Tag Archives: deep learning

smARTcities SALON in Vaughan, Ontario, Canada on March 22, 2018

Thank goodness for the March 15, 2018 notice from the Art/Sci Salon in Toronto (received via email) announcing an event on smart cities being held in the nearby city of Vaughan (it borders Toronto to the north). It’s led me on quite the chase as I’ve delved into a reference to Smart City projects taking place across the country and the results follow after this bit about the event.

smARTcities SALON

From the announcement,


Smart City projects are currently underway across the country, including
Google SideWalk at Toronto Harbourfront. Canada’s first Smart Hospital
is currently under construction in the City of Vaughan. It’s an example
of the city working towards building a reputation as one of the world’s
leading Smart Cities, by adopting new technologies consistent with
priorities defined by citizen collaboration.

Hon. Maurizio Bevilacqua, P.C., Mayor chairs the Smart City Advisory
Task Force leading historic transformation in Vaughan. Working to become
a Smart City is a chance to encourage civic engagement, accelerate
economic growth, and generate efficiencies. His opening address will
outline some of the priorities and opportunities that our panel will


Lilian Radovac, PhD., Assistant Professor, Institute of Communication,
Culture, Information & Technology, University of Toronto. Lilian is a
historian of urban sounds and cultures and has a critical interest in
SmartCity initiatives in two of the cities she has called home: New York
City and Toronto..

Oren Berkovich is the CEO of Singularity University in Canada, an
educational institution and a global network of experts and
entrepreneurs that work together on solving the world’s biggest
challenges. As a catalyst for long-term growth Oren spends his time
connecting people with ideas to facilitate strategic conversations about
the future.

Frank Di Palma, the Chief Information Officer for the City of Vaughan,
is a graduate of York University with more than 20 years experience in
IT operations and services. Frank leads the many SmartCity initiatives
already underway at Vaughan City Hall.

Ron Wild, artist and Digital Art/Science Collaborator, will moderate the

Audience Participation opportunities will enable attendees to forward
questions for consideration by the panel.

You can register for the smARTcities SALON here on Eventbrite,

Art Exhibition Reception

Following the panel discussion, the audience is invited to view the art exhibition ‘smARTcities; exploring the digital frontier.’ Works commissioned by Vaughan specifically for the exhibition, including the SmartCity Map and SmartHospital Map will be shown as well as other Art/Science-themed works. Many of these ‘maps’ were made by Ron in collaboration with mathematicians, scientists, and medical researchers, some of who will be in attendance. Further examples of Ron’s art can be found HERE

Please click through to buy a FREE ticket so we know how many guests to expect. Thank you.

This event can be reached by taking the subway up the #1 west line to the new Vaughan Metropolitan Centre terminal station. Take the #20 bus to the Vaughan Mills transfer loop; transfer there to the #4/A which will take you to the stop right at City Hall. Free parking is available for those coming by car. Car-pooling and ride-sharing is encouraged. The facility is fully accessible.

Here’s one of Wild’s pieces,

144×96″ triptych, Vaughan, 2018 Artist: mrowade (Ron Wild?)

I’m pretty sure that mrowade is Ron Wild.

Smart Cities, the rest of the country, and Vancouver

Much to my surprise, I covered the ‘Smart Cities’ story in its early (but not earliest) days (and before it was Smart Cities) in two posts: January 30, 2015 and January 27,2016 about the National Research Council of Canada (NRC) and its cities and technology public engagement exercises.

David Vogt in a July 12, 2016 posting on the Urban Opus website provides some catch up information,

Canada’s National Research Council (NRC) has identified Cities of the Future as a game-changing technology and economic opportunity.  Following a national dialogue, an Executive Summit was held in Toronto on March 31, 2016, resulting in an important summary report that will become the seed for Canadian R&D strategy in this sector.

The conclusion so far is that the opportunity for Canada is to muster leadership in the following three areas (in order):

  1. Better Infrastructure and Infrastructure Management
  2. Efficient Transportation; and
  3. Renewable Energy

The National Research Council (NRC) offers a more balanced view of the situation on its “NRC capabilities in smart infrastructure and cities of the future” webpage,

Key opportunities for Canada

North America is one of the most urbanised regions in the world (82 % living in urban areas in 2014).
With growing urbanisation, sustainable development challenges will be increasingly concentrated in cities, requiring technology solutions.
Smart cities are data-driven, relying on broadband and telecommunications, sensors, social media, data collection and integration, automation, analytics and visualization to provide real-time situational analysis.
Most infrastructure will be “smart” by 2030 and transportation systems will be intelligent, adaptive and connected.
Renewable energy, energy storage, power quality and load measurement will contribute to smart grid solutions that are integrated with transportation.
“Green”, sustainable and high-performing construction and infrastructure materials are in demand.

Canadian challenges

High energy use: Transportation accounts for roughly 23% of Canada’s total greenhouse gas emissions, followed closely by the energy consumption of buildings, which accounts for 12% of Canada’s greenhouse gas emissions (Canada’s United Nations Framework Convention on Climate Change report).
Traffic congestion in Canadian cities is increasing, contributing to loss of productivity, increased stress for citizens as well as air and noise pollution.
Canadian cities are susceptible to extreme weather and events related to climate change (e.g., floods, storms).
Changing demographics: aging population (need for accessible transportation options, housing, medical and recreational services) and diverse (immigrant) populations.
Financial and jurisdictional issues: the inability of municipalities (who have primary responsibility) to finance R&D or large-scale solutions without other government assistance.

Opportunities being examined
Living lab

Test bed for smart city technology in order to quantify and demonstrate the benefits of smart cities.
Multiple partnering opportunities (e.g. municipalities, other government organizations, industry associations, universities, social sciences, urban planning).

The integrated city

Efficient transportation: integration of personal mobility and freight movement as key city and inter-city infrastructure.
Efficient and integrated transportation systems linked to city infrastructure.
Planning urban environments for mobility while repurposing redundant infrastructures (converting parking to the food-water-energy nexus) as population shifts away from personal transportation.


Sustainable urban bio-cycling.
‎System approach to the development of the technology platforms required to address the nexus.

Key enabling platform technologies
Artificial intelligence

Computer vision and image understanding
Adaptive robots; future robotic platforms for part manufacturing
Understanding human emotions from language
Next generation information extraction using deep learning
Speech recognition
Artificial intelligence to optimize talent management for human resources


Smart materials
Self-assembled nanostructures

Big data analytics

Predictive equipment maintenance
Energy management
Artificial intelligence for optimizing energy storage and distribution
Understanding and tracking of hazardous chemical elements
Process and design optimization

Printed electronics for Internet of Things

Inks and materials
Printing technologies
Large area, flexible, stretchable, printed electronics components
Applications: sensors for Internet of Things, wearables, antenna, radio-frequency identification tags, smart surfaces, packaging, security, signage

If you’re curious about the government’s plan with regard to implementation, this NRC webpage provides some fascinating insight into their hopes if not the reality. (I have mentioned artificial intelligence and the federal government before in a March 16, 2018 posting about the federal budget and science; scroll down approximately 50% of the way to the subsection titled, Budget 2018: Who’s watching over us? and scan for Michael Karlin’s name.)

As for the current situation, there’s a Smart Cities Challenge taking place. Both Toronto and Vancouver have webpages dedicated to their response to the challenge. (You may want to check your own city’s website to find if it’s participating.)I have a preference for the Toronto page as they immediately state that they’re participating in this challenge and they provide an explanation for what they want from you. Vancouver’s page is by comparison a bit confusing with two videos being immediately presented to the reader and from there too many graphics competing for your attention. They do, however, offer something valuable, links to explanations for smart cities and for the challenge.

Here’s a description of the Smart Cities Challenge (from its webpage),

The Smart Cities Challenge

The Smart Cities Challenge is a pan-Canadian competition open to communities of all sizes, including municipalities, regional governments and Indigenous communities (First Nations, Métis and Inuit). The Challenge encourages communities to adopt a smart cities approach to improve the lives of their residents through innovation, data and connected technology.

  • One prize of up to $50 million open to all communities, regardless of population;
  • Two prizes of up to $10 million open to all communities with populations under 500,000 people; and
  • One prize of up to $5 million open to all communities with populations under 30,000 people.

Infrastructure Canada is engaging Indigenous leaders, communities and organizations to finalize the design of a competition specific to Indigenous communities that will reflect their unique realities and issues. Indigenous communities are also eligible to compete for all the prizes in the current competition.

The Challenge will be an open and transparent process. Communities that submit proposals will also post them online, so that residents and stakeholders can see them. An independent Jury will be appointed to select finalists and winners.

Applications are due by April 24, 2018. Communities interested in participating should visit the
Impact Canada Challenge Platform for the applicant guide and more information.

Finalists will be announced in the Summer of 2018 and winners in Spring 2019 according to the information on the Impact Canada Challenge Platform.

It’s not clear to me if she’s leading Vancouver’s effort to win the Smart Cities Challenge but Jessie Adcock’s (City of Vancouver Chief Digital Officer) Twitter feed certainly features information on the topic and, I suspect, if you’re looking for the most up-to-date information on Vancovuer’s participation, you’re more likely to find it on her feed than on the City of Vancouver’s Smart Cities Challenge webpage.

IBM to build brain-inspired AI supercomputing system equal to 64 million neurons for US Air Force

This is the second IBM computer announcement I’ve stumbled onto within the last 4 weeks or so,  which seems like a veritable deluge given the last time I wrote about IBM’s computing efforts was in an Oct. 8, 2015 posting about carbon nanotubes,. I believe that up until now that was my  most recent posting about IBM and computers.

Moving onto the news, here’s more from a June 23, 3017 news item on Nanotechnology Now,

IBM (NYSE: IBM) and the U.S. Air Force Research Laboratory (AFRL) today [June 23, 2017] announced they are collaborating on a first-of-a-kind brain-inspired supercomputing system powered by a 64-chip array of the IBM TrueNorth Neurosynaptic System. The scalable platform IBM is building for AFRL will feature an end-to-end software ecosystem designed to enable deep neural-network learning and information discovery. The system’s advanced pattern recognition and sensory processing power will be the equivalent of 64 million neurons and 16 billion synapses, while the processor component will consume the energy equivalent of a dim light bulb – a mere 10 watts to power.

A June 23, 2017 IBM news release, which originated the news item, describes the proposed collaboration, which is based on IBM’s TrueNorth brain-inspired chip architecture (see my Aug. 8, 2014 posting for more about TrueNorth),

IBM researchers believe the brain-inspired, neural network design of TrueNorth will be far more efficient for pattern recognition and integrated sensory processing than systems powered by conventional chips. AFRL is investigating applications of the system in embedded, mobile, autonomous settings where, today, size, weight and power (SWaP) are key limiting factors.

The IBM TrueNorth Neurosynaptic System can efficiently convert data (such as images, video, audio and text) from multiple, distributed sensors into symbols in real time. AFRL will combine this “right-brain” perception capability of the system with the “left-brain” symbol processing capabilities of conventional computer systems. The large scale of the system will enable both “data parallelism” where multiple data sources can be run in parallel against the same neural network and “model parallelism” where independent neural networks form an ensemble that can be run in parallel on the same data.

“AFRL was the earliest adopter of TrueNorth for converting data into decisions,” said Daniel S. Goddard, director, information directorate, U.S. Air Force Research Lab. “The new neurosynaptic system will be used to enable new computing capabilities important to AFRL’s mission to explore, prototype and demonstrate high-impact, game-changing technologies that enable the Air Force and the nation to maintain its superior technical advantage.”

“The evolution of the IBM TrueNorth Neurosynaptic System is a solid proof point in our quest to lead the industry in AI hardware innovation,” said Dharmendra S. Modha, IBM Fellow, chief scientist, brain-inspired computing, IBM Research – Almaden. “Over the last six years, IBM has expanded the number of neurons per system from 256 to more than 64 million – an 800 percent annual increase over six years.’’

The system fits in a 4U-high (7”) space in a standard server rack and eight such systems will enable the unprecedented scale of 512 million neurons per rack. A single processor in the system consists of 5.4 billion transistors organized into 4,096 neural cores creating an array of 1 million digital neurons that communicate with one another via 256 million electrical synapses.    For CIFAR-100 dataset, TrueNorth achieves near state-of-the-art accuracy, while running at >1,500 frames/s and using 200 mW (effectively >7,000 frames/s per Watt) – orders of magnitude lower speed and energy than a conventional computer running inference on the same neural network.

The IBM TrueNorth Neurosynaptic System was originally developed under the auspices of Defense Advanced Research Projects Agency’s (DARPA) Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program in collaboration with Cornell University. In 2016, the TrueNorth Team received the inaugural Misha Mahowald Prize for Neuromorphic Engineering and TrueNorth was accepted into the Computer History Museum.  Research with TrueNorth is currently being performed by more than 40 universities, government labs, and industrial partners on five continents.

There is an IBM video accompanying this news release, which seems more promotional than informational,

The IBM scientist featured in the video has a Dec. 19, 2016 posting on an IBM research blog which provides context for this collaboration with AFRL,

2016 was a big year for brain-inspired computing. My team and I proved in our paper “Convolutional networks for fast, energy-efficient neuromorphic computing” that the value of this breakthrough is that it can perform neural network inference at unprecedented ultra-low energy consumption. Simply stated, our TrueNorth chip’s non-von Neumann architecture mimics the brain’s neural architecture — giving it unprecedented efficiency and scalability over today’s computers.

The brain-inspired TrueNorth processor [is] a 70mW reconfigurable silicon chip with 1 million neurons, 256 million synapses, and 4096 parallel and distributed neural cores. For systems, we present a scale-out system loosely coupling 16 single-chip boards and a scale-up system tightly integrating 16 chips in a 4´4 configuration by exploiting TrueNorth’s native tiling.

For the scale-up systems we summarize our approach to physical placement of neural network, to reduce intra- and inter-chip network traffic. The ecosystem is in use at over 30 universities and government / corporate labs. Our platform is a substrate for a spectrum of applications from mobile and embedded computing to cloud and supercomputers.
TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications

TrueNorth, once loaded with a neural network model, can be used in real-time as a sensory streaming inference engine, performing rapid and accurate classifications while using minimal energy. TrueNorth’s 1 million neurons consume only 70 mW, which is like having a neurosynaptic supercomputer the size of a postage stamp that can run on a smartphone battery for a week.

Recently, in collaboration with Lawrence Livermore National Laboratory, U.S. Air Force Research Laboratory, and U.S. Army Research Laboratory, we published our fifth paper at IEEE’s prestigious Supercomputing 2016 conference that summarizes the results of the team’s 12.5-year journey (see the associated graphic) to unlock this value proposition. [keep scrolling for the graphic]

Applying the mind of a chip

Three of our partners, U.S. Army Research Lab, U.S. Air Force Research Lab and Lawrence Livermore National Lab, contributed sections to the Supercomputing paper each showcasing a different TrueNorth system, as summarized by my colleagues Jun Sawada, Brian Taba, Pallab Datta, and Ben Shaw:

U.S. Army Research Lab (ARL) prototyped a computational offloading scheme to illustrate how TrueNorth’s low power profile enables computation at the point of data collection. Using the single-chip NS1e board and an Android tablet, ARL researchers created a demonstration system that allows visitors to their lab to hand write arithmetic expressions on the tablet, with handwriting streamed to the NS1e for character recognition, and recognized characters sent back to the tablet for arithmetic calculation.

Of course, the point here is not to make a handwriting calculator, it is to show how TrueNorth’s low power and real time pattern recognition might be deployed at the point of data collection to reduce latency, complexity and transmission bandwidth, as well as back-end data storage requirements in distributed systems.

U.S. Air Force Research Lab (AFRL) contributed another prototype application utilizing a TrueNorth scale-out system to perform a data-parallel text extraction and recognition task. In this application, an image of a document is segmented into individual characters that are streamed to AFRL’s NS1e16 TrueNorth system for parallel character recognition. Classification results are then sent to an inference-based natural language model to reconstruct words and sentences. This system can process 16,000 characters per second! AFRL plans to implement the word and sentence inference algorithms on TrueNorth, as well.

Lawrence Livermore National Lab (LLNL) has a 16-chip NS16e scale-up system to explore the potential of post-von Neumann computation through larger neural models and more complex algorithms, enabled by the native tiling characteristics of the TrueNorth chip. For the Supercomputing paper, they contributed a single-chip application performing in-situ process monitoring in an additive manufacturing process. LLNL trained a TrueNorth network to recognize seven classes related to track weld quality in welds produced by a selective laser melting machine. Real-time weld quality determination allows for closed-loop process improvement and immediate rejection of defective parts. This is one of several applications LLNL is developing to showcase TrueNorth as a scalable platform for low-power, real-time inference.

[downloaded from https://www.ibm.com/blogs/research/2016/12/the-brains-architecture-efficiency-on-a-chip/] Courtesy: IBM

I gather this 2017 announcement is the latest milestone on the TrueNorth journey.

An explanation of neural networks from the Massachusetts Institute of Technology (MIT)

I always enjoy the MIT ‘explainers’ and have been a little sad that I haven’t stumbled across one in a while. Until now, that is. Here’s an April 14, 201 neural network ‘explainer’ (in its entirety) by Larry Hardesty (?),

In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.”

Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department.

Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory.

The technique then enjoyed a resurgence in the 1980s, fell into eclipse again in the first decade of the new century, and has returned like gangbusters in the second, fueled largely by the increased processing power of graphics chips.

“There’s this idea that ideas in science are a bit like epidemics of viruses,” says Tomaso Poggio, the Eugene McDermott Professor of Brain and Cognitive Sciences at MIT, an investigator at MIT’s McGovern Institute for Brain Research, and director of MIT’s Center for Brains, Minds, and Machines. “There are apparently five or six basic strains of flu viruses, and apparently each one comes back with a period of around 25 years. People get infected, and they develop an immune response, and so they don’t get infected for the next 25 years. And then there is a new generation that is ready to be infected by the same strain of virus. In science, people fall in love with an idea, get excited about it, hammer it to death, and then get immunized — they get tired of it. So ideas should have the same kind of periodicity!”

Weighty matters

Neural nets are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. Usually, the examples have been hand-labeled in advance. An object recognition system, for instance, might be fed thousands of labeled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with particular labels.

Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.

To each of its incoming connections, a node will assign a number known as a “weight.” When the network is active, the node receives a different data item — a different number — over each of its connections and multiplies it by the associated weight. It then adds the resulting products together, yielding a single number. If that number is below a threshold value, the node passes no data to the next layer. If the number exceeds the threshold value, the node “fires,” which in today’s neural nets generally means sending the number — the sum of the weighted inputs — along all its outgoing connections.

When a neural net is being trained, all of its weights and thresholds are initially set to random values. Training data is fed to the bottom layer — the input layer — and it passes through the succeeding layers, getting multiplied and added together in complex ways, until it finally arrives, radically transformed, at the output layer. During training, the weights and thresholds are continually adjusted until training data with the same labels consistently yield similar outputs.

Minds and machines

The neural nets described by McCullough and Pitts in 1944 had thresholds and weights, but they weren’t arranged into layers, and the researchers didn’t specify any training mechanism. What McCullough and Pitts showed was that a neural net could, in principle, compute any function that a digital computer could. The result was more neuroscience than computer science: The point was to suggest that the human brain could be thought of as a computing device.

Neural nets continue to be a valuable tool for neuroscientific research. For instance, particular network layouts or rules for adjusting weights and thresholds have reproduced observed features of human neuroanatomy and cognition, an indication that they capture something about how the brain processes information.

The first trainable neural network, the Perceptron, was demonstrated by the Cornell University psychologist Frank Rosenblatt in 1957. The Perceptron’s design was much like that of the modern neural net, except that it had only one layer with adjustable weights and thresholds, sandwiched between input and output layers.

Perceptrons were an active area of research in both psychology and the fledgling discipline of computer science until 1959, when Minsky and Papert published a book titled “Perceptrons,” which demonstrated that executing certain fairly common computations on Perceptrons would be impractically time consuming.

“Of course, all of these limitations kind of disappear if you take machinery that is a little more complicated — like, two layers,” Poggio says. But at the time, the book had a chilling effect on neural-net research.

“You have to put these things in historical context,” Poggio says. “They were arguing for programming — for languages like Lisp. Not many years before, people were still using analog computers. It was not clear at all at the time that programming was the way to go. I think they went a little bit overboard, but as usual, it’s not black and white. If you think of this as this competition between analog computing and digital computing, they fought for what at the time was the right thing.”


By the 1980s, however, researchers had developed algorithms for modifying neural nets’ weights and thresholds that were efficient enough for networks with more than one layer, removing many of the limitations identified by Minsky and Papert. The field enjoyed a renaissance.

But intellectually, there’s something unsatisfying about neural nets. Enough training may revise a network’s settings to the point that it can usefully classify data, but what do those settings mean? What image features is an object recognizer looking at, and how does it piece them together into the distinctive visual signatures of cars, houses, and coffee cups? Looking at the weights of individual connections won’t answer that question.

In recent years, computer scientists have begun to come up with ingenious methods for deducing the analytic strategies adopted by neural nets. But in the 1980s, the networks’ strategies were indecipherable. So around the turn of the century, neural networks were supplanted by support vector machines, an alternative approach to machine learning that’s based on some very clean and elegant mathematics.

The recent resurgence in neural networks — the deep-learning revolution — comes courtesy of the computer-game industry. The complex imagery and rapid pace of today’s video games require hardware that can keep up, and the result has been the graphics processing unit (GPU), which packs thousands of relatively simple processing cores on a single chip. It didn’t take long for researchers to realize that the architecture of a GPU is remarkably like that of a neural net.

Modern GPUs enabled the one-layer networks of the 1960s and the two- to three-layer networks of the 1980s to blossom into the 10-, 15-, even 50-layer networks of today. That’s what the “deep” in “deep learning” refers to — the depth of the network’s layers. And currently, deep learning is responsible for the best-performing systems in almost every area of artificial-intelligence research.

Under the hood

The networks’ opacity is still unsettling to theorists, but there’s headway on that front, too. In addition to directing the Center for Brains, Minds, and Machines (CBMM), Poggio leads the center’s research program in Theoretical Frameworks for Intelligence. Recently, Poggio and his CBMM colleagues have released a three-part theoretical study of neural networks.

The first part, which was published last month in the International Journal of Automation and Computing, addresses the range of computations that deep-learning networks can execute and when deep networks offer advantages over shallower ones. Parts two and three, which have been released as CBMM technical reports, address the problems of global optimization, or guaranteeing that a network has found the settings that best accord with its training data, and overfitting, or cases in which the network becomes so attuned to the specifics of its training data that it fails to generalize to other instances of the same categories.

There are still plenty of theoretical questions to be answered, but CBMM researchers’ work could help ensure that neural networks finally break the generational cycle that has brought them in and out of favor for seven decades.

This image from MIT illustrates a ‘modern’ neural network,

Most applications of deep learning use “convolutional” neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster feeds data to multiple nodes (orange and green) of the next layer. Image: Jose-Luis Olivares/MIT

h/t phys.org April 17, 2017

One final note, I wish the folks at MIT had an ‘explainer’ archive. I’m not sure how to find any more ‘explainers on MIT’s website.

Vector Institute and Canada’s artificial intelligence sector

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.

In addition, Vector is expected to receive funding from the Province of Ontario and more than 30 top Canadian and global companies eager to tap this pool of talent to grow their businesses. The institute will also work closely with other Ontario universities with AI talent.

(See my March 24, 2017 posting; scroll down about 25% for the science part, including the Pan-Canadian Artificial Intelligence Strategy of the budget.)

Not obvious in last week’s coverage of the Pan-Canadian Artificial Intelligence Strategy is that the much lauded Hinton has been living in the US and working for Google. These latest announcements (Pan-Canadian AI Strategy and Vector Institute) mean that he’s moving back.

A March 28, 2017 article by Kate Allen for TorontoStar.com provides more details about the Vector Institute, Hinton, and the Canadian ‘brain drain’ as it applies to artificial intelligence, (Note:  A link has been removed)

Toronto will host a new institute devoted to artificial intelligence, a major gambit to bolster a field of research pioneered in Canada but consistently drained of talent by major U.S. technology companies like Google, Facebook and Microsoft.

The Vector Institute, an independent non-profit affiliated with the University of Toronto, will hire about 25 new faculty and research scientists. It will be backed by more than $150 million in public and corporate funding in an unusual hybridization of pure research and business-minded commercial goals.

The province will spend $50 million over five years, while the federal government, which announced a $125-million Pan-Canadian Artificial Intelligence Strategy in last week’s budget, is providing at least $40 million, backers say. More than two dozen companies have committed millions more over 10 years, including $5 million each from sponsors including Google, Air Canada, Loblaws, and Canada’s five biggest banks [Bank of Montreal (BMO). Canadian Imperial Bank of Commerce ({CIBC} President’s Choice Financial},  Royal Bank of Canada (RBC), Scotiabank (Tangerine), Toronto-Dominion Bank (TD Canada Trust)].

The mode of artificial intelligence that the Vector Institute will focus on, deep learning, has seen remarkable results in recent years, particularly in image and speech recognition. Geoffrey Hinton, considered the “godfather” of deep learning for the breakthroughs he made while a professor at U of T, has worked for Google since 2013 in California and Toronto.

Hinton will move back to Canada to lead a research team based at the tech giant’s Toronto offices and act as chief scientific adviser of the new institute.

Researchers trained in Canadian artificial intelligence labs fill the ranks of major technology companies, working on tools like instant language translation, facial recognition, and recommendation services. Academic institutions and startups in Toronto, Waterloo, Montreal and Edmonton boast leaders in the field, but other researchers have left for U.S. universities and corporate labs.

The goals of the Vector Institute are to retain, repatriate and attract AI talent, to create more trained experts, and to feed that expertise into existing Canadian companies and startups.

Hospitals are expected to be a major partner, since health care is an intriguing application for AI. Last month, researchers from Stanford University announced they had trained a deep learning algorithm to identify potentially cancerous skin lesions with accuracy comparable to human dermatologists. The Toronto company Deep Genomics is using deep learning to read genomes and identify mutations that may lead to disease, among other things.

Intelligent algorithms can also be applied to tasks that might seem less virtuous, like reading private data to better target advertising. Zemel [Richard Zemel, the institute’s research director and a professor of computer science at U of T] says the centre is creating an ethics working group [emphasis mine] and maintaining ties with organizations that promote fairness and transparency in machine learning. As for privacy concerns, “that’s something we are well aware of. We don’t have a well-formed policy yet but we will fairly soon.”

The institute’s annual funding pales in comparison to the revenues of the American tech giants, which are measured in tens of billions. The risk the institute’s backers are taking is simply creating an even more robust machine learning PhD mill for the U.S.

“They obviously won’t all stay in Canada, but Toronto industry is very keen to get them,” Hinton said. “I think Trump might help there.” Two researchers on Hinton’s new Toronto-based team are Iranian, one of the countries targeted by U.S. President Donald Trump’s travel bans.

Ethics do seem to be a bit of an afterthought. Presumably the Vector Institute’s ‘ethics working group’ won’t include any regular folks. Is there any thought to what the rest of us think about these developments? As there will also be some collaboration with other proposed AI institutes including ones at the University of Montreal (Université de Montréal) and the University of Alberta (Kate McGillivray’s article coming up shortly mentions them), might the ethics group be centered in either Edmonton or Montreal? Interestingly, two Canadians (Timothy Caulfield at the University of Alberta and Eric Racine at Université de Montréa) testified at the US Commission for the Study of Bioethical Issues Feb. 10 – 11, 2014 meeting, the Brain research, ethics, and nanotechnology. Still speculating here but I imagine Caulfield and/or Racine could be persuaded to extend their expertise in ethics and the human brain to AI and its neural networks.

Getting back to the topic at hand the ‘AI sceneCanada’, Allen’s article is worth reading in its entirety if you have the time.

Kate McGillivray’s March 29, 2017 article for the Canadian Broadcasting Corporation’s (CBC) news online provides more details about the Canadian AI situation and the new strategies,

With artificial intelligence set to transform our world, a new institute is putting Toronto to the front of the line to lead the charge.

The Vector Institute for Artificial Intelligence, made possible by funding from the federal government revealed in the 2017 budget, will move into new digs in the MaRS Discovery District by the end of the year.

Vector’s funding comes partially from a $125 million investment announced in last Wednesday’s federal budget to launch a pan-Canadian artificial intelligence strategy, with similar institutes being established in Montreal and Edmonton.

“[A.I.] cuts across pretty well every sector of the economy,” said Dr. Alan Bernstein, CEO and president of the Canadian Institute for Advanced Research, the organization tasked with administering the federal program.

“Silicon Valley and England and other places really jumped on it, so we kind of lost the lead a little bit. I think the Canadian federal government has now realized that,” he said.

Stopping up the brain drain

Critical to the strategy’s success is building a homegrown base of A.I. experts and innovators — a problem in the last decade, despite pioneering work on so-called “Deep Learning” by Canadian scholars such as Yoshua Bengio and Geoffrey Hinton, a former University of Toronto professor who will now serve as Vector’s chief scientific advisor.

With few university faculty positions in Canada and with many innovative companies headquartered elsewhere, it has been tough to keep the few graduates specializing in A.I. in town.

“We were paying to educate people and shipping them south,” explained Ed Clark, chair of the Vector Institute and business advisor to Ontario Premier Kathleen Wynne.

The existence of that “fantastic science” will lean heavily on how much buy-in Vector and Canada’s other two A.I. centres get.

Toronto’s portion of the $125 million is a “great start,” said Bernstein, but taken alone, “it’s not enough money.”

“My estimate of the right amount of money to make a difference is a half a billion or so, and I think we will get there,” he said.

Jessica Murphy’s March 29, 2017 article for the British Broadcasting Corporation’s (BBC) news online offers some intriguing detail about the Canadian AI scene,

Canadian researchers have been behind some recent major breakthroughs in artificial intelligence. Now, the country is betting on becoming a big player in one of the hottest fields in technology, with help from the likes of Google and RBC [Royal Bank of Canada].

In an unassuming building on the University of Toronto’s downtown campus, Geoff Hinton laboured for years on the “lunatic fringe” of academia and artificial intelligence, pursuing research in an area of AI called neural networks.

Also known as “deep learning”, neural networks are computer programs that learn in similar way to human brains. The field showed early promise in the 1980s, but the tech sector turned its attention to other AI methods after that promise seemed slow to develop.

“The approaches that I thought were silly were in the ascendancy and the approach that I thought was the right approach was regarded as silly,” says the British-born [emphasis mine] professor, who splits his time between the university and Google, where he is a vice-president of engineering fellow.

Neural networks are used by the likes of Netflix to recommend what you should binge watch and smartphones with voice assistance tools. Google DeepMind’s AlphaGo AI used them to win against a human in the ancient game of Go in 2016.

Foteini Agrafioti, who heads up the new RBC Research in Machine Learning lab at the University of Toronto, said those recent innovations made AI attractive to researchers and the tech industry.

“Anything that’s powering Google’s engines right now is powered by deep learning,” she says.

Developments in the field helped jumpstart innovation and paved the way for the technology’s commercialisation. They also captured the attention of Google, IBM and Microsoft, and kicked off a hiring race in the field.

The renewed focus on neural networks has boosted the careers of early Canadian AI machine learning pioneers like Hinton, the University of Montreal’s Yoshua Bengio, and University of Alberta’s Richard Sutton.

Money from big tech is coming north, along with investments by domestic corporations like banking multinational RBC and auto parts giant Magna, and millions of dollars in government funding.

Former banking executive Ed Clark will head the institute, and says the goal is to make Toronto, which has the largest concentration of AI-related industries in Canada, one of the top five places in the world for AI innovation and business.

The founders also want it to serve as a magnet and retention tool for top talent aggressively head-hunted by US firms.

Clark says they want to “wake up” Canadian industry to the possibilities of AI, which is expected to have a massive impact on fields like healthcare, banking, manufacturing and transportation.

Google invested C$4.5m (US$3.4m/£2.7m) last November [2016] in the University of Montreal’s Montreal Institute for Learning Algorithms.

Microsoft is funding a Montreal startup, Element AI. The Seattle-based company also announced it would acquire Montreal-based Maluuba and help fund AI research at the University of Montreal and McGill University.

Thomson Reuters and General Motors both recently moved AI labs to Toronto.

RBC is also investing in the future of AI in Canada, including opening a machine learning lab headed by Agrafioti, co-funding a program to bring global AI talent and entrepreneurs to Toronto, and collaborating with Sutton and the University of Alberta’s Machine Intelligence Institute.

Canadian tech also sees the travel uncertainty created by the Trump administration in the US as making Canada more attractive to foreign talent. (One of Clark’s the selling points is that Toronto as an “open and diverse” city).

This may reverse the ‘brain drain’ but it appears Canada’s role as a ‘branch plant economy’ for foreign (usually US) companies could become an important discussion once more. From the ‘Foreign ownership of companies of Canada’ Wikipedia entry (Note: Links have been removed),

Historically, foreign ownership was a political issue in Canada in the late 1960s and early 1970s, when it was believed by some that U.S. investment had reached new heights (though its levels had actually remained stable for decades), and then in the 1980s, during debates over the Free Trade Agreement.

But the situation has changed, since in the interim period Canada itself became a major investor and owner of foreign corporations. Since the 1980s, Canada’s levels of investment and ownership in foreign companies have been larger than foreign investment and ownership in Canada. In some smaller countries, such as Montenegro, Canadian investment is sizable enough to make up a major portion of the economy. In Northern Ireland, for example, Canada is the largest foreign investor. By becoming foreign owners themselves, Canadians have become far less politically concerned about investment within Canada.

Of note is that Canada’s largest companies by value, and largest employers, tend to be foreign-owned in a way that is more typical of a developing nation than a G8 member. The best example is the automotive sector, one of Canada’s most important industries. It is dominated by American, German, and Japanese giants. Although this situation is not unique to Canada in the global context, it is unique among G-8 nations, and many other relatively small nations also have national automotive companies.

It’s interesting to note that sometimes Canadian companies are the big investors but that doesn’t change our basic position. And, as I’ve noted in other postings (including the March 24, 2017 posting), these government investments in science and technology won’t necessarily lead to a move away from our ‘branch plant economy’ towards an innovative Canada.

You can find out more about the Vector Institute for Artificial Intelligence here.

BTW, I noted that reference to Hinton as ‘British-born’ in the BBC article. He was educated in the UK and subsidized by UK taxpayers (from his Wikipedia entry; Note: Links have been removed),

Hinton was educated at King’s College, Cambridge graduating in 1970, with a Bachelor of Arts in experimental psychology.[1] He continued his study at the University of Edinburgh where he was awarded a PhD in artificial intelligence in 1977 for research supervised by H. Christopher Longuet-Higgins.[3][12]

It seems Canadians are not the only ones to experience  ‘brain drains’.

Finally, I wrote at length about a recent initiative taking place between the University of British Columbia (Vancouver, Canada) and the University of Washington (Seattle, Washington), the Cascadia Urban Analytics Cooperative in a Feb. 28, 2017 posting noting that the initiative is being funded by Microsoft to the tune $1M and is part of a larger cooperative effort between the province of British Columbia and the state of Washington. Artificial intelligence is not the only area where US technology companies are hedging their bets (against Trump’s administration which seems determined to terrify people from crossing US borders) by investing in Canada.

For anyone interested in a little more information about AI in the US and China, there’s today’s (March 31, 2017)earlier posting: China, US, and the race for artificial intelligence research domination.

The Canadian science scene and the 2017 Canadian federal budget

There’s not much happening in the 2017-18 budget in terms of new spending according to Paul Wells’ March 22, 2017 article for TheStar.com,

This is the 22nd or 23rd federal budget I’ve covered. And I’ve never seen the like of the one Bill Morneau introduced on Wednesday [March 22, 2017].

Not even in the last days of the Harper Conservatives did a budget provide for so little new spending — $1.3 billion in the current budget year, total, in all fields of government. That’s a little less than half of one per cent of all federal program spending for this year.

But times are tight. The future is a place where we can dream. So the dollars flow more freely in later years. In 2021-22, the budget’s fifth planning year, new spending peaks at $8.2 billion. Which will be about 2.4 per cent of all program spending.

He’s not alone in this 2017 federal budget analysis; CBC (Canadian Broadcasting Corporation) pundits, Chantal Hébert, Andrew Coyne, and Jennifer Ditchburn said much the same during their ‘At Issue’ segment of the March 22, 2017 broadcast of The National (news).

Before I focus on the science and technology budget, here are some general highlights from the CBC’s March 22, 2017 article on the 2017-18 budget announcement (Note: Links have been removed,

Here are highlights from the 2017 federal budget:

  • Deficit: $28.5 billion, up from $25.4 billion projected in the fall.
  • Trend: Deficits gradually decline over next five years — but still at $18.8 billion in 2021-22.
  • Housing: $11.2 billion over 11 years, already budgeted, will go to a national housing strategy.
  • Child care: $7 billion over 10 years, already budgeted, for new spaces, starting 2018-19.
  • Indigenous: $3.4 billion in new money over five years for infrastructure, health and education.
  • Defence: $8.4 billion in capital spending for equipment pushed forward to 2035.
  • Care givers: New care-giving benefit up to 15 weeks, starting next year.
  • Skills: New agency to research and measure skills development, starting 2018-19.
  • Innovation: $950 million over five years to support business-led “superclusters.”
  • Startups: $400 million over three years for a new venture capital catalyst initiative.
  • AI: $125 million to launch a pan-Canadian Artificial Intelligence Strategy.
  • Coding kids: $50 million over two years for initiatives to teach children to code.
  • Families: Option to extend parental leave up to 18 months.
  • Uber tax: GST to be collected on ride-sharing services.
  • Sin taxes: One cent more on a bottle of wine, five cents on 24 case of beer.
  • Bye-bye: No more Canada Savings Bonds.
  • Transit credit killed: 15 per cent non-refundable public transit tax credit phased out this year.

You can find the entire 2017-18 budget here.

Science and the 2017-18 budget

For anyone interested in the science news, you’ll find most of that in the 2017 budget’s Chapter 1 — Skills, Innovation and Middle Class jobs. As well, Wayne Kondro has written up a précis in his March 22, 2017 article for Science (magazine),

Finance officials, who speak on condition of anonymity during the budget lock-up, indicated the budgets of the granting councils, the main source of operational grants for university researchers, will be “static” until the government can assess recommendations that emerge from an expert panel formed in 2015 and headed by former University of Toronto President David Naylor to review basic science in Canada [highlighted in my June 15, 2016 posting ; $2M has been allocated for the advisor and associated secretariat]. Until then, the officials said, funding for the Natural Sciences and Engineering Research Council of Canada (NSERC) will remain at roughly $848 million, whereas that for the Canadian Institutes of Health Research (CIHR) will remain at $773 million, and for the Social Sciences and Humanities Research Council [SSHRC] at $547 million.

NSERC, though, will receive $8.1 million over 5 years to administer a PromoScience Program that introduces youth, particularly unrepresented groups like Aboriginal people and women, to science, technology, engineering, and mathematics through measures like “space camps and conservation projects.” CIHR, meanwhile, could receive modest amounts from separate plans to identify climate change health risks and to reduce drug and substance abuse, the officials added.

… Canada’s Innovation and Skills Plan, would funnel $600 million over 5 years allocated in 2016, and $112.5 million slated for public transit and green infrastructure, to create Silicon Valley–like “super clusters,” which the budget defined as “dense areas of business activity that contain large and small companies, post-secondary institutions and specialized talent and infrastructure.” …

… The Canadian Institute for Advanced Research will receive $93.7 million [emphasis mine] to “launch a Pan-Canadian Artificial Intelligence Strategy … (to) position Canada as a world-leading destination for companies seeking to invest in artificial intelligence and innovation.”

… Among more specific measures are vows to: Use $87.7 million in previous allocations to the Canada Research Chairs program to create 25 “Canada 150 Research Chairs” honoring the nation’s 150th year of existence, provide $1.5 million per year to support the operations of the office of the as-yet-unappointed national science adviser [see my Dec. 7, 2016 post for information about the job posting, which is now closed]; provide $165.7 million [emphasis mine] over 5 years for the nonprofit organization Mitacs to create roughly 6300 more co-op positions for university students and grads, and provide $60.7 million over five years for new Canadian Space Agency projects, particularly for Canadian participation in the National Aeronautics and Space Administration’s next Mars Orbiter Mission.

Kondros was either reading an earlier version of the budget or made an error regarding Mitacs (from the budget in the “A New, Ambitious Approach to Work-Integrated Learning” subsection),

Mitacs has set an ambitious goal of providing 10,000 work-integrated learning placements for Canadian post-secondary students and graduates each year—up from the current level of around 3,750 placements. Budget 2017 proposes to provide $221 million [emphasis mine] over five years, starting in 2017–18, to achieve this goal and provide relevant work experience to Canadian students.

As well, the budget item for the Pan-Canadian Artificial Intelligence Strategy is $125M.

Moving from Kondros’ précis, the budget (in the “Positioning National Research Council Canada Within the Innovation and Skills Plan” subsection) announces support for these specific areas of science,

Stem Cell Research

The Stem Cell Network, established in 2001, is a national not-for-profit organization that helps translate stem cell research into clinical applications, commercial products and public policy. Its research holds great promise, offering the potential for new therapies and medical treatments for respiratory and heart diseases, cancer, diabetes, spinal cord injury, multiple sclerosis, Crohn’s disease, auto-immune disorders and Parkinson’s disease. To support this important work, Budget 2017 proposes to provide the Stem Cell Network with renewed funding of $6 million in 2018–19.

Space Exploration

Canada has a long and proud history as a space-faring nation. As our international partners prepare to chart new missions, Budget 2017 proposes investments that will underscore Canada’s commitment to innovation and leadership in space. Budget 2017 proposes to provide $80.9 million on a cash basis over five years, starting in 2017–18, for new projects through the Canadian Space Agency that will demonstrate and utilize Canadian innovations in space, including in the field of quantum technology as well as for Mars surface observation. The latter project will enable Canada to join the National Aeronautics and Space Administration’s (NASA’s) next Mars Orbiter Mission.

Quantum Information

The development of new quantum technologies has the potential to transform markets, create new industries and produce leading-edge jobs. The Institute for Quantum Computing is a world-leading Canadian research facility that furthers our understanding of these innovative technologies. Budget 2017 proposes to provide the Institute with renewed funding of $10 million over two years, starting in 2017–18.

Social Innovation

Through community-college partnerships, the Community and College Social Innovation Fund fosters positive social outcomes, such as the integration of vulnerable populations into Canadian communities. Following the success of this pilot program, Budget 2017 proposes to invest $10 million over two years, starting in 2017–18, to continue this work.

International Research Collaborations

The Canadian Institute for Advanced Research (CIFAR) connects Canadian researchers with collaborative research networks led by eminent Canadian and international researchers on topics that touch all humanity. Past collaborations facilitated by CIFAR are credited with fostering Canada’s leadership in artificial intelligence and deep learning. Budget 2017 proposes to provide renewed and enhanced funding of $35 million over five years, starting in 2017–18.

Earlier this week, I highlighted Canada’s strength in the field of regenerative medicine, specifically stem cells in a March 21, 2017 posting. The $6M in the current budget doesn’t look like increased funding but rather a one-year extension. I’m sure they’re happy to receive it  but I imagine it’s a little hard to plan major research projects when you’re not sure how long your funding will last.

As for Canadian leadership in artificial intelligence, that was news to me. Here’s more from the budget,

Canada a Pioneer in Deep Learning in Machines and Brains

CIFAR’s Learning in Machines & Brains program has shaken up the field of artificial intelligence by pioneering a technique called “deep learning,” a computer technique inspired by the human brain and neural networks, which is now routinely used by the likes of Google and Facebook. The program brings together computer scientists, biologists, neuroscientists, psychologists and others, and the result is rich collaborations that have propelled artificial intelligence research forward. The program is co-directed by one of Canada’s foremost experts in artificial intelligence, the Université de Montréal’s Yoshua Bengio, and for his many contributions to the program, the University of Toronto’s Geoffrey Hinton, another Canadian leader in this field, was awarded the title of Distinguished Fellow by CIFAR in 2014.

Meanwhile, from chapter 1 of the budget in the subsection titled “Preparing for the Digital Economy,” there is this provision for children,

Providing educational opportunities for digital skills development to Canadian girls and boys—from kindergarten to grade 12—will give them the head start they need to find and keep good, well-paying, in-demand jobs. To help provide coding and digital skills education to more young Canadians, the Government intends to launch a competitive process through which digital skills training organizations can apply for funding. Budget 2017 proposes to provide $50 million over two years, starting in 2017–18, to support these teaching initiatives.

I wonder if BC Premier Christy Clark is heaving a sigh of relief. At the 2016 #BCTECH Summit, she announced that students in BC would learn to code at school and in newly enhanced coding camp programmes (see my Jan. 19, 2016 posting). Interestingly, there was no mention of additional funding to support her initiative. I guess this money from the federal government comes at a good time as we will have a provincial election later this spring where she can announce the initiative again and, this time, mention there’s money for it.

Attracting brains from afar

Ivan Semeniuk in his March 23, 2017 article (for the Globe and Mail) reads between the lines to analyze the budget’s possible impact on Canadian science,

But a between-the-lines reading of the budget document suggests the government also has another audience in mind: uneasy scientists from the United States and Britain.

The federal government showed its hand at the 2017 #BCTECH Summit. From a March 16, 2017 article by Meera Bains for the CBC news online,

At the B.C. tech summit, Navdeep Bains, Canada’s minister of innovation, said the government will act quickly to fast track work permits to attract highly skilled talent from other countries.

“We’re taking the processing time, which takes months, and reducing it to two weeks for immigration processing for individuals [who] need to come here to help companies grow and scale up,” Bains said.

“So this is a big deal. It’s a game changer.”

That change will happen through the Global Talent Stream, a new program under the federal government’s temporary foreign worker program.  It’s scheduled to begin on June 12, 2017.

U.S. companies are taking notice and a Canadian firm, True North, is offering to help them set up shop.

“What we suggest is that they think about moving their operations, or at least a chunk of their operations, to Vancouver, set up a Canadian subsidiary,” said the company’s founder, Michael Tippett.

“And that subsidiary would be able to house and accommodate those employees.”

Industry experts says while the future is unclear for the tech sector in the U.S., it’s clear high tech in B.C. is gearing up to take advantage.

US business attempts to take advantage of Canada’s relative stability and openness to immigration would seem to be the motive for at least one cross border initiative, the Cascadia Urban Analytics Cooperative. From my Feb. 28, 2017 posting,

There was some big news about the smallest version of the Cascadia region on Thursday, Feb. 23, 2017 when the University of British Columbia (UBC) , the University of Washington (state; UW), and Microsoft announced the launch of the Cascadia Urban Analytics Cooperative. From the joint Feb. 23, 2017 news release (read on the UBC website or read on the UW website),

In an expansion of regional cooperation, the University of British Columbia and the University of Washington today announced the establishment of the Cascadia Urban Analytics Cooperative to use data to help cities and communities address challenges from traffic to homelessness. The largest industry-funded research partnership between UBC and the UW, the collaborative will bring faculty, students and community stakeholders together to solve problems, and is made possible thanks to a $1-million gift from Microsoft.

Today’s announcement follows last September’s [2016] Emerging Cascadia Innovation Corridor Conference in Vancouver, B.C. The forum brought together regional leaders for the first time to identify concrete opportunities for partnerships in education, transportation, university research, human capital and other areas.

A Boston Consulting Group study unveiled at the conference showed the region between Seattle and Vancouver has “high potential to cultivate an innovation corridor” that competes on an international scale, but only if regional leaders work together. The study says that could be possible through sustained collaboration aided by an educated and skilled workforce, a vibrant network of research universities and a dynamic policy environment.

It gets better, it seems Microsoft has been positioning itself for a while if Matt Day’s analysis is correct (from my Feb. 28, 2017 posting),

Matt Day in a Feb. 23, 2017 article for the The Seattle Times provides additional perspective (Note: Links have been removed),

Microsoft’s effort to nudge Seattle and Vancouver, B.C., a bit closer together got an endorsement Thursday [Feb. 23, 2017] from the leading university in each city.

The partnership has its roots in a September [2016] conference in Vancouver organized by Microsoft’s public affairs and lobbying unit [emphasis mine.] That gathering was aimed at tying business, government and educational institutions in Microsoft’s home region in the Seattle area closer to its Canadian neighbor.

Microsoft last year [2016] opened an expanded office in downtown Vancouver with space for 750 employees, an outpost partly designed to draw to the Northwest more engineers than the company can get through the U.S. guest worker system [emphasis mine].

This was all prior to President Trump’s legislative moves in the US, which have at least one Canadian observer a little more gleeful than I’m comfortable with. From a March 21, 2017 article by Susan Lum  for CBC News online,

U.S. President Donald Trump’s efforts to limit travel into his country while simultaneously cutting money from science-based programs provides an opportunity for Canada’s science sector, says a leading Canadian researcher.

“This is Canada’s moment. I think it’s a time we should be bold,” said Alan Bernstein, president of CIFAR [which on March 22, 2017 was awarded $125M to launch the Pan Canada Artificial Intelligence Strategy in the Canadian federal budget announcement], a global research network that funds hundreds of scientists in 16 countries.

Bernstein believes there are many reasons why Canada has become increasingly attractive to scientists around the world, including the political climate in the United States and the Trump administration’s travel bans.

Thankfully, Bernstein calms down a bit,

“It used to be if you were a bright young person anywhere in the world, you would want to go to Harvard or Berkeley or Stanford, or what have you. Now I think you should give pause to that,” he said. “We have pretty good universities here [emphasis mine]. We speak English. We’re a welcoming society for immigrants.”​

Bernstein cautions that Canada should not be seen to be poaching scientists from the United States — but there is an opportunity.

“It’s as if we’ve been in a choir of an opera in the back of the stage and all of a sudden the stars all left the stage. And the audience is expecting us to sing an aria. So we should sing,” Bernstein said.

Bernstein said the federal government, with this week’s so-called innovation budget, can help Canada hit the right notes.

“Innovation is built on fundamental science, so I’m looking to see if the government is willing to support, in a big way, fundamental science in the country.”

Pretty good universities, eh? Thank you, Dr. Bernstein, for keeping some of the boosterism in check. Let’s leave the chest thumping to President Trump and his cronies.

Ivan Semeniuk’s March 23, 2017 article (for the Globe and Mail) provides more details about the situation in the US and in Britain,

Last week, Donald Trump’s first budget request made clear the U.S. President would significantly reduce or entirely eliminate research funding in areas such as climate science and renewable energy if permitted by Congress. Even the National Institutes of Health, which spearheads medical research in the United States and is historically supported across party lines, was unexpectedly targeted for a $6-billion (U.S.) cut that the White House said could be achieved through “efficiencies.”

In Britain, a recent survey found that 42 per cent of academics were considering leaving the country over worries about a less welcoming environment and the loss of research money that a split with the European Union is expected to bring.

In contrast, Canada’s upbeat language about science in the budget makes a not-so-subtle pitch for diversity and talent from abroad, including $117.6-million to establish 25 research chairs with the aim of attracting “top-tier international scholars.”

For good measure, the budget also includes funding for science promotion and $2-million annually for Canada’s yet-to-be-hired Chief Science Advisor, whose duties will include ensuring that government researchers can speak freely about their work.

“What we’ve been hearing over the last few months is that Canada is seen as a beacon, for its openness and for its commitment to science,” said Ms. Duncan [Kirsty Duncan, Minister of Science], who did not refer directly to either the United States or Britain in her comments.

Providing a less optimistic note, Erica Alini in her March 22, 2017 online article for Global News mentions a perennial problem, the Canadian brain drain,

The budget includes a slew of proposed reforms and boosted funding for existing training programs, as well as new skills-development resources for unemployed and underemployed Canadians not covered under current EI-funded programs.

There are initiatives to help women and indigenous people get degrees or training in science, technology, engineering and mathematics (the so-called STEM subjects) and even to teach kids as young as kindergarten-age to code.

But there was no mention of how to make sure Canadians with the right skills remain in Canada, TD’s DePratto {Toronto Dominion Bank} Economics; TD is currently experiencing a scandal {March 13, 2017 Huffington Post news item}] told Global News.

Canada ranks in the middle of the pack compared to other advanced economies when it comes to its share of its graduates in STEM fields, but the U.S. doesn’t shine either, said DePratto [Brian DePratto, senior economist at TD .

The key difference between Canada and the U.S. is the ability to retain domestic talent and attract brains from all over the world, he noted.

To be blunt, there may be some opportunities for Canadian science but it does well to remember (a) US businesses have no particular loyalty to Canada and (b) all it takes is an election to change any perceived advantages to disadvantages.

Digital policy and intellectual property issues

Dubbed by some as the ‘innovation’ budget (official title:  Building a Strong Middle Class), there is an attempt to address a longstanding innovation issue (from a March 22, 2017 posting by Michael Geist on his eponymous blog (Note: Links have been removed),

The release of today’s [march 22, 2017] federal budget is expected to include a significant emphasis on innovation, with the government revealing how it plans to spend (or re-allocate) hundreds of millions of dollars that is intended to support innovation. Canada’s dismal innovation record needs attention, but spending our way to a more innovative economy is unlikely to yield the desired results. While Navdeep Bains, the Innovation, Science and Economic Development Minister, has talked for months about the importance of innovation, Toronto Star columnist Paul Wells today delivers a cutting but accurate assessment of those efforts:

“This government is the first with a minister for innovation! He’s Navdeep Bains. He frequently posts photos of his meetings on Twitter, with the hashtag “#innovation.” That’s how you know there is innovation going on. A year and a half after he became the minister for #innovation, it’s not clear what Bains’s plans are. It’s pretty clear that within the government he has less than complete control over #innovation. There’s an advisory council on economic growth, chaired by the McKinsey guru Dominic Barton, which periodically reports to the government urging more #innovation.

There’s a science advisory panel, chaired by former University of Toronto president David Naylor, that delivered a report to Science Minister Kirsty Duncan more than three months ago. That report has vanished. One presumes that’s because it offered some advice. Whatever Bains proposes, it will have company.”

Wells is right. Bains has been very visible with plenty of meetings and public photo shoots but no obvious innovation policy direction. This represents a missed opportunity since Bains has plenty of policy tools at his disposal that could advance Canada’s innovation framework without focusing on government spending.

For example, Canada’s communications system – wireless and broadband Internet access – falls directly within his portfolio and is crucial for both business and consumers. Yet Bains has been largely missing in action on the file. He gave approval for the Bell – MTS merger that virtually everyone concedes will increase prices in the province and make the communications market less competitive. There are potential policy measures that could bring new competitors into the market (MVNOs [mobile virtual network operators] and municipal broadband) and that could make it easier for consumers to switch providers (ban on unlocking devices). Some of this falls to the CRTC, but government direction and emphasis would make a difference.

Even more troubling has been his near total invisibility on issues relating to new fees or taxes on Internet access and digital services. Canadian Heritage Minister Mélanie Joly has taken control of the issue with the possibility that Canadians could face increased costs for their Internet access or digital services through mandatory fees to contribute to Canadian content.  Leaving aside the policy objections to such an approach (reducing affordable access and the fact that foreign sources now contribute more toward Canadian English language TV production than Canadian broadcasters and distributors), Internet access and e-commerce are supposed to be Bains’ issue and they have a direct connection to the innovation file. How is it possible for the Innovation, Science and Economic Development Minister to have remained silent for months on the issue?

Bains has been largely missing on trade related innovation issues as well. My Globe and Mail column today focuses on a digital-era NAFTA, pointing to likely U.S. demands on data localization, data transfers, e-commerce rules, and net neutrality.  These are all issues that fall under Bains’ portfolio and will impact investment in Canadian networks and digital services. There are innovation opportunities for Canada here, but Bains has been content to leave the policy issues to others, who will be willing to sacrifice potential gains in those areas.

Intellectual property policy is yet another area that falls directly under Bains’ mandate with an obvious link to innovation, but he has done little on the file. Canada won a huge NAFTA victory late last week involving the Canadian patent system, which was challenged by pharmaceutical giant Eli Lilly. Why has Bains not promoted the decision as an affirmation of how Canada’s intellectual property rules?

On the copyright front, the government is scheduled to conduct a review of the Copyright Act later this year, but it is not clear whether Bains will take the lead or again cede responsibility to Joly. The Copyright Act is statutorily under the Industry Minister and reform offers the chance to kickstart innovation. …

For anyone who’s not familiar with this area, innovation is often code for commercialization of science and technology research efforts. These days, digital service and access policies and intellectual property policies are all key to research and innovation efforts.

The country that’s most often (except in mainstream Canadian news media) held up as an example of leadership in innovation is Estonia. The Economist profiled the country in a July 31, 2013 article and a July 7, 2016 article on apolitical.co provides and update.


Science monies for the tri-council science funding agencies (NSERC, SSHRC, and CIHR) are more or less flat but there were a number of line items in the federal budget which qualify as science funding. The $221M over five years for Mitacs, the $125M for the Pan-Canadian Artificial Intelligence Strategy, additional funding for the Canada research chairs, and some of the digital funding could also be included as part of the overall haul. This is in line with the former government’s (Stephen Harper’s Conservatives) penchant for keeping the tri-council’s budgets under control while spreading largesse elsewhere (notably the Perimeter Institute, TRIUMF [Canada’s National Laboratory for Particle and Nuclear Physics], and, in the 2015 budget, $243.5-million towards the Thirty Metre Telescope (TMT) — a massive astronomical observatory to be constructed on the summit of Mauna Kea, Hawaii, a $1.5-billion project). This has lead to some hard feelings in the past with regard to ‘big science’ projects getting what some have felt is an undeserved boost in finances while the ‘small fish’ are left scrabbling for the ever-diminishing (due to budget cuts in years past and inflation) pittances available from the tri-council agencies.

Mitacs, which started life as a federally funded Network Centre for Excellence focused on mathematics, has since shifted focus to become an innovation ‘champion’. You can find Mitacs here and you can find the organization’s March 2016 budget submission to the House of Commons Standing Committee on Finance here. At the time, they did not request a specific amount of money; they just asked for more.

The amount Mitacs expects to receive this year is over $40M which represents more than double what they received from the federal government and almost of 1/2 of their total income in the 2015-16 fiscal year according to their 2015-16 annual report (see p. 327 for the Mitacs Statement of Operations to March 31, 2016). In fact, the federal government forked over $39,900,189. in the 2015-16 fiscal year to be their largest supporter while Mitacs’ total income (receipts) was $81,993,390.

It’s a strange thing but too much money, etc. can be as bad as too little. I wish the folks Mitacs nothing but good luck with their windfall.

I don’t see anything in the budget that encourages innovation and investment from the industrial sector in Canada.

Finallyl, innovation is a cultural issue as much as it is a financial issue and having worked with a number of developers and start-up companies, the most popular business model is to develop a successful business that will be acquired by a large enterprise thereby allowing the entrepreneurs to retire before the age of 30 (or 40 at the latest). I don’t see anything from the government acknowledging the problem let alone any attempts to tackle it.

All in all, it was a decent budget with nothing in it to seriously offend anyone.

US white paper on neuromorphic computing (or the nanotechnology-inspired Grand Challenge for future computing)

The US has embarked on a number of what is called “Grand Challenges.” I first came across the concept when reading about the Bill and Melinda Gates (of Microsoft fame) Foundation. I gather these challenges are intended to provide funding for research that advances bold visions.

There is the US National Strategic Computing Initiative established on July 29, 2015 and its first anniversary results were announced one year to the day later. Within that initiative a nanotechnology-inspired Grand Challenge for Future Computing was issued and, according to a July 29, 2016 news item on Nanowerk, a white paper on the topic has been issued (Note: A link has been removed),

Today [July 29, 2016), Federal agencies participating in the National Nanotechnology Initiative (NNI) released a white paper (pdf) describing the collective Federal vision for the emerging and innovative solutions needed to realize the Nanotechnology-Inspired Grand Challenge for Future Computing.

The grand challenge, announced on October 20, 2015, is to “create a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain.” The white paper describes the technical priorities shared by the agencies, highlights the challenges and opportunities associated with these priorities, and presents a guiding vision for the research and development (R&D) needed to achieve key technical goals. By coordinating and collaborating across multiple levels of government, industry, academia, and nonprofit organizations, the nanotechnology and computer science communities can look beyond the decades-old approach to computing based on the von Neumann architecture and chart a new path that will continue the rapid pace of innovation beyond the next decade.

A July 29, 2016 US National Nanotechnology Coordination Office news release, which originated the news item, further and succinctly describes the contents of the paper,

“Materials and devices for computing have been and will continue to be a key application domain in the field of nanotechnology. As evident by the R&D topics highlighted in the white paper, this challenge will require the convergence of nanotechnology, neuroscience, and computer science to create a whole new paradigm for low-power computing with revolutionary, brain-like capabilities,” said Dr. Michael Meador, Director of the National Nanotechnology Coordination Office. …

The white paper was produced as a collaboration by technical staff at the Department of Energy, the National Science Foundation, the Department of Defense, the National Institute of Standards and Technology, and the Intelligence Community. …

The white paper titled “A Federal Vision for Future Computing: A Nanotechnology-Inspired Grand Challenge” is 15 pp. and it offers tidbits such as this (Note: Footnotes not included),

A new materials base may be needed for future electronic hardware. While most of today’s electronics use silicon, this approach is unsustainable if billions of disposable and short-lived sensor nodes are needed for the coming Internet-of-Things (IoT). To what extent can the materials base for the implementation of future information technology (IT) components and systems support sustainability through recycling and bio-degradability? More sustainable materials, such as compostable or biodegradable systems (polymers, paper, etc.) that can be recycled or reused,  may play an important role. The potential role for such alternative materials in the fabrication of integrated systems needs to be explored as well. [p. 5]

The basic architecture of computers today is essentially the same as those built in the 1940s—the von Neumann architecture—with separate compute, high-speed memory, and high-density storage components that are electronically interconnected. However, it is well known that continued performance increases using this architecture are not feasible in the long term, with power density constraints being one of the fundamental roadblocks.7 Further advances in the current approach using multiple cores, chip multiprocessors, and associated architectures are plagued by challenges in software and programming models. Thus,  research and development is required in radically new and different computing architectures involving processors, memory, input-output devices, and how they behave and are interconnected. [p. 7]

Neuroscience research suggests that the brain is a complex, high-performance computing system with low energy consumption and incredible parallelism. A highly plastic and flexible organ, the human brain is able to grow new neurons, synapses, and connections to cope with an ever-changing environment. Energy efficiency, growth, and flexibility occur at all scales, from molecular to cellular, and allow the brain, from early to late stage, to never stop learning and to act with proactive intelligence in both familiar and novel situations. Understanding how these mechanisms work and cooperate within and across scales has the potential to offer tremendous technical insights and novel engineering frameworks for materials, devices, and systems seeking to perform efficient and autonomous computing. This research focus area is the most synergistic with the national BRAIN Initiative. However, unlike the BRAIN Initiative, where the goal is to map the network connectivity of the brain, the objective here is to understand the nature, methods, and mechanisms for computation,  and how the brain performs some of its tasks. Even within this broad paradigm,  one can loosely distinguish between neuromorphic computing and artificial neural network (ANN) approaches. The goal of neuromorphic computing is oriented towards a hardware approach to reverse engineering the computational architecture of the brain. On the other hand, ANNs include algorithmic approaches arising from machinelearning,  which in turn could leverage advancements and understanding in neuroscience as well as novel cognitive, mathematical, and statistical techniques. Indeed, the ultimate intelligent systems may as well be the result of merging existing ANN (e.g., deep learning) and bio-inspired techniques. [p. 8]

As government documents go, this is quite readable.

For anyone interested in learning more about the future federal plans for computing in the US, there is a July 29, 2016 posting on the White House blog celebrating the first year of the US National Strategic Computing Initiative Strategic Plan (29 pp. PDF; awkward but that is the title).

Deep learning and some history from the Swiss National Science Foundation (SNSF)

A June 27, 2016 news item on phys.org provides a measured analysis of deep learning and its current state of development (from a Swiss perspective),

In March 2016, the world Go champion Lee Sedol lost 1-4 against the artificial intelligence AlphaGo. For many, this was yet another defeat for humanity at the hands of the machines. Indeed, the success of the AlphaGo software was forged in an area of artificial intelligence that has seen huge progress over the last decade. Deep learning, as it’s called, uses artificial neural networks to process algorithmic calculations. This software architecture therefore mimics biological neural networks.

Much of the progress in deep learning is thanks to the work of Jürgen Schmidhuber, director of the IDSIA (Istituto Dalle Molle di Studi sull’Intelligenza Artificiale) which is located in the suburbs of Lugano. The IDSIA doctoral student Shane Legg and a group of former colleagues went on to found DeepMind, the startup acquired by Google in early 2014 for USD 500 million. The DeepMind algorithms eventually wound up in AlphaGo.

“Schmidhuber is one of the best at deep learning,” says Boi Faltings of the EPFL Artificial Intelligence Lab. “He never let go of the need to keep working at it.” According to Stéphane Marchand-Maillet of the University of Geneva computing department, “he’s been in the race since the very beginning.”

A June 27, 2016 SNSF news release (first published as a story in Horizons no. 109 June 2016) by Fabien Goubet, which originated the news item, goes on to provide a brief history,

The real strength of deep learning is structural recognition, and winning at Go is just an illustration of this, albeit a rather resounding one. Elsewhere, and for some years now, we have seen it applied to an entire spectrum of areas, such as visual and vocal recognition, online translation tools and smartphone personal assistants. One underlying principle of machine learning is that algorithms must first be trained using copious examples. Naturally, this has been helped by the deluge of user-generated content spawned by smartphones and web 2.0, stretching from Facebook photo comments to official translations published on the Internet. By feeding a machine thousands of accurately tagged images of cats, for example, it learns first to recognise those cats and later any image of a cat, including those it hasn’t been fed.

Deep learning isn’t new; it just needed modern computers to come of age. As far back as the early 1950s, biologists tried to lay out formal principles to explain the working of the brain’s cells. In 1956, the psychologist Frank Rosenblatt of the New York State Aeronautical Laboratory published a numerical model based on these concepts, thereby creating the very first artificial neural network. Once integrated into a calculator, it learned to recognise rudimentary images.

“This network only contained eight neurones organised in a single layer. It could only recognise simple characters”, says Claude Touzet of the Adaptive and Integrative Neuroscience Laboratory of Aix-Marseille University. “It wasn’t until 1985 that we saw the second generation of artificial neural networks featuring multiple layers and much greater performance”. This breakthrough was made simultaneously by three researchers: Yann LeCun in Paris, Geoffrey Hinton in Toronto and Terrence Sejnowski in Baltimore.

Byte-size learning

In multilayer networks, each layer learns to recognise the precise visual characteristics of a shape. The deeper the layer, the more abstract the characteristics. With cat photos, the first layer analyses pixel colour, and the following layer recognises the general form of the cat. This structural design can support calculations being made upon thousands of layers, and it was this aspect of the architecture that gave rise to the name ‘deep learning’.

Marchand-Maillet explains: “Each artificial neurone is assigned an input value, which it computes using a mathematical function, only firing if the output exceeds a pre-defined threshold”. In this way, it reproduces the behaviour of real neurones, which only fire and transmit information when the input signal (the potential difference across the entire neural circuit) reaches a certain level. In the artificial model, the results of a single layer are weighted, added up and then sent as the input signal to the following layer, which processes that input using different functions, and so on and so forth.

For example, if a system is trained with great quantities of photos of apples and watermelons, it will progressively learn to distinguish them on the basis of diameter, says Marchand-Maillet. If it cannot decide (e.g., when processing a picture of a tiny watermelon), the subsequent layers take over by analysing the colours or textures of the fruit in the photo, and so on. In this way, every step in the process further refines the assessment.

Video games to the rescue

For decades, the frontier of computing held back more complex applications, even at the cutting edge. Industry walked away, and deep learning only survived thanks to the video games sector, which eventually began producing graphics chips, or GPUs, with an unprecedented power at accessible prices: up to 6 teraflops (i.e., 6 trillion calculations per second) for a few hundred dollars. “There’s no doubt that it was this calculating power that laid the ground for the quantum leap in deep learning”, says Touzet. GPUs are also very good at parallel calculations, a useful function for executing the innumerable simultaneous operations required by neural networks.
Although image analysis is getting great results, things are more complicated for sequential data objects such as natural spoken language and video footage. This has formed part of Schmidhuber’s work since 1989, and his response has been to develop recurrent neural networks in which neurones communicate with each other in loops, feeding processed data back into the initial layers.

Such sequential data analysis is highly dependent on context and precursory data. In Lugano, networks have been instructed to memorise the order of a chain of events. Long Short Term Memory (LSTM) networks can distinguish ‘boat’ from ‘float’ by recalling the sound that preceded ‘oat’ (i.e., either ‘b’ or ‘fl’). “Recurrent neural networks are more powerful than other approaches such as the Hidden Markov models”, says Schmidhuber, who also notes that Google Voice integrated LSTMs in 2015. “With looped networks, the number of layers is potentially infinite”, says Faltings [?].

For Schmidhuber, deep learning is just one aspect of artificial intelligence; the real thing will lead to “the most important change in the history of our civilisation”. But Marchand-Maillet sees deep learning as “a bit of hype, leading us to believe that artificial intelligence can learn anything provided there’s data. But it’s still an open question as to whether deep learning can really be applied to every last domain”.

It’s nice to get an historical perspective and eye-opening to realize that scientists have been working on these concepts since the 1950s.

Deep learning for cosmetics

Deep learning seems to be a synonym for artificial intelligence if a May 24, 2016 Insilico Medicine news release on EurekAlert about its use in the fields of cosmetics and as an alternative to testing animals is to be believed (Note: Links have been removed),

In addition to heading Insilico Medicine, Inc, a big data analytics company focused on applying advanced signaling pathway activation analysis and deep learning methods to biomarker and drug discovery in cancer and age-related diseases, Alex Zhavoronkov, PhD is the co-founder and principal scientist of Youth Laboratories, a company focusing on applying machine learning methods to evaluating the condition of human skin and general health status using multimodal inputs. The company developed an app called RYNKL, a mobile app for evaluating the effectiveness of various anti-aging interventions by analyzing “wrinkleness” and other parameters. The app was developed using funds from a Kickstarter crowdfunding campaign and is now being extensively tested and improved. The company also developed a platform for running online beauty competitions, where humans are evaluated by a panel of robot judges. Teams of programmers also compete on the development of most innovative algorithms to evaluate humans.

“One of my goals in life is to minimize unnecessary animal testing in areas, where computer simulations can be even more relevant to humans. Serendipitously, some of our approaches find surprising new applications in the beauty industry, which has moved away from human testing and is moving towards personalizing cosmetics and beauty products. We are happy to present our research results to a very relevant audience at this major industry event”, said Alex Zhavoronkov, CEO of Insilico Medicine, Inc.

Artificial intelligence is entering every aspect of our daily life. Deep learning systems are already outperforming humans in image and text recognition and we would like to bring some of the most innovative players like Insilico Medicine, who dare to work with gene expression, imaging and drug data to find novel ways to keep us healthy, young and beautiful”, said Irina Kremlin, director of INNOCOS.

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

Deep biomarkers of human aging: Application of deep neural networks to biomarker development by Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov. Aging May 2016 vol. 8, no. 5

This is an open access paper.

You can find out more about In Silico Medicine here and RINKL here. I was not able to find a website for Youth Laboratories.