of Maryland showed that certain neural networks—abstract webs that pass information from node to node like neurons in the brain—can succinctly describe wide swathes of quantum systems.

An artist’s rendering of a neural network with two layers. At the top is a real quantum system, like atoms in an optical lattice. Below is a network of hidden neurons that capture their interactions (Credit: E. Edwards/JQI)

A June 12, 2017 JQI news release by Chris Cesare, which originated the news item, describes how neural networks can represent quantum entanglement,

Dongling Deng, a JQI Postdoctoral Fellow who is a member of CMTC and the paper’s first author, says that researchers who use computers to study quantum systems might benefit from the simple descriptions that neural networks provide. “If we want to numerically tackle some quantum problem,” Deng says, “we first need to find an efficient representation.”

On paper and, more importantly, on computers, physicists have many ways of representing quantum systems. Typically these representations comprise lists of numbers describing the likelihood that a system will be found in different quantum states. But it becomes difficult to extract properties or predictions from a digital description as the number of quantum particles grows, and the prevailing wisdom has been that entanglement—an exotic quantum connection between particles—plays a key role in thwarting simple representations.

The neural networks used by Deng and his collaborators—CMTC Director and JQI Fellow Sankar Das Sarma and Fudan University physicist and former JQI Postdoctoral Fellow Xiaopeng Li—can efficiently represent quantum systems that harbor lots of entanglement, a surprising improvement over prior methods.

What’s more, the new results go beyond mere representation. “This research is unique in that it does not just provide an efficient representation of highly entangled quantum states,” Das Sarma says. “It is a new way of solving intractable, interacting quantum many-body problems that uses machine learning tools to find exact solutions.”

Neural networks and their accompanying learning techniques powered AlphaGo, the computer program that beat some of the world’s best Go players last year (link is external) (and the top player this year (link is external)). The news excited Deng, an avid fan of the board game. Last year, around the same time as AlphaGo’s triumphs, a paper appeared that introduced the idea of using neural networks to represent quantum states (link is external), although it gave no indication of exactly how wide the tool’s reach might be. “We immediately recognized that this should be a very important paper,” Deng says, “so we put all our energy and time into studying the problem more.”

The result was a more complete account of the capabilities of certain neural networks to represent quantum states. In particular, the team studied neural networks that use two distinct groups of neurons. The first group, called the visible neurons, represents real quantum particles, like atoms in an optical lattice or ions in a chain. To account for interactions between particles, the researchers employed a second group of neurons—the hidden neurons—which link up with visible neurons. These links capture the physical interactions between real particles, and as long as the number of connections stays relatively small, the neural network description remains simple.

Specifying a number for each connection and mathematically forgetting the hidden neurons can produce a compact representation of many interesting quantum states, including states with topological characteristics and some with surprising amounts of entanglement.

Beyond its potential as a tool in numerical simulations, the new framework allowed Deng and collaborators to prove some mathematical facts about the families of quantum states represented by neural networks. For instance, neural networks with only short-range interactions—those in which each hidden neuron is only connected to a small cluster of visible neurons—have a strict limit on their total entanglement. This technical result, known as an area law, is a research pursuit of many condensed matter physicists.

These neural networks can’t capture everything, though. “They are a very restricted regime,” Deng says, adding that they don’t offer an efficient universal representation. If they did, they could be used to simulate a quantum computer with an ordinary computer, something physicists and computer scientists think is very unlikely. Still, the collection of states that they do represent efficiently, and the overlap of that collection with other representation methods, is an open problem that Deng says is ripe for further exploration.

Here’s a link to and a citation for the paper,

Quantum Entanglement in Neural Network States by Dong-Ling Deng, Xiaopeng Li, and S. Das Sarma. Phys. Rev. X 7, 021021 – Published 11 May 2017

This paper is open access.

Blue Brain and the multidimensional universe

Blue Brain is a Swiss government brain research initiative which officially came to life in 2006 although the initial agreement between the École Politechnique Fédérale de Lausanne (EPFL) and IBM was signed in 2005 (according to the project’s Timeline page). Moving on, the project’s latest research reveals something astounding (from a June 12, 2017 Frontiers Publishing press release on EurekAlert),

For most people, it is a stretch of the imagination to understand the world in four dimensions but a new study has discovered structures in the brain with up to eleven dimensions – ground-breaking work that is beginning to reveal the brain’s deepest architectural secrets.

Using algebraic topology in a way that it has never been used before in neuroscience, a team from the Blue Brain Project has uncovered a universe of multi-dimensional geometrical structures and spaces within the networks of the brain.

The research, published today in Frontiers in Computational Neuroscience, shows that these structures arise when a group of neurons forms a clique: each neuron connects to every other neuron in the group in a very specific way that generates a precise geometric object. The more neurons there are in a clique, the higher the dimension of the geometric object.

“We found a world that we had never imagined,” says neuroscientist Henry Markram, director of Blue Brain Project and professor at the EPFL in Lausanne, Switzerland, “there are tens of millions of these objects even in a small speck of the brain, up through seven dimensions. In some networks, we even found structures with up to eleven dimensions.”

Markram suggests this may explain why it has been so hard to understand the brain. “The mathematics usually applied to study networks cannot detect the high-dimensional structures and spaces that we now see clearly.”

If 4D worlds stretch our imagination, worlds with 5, 6 or more dimensions are too complex for most of us to comprehend. This is where algebraic topology comes in: a branch of mathematics that can describe systems with any number of dimensions. The mathematicians who brought algebraic topology to the study of brain networks in the Blue Brain Project were Kathryn Hess from EPFL and Ran Levi from Aberdeen University.

“Algebraic topology is like a telescope and microscope at the same time. It can zoom into networks to find hidden structures – the trees in the forest – and see the empty spaces – the clearings – all at the same time,” explains Hess.

In 2015, Blue Brain published the first digital copy of a piece of the neocortex – the most evolved part of the brain and the seat of our sensations, actions, and consciousness. In this latest research, using algebraic topology, multiple tests were performed on the virtual brain tissue to show that the multi-dimensional brain structures discovered could never be produced by chance. Experiments were then performed on real brain tissue in the Blue Brain’s wet lab in Lausanne confirming that the earlier discoveries in the virtual tissue are biologically relevant and also suggesting that the brain constantly rewires during development to build a network with as many high-dimensional structures as possible.

When the researchers presented the virtual brain tissue with a stimulus, cliques of progressively higher dimensions assembled momentarily to enclose high-dimensional holes, that the researchers refer to as cavities. “The appearance of high-dimensional cavities when the brain is processing information means that the neurons in the network react to stimuli in an extremely organized manner,” says Levi. “It is as if the brain reacts to a stimulus by building then razing a tower of multi-dimensional blocks, starting with rods (1D), then planks (2D), then cubes (3D), and then more complex geometries with 4D, 5D, etc. The progression of activity through the brain resembles a multi-dimensional sandcastle that materializes out of the sand and then disintegrates.”

The big question these researchers are asking now is whether the intricacy of tasks we can perform depends on the complexity of the multi-dimensional “sandcastles” the brain can build. Neuroscience has also been struggling to find where the brain stores its memories. “They may be ‘hiding’ in high-dimensional cavities,” Markram speculates.

###

About Blue Brain

The aim of the Blue Brain Project, a Swiss brain initiative founded and directed by Professor Henry Markram, is to build accurate, biologically detailed digital reconstructions and simulations of the rodent brain, and ultimately, the human brain. The supercomputer-based reconstructions and simulations built by Blue Brain offer a radically new approach for understanding the multilevel structure and function of the brain. http://bluebrain.epfl.ch

About Frontiers

Frontiers is a leading community-driven open-access publisher. By taking publishing entirely online, we drive innovation with new technologies to make peer review more efficient and transparent. We provide impact metrics for articles and researchers, and merge open access publishing with a research network platform – Loop – to catalyse research dissemination, and popularize research to the public, including children. Our goal is to increase the reach and impact of research articles and their authors. Frontiers has received the ALPSP Gold Award for Innovation in Publishing in 2014. http://www.frontiersin.org.

Here’s a link to and a citation for the paper,

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function by Michael W. Reimann, Max Nolte, Martina Scolamiero, Katharine Turner, Rodrigo Perin, Giuseppe Chindemi, Paweł Dłotko, Ran Levi, Kathryn Hess, and Henry Markram. Front. Comput. Neurosci., 12 June 2017 | https://doi.org/10.3389/fncom.2017.00048

This paper is open access.

Artificial intelligence (AI) company (in Montréal, Canada) attracts $135M in funding from Microsoft, Intel, Nvidia and others

It seems there’s a push on to establish Canada as a centre for artificial intelligence research and, if the federal and provincial governments have their way, for commercialization of said research. As always, there seems to be a bit of competition between Toronto (Ontario) and Montréal (Québec) as to which will be the dominant hub for the Canadian effort if one is to take Braga’s word for the situation.

In any event, Toronto seemed to have a mild advantage over Montréal initially with the 2017 Canadian federal government  budget announcement that the Canadian Institute for Advanced Research (CIFAR), based in Toronto, would launch a Pan-Canadian Artificial Intelligence Strategy and with an announcement from the University of Toronto shortly after (from my March 31, 2017 posting),

On the heels of the March 22, 2017 federal budget announcement of $125M for a Pan-Canadian Artificial Intelligence Strategy, the University of Toronto (U of T) has announced the inception of the Vector Institute for Artificial Intelligence in a March 28, 2017 news release by Jennifer Robinson (Note: Links have been removed),

A team of globally renowned researchers at the University of Toronto is driving the planning of a new institute staking Toronto’s and Canada’s claim as the global leader in AI.

Geoffrey Hinton, a University Professor Emeritus in computer science at U of T and vice-president engineering fellow at Google, will serve as the chief scientific adviser of the newly created Vector Institute based in downtown Toronto.

“The University of Toronto has long been considered a global leader in artificial intelligence research,” said U of T President Meric Gertler. “It’s wonderful to see that expertise act as an anchor to bring together researchers, government and private sector actors through the Vector Institute, enabling them to aim even higher in leading advancements in this fast-growing, critical field.”

As part of the Government of Canada’s Pan-Canadian Artificial Intelligence Strategy, Vector will share $125 million in federal funding with fellow institutes in Montreal and Edmonton. All three will conduct research and secure talent to cement Canada’s position as a world leader in AI.

However, Montréal and the province of Québec are no slouches when it comes to supporting to technology. From a June 14, 2017 article by Matthew Braga for CBC (Canadian Broadcasting Corporation) news online (Note: Links have been removed),

One of the most promising new hubs for artificial intelligence research in Canada is going international, thanks to a $135 million investment with contributions from some of the biggest names in tech.

The company, Montreal-based Element AI, was founded last October [2016] to help companies that might not have much experience in artificial intelligence start using the technology to change the way they do business.

It’s equal parts general research lab and startup incubator, with employees working to develop new and improved techniques in artificial intelligence that might not be fully realized for years, while also commercializing products and services that can be sold to clients today.

It was co-founded by Yoshua Bengio — one of the pioneers of a type of AI research called machine learning — along with entrepreneurs Jean-François Gagné and Nicolas Chapados, and the Canadian venture capital fund Real Ventures.

In an interview, Bengio and Gagné said the money from the company’s funding round will be used to hire 250 new employees by next January. A hundred will be based in Montreal, but an additional 100 employees will be hired for a new office in Toronto, and the remaining 50 for an Element AI office in Asia — its first international outpost.

They will join more than 100 employees who work for Element AI today, having left jobs at Amazon, Uber and Google, among others, to work at the company’s headquarters in Montreal.

The expansion is a big vote of confidence in Element AI’s strategy from some of the world’s biggest technology companies. Microsoft, Intel and Nvidia all contributed to the round, and each is a key player in AI research and development.

The company has some not unexpected plans and partners (from the Braga, article, Note: A link has been removed),

The Series A round was led by Data Collective, a Silicon Valley-based venture capital firm, and included participation by Fidelity Investments Canada, National Bank of Canada, and Real Ventures.

What will it help the company do? Scale, its founders say.

“We’re looking at domain experts, artificial intelligence experts,” Gagné said. “We already have quite a few, but we’re looking at people that are at the top of their game in their domains.

“And at this point, it’s no longer just pure artificial intelligence, but people who understand, extremely well, robotics, industrial manufacturing, cybersecurity, and financial services in general, which are all the areas we’re going after.”

Gagné says that Element AI has already delivered 10 projects to clients in those areas, and have many more in development. In one case, Element AI has been helping a Japanese semiconductor company better analyze the data collected by the assembly robots on its factory floor, in a bid to reduce manufacturing errors and improve the quality of the company’s products.

There’s more to investment in Québec’s AI sector than Element AI (from the Braga article; Note: Links have been removed),

Element AI isn’t the only organization in Canada that investors are interested in.

In September, the Canadian government announced $213 million in funding for a handful of Montreal universities, while both Google and Microsoft announced expansions of their Montreal AI research groups in recent months alongside investments in local initiatives. The province of Quebec has pledged $100 million for AI initiatives by 2022.

Braga goes on to note some other initiatives but at that point the article’s focus is exclusively Toronto.

For more insight into the AI situation in Québec, there’s Dan Delmar’s May 23, 2017 article for the Montreal Express (Note: Links have been removed),

Advocating for massive government spending with little restraint admittedly deviates from the tenor of these columns, but the AI business is unlike any other before it. [emphasis misn] Having leaders acting as fervent advocates for the industry is crucial; resisting the coming technological tide is, as the Borg would say, futile.

The roughly 250 AI researchers who call Montreal home are not simply part of a niche industry. Quebec’s francophone character and Montreal’s multilingual citizenry are certainly factors favouring the development of language technology, but there’s ample opportunity for more ambitious endeavours with broader applications.

AI isn’t simply a technological breakthrough; it is the technological revolution. [emphasis mine] In the coming decades, modern computing will transform all industries, eliminating human inefficiencies and maximizing opportunities for innovation and growth — regardless of the ethical dilemmas that will inevitably arise.

“By 2020, we’ll have computers that are powerful enough to simulate the human brain,” said (in 2009) futurist Ray Kurzweil, author of The Singularity Is Near, a seminal 2006 book that has inspired a generation of AI technologists. Kurzweil’s projections are not science fiction but perhaps conservative, as some forms of AI already effectively replace many human cognitive functions. “By 2045, we’ll have expanded the intelligence of our human-machine civilization a billion-fold. That will be the singularity.”

The singularity concept, borrowed from physicists describing event horizons bordering matter-swallowing black holes in the cosmos, is the point of no return where human and machine intelligence will have completed their convergence. That’s when the machines “take over,” so to speak, and accelerate the development of civilization beyond traditional human understanding and capability.

The claims I’ve highlighted in Delmar’s article have been made before for other technologies, “xxx is like no other business before’ and “it is a technological revolution.”  Also if you keep scrolling down to the bottom of the article, you’ll find Delmar is a ‘public relations consultant’ which, if you look at his LinkedIn profile, you’ll find means he’s a managing partner in a PR firm known as Provocateur.

Bertrand Marotte’s May 20, 2017 article for the Montreal Gazette offers less hyperbole along with additional detail about the Montréal scene (Note: Links have been removed),

It might seem like an ambitious goal, but key players in Montreal’s rapidly growing artificial-intelligence sector are intent on transforming the city into a Silicon Valley of AI.

Certainly, the flurry of activity these days indicates that AI in the city is on a roll. Impressive amounts of cash have been flowing into academia, public-private partnerships, research labs and startups active in AI in the Montreal area.

…, researchers at Microsoft Corp. have successfully developed a computing system able to decipher conversational speech as accurately as humans do. The technology makes the same, or fewer, errors than professional transcribers and could be a huge boon to major users of transcription services like law firms and the courts.

Setting the goal of attaining the critical mass of a Silicon Valley is “a nice point of reference,” said tech entrepreneur Jean-François Gagné, co-founder and chief executive officer of Element AI, an artificial intelligence startup factory launched last year.

The idea is to create a “fluid, dynamic ecosystem” in Montreal where AI research, startup, investment and commercialization activities all mesh productively together, said Gagné, who founded Element with researcher Nicolas Chapados and Université de Montréal deep learning pioneer Yoshua Bengio.

“Artificial intelligence is seen now as a strategic asset to governments and to corporations. The fight for resources is global,” he said.

The rise of Montreal — and rival Toronto — as AI hubs owes a lot to provincial and federal government funding.

Ottawa promised $213 million last September to fund AI and big data research at four Montreal post-secondary institutions. Quebec has earmarked $100 million over the next five years for the development of an AI “super-cluster” in the Montreal region.

The provincial government also created a 12-member blue-chip committee to develop a strategic plan to make Quebec an AI hub, co-chaired by Claridge Investments Ltd. CEO Pierre Boivin and Université de Montréal rector Guy Breton.

But private-sector money has also been flowing in, particularly from some of the established tech giants competing in an intense AI race for innovative breakthroughs and the best brains in the business.

Montreal’s rich talent pool is a major reason Waterloo, Ont.-based language-recognition startup Maluuba decided to open a research lab in the city, said the company’s vice-president of product development, Mohamed Musbah.

“It’s been incredible so far. The work being done in this space is putting Montreal on a pedestal around the world,” he said.

Microsoft struck a deal this year to acquire Maluuba, which is working to crack one of the holy grails of deep learning: teaching machines to read like the human brain does. Among the company’s software developments are voice assistants for smartphones.

Maluuba has also partnered with an undisclosed auto manufacturer to develop speech recognition applications for vehicles. Voice recognition applied to cars can include such things as asking for a weather report or making remote requests for the vehicle to unlock itself.

Marotte’s Twitter profile describes him as a freelance writer, editor, and translator.

Meet Pepper, a robot for health care clinical settings

A Canadian project to introduce robots like Pepper into clinical settings (aside: can seniors’ facilities be far behind?) is the subject of a June 23, 2017 news item on phys.org,

McMaster and Ryerson universities today announced the Smart Robots for Health Communication project, a joint research initiative designed to introduce social robotics and artificial intelligence into clinical health care.

A June 22, 2017 McMaster University news release, which originated the news item, provides more detail,

With the help of Softbank’s humanoid robot Pepper and IBM Bluemix Watson Cognitive Services, the researchers will study health information exchange through a state-of-the-art human-robot interaction system. The project is a collaboration between David Harris Smith, professor in the Department of Communication Studies and Multimedia at McMaster University, Frauke Zeller, professor in the School of Professional Communication at Ryerson University and Hermenio Lima, a dermatologist and professor of medicine at McMaster’s Michael G. DeGroote School of Medicine. His main research interests are in the area of immunodermatology and technology applied to human health.

The research project involves the development and analysis of physical and virtual human-robot interactions, and has the capability to improve healthcare outcomes by helping healthcare professionals better understand patients’ behaviour.

Zeller and Harris Smith have previously worked together on hitchBOT, the friendly hitchhiking robot that travelled across Canada and has since found its new home in the [Canada] Science and Technology Museum in Ottawa.

“Pepper will help us highlight some very important aspects and motives of human behaviour and communication,” said Zeller.

Designed to be used in professional environments, Pepper is a humanoid robot that can interact with people, ‘read’ emotions, learn, move and adapt to its environment, and even recharge on its own. Pepper is able to perform facial recognition and develop individualized relationships when it interacts with people.

Lima, the clinic director, said: “We are excited to have the opportunity to potentially transform patient engagement in a clinical setting, and ultimately improve healthcare outcomes by adapting to clients’ communications needs.”

At Ryerson, Pepper was funded by the Co-lab in the Faculty of Communication and Design. FCAD’s Co-lab provides strategic leadership, technological support and acquisitions of technologies that are shaping the future of communications.

“This partnership is a testament to the collaborative nature of innovation,” said dean of FCAD, Charles Falzon. “I’m thrilled to support this multidisciplinary project that pushes the boundaries of research, and allows our faculty and students to find uses for emerging tech inside and outside the classroom.”

“This project exemplifies the value that research in the Humanities can bring to the wider world, in this case building understanding and enhancing communications in critical settings such as health care,” says McMaster’s Dean of Humanities, Ken Cruikshank.

The integration of IBM Watson cognitive computing services with the state-of-the-art social robot Pepper, offers a rich source of research potential for the projects at Ryerson and McMaster. This integration is also supported by IBM Canada and [Southern Ontario Smart Computing Innovation Platform] SOSCIP by providing the project access to high performance research computing resources and staff in Ontario.

“We see this as the initiation of an ongoing collaborative university and industry research program to develop and test applications of embodied AI, a research program that is well-positioned to integrate and apply emerging improvements in machine learning and social robotics innovations,” said Harris Smith.

I just went to a presentation at the facility where my mother lives and it was all about delivering more individualized and better care for residents. Given that most seniors in British Columbia care facilities do not receive the number of service hours per resident recommended by the province due to funding issues, it seemed a well-meaning initiative offered in the face of daunting odds against success. Now with this news, I wonder what impact ‘Pepper’ might ultimately have on seniors and on the people who currently deliver service. Of course, this assumes that researchers will be able to tackle problems with understanding various accents and communication strategies, which are strongly influenced by culture and, over time, the aging process.

After writing that last paragraph I stumbled onto this June 27, 2017 Sage Publications press release on EurekAlert about a related matter,

Existing digital technologies must be exploited to enable a paradigm shift in current healthcare delivery which focuses on tests, treatments and targets rather than the therapeutic benefits of empathy. Writing in the Journal of the Royal Society of Medicine, Dr Jeremy Howick and Dr Sian Rees of the Oxford Empathy Programme, say a new paradigm of empathy-based medicine is needed to improve patient outcomes, reduce practitioner burnout and save money.

Empathy-based medicine, they write, re-establishes relationship as the heart of healthcare. “Time pressure, conflicting priorities and bureaucracy can make practitioners less likely to express empathy. By re-establishing the clinical encounter as the heart of healthcare, and exploiting available technologies, this can change”, said Dr Howick, a Senior Researcher in Oxford University’s Nuffield Department of Primary Care Health Sciences.

Technology is already available that could reduce the burden of practitioner paperwork by gathering basic information prior to consultation, for example via email or a mobile device in the waiting room.

During the consultation, the computer screen could be placed so that both patient and clinician can see it, a help to both if needed, for example, to show infographics on risks and treatment options to aid decision-making and the joint development of a treatment plan.

Dr Howick said: “The spread of alternatives to face-to-face consultations is still in its infancy, as is our understanding of when a machine will do and when a person-to-person relationship is needed.” However, he warned, technology can also get in the way. A computer screen can become a barrier to communication rather than an aid to decision-making. “Patients and carers need to be involved in determining the need for, and designing, new technologies”, he said.

I sincerely hope that the Canadian project has taken into account some of the issues described in the ’empathy’ press release and in the article, which can be found here,

Overthrowing barriers to empathy in healthcare: empathy in the age of the Internet
by J Howick and S Rees. Journaly= of the Royal Society of Medicine Article first published online: June 27, 2017 DOI: https://doi.org/10.1177/0141076817714443

This article is open access.

Hacking the human brain with a junction-based artificial synaptic device

Earlier today I published a piece featuring Dr. Wei Lu’s work on memristors and the movement to create an artificial brain (my June 28, 2017 posting: Dr. Wei Lu and bio-inspired ‘memristor’ chips). For this posting I’m featuring a non-memristor (if I’ve properly understood the technology) type of artificial synapse. From a June 28, 2017 news item on Nanowerk,

One of the greatest challenges facing artificial intelligence development is understanding the human brain and figuring out how to mimic it.

Now, one group reports in ACS Nano (“Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device”) that they have developed an artificial synapse capable of simulating a fundamental function of our nervous system — the release of inhibitory and stimulatory signals from the same “pre-synaptic” terminal.

Unfortunately, the American Chemical Society news release on EurekAlert, which originated the news item, doesn’t provide too much more detail,

The human nervous system is made up of over 100 trillion synapses, structures that allow neurons to pass electrical and chemical signals to one another. In mammals, these synapses can initiate and inhibit biological messages. Many synapses just relay one type of signal, whereas others can convey both types simultaneously or can switch between the two. To develop artificial intelligence systems that better mimic human learning, cognition and image recognition, researchers are imitating synapses in the lab with electronic components. Most current artificial synapses, however, are only capable of delivering one type of signal. So, Han Wang, Jing Guo and colleagues sought to create an artificial synapse that can reconfigurably send stimulatory and inhibitory signals.

The researchers developed a synaptic device that can reconfigure itself based on voltages applied at the input terminal of the device. A junction made of black phosphorus and tin selenide enables switching between the excitatory and inhibitory signals. This new device is flexible and versatile, which is highly desirable in artificial neural networks. In addition, the artificial synapses may simplify the design and functions of nervous system simulations.

Here’s how I concluded that this is not a memristor-type device (from the paper [first paragraph, final sentence]; a link and citation will follow; Note: Links have been removed)),

The conventional memristor-type [emphasis mine](14-20) and transistor-type(21-25) artificial synapses can realize synaptic functions in a single semiconductor device but lacks the ability [emphasis mine] to dynamically reconfigure between excitatory and inhibitory responses without the addition of a modulating terminal.

Here’s a link to and a citation for the paper,

Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device by
He Tian, Xi Cao, Yujun Xie, Xiaodong Yan, Andrew Kostelec, Don DiMarzio, Cheng Chang, Li-Dong Zhao, Wei Wu, Jesse Tice, Judy J. Cha, Jing Guo, and Han Wang. ACS Nano, Article ASAP DOI: 10.1021/acsnano.7b03033 Publication Date (Web): June 28, 2017

Copyright © 2017 American Chemical Society

This paper is behind a paywall.

Self-learning neuromorphic chip

There aren’t many details about this chip and so far as I can tell this technology is not based on a memristor. From a May 16, 2017 news item on plys.org,

Today [May 16, 2017], at the imec technology forum (ITF2017), imec demonstrated the world’s first self-learning neuromorphic chip. The brain-inspired chip, based on OxRAM technology, has the capability of self-learning and has been demonstrated to have the ability to compose music.

Here’s a sample,

A May 16, 2017 imec press release, which originated the news item, expands on the theme,

The human brain is a dream for computer scientists: it has a huge computing power while consuming only a few tens of Watts. Imec researchers are combining state-of-the-art hardware and software to design chips that feature these desirable characteristics of a self-learning system. Imec’s ultimate goal is to design the process technology and building blocks to make artificial intelligence to be energy efficient so that that it can be integrated into sensors. Such intelligent sensors will drive the internet of things forward. This would not only allow machine learning to be present in all sensors but also allow on-field learning capability to further improve the learning.

By co-optimizing the hardware and the software, the chip features machine learning and intelligence characteristics on a small area, while consuming only very little power. The chip is self-learning, meaning that is makes associations between what it has experienced and what it experiences. The more it experiences, the stronger the connections will be. The chip presented today has learned to compose new music and the rules for the composition are learnt on the fly.

It is imec’s ultimate goal to further advance both hardware and software to achieve very low-power, high-performance, low-cost and highly miniaturized neuromorphic chips that can be applied in many domains ranging for personal health, energy, traffic management etc. For example, neuromorphic chips integrated into sensors for health monitoring would enable to identify a particular heartrate change that could lead to heart abnormalities, and would learn to recognize slightly different ECG patterns that vary between individuals. Such neuromorphic chips would thus enable more customized and patient-centric monitoring.

“Because we have hardware, system design and software expertise under one roof, imec is ideally positioned to drive neuromorphic computing forward,” says Praveen Raghavan, distinguished member of the technical Staff at imec. “Our chip has evolved from co-optimizing logic, memory, algorithms and system in a holistic way. This way, we succeeded in developing the building blocks for such a self-learning system.”

About ITF

The Imec Technology Forum (ITF) is imec’s series of internationally acclaimed events with a clear focus on the technologies that will drive groundbreaking innovation in healthcare, smart cities and mobility, ICT, logistics and manufacturing, and energy.

At ITF, some of the world’s greatest minds in technology take the stage. Their talks cover a wide range of domains – such as advanced chip scaling, smart imaging, sensor and communication systems, the IoT, supercomputing, sustainable energy and battery technology, and much more. As leading innovators in their fields, they also present early insights in market trends, evolutions, and breakthroughs in nanoelectronics and digital technology: What will be successful and what not, in five or even ten years from now? How will technology evolve, and how fast? And who can help you implement your technology roadmaps?

About imec

Imec is the world-leading research and innovation hub in nano-electronics and digital technologies. The combination of our widely-acclaimed leadership in microchip technology and profound software and ICT expertise is what makes us unique. By leveraging our world-class infrastructure and local and global ecosystem of partners across a multitude of industries, we create groundbreaking innovation in application domains such as healthcare, smart cities and mobility, logistics and manufacturing, and energy.

As a trusted partner for companies, start-ups and universities we bring together close to 3,500 brilliant minds from over 75 nationalities. Imec is headquartered in Leuven, Belgium and also has distributed R&D groups at a number of Flemish universities, in the Netherlands, Taiwan, USA, China, and offices in India and Japan. In 2016, imec’s revenue (P&L) totaled 496 million euro. Further information on imec can be found at www.imec.be.

Imec is a registered trademark for the activities of IMEC International (a legal entity set up under Belgian law as a “stichting van openbaar nut”), imec Belgium (IMEC vzw supported by the Flemish Government), imec the Netherlands (Stichting IMEC Nederland, part of Holst Centre which is supported by the Dutch Government), imec Taiwan (IMEC Taiwan Co.) and imec China (IMEC Microelectronics (Shanghai) Co. Ltd.) and imec India (Imec India Private Limited), imec Florida (IMEC USA nanoelectronics design center).

I don’t usually include the ‘abouts’ but I was quite intrigued by imec. For anyone curious about the ITF (imec Forums), here’s a website with a listing all of the previously held and upcoming 2017 forums.

May/June 2017 scienceish events in Canada (mostly in Vancouver)

I have five* events for this posting

(1) Science and You (Montréal)

The latest iteration of the Science and You conference took place May 4 – 6, 2017 at McGill University (Montréal, Québec). That’s the sad news, the good news is that they have recorded and released the sessions onto YouTube. (This is the first time the conference has been held outside of Europe, in fact, it’s usually held in France.) Here’s why you might be interested (from the 2017 conference page),

The animator of the conference will be Véronique Morin:

Véronique Morin is science journalist and communicator, first president of the World Federation of Science Journalists (WFSJ) and serves as judge for science communication awards. She worked for a science program on Quebec’s public TV network, CBCRadio-Canada, TVOntario, and as a freelancer is also a contributor to -among others-  The Canadian Medical Journal, University Affairs magazine, NewsDeeply, while pursuing documentary projects.

Let’s talk about S …

Holding the attention of an audience full of teenagers may seem impossible… particularly on topics that might be seen as boring, like sciences! Yet, it’s essential to demistify science in order to make it accessible, even appealing in the eyes of futur citizens.
How can we encourage young adults to ask themselves questions about the surrounding world, nature and science? How can we make them discover sciences with and without digital tools?

Find out tips and tricks used by our speakers Kristin Alford and Amanda Tyndall.

Kristin Alford
Dr Kristin Alford is a futurist and the inaugural Director of MOD., a futuristic museum of discovery at the University of South Australia. Her mind is presently occupied by the future of work and provoking young adults to ask questions about the role of science at the intersection of art and innovation.

Internet Website

Amanda Tyndall
Over 20 years of  science communication experience with organisations such as Café Scientifique, The Royal Institution of Great Britain (and Australia’s Science Exchange), the Science Museum in London and now with the Edinburgh International Science Festival. Particularly interested in engaging new audiences through linkages with the arts and digital/creative industries.

Internet Website

A troll in the room

Increasingly used by politicians, social media can reach thousand of people in few seconds. Relayed to infinity, the message seems truthful, but is it really? At a time of fake news and alternative facts, how can we, as a communicator or a journalist, take up the challenge of disinformation?
Discover the traps and tricks of disinformation in the age of digital technologies with our two fact-checking experts, Shawn Otto and Vanessa Schipani, who will offer concrete solutions to unravel the true from the false..

 

Shawn Otto
Shawn Otto was awarded the IEEE-USA (“I-Triple-E”) National Distinguished Public Service Award for his work elevating science in America’s national public dialogue. He is cofounder and producer of the US presidential science debates at ScienceDebate.org. He is also an award-winning screenwriter and novelist, best known for writing and co-producing the Academy Award-nominated movie House of Sand and Fog.

Vanessa Schipani
Vanessa is a science journalist at FactCheck.org, which monitors U.S. politicians’ claims for accuracy. Previously, she wrote for outlets in the U.S., Europe and Japan, covering topics from quantum mechanics to neuroscience. She has bachelor’s degrees in zoology and philosophy and a master’s in the history and philosophy of science.

At 20,000 clicks from the extreme

Sharing living from a space station, ship or submarine. The examples of social media use in extreme conditions are multiplying and the public is asking for more. How to use public tools to highlight practices and discoveries? How to manage the use of social networks of a large organisation? What pitfalls to avoid? What does this mean for citizens and researchers?
Find out with Phillipe Archambault and Leslie Elliott experts in extrem conditions.

Philippe Archambault

Professor Philippe Archambault is a marine ecologist at Laval University, the director of the Notre Golfe network and president of the 4th World Conference on Marine Biodiversity. His research on the influence of global changes on biodiversity and the functioning of ecosystems has led him to work in all four corners of our oceans from the Arctic to the Antarctic, through Papua New Guinea and the French Polynesia.

Website

Leslie Elliott

Leslie Elliott leads a team of communicators at Ocean Networks Canada in Victoria, British Columbia, home to Canada’s world-leading ocean observatories in the Pacific and Arctic Oceans. Audiences can join robots equipped with high definition cameras via #livedive to discover more about our ocean.

Website

Science is not a joke!

Science and humor are two disciplines that might seem incompatible … and yet, like the ig-Nobels, humour can prove to be an excellent way to communicate a scientific message. This, however, can prove to be quite challenging since one needs to ensure they employ the right tone and language to both captivate the audience while simultaneously communicating complex topics.

Patrick Baud and Brian Malow, both well-renowned scientific communicators, will give you with the tools you need to capture your audience and also convey a proper scientific message. You will be surprised how, even in Science, a good dose of humour can make you laugh and think.

Patrick Baud
Patrick Baud is a French author who was born on June 30, 1979, in Avignon. He has been sharing for many years his passion for tales of fantasy, and the marvels and curiosities of the world, through different media: radio, web, novels, comic strips, conferences, and videos. His YouTube channel “Axolot”, was created in 2013, and now has over 420,000 followers.

Internet Website
Youtube

Brian Malow
Brian Malow is Earth’s Premier Science Comedian (self-proclaimed).  Brian has made science videos for Time Magazine and contributed to Neil deGrasse Tyson’s radio show.  He worked in science communications at a museum, blogged for Scientific American, and trains scientists to be better communicators.

Internet Website
YouTube

I don’t think they’ve managed to get everything up on YouTube yet but the material I’ve found has been subtitled (into French or English, depending on which language the speaker used).

Here are the opening day’s talks on YouTube with English subtitles or French subtitles when appropriate. You can also find some abstracts for the panel presentations here. I was particularly in this panel (S3 – The Importance of Reaching Out to Adults in Scientific Culture), Note: I have searched out the French language descriptions for those unavailable in English,

Organized by Coeur des sciences, Université du Québec à Montréal (UQAM)
Animator: Valérie Borde, Freelance Science Journalist

Anouk Gingras, Musée de la civilisation, Québec
Text not available in English

[La science au Musée de la civilisation c’est :
• Une cinquantaine d’expositions et espaces découvertes
• Des thèmes d’actualité, liés à des enjeux sociaux, pour des exposition souvent destinées aux adultes
• Un potentiel de nouveaux publics en lien avec les autres thématiques présentes au Musée (souvent non scientifiques)
L’exposition Nanotechnologies : l’invisible révolution :
• Un thème d’actualité suscitant une réflexion
• Un sujet sensible menant à la création d’un parcours d’exposition polarisé : choix entre « oui » ou « non » au développement des nanotechnologies pour l’avenir
• L’utilisation de divers éléments pour rapprocher le sujet du visiteur

  • Les nanotechnologies dans la science-fiction
  • Les objets du quotidien contenant des nanoparticules
  • Les objets anciens qui utilisant les nanotechnologies
  • Divers microscopes retraçant l’histoire des nanotechnologies

• Une forme d’interaction suscitant la réflexion du visiteur via un objet sympatique : le canard  de plastique jaune, muni d’une puce RFID

  • Sept stations de consultation qui incitent le visiteur à se prononcer et à réfléchir sur des questions éthiques liées au développement des nanotechnologies
  • Une compilation des données en temps réel
  • Une livraison des résultats personnalisée
  • Une mesure des visiteurs dont l’opinion s’est modifiée à la suite de la visite de l’exposition

Résultats de fréquentation :
• Public de jeunes adultes rejoint (51%)
• Plus d’hommes que de femmes ont visité l’exposition
• Parcours avec canard: incite à la réflexion et augmente l’attention
• 3 visiteurs sur 4 prennent le canard; 92% font l’activité en entier]

Marie Lambert-Chan, Québec Science
Capting the attention of adult readership : challenging mission, possible mission
Since 1962, Québec Science Magazine is the only science magazine aimed at an adult readership in Québec. Our mission : covering topical subjects related to science and technology, as well as social issues from a scientific point of view. Each year, we print eight issues, with a circulation of 22,000 copies. Furthermore, the magazine has received several awards and accolades. In 2017, Québec Science Magazine was honored by the Canadian Magazine Awards/Grands Prix du Magazine and was named Best Magazine in Science, Business and Politics category.
Although we have maintained a solid reputation among scientists and the media industry, our magazine is still relatively unknown to the general public. Why is that ? How is it that, through all those years, we haven’t found the right angle to engage a broader readership ?
We are still searching for definitive answers, but here are our observations :
Speaking science to adults is much more challenging than it is with children, who can marvel endlessly at the smallest things. Unfortunately, adults lose this capacity to marvel and wonder for various reasons : they have specific interests, they failed high-school science, they don’t feel competent enough to understand scientific phenomena. How do we bring the wonder back ? This is our mission. Not impossible, and hopefully soon to be accomplished. One noticible example is the number of reknown scientists interviewed during the popular talk-show Tout le monde en parle, leading us to believe the general public may have an interest in science.
However, to accomplish our mission, we have to recount science. According to the Bulgarian writer and blogger Maria Popova, great science writing should explain, elucidate and enchant . To explain : to make the information clear and comprehensible. To elucidate : to reveal all the interconnections between the pieces of information. To enchant : to go beyond the scientific terms and information and tell a story, thus giving a kaleidoscopic vision of the subject. This is how we intend to capture our readership’s attention.
Our team aims to accomplish this challenge. Although, to be perfectly honest, it would be much easier if we had more resources, financial-wise or human-wise. However, we don’t lack ideas. We dream of major scientific investigations, conferences organized around themes from the magazine’s issues, Web documentaries, podcasts… Such initiatives would give us the visibility we desperately crave.
That said, even in the best conditions, would be have more subscribers ? Perhaps. But it isn’t assured. Even if our magazine is aimed at adult readership, we are convinced that childhood and science go hand in hand, and is even decisive for the children’s future. At the moment, school programs are not in place for continuous scientific development. It is possible to develop an interest for scientific culture as adults, but it is much easier to achieve this level of curiosity if it was previously fostered.

Robert Lamontagne, Université de Montréal
Since the beginning of my career as an astrophysicist, I have been interested in scientific communication to non-specialist audiences. I have presented hundreds of lectures describing the phenomena of the cosmos. Initially, these were mainly offered in amateur astronomers’ clubs or in high-schools and Cégeps. Over the last few years, I have migrated to more general adult audiences in the context of cultural activities such as the “Festival des Laurentides”, the Arts, Culture and Society activities in Repentigny and, the Université du troisième âge (UTA) or Senior’s University.
The Quebec branch of the UTA, sponsored by the Université de Sherbrooke (UdeS), exists since 1976. Seniors universities, created in Toulouse, France, are part of a worldwide movement. The UdeS and its senior’s university antennas are members of the International Association of the Universities of the Third Age (AIUTA). The UTA is made up of 28 antennas located in 10 regions and reaches more than 10,000 people per year. Antenna volunteers prepare educational programming by drawing on a catalog of courses, seminars and lectures, covering a diverse range of subjects ranging from history and politics to health, science, or the environment.
The UTA is aimed at people aged 50 and over who wish to continue their training and learn throughout their lives. It is an attentive, inquisitive, educated public and, given the demographics in Canada, its number is growing rapidly. This segment of the population is often well off and very involved in society.
I usually use a two-prong approach.
• While remaining rigorous, the content is articulated around a few ideas, avoiding analytical expressions in favor of a qualitative description.
• The narrative framework, the story, which allows to contextualize the scientific content and to forge links with the audience.

Sophie Malavoy, Coeur des sciences – UQAM

Many obstacles need to be overcome in order to reach out to adults, especially those who aren’t in principle interested in science.
• Competing against cultural activities such as theater, movies, etc.
• The idea that science is complex and dull
• A feeling of incompetence. « I’ve always been bad in math and physics»
• Funding shortfall for activities which target adults
How to reach out to those adults?
• To put science into perspective. To bring its relevance out by making links with current events and big issues (economic, heath, environment, politic). To promote a transdisciplinary approach which includes humanities and social sciences.
• To stake on originality by offering uncommon and ludic experiences (scientific walks in the city, street performances, etc.)
• To bridge between science and popular activities to the public (science/music; science/dance; science/theater; science/sports; science/gastronomy; science/literature)
• To reach people with emotions without sensationalism. To boost their curiosity and ability to wonder.
• To put a human face on science, by insisting not only on the results of a research but on its process. To share the adventure lived by researchers.
• To liven up people’s feeling of competence. To insist on the scientific method.
• To invite non-scientists (citizens groups, communities, consumers, etc.) to the reflections on science issues (debate, etc.).  To move from dissemination of science to dialog

Didier Pourquery, The Conversation France
Text not available in English

[Depuis son lancement en septembre 2015 la plateforme The Conversation France (2 millions de pages vues par mois) n’a cessé de faire progresser son audience. Selon une étude menée un an après le lancement, la structure de lectorat était la suivante
Pour accrocher les adultes et les ainés deux axes sont intéressants ; nous les utilisons autant sur notre site que sur notre newsletter quotidienne – 26.000 abonnés- ou notre page Facebook (11500 suiveurs):
1/ expliquer l’actualité : donner les clefs pour comprendre les débats scientifiques qui animent la société ; mettre de la science dans les discussions (la mission du site est de  « nourrir le débat citoyen avec de l’expertise universitaire et de la recherche »). L’idée est de poser des questions de compréhension simple au moment où elles apparaissent dans le débat (en période électorale par exemple : qu’est-ce que le populisme ? Expliqué par des chercheurs de Sciences Po incontestables.)
Exemples : comprendre les conférences climat -COP21, COP22 – ; comprendre les débats de société (Gestation pour autrui); comprendre l’économie (revenu universel); comprendre les maladies neurodégénératives (Alzheimer) etc.
2/ piquer la curiosité : utiliser les formules classiques (le saviez-vous ?) appliquées à des sujets surprenants (par exemple : «  Que voit un chien quand il regarde la télé ? » a eu 96.000 pages vues) ; puis jouer avec ces articles sur les réseaux sociaux. Poser des questions simples et surprenantes. Par exemple : ressemblez-vous à votre prénom ? Cet article académique très sérieux a comptabilisé 95.000 pages vues en français et 171.000 en anglais.
3/ Susciter l’engagement : faire de la science participative simple et utile. Par exemple : appeler nos lecteurs à surveiller l’invasion de moustiques tigres partout sur le territoire. Cet article a eu 112.000 pages vues et a été republié largement sur d’autres sites. Autre exemple : appeler les lecteurs à photographier les punaises de leur environnement.]

Here are my very brief and very rough translations. (1) Anouk Gingras is focused largely on a nanotechnology exhibit and whether or not visitors went through it and participated in various activities. She doesn’t seem specifically focused on science communication for adults but they are doing some very interesting and related work at Québec’s Museum of Civilization. (2) Didier Pourquery is describing an online initiative known as ‘The Conversation France’ (strange—why not La conversation France?). Moving on, there’s a website with a daily newsletter (blog?) and a Facebook page. They have two main projects, one is a discussion of current science issues in society, which is informed with and by experts but is not exclusive to experts, and more curiosity-based science questions and discussion such as What does a dog see when it watches television?

Serendipity! I hadn’t stumbled across this conference when I posted my May 12, 2017 piece on the ‘insanity’ of science outreach in Canada. It’s good to see I’m not the only one focused on science outreach for adults and that there is some action, although seems to be a Québec-only effort.

(2) Ingenious—a book launch in Vancouver

The book will be launched on Thursday, June 1, 2017 at the Vancouver Public Library’s Central Branch (from the Ingenious: An Evening of Canadian Innovation event page)

Ingenious: An Evening of Canadian Innovation
Thursday, June 1, 2017 (6:30 pm – 8:00 pm)
Central Branch
Description

Gov. Gen. David Johnston and OpenText Corp. chair Tom Jenkins discuss Canadian innovation and their book Ingenious: How Canadian Innovators Made the World Smarter, Smaller, Kinder, Safer, Healthier, Wealthier and Happier.

Books will be available for purchase and signing.

Doors open at 6 p.m.

INGENIOUS : HOW CANADIAN INNOVATORS MADE THE WORLD SMARTER, SMALLER, KINDER, SAFER, HEALTHIER, WEALTHIER, AND HAPPIER

Address:

350 West Georgia St.
VancouverV6B 6B1

Get Directions

  • Phone:

Location Details:

Alice MacKay Room, Lower Level

I do have a few more details about the authors and their book. First, there’s this from the Ottawa Writer’s Festival March 28, 2017 event page,

To celebrate Canada’s 150th birthday, Governor General David Johnston and Tom Jenkins have crafted a richly illustrated volume of brilliant Canadian innovations whose widespread adoption has made the world a better place. From Bovril to BlackBerrys, lightbulbs to liquid helium, peanut butter to Pablum, this is a surprising and incredibly varied collection to make Canadians proud, and to our unique entrepreneurial spirit.

Successful innovation is always inspired by at least one of three forces — insight, necessity, and simple luck. Ingenious moves through history to explore what circumstances, incidents, coincidences, and collaborations motivated each great Canadian idea, and what twist of fate then brought that idea into public acceptance. Above all, the book explores what goes on in the mind of an innovator, and maps the incredible spectrum of personalities that have struggled to improve the lot of their neighbours, their fellow citizens, and their species.

From the marvels of aboriginal invention such as the canoe, snowshoe, igloo, dogsled, lifejacket, and bunk bed to the latest pioneering advances in medicine, education, philanthropy, science, engineering, community development, business, the arts, and the media, Canadians have improvised and collaborated their way to international admiration. …

Then, there’s this April 5, 2017 item on Canadian Broadcasting Corporation’s (CBC) news online,

From peanut butter to the electric wheelchair, the stories behind numerous life-changing Canadian innovations are detailed in a new book.

Gov. Gen. David Johnston and Tom Jenkins, chair of the National Research Council and former CEO of OpenText, are the authors of Ingenious: How Canadian Innovators Made the World Smarter, Smaller, Kinder, Safer, Healthier, Wealthier and Happier. The authors hope their book reinforces and extends the culture of innovation in Canada.

“We started wanting to tell 50 stories of Canadian innovators, and what has amazed Tom and myself is how many there are,” Johnston told The Homestretch on Wednesday. The duo ultimately chronicled 297 innovations in the book, including the pacemaker, life jacket and chocolate bars.

“Innovations are not just technological, not just business, but they’re social innovations as well,” Johnston said.

Many of those innovations, and the stories behind them, are not well known.

“We’re sort of a humble people,” Jenkins said. “We’re pretty quiet. We don’t brag, we don’t talk about ourselves very much, and so we then lead ourselves to believe as a culture that we’re not really good inventors, the Americans are. And yet we knew that Canadians were actually great inventors and innovators.”

‘Opportunities and challenges’

For Johnston, his favourite story in the book is on the light bulb.

“It’s such a symbol of both our opportunities and challenges,” he said. “The light bulb was invented in Canada, not the United States. It was two inventors back in the 1870s that realized that if you passed an electric current through a resistant metal it would glow, and they patented that, but then they didn’t have the money to commercialize it.”

American inventor Thomas Edison went on to purchase that patent and made changes to the original design.

Johnston and Jenkins are also inviting readers to share their own innovation stories, on the book’s website.

I’m looking forward to the talk and wondering if they’ve included the botox and cellulose nanocrystal (CNC) stories to the book. BTW, Tom Jenkins was the chair of a panel examining Canadian research and development and lead author of the panel’s report (Innovation Canada: A Call to Action) for the then Conservative government (it’s also known as the Jenkins report). You can find out more about in my Oct. 21, 2011 posting.

(3) Made in Canada (Vancouver)

This is either fortuitous or there’s some very high level planning involved in the ‘Made in Canada; Inspiring Creativity and Innovation’ show which runs from April 21 – Sept. 4, 2017 at Vancouver’s Science World (also known as the Telus World of Science). From the Made in Canada; Inspiring Creativity and Innovation exhibition page,

Celebrate Canadian creativity and innovation, with Science World’s original exhibition, Made in Canada, presented by YVR [Vancouver International Airport] — where you drive the creative process! Get hands-on and build the fastest bobsled, construct a stunning piece of Vancouver architecture and create your own Canadian sound mashup, to share with friends.

Vote for your favourite Canadian inventions and test fly a plane of your design. Discover famous (and not-so-famous, but super neat) Canadian inventions. Learn about amazing, local innovations like robots that teach themselves, one-person electric cars and a computer that uses parallel universes.

Imagine what you can create here, eh!!

You can find more information here.

One quick question, why would Vancouver International Airport be presenting this show? I asked that question of Science World’s Communications Coordinator, Jason Bosher, and received this response,

 YVR is the presenting sponsor. They donated money to the exhibition and they also contributed an exhibit for the “We Move” themed zone in the Made in Canada exhibition. The YVR exhibit details the history of the YVR airport, it’s geographic advantage and some of the planes they have seen there.

I also asked if there was any connection between this show and the ‘Ingenious’ book launch,

Some folks here are aware of the book launch. It has to do with the Canada 150 initiative and nothing to do with the Made in Canada exhibition, which was developed here at Science World. It is our own original exhibition.

So there you have it.

(4) Robotics, AI, and the future of work (Ottawa)

I’m glad to finally stumble across a Canadian event focusing on the topic of artificial intelligence (AI), robotics and the future of work. Sadly (for me), this is taking place in Ottawa. Here are more details  from the May 25, 2017 notice (received via email) from the Canadian Science Policy Centre (CSPC),

CSPC is Partnering with CIFAR {Canadian Institute for Advanced Research]
The Second Annual David Dodge Lecture

Join CIFAR and Senior Fellow Daron Acemoglu for
the Second Annual David Dodge CIFAR Lecture in Ottawa on June 13.
June 13, 2017 | 12 – 2 PM [emphasis mine]
Fairmont Château Laurier, Drawing Room | 1 Rideau St, Ottawa, ON
Along with the backlash against globalization and the outsourcing of jobs, concern is also growing about the effect that robotics and artificial intelligence will have on the labour force in advanced industrial nations. World-renowned economist Acemoglu, author of the best-selling book Why Nations Fail, will discuss how technology is changing the face of work and the composition of labour markets. Drawing on decades of data, Acemoglu explores the effects of widespread automation on manufacturing jobs, the changes we can expect from artificial intelligence technologies, and what responses to these changes might look like. This timely discussion will provide valuable insights for current and future leaders across government, civil society, and the private sector.

Daron Acemoglu is a Senior Fellow in CIFAR’s Insitutions, Organizations & Growth program, and the Elizabeth and James Killian Professor of Economics at the Massachusetts Institute of Technology.

Tickets: $15 (A light lunch will be served.)

You can find a registration link here. Also, if you’re interested in the Canadian efforts in the field of artificial intelligence you can find more in my March 24, 2017 posting (scroll down about 25% of the way and then about 40% of the way) on the 2017 Canadian federal budget and science where I first noted the $93.7M allocated to CIFAR for launching a Pan-Canadian Artificial Intelligence Strategy.

(5) June 2017 edition of the Curiosity Collider Café (Vancouver)

This is an art/science (also known called art/sci and SciArt) that has taken place in Vancouver every few months since April 2015. Here’s more about the June 2017 edition (from the Curiosity Collider events page),

Collider Cafe

When
8:00pm on Wednesday, June 21st, 2017. Door opens at 7:30pm.

Where
Café Deux Soleils. 2096 Commercial Drive, Vancouver, BC (Google Map).

Cost
$5.00-10.00 cover at the door (sliding scale). Proceeds will be used to cover the cost of running this event, and to fund future Curiosity Collider events. Curiosity Collider is a registered BC non-profit organization.

***

#ColliderCafe is a space for artists, scientists, makers, and anyone interested in art+science. Meet, discover, connect, create. How do you explore curiosity in your life? Join us and discover how our speakers explore their own curiosity at the intersection of art & science.

The event will start promptly at 8pm (doors open at 7:30pm). $5.00-10.00 (sliding scale) cover at the door. Proceeds will be used to cover the cost of running this event, and to fund future Curiosity Collider events. Curiosity Collider is a registered BC non-profit organization.

Enjoy!

*I changed ‘three’ events to ‘five’ events and added a number to each event for greater reading ease on May 31, 2017.

Machine learning programs learn bias

The notion of bias in artificial intelligence (AI)/algorithms/robots is gaining prominence (links to other posts featuring algorithms and bias are at the end of this post). The latest research concerns machine learning where an artificial intelligence system trains itself with ordinary human language from the internet. From an April 13, 2017 American Association for the Advancement of Science (AAAS) news release on EurekAlert,

As artificial intelligence systems “learn” language from existing texts, they exhibit the same biases that humans do, a new study reveals. The results not only provide a tool for studying prejudicial attitudes and behavior in humans, but also emphasize how language is intimately intertwined with historical biases and cultural stereotypes. A common way to measure biases in humans is the Implicit Association Test (IAT), where subjects are asked to pair two concepts they find similar, in contrast to two concepts they find different; their response times can vary greatly, indicating how well they associated one word with another (for example, people are more likely to associate “flowers” with “pleasant,” and “insects” with “unpleasant”). Here, Aylin Caliskan and colleagues developed a similar way to measure biases in AI systems that acquire language from human texts; rather than measuring lag time, however, they used the statistical number of associations between words, analyzing roughly 2.2 million words in total. Their results demonstrate that AI systems retain biases seen in humans. For example, studies of human behavior show that the exact same resume is 50% more likely to result in an opportunity for an interview if the candidate’s name is European American rather than African-American. Indeed, the AI system was more likely to associate European American names with “pleasant” stimuli (e.g. “gift,” or “happy”). In terms of gender, the AI system also reflected human biases, where female words (e.g., “woman” and “girl”) were more associated than male words with the arts, compared to mathematics. In a related Perspective, Anthony G. Greenwald discusses these findings and how they could be used to further analyze biases in the real world.

There are more details about the research in this April 13, 2017 Princeton University news release on EurekAlert (also on ScienceDaily),

In debates over the future of artificial intelligence, many experts think of the new systems as coldly logical and objectively rational. But in a new study, researchers have demonstrated how machines can be reflections of us, their creators, in potentially problematic ways. Common machine learning programs, when trained with ordinary human language available online, can acquire cultural biases embedded in the patterns of wording, the researchers found. These biases range from the morally neutral, like a preference for flowers over insects, to the objectionable views of race and gender.

Identifying and addressing possible bias in machine learning will be critically important as we increasingly turn to computers for processing the natural language humans use to communicate, for instance in doing online text searches, image categorization and automated translations.

“Questions about fairness and bias in machine learning are tremendously important for our society,” said researcher Arvind Narayanan, an assistant professor of computer science and an affiliated faculty member at the Center for Information Technology Policy (CITP) at Princeton University, as well as an affiliate scholar at Stanford Law School’s Center for Internet and Society. “We have a situation where these artificial intelligence systems may be perpetuating historical patterns of bias that we might find socially unacceptable and which we might be trying to move away from.”

The paper, “Semantics derived automatically from language corpora contain human-like biases,” published April 14  [2017] in Science. Its lead author is Aylin Caliskan, a postdoctoral research associate and a CITP fellow at Princeton; Joanna Bryson, a reader at University of Bath, and CITP affiliate, is a coauthor.

As a touchstone for documented human biases, the study turned to the Implicit Association Test, used in numerous social psychology studies since its development at the University of Washington in the late 1990s. The test measures response times (in milliseconds) by human subjects asked to pair word concepts displayed on a computer screen. Response times are far shorter, the Implicit Association Test has repeatedly shown, when subjects are asked to pair two concepts they find similar, versus two concepts they find dissimilar.

Take flower types, like “rose” and “daisy,” and insects like “ant” and “moth.” These words can be paired with pleasant concepts, like “caress” and “love,” or unpleasant notions, like “filth” and “ugly.” People more quickly associate the flower words with pleasant concepts, and the insect terms with unpleasant ideas.

The Princeton team devised an experiment with a program where it essentially functioned like a machine learning version of the Implicit Association Test. Called GloVe, and developed by Stanford University researchers, the popular, open-source program is of the sort that a startup machine learning company might use at the heart of its product. The GloVe algorithm can represent the co-occurrence statistics of words in, say, a 10-word window of text. Words that often appear near one another have a stronger association than those words that seldom do.

The Stanford researchers turned GloVe loose on a huge trawl of contents from the World Wide Web, containing 840 billion words. Within this large sample of written human culture, Narayanan and colleagues then examined sets of so-called target words, like “programmer, engineer, scientist” and “nurse, teacher, librarian” alongside two sets of attribute words, such as “man, male” and “woman, female,” looking for evidence of the kinds of biases humans can unwittingly possess.

In the results, innocent, inoffensive biases, like for flowers over bugs, showed up, but so did examples along lines of gender and race. As it turned out, the Princeton machine learning experiment managed to replicate the broad substantiations of bias found in select Implicit Association Test studies over the years that have relied on live, human subjects.

For instance, the machine learning program associated female names more with familial attribute words, like “parents” and “wedding,” than male names. In turn, male names had stronger associations with career attributes, like “professional” and “salary.” Of course, results such as these are often just objective reflections of the true, unequal distributions of occupation types with respect to gender–like how 77 percent of computer programmers are male, according to the U.S. Bureau of Labor Statistics.

Yet this correctly distinguished bias about occupations can end up having pernicious, sexist effects. An example: when foreign languages are naively processed by machine learning programs, leading to gender-stereotyped sentences. The Turkish language uses a gender-neutral, third person pronoun, “o.” Plugged into the well-known, online translation service Google Translate, however, the Turkish sentences “o bir doktor” and “o bir hem?ire” with this gender-neutral pronoun are translated into English as “he is a doctor” and “she is a nurse.”

“This paper reiterates the important point that machine learning methods are not ‘objective’ or ‘unbiased’ just because they rely on mathematics and algorithms,” said Hanna Wallach, a senior researcher at Microsoft Research New York City, who was not involved in the study. “Rather, as long as they are trained using data from society and as long as society exhibits biases, these methods will likely reproduce these biases.”

Another objectionable example harkens back to a well-known 2004 paper by Marianne Bertrand of the University of Chicago Booth School of Business and Sendhil Mullainathan of Harvard University. The economists sent out close to 5,000 identical resumes to 1,300 job advertisements, changing only the applicants’ names to be either traditionally European American or African American. The former group was 50 percent more likely to be offered an interview than the latter. In an apparent corroboration of this bias, the new Princeton study demonstrated that a set of African American names had more unpleasantness associations than a European American set.

Computer programmers might hope to prevent cultural stereotype perpetuation through the development of explicit, mathematics-based instructions for the machine learning programs underlying AI systems. Not unlike how parents and mentors try to instill concepts of fairness and equality in children and students, coders could endeavor to make machines reflect the better angels of human nature.

“The biases that we studied in the paper are easy to overlook when designers are creating systems,” said Narayanan. “The biases and stereotypes in our society reflected in our language are complex and longstanding. Rather than trying to sanitize or eliminate them, we should treat biases as part of the language and establish an explicit way in machine learning of determining what we consider acceptable and unacceptable.”

Here’s a link to and a citation for the Princeton paper,

Semantics derived automatically from language corpora contain human-like biases by Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan. Science  14 Apr 2017: Vol. 356, Issue 6334, pp. 183-186 DOI: 10.1126/science.aal4230

This paper appears to be open access.

Links to more cautionary posts about AI,

Aug 5, 2009: Autonomous algorithms; intelligent windows; pretty nano pictures

June 14, 2016:  Accountability for artificial intelligence decision-making

Oct. 25, 2016 Removing gender-based stereotypes from algorithms

March 1, 2017: Algorithms in decision-making: a government inquiry in the UK

There’s also a book which makes some of the current use of AI programmes and big data quite accessible reading: Cathy O’Neil’s ‘Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy’.

Emerging technology and the law

I have three news bits about legal issues that are arising as a consequence of emerging technologies.

Deep neural networks, art, and copyright

Caption: The rise of automated art opens new creative avenues, coupled with new problems for copyright protection. Credit: Provided by: Alexander Mordvintsev, Christopher Olah and Mike Tyka

Presumably this artwork is a demonstration of automated art although they never really do explain how in the news item/news release. An April 26, 2017 news item on ScienceDaily announces research into copyright and the latest in using neural networks to create art,

In 1968, sociologist Jean Baudrillard wrote on automatism that “contained within it is the dream of a dominated world […] that serves an inert and dreamy humanity.”

With the growing popularity of Deep Neural Networks (DNN’s), this dream is fast becoming a reality.

Dr. Jean-Marc Deltorn, researcher at the Centre d’études internationales de la propriété intellectuelle in Strasbourg, argues that we must remain a responsive and responsible force in this process of automation — not inert dominators. As he demonstrates in a recent Frontiers in Digital Humanities paper, the dream of automation demands a careful study of the legal problems linked to copyright.

An April 26, 2017 Frontiers (publishing) news release on EurekAlert, which originated the news item, describes the research in more detail,

For more than half a century, artists have looked to computational processes as a way of expanding their vision. DNN’s are the culmination of this cross-pollination: by learning to identify a complex number of patterns, they can generate new creations.

These systems are made up of complex algorithms modeled on the transmission of signals between neurons in the brain.

DNN creations rely in equal measure on human inputs and the non-human algorithmic networks that process them.

Inputs are fed into the system, which is layered. Each layer provides an opportunity for a more refined knowledge of the inputs (shape, color, lines). Neural networks compare actual outputs to expected ones, and correct the predictive error through repetition and optimization. They train their own pattern recognition, thereby optimizing their learning curve and producing increasingly accurate outputs.

The deeper the layers are, the higher the level of abstraction. The highest layers are able to identify the contents of a given input with reasonable accuracy, after extended periods of training.

Creation thus becomes increasingly automated through what Deltorn calls “the arcane traceries of deep architecture”. The results are sufficiently abstracted from their sources to produce original creations that have been exhibited in galleries, sold at auction and performed at concerts.

The originality of DNN’s is a combined product of technological automation on one hand, human inputs and decisions on the other.

DNN’s are gaining popularity. Various platforms (such as DeepDream) now allow internet users to generate their very own new creations . This popularization of the automation process calls for a comprehensive legal framework that ensures a creator’s economic and moral rights with regards to his work – copyright protection.

Form, originality and attribution are the three requirements for copyright. And while DNN creations satisfy the first of these three, the claim to originality and attribution will depend largely on a given country legislation and on the traceability of the human creator.

Legislation usually sets a low threshold to originality. As DNN creations could in theory be able to create an endless number of riffs on source materials, the uncurbed creation of original works could inflate the existing number of copyright protections.

Additionally, a small number of national copyright laws confers attribution to what UK legislation defines loosely as “the person by whom the arrangements necessary for the creation of the work are undertaken.” In the case of DNN’s, this could mean anybody from the programmer to the user of a DNN interface.

Combined with an overly supple take on originality, this view on attribution would further increase the number of copyrightable works.

The risk, in both cases, is that artists will be less willing to publish their own works, for fear of infringement of DNN copyright protections.

In order to promote creativity – one seminal aim of copyright protection – the issue must be limited to creations that manifest a personal voice “and not just the electric glint of a computational engine,” to quote Deltorn. A delicate act of discernment.

DNN’s promise new avenues of creative expression for artists – with potential caveats. Copyright protection – a “catalyst to creativity” – must be contained. Many of us gently bask in the glow of an increasingly automated form of technology. But if we want to safeguard the ineffable quality that defines much art, it might be a good idea to hone in more closely on the differences between the electric and the creative spark.

This research is and be will part of a broader Frontiers Research Topic collection of articles on Deep Learning and Digital Humanities.

Here’s a link to and a citation for the paper,

Deep Creations: Intellectual Property and the Automata by Jean-Marc Deltorn. Front. Digit. Humanit., 01 February 2017 | https://doi.org/10.3389/fdigh.2017.00003

This paper is open access.

Conference on governance of emerging technologies

I received an April 17, 2017 notice via email about this upcoming conference. Here’s more from the Fifth Annual Conference on Governance of Emerging Technologies: Law, Policy and Ethics webpage,

The Fifth Annual Conference on Governance of Emerging Technologies:

Law, Policy and Ethics held at the new

Beus Center for Law & Society in Phoenix, AZ

May 17-19, 2017!

Call for Abstracts – Now Closed

The conference will consist of plenary and session presentations and discussions on regulatory, governance, legal, policy, social and ethical aspects of emerging technologies, including (but not limited to) nanotechnology, synthetic biology, gene editing, biotechnology, genomics, personalized medicine, human enhancement technologies, telecommunications, information technologies, surveillance technologies, geoengineering, neuroscience, artificial intelligence, and robotics. The conference is premised on the belief that there is much to be learned and shared from and across the governance experience and proposals for these various emerging technologies.

Keynote Speakers:

Gillian HadfieldRichard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics USC [University of Southern California] Gould School of Law

Shobita Parthasarathy, Associate Professor of Public Policy and Women’s Studies, Director, Science, Technology, and Public Policy Program University of Michigan

Stuart Russell, Professor at [University of California] Berkeley, is a computer scientist known for his contributions to artificial intelligence

Craig Shank, Vice President for Corporate Standards Group in Microsoft’s Corporate, External and Legal Affairs (CELA)

Plenary Panels:

Innovation – Responsible and/or Permissionless

Ellen-Marie Forsberg, Senior Researcher/Research Manager at Oslo and Akershus University College of Applied Sciences

Adam Thierer, Senior Research Fellow with the Technology Policy Program at the Mercatus Center at George Mason University

Wendell Wallach, Consultant, ethicist, and scholar at Yale University’s Interdisciplinary Center for Bioethics

 Gene Drives, Trade and International Regulations

Greg Kaebnick, Director, Editorial Department; Editor, Hastings Center Report; Research Scholar, Hastings Center

Jennifer Kuzma, Goodnight-North Carolina GlaxoSmithKline Foundation Distinguished Professor in Social Sciences in the School of Public and International Affairs (SPIA) and co-director of the Genetic Engineering and Society (GES) Center at North Carolina State University

Andrew Maynard, Senior Sustainability Scholar, Julie Ann Wrigley Global Institute of Sustainability Director, Risk Innovation Lab, School for the Future of Innovation in Society Professor, School for the Future of Innovation in Society, Arizona State University

Gary Marchant, Regents’ Professor of Law, Professor of Law Faculty Director and Faculty Fellow, Center for Law, Science & Innovation, Arizona State University

Marc Saner, Inaugural Director of the Institute for Science, Society and Policy, and Associate Professor, University of Ottawa Department of Geography

Big Data

Anupam Chander, Martin Luther King, Jr. Professor of Law and Director, California International Law Center, UC Davis School of Law

Pilar Ossorio, Professor of Law and Bioethics, University of Wisconsin, School of Law and School of Medicine and Public Health; Morgridge Institute for Research, Ethics Scholar-in-Residence

George Poste, Chief Scientist, Complex Adaptive Systems Initiative (CASI) (http://www.casi.asu.edu/), Regents’ Professor and Del E. Webb Chair in Health Innovation, Arizona State University

Emily Shuckburgh, climate scientist and deputy head of the Polar Oceans Team at the British Antarctic Survey, University of Cambridge

 Responsible Development of AI

Spring Berman, Ira A. Fulton Schools of Engineering, Arizona State University

John Havens, The IEEE [Institute of Electrical and Electronics Engineers] Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Subbarao Kambhampati, Senior Sustainability Scientist, Julie Ann Wrigley Global Institute of Sustainability, Professor, School of Computing, Informatics and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University

Wendell Wallach, Consultant, Ethicist, and Scholar at Yale University’s Interdisciplinary Center for Bioethics

Existential and Catastrophic Ricks [sic]

Tony Barrett, Co-Founder and Director of Research of the Global Catastrophic Risk Institute

Haydn Belfield,  Academic Project Administrator, Centre for the Study of Existential Risk at the University of Cambridge

Margaret E. Kosal Associate Director, Sam Nunn School of International Affairs, Georgia Institute of Technology

Catherine Rhodes,  Academic Project Manager, Centre for the Study of Existential Risk at CSER, University of Cambridge

These were the panels that are of interest to me; there are others on the homepage.

Here’s some information from the Conference registration webpage,

Early Bird Registration – $50 off until May 1! Enter discount code: earlybirdGETs50

New: Group Discount – Register 2+ attendees together and receive an additional 20% off for all group members!

Click Here to Register!

Conference registration fees are as follows:

  • General (non-CLE) Registration: $150.00
  • CLE Registration: $350.00
  • *Current Student / ASU Law Alumni Registration: $50.00
  • ^Cybsersecurity sessions only (May 19): $100 CLE / $50 General / Free for students (registration info coming soon)

There you have it.

Neuro-techno future laws

I’m pretty sure this isn’t the first exploration of potential legal issues arising from research into neuroscience although it’s the first one I’ve stumbled across. From an April 25, 2017 news item on phys.org,

New human rights laws to prepare for advances in neurotechnology that put the ‘freedom of the mind’ at risk have been proposed today in the open access journal Life Sciences, Society and Policy.

The authors of the study suggest four new human rights laws could emerge in the near future to protect against exploitation and loss of privacy. The four laws are: the right to cognitive liberty, the right to mental privacy, the right to mental integrity and the right to psychological continuity.

An April 25, 2017 Biomed Central news release on EurekAlert, which originated the news item, describes the work in more detail,

Marcello Ienca, lead author and PhD student at the Institute for Biomedical Ethics at the University of Basel, said: “The mind is considered to be the last refuge of personal freedom and self-determination, but advances in neural engineering, brain imaging and neurotechnology put the freedom of the mind at risk. Our proposed laws would give people the right to refuse coercive and invasive neurotechnology, protect the privacy of data collected by neurotechnology, and protect the physical and psychological aspects of the mind from damage by the misuse of neurotechnology.”

Advances in neurotechnology, such as sophisticated brain imaging and the development of brain-computer interfaces, have led to these technologies moving away from a clinical setting and into the consumer domain. While these advances may be beneficial for individuals and society, there is a risk that the technology could be misused and create unprecedented threats to personal freedom.

Professor Roberto Andorno, co-author of the research, explained: “Brain imaging technology has already reached a point where there is discussion over its legitimacy in criminal court, for example as a tool for assessing criminal responsibility or even the risk of reoffending. Consumer companies are using brain imaging for ‘neuromarketing’, to understand consumer behaviour and elicit desired responses from customers. There are also tools such as ‘brain decoders’ which can turn brain imaging data into images, text or sound. All of these could pose a threat to personal freedom which we sought to address with the development of four new human rights laws.”

The authors explain that as neurotechnology improves and becomes commonplace, there is a risk that the technology could be hacked, allowing a third-party to ‘eavesdrop’ on someone’s mind. In the future, a brain-computer interface used to control consumer technology could put the user at risk of physical and psychological damage caused by a third-party attack on the technology. There are also ethical and legal concerns over the protection of data generated by these devices that need to be considered.

International human rights laws make no specific mention to neuroscience, although advances in biomedicine have become intertwined with laws, such as those concerning human genetic data. Similar to the historical trajectory of the genetic revolution, the authors state that the on-going neurorevolution will force a reconceptualization of human rights laws and even the creation of new ones.

Marcello Ienca added: “Science-fiction can teach us a lot about the potential threat of technology. Neurotechnology featured in famous stories has in some cases already become a reality, while others are inching ever closer, or exist as military and commercial prototypes. We need to be prepared to deal with the impact these technologies will have on our personal freedom.”

Here’s a link to and a citation for the paper,

Towards new human rights in the age of neuroscience and neurotechnology by Marcello Ienca and Roberto Andorno. Life Sciences, Society and Policy201713:5 DOI: 10.1186/s40504-017-0050-1 Published: 26 April 2017

©  The Author(s). 2017

This paper is open access.

Does understanding your pet mean understanding artificial intelligence better?

Heather Roff’s take on artificial intelligence features an approach I haven’t seen before. From her March 30, 2017 essay for The Conversation (h/t March 31, 2017 news item on phys.org),

It turns out, though, that we already have a concept we can use when we think about AI: It’s how we think about animals. As a former animal trainer (albeit briefly) who now studies how people use AI, I know that animals and animal training can teach us quite a lot about how we ought to think about, approach and interact with artificial intelligence, both now and in the future.

Using animal analogies can help regular people understand many of the complex aspects of artificial intelligence. It can also help us think about how best to teach these systems new skills and, perhaps most importantly, how we can properly conceive of their limitations, even as we celebrate AI’s new possibilities.
Looking at constraints

As AI expert Maggie Boden explains, “Artificial intelligence seeks to make computers do the sorts of things that minds can do.” AI researchers are working on teaching computers to reason, perceive, plan, move and make associations. AI can see patterns in large data sets, predict the likelihood of an event occurring, plan a route, manage a person’s meeting schedule and even play war-game scenarios.

Many of these capabilities are, in themselves, unsurprising: Of course a robot can roll around a space and not collide with anything. But somehow AI seems more magical when the computer starts to put these skills together to accomplish tasks.

Thinking of AI as a trainable animal isn’t just useful for explaining it to the general public. It is also helpful for the researchers and engineers building the technology. If an AI scholar is trying to teach a system a new skill, thinking of the process from the perspective of an animal trainer could help identify potential problems or complications.

For instance, if I try to train my dog to sit, and every time I say “sit” the buzzer to the oven goes off, then my dog will begin to associate sitting not only with my command, but also with the sound of the oven’s buzzer. In essence, the buzzer becomes another signal telling the dog to sit, which is called an “accidental reinforcement.” If we look for accidental reinforcements or signals in AI systems that are not working properly, then we’ll know better not only what’s going wrong, but also what specific retraining will be most effective.

This requires us to understand what messages we are giving during AI training, as well as what the AI might be observing in the surrounding environment. The oven buzzer is a simple example; in the real world it will be far more complicated.

Before we welcome our AI overlords and hand over our lives and jobs to robots, we ought to pause and think about the kind of intelligences we are creating. …

Source: pixabay.com

It’s just last year (2016) that an AI system beat a human Go master player. Here’s how a March 17, 2016 article by John Russell for TechCrunch described the feat (Note: Links have been removed),

Much was written of an historic moment for artificial intelligence last week when a Google-developed AI beat one of the planet’s most sophisticated players of Go, an East Asia strategy game renowned for its deep thinking and strategy.

Go is viewed as one of the ultimate tests for an AI given the sheer possibilities on hand. “There are 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 possible positions [in the game] — that’s more than the number of atoms in the universe, and more than a googol times larger than chess,” Google said earlier this year.

If you missed the series — which AlphaGo, the AI, won 4-1 — or were unsure of exactly why it was so significant, Google summed the general importance up in a post this week.

Far from just being a game, Demis Hassabis, CEO and Co-Founder of DeepMind — the Google-owned company behind AlphaGo — said the AI’s development is proof that it can be used to solve problems in ways that humans may be not be accustomed or able to do:

We’ve learned two important things from this experience. First, this test bodes well for AI’s potential in solving other problems. AlphaGo has the ability to look “globally” across a board—and find solutions that humans either have been trained not to play or would not consider. This has huge potential for using AlphaGo-like technology to find solutions that humans don’t necessarily see in other areas.

I find Roff’s thesis intriguing and is likely applicable to the short-term but in the longer term and in light of the attempts to  create devices that mimic neural plasticity and neuromorphic engineering  I don’t find her thesis convincing.