Tag Archives: IBM

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.

Brain stuff: quantum entanglement and a multi-dimensional universe

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

Quantum entanglement and neural networks

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper is open access.

Blue Brain and the multidimensional universe

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

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

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

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

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

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

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

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

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

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

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


About Blue Brain

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

About Frontiers

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

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

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

This paper is open access.

Health technology and the Canadian Broadcasting Corporation’s (CBC) two-tier health system ‘Viewpoint’

There’s a lot of talk and handwringing about Canada’s health care system, which ebbs and flows in almost predictable cycles. Jesse Hirsh in a May 16, 2017 ‘Viewpoints’ segment (an occasional series run as part the of the CBC’s [Canadian Broadcasting Corporation] flagship, daily news programme, The National) dared to reframe the discussion as one about technology and ‘those who get it’  [the technologically literate] and ‘those who don’t’,  a state Hirsh described as being illiterate as you can see and hear in the following video.

I don’t know about you but I’m getting tired of being called illiterate when I don’t know something. To be illiterate means you can’t read and write and as it turns out I do both of those things on a daily basis (sometimes even in two languages). Despite my efforts, I’m ignorant about any number of things and those numbers keep increasing day by day. BTW, Is there anyone who isn’t having trouble keeping up?

Moving on from my rhetorical question, Hirsh has a point about the tech divide and about the need for discussion. It’s a point that hadn’t occurred to me (although I think he’s taking it in the wrong direction). In fact, this business of a tech divide already exists if you consider that people who live in rural environments and need the latest lifesaving techniques or complex procedures or access to highly specialized experts have to travel to urban centres. I gather that Hirsh feels that this divide isn’t necessarily going to be an urban/rural split so much as an issue of how technically literate you and your doctor are.  That’s intriguing but then his argumentation gets muddled. Confusingly, he seems to be suggesting that the key to the split is your access (not your technical literacy) to artificial intelligence (AI) and algorithms (presumably he’s referring to big data and data analytics). I expect access will come down more to money than technological literacy.

For example, money is likely to be a key issue when you consider his big pitch is for access to IBM’s Watson computer. (My Feb. 28, 2011 posting titled: Engineering, entertainment, IBM’s Watson, and product placement focuses largely on Watson, its winning appearances on the US television game show, Jeopardy, and its subsequent adoption into the University of Maryland’s School of Medicine in a project to bring Watson into the examining room with patients.)

Hirsh’s choice of IBM’s Watson is particularly interesting for a number of reasons. (1) Presumably there are companies other than IBM in this sector. Why do they not rate a mention?  (2) Given the current situation with IBM and the Canadian federal government’s introduction of the Phoenix payroll system (a PeopleSoft product customized by IBM), which is  a failure of monumental proportions (a Feb. 23, 2017 article by David Reevely for the Ottawa Citizen and a May 25, 2017 article by Jordan Press for the National Post), there may be a little hesitation, if not downright resistance, to a large scale implementation of any IBM product or service, regardless of where the blame lies. (3) Hirsh notes on the home page for his eponymous website,

I’m presently spending time at the IBM Innovation Space in Toronto Canada, investigating the impact of artificial intelligence and cognitive computing on all sectors and industries.

Yes, it would seem he has some sort of relationship with IBM not referenced in his Viewpoints segment on The National. Also, his description of the relationship isn’t especially illuminating but perhaps it.s this? (from the IBM Innovation Space  – Toronto Incubator Application webpage),

Our incubator

The IBM Innovation Space is a Toronto-based incubator that provides startups with a collaborative space to innovate and disrupt the market. Our goal is to provide you with the tools needed to take your idea to the next level, introduce you to the right networks and help you acquire new clients. Our unique approach, specifically around client engagement, positions your company for optimal growth and revenue at an accelerated pace.


IBM Bluemix
IBM Global Entrepreneur
Softlayer – an IBM Company

Startups partnered with the IBM Innovation Space can receive up to $120,000 in IBM credits at no charge for up to 12 months through the Global Entrepreneurship Program (GEP). These credits can be used in our products such our IBM Bluemix developer platform, Softlayer cloud services, and our world-renowned IBM Watson ‘cognitive thinking’ APIs. We provide you with enterprise grade technology to meet your clients’ needs, large or small.

Collaborative workspace in the heart of Downtown Toronto
Mentorship opportunities available with leading experts
Access to large clients to scale your startup quickly and effectively
Weekly programming ranging from guest speakers to collaborative activities
Help with funding and access to local VCs and investors​

Final comments

While I have some issues with Hirsh’s presentation, I agree that we should be discussing the issues around increased automation of our health care system. A friend of mine’s husband is a doctor and according to him those prescriptions and orders you get when leaving the hospital? They are not made up by a doctor so much as they are spit up by a computer based on the data that the doctors and nurses have supplied.

GIGO, bias, and de-skilling

Leaving aside the wonders that Hirsh describes, there’s an oldish saying in the computer business, garbage in/garbage out (gigo). At its simplest, who’s going to catch a mistake? (There are lots of mistakes made in hospitals and other health care settings.)

There are also issues around the quality of research. Are all the research papers included in the data used by the algorithms going to be considered equal? There’s more than one case where a piece of problematic research has been accepted uncritically, even if it get through peer review, and subsequently cited many times over. One of the ways to measure impact, i.e., importance, is to track the number of citations. There’s also the matter of where the research is published. A ‘high impact’ journal, such as Nature, Science, or Cell, automatically gives a piece of research a boost.

There are other kinds of bias as well. Increasingly, there’s discussion about algorithms being biased and about how machine learning (AI) can become biased. (See my May 24, 2017 posting: Machine learning programs learn bias, which highlights the issues and cites other FrogHeart posts on that and other related topics.)

These problems are to a large extent already present. Doctors have biases and research can be wrong and it can take a long time before there are corrections. However, the advent of an automated health diagnosis and treatment system is likely to exacerbate the problems. For example, if you don’t agree with your doctor’s diagnosis or treatment, you can search other opinions. What happens when your diagnosis and treatment have become data? Will the system give you another opinion? Who will you talk to? The doctor who got an answer from ‘Watson”? Is she or he going to debate Watson? Are you?

This leads to another issue and that’s automated systems getting more credit than they deserve. Futurists such as Hirsh tend to underestimate people and overestimate the positive impact that automation will have. A computer, data analystics, or an AI system are tools not gods. You’ll have as much luck petitioning one of those tools as you would Zeus.

The unasked question is how will your doctor or other health professional gain experience and skills if they never have to practice the basic, boring aspects of health care (asking questions for a history, reading medical journals to keep up with the research, etc.) and leave them to the computers? There had to be  a reason for calling it a medical ‘practice’.

There are definitely going to be advantages to these technological innovations but thoughtful adoption of these practices (pun intended) should be our goal.

Who owns your data?

Another issue which is increasingly making itself felt is ownership of data. Jacob Brogan has written a provocative May 23, 2017 piece for slate.com asking that question about the data Ancestry.com gathers for DNA testing (Note: Links have been removed),

AncestryDNA’s pitch to consumers is simple enough. For $99 (US), the company will analyze a sample of your saliva and then send back information about your “ethnic mix.” While that promise may be scientifically dubious, it’s a relatively clear-cut proposal. Some, however, worry that the service might raise significant privacy concerns.

After surveying AncestryDNA’s terms and conditions, consumer protection attorney Joel Winston found a few issues that troubled him. As he noted in a Medium post last week, the agreement asserts that it grants the company “a perpetual, royalty-free, world-wide, transferable license to use your DNA.” (The actual clause is considerably longer.) According to Winston, “With this single contractual provision, customers are granting Ancestry.com the broadest possible rights to own and exploit their genetic information.”

Winston also noted a handful of other issues that further complicate the question of ownership. Since we share much of our DNA with our relatives, he warned, “Even if you’ve never used Ancestry.com, but one of your genetic relatives has, the company may already own identifiable portions of your DNA.” [emphasis mine] Theoretically, that means information about your genetic makeup could make its way into the hands of insurers or other interested parties, whether or not you’ve sent the company your spit. (Maryam Zaringhalam explored some related risks in a recent Slate article.) Further, Winston notes that Ancestry’s customers waive their legal rights, meaning that they cannot sue the company if their information gets used against them in some way.

Over the weekend, Eric Heath, Ancestry’s chief privacy officer, responded to these concerns on the company’s own site. He claims that the transferable license is necessary for the company to provide its customers with the service that they’re paying for: “We need that license in order to move your data through our systems, render it around the globe, and to provide you with the results of our analysis work.” In other words, it allows them to send genetic samples to labs (Ancestry uses outside vendors), store the resulting data on servers, and furnish the company’s customers with the results of the study they’ve requested.

Speaking to me over the phone, Heath suggested that this license was akin to the ones that companies such as YouTube employ when users upload original content. It grants them the right to shift that data around and manipulate it in various ways, but isn’t an assertion of ownership. “We have committed to our users that their DNA data is theirs. They own their DNA,” he said.

I’m glad to see the company’s representatives are open to discussion and, later in the article, you’ll see there’ve already been some changes made. Still, there is no guarantee that the situation won’t again change, for ill this time.

What data do they have and what can they do with it?

It’s not everybody who thinks data collection and data analytics constitute problems. While some people might balk at the thought of their genetic data being traded around and possibly used against them, e.g., while hunting for a job, or turned into a source of revenue, there tends to be a more laissez-faire attitude to other types of data. Andrew MacLeod’s May 24, 2017 article for thetyee.ca highlights political implications and privacy issues (Note: Links have been removed),

After a small Victoria [British Columbia, Canada] company played an outsized role in the Brexit vote, government information and privacy watchdogs in British Columbia and Britain have been consulting each other about the use of social media to target voters based on their personal data.

The U.K.’s information commissioner, Elizabeth Denham [Note: Denham was formerly B.C.’s Office of the Information and Privacy Commissioner], announced last week [May 17, 2017] that she is launching an investigation into “the use of data analytics for political purposes.”

The investigation will look at whether political parties or advocacy groups are gathering personal information from Facebook and other social media and using it to target individuals with messages, Denham said.

B.C.’s Office of the Information and Privacy Commissioner confirmed it has been contacted by Denham.

Macleod’s March 6, 2017 article for thetyee.ca provides more details about the company’s role (note: Links have been removed),

The “tiny” and “secretive” British Columbia technology company [AggregateIQ; AIQ] that played a key role in the Brexit referendum was until recently listed as the Canadian office of a much larger firm that has 25 years of experience using behavioural research to shape public opinion around the world.

The larger firm, SCL Group, says it has worked to influence election outcomes in 19 countries. Its associated company in the U.S., Cambridge Analytica, has worked on a wide range of campaigns, including Donald Trump’s presidential bid.

In late February [2017], the Telegraph reported that campaign disclosures showed that Vote Leave campaigners had spent £3.5 million — about C$5.75 million [emphasis mine] — with a company called AggregateIQ, run by CEO Zack Massingham in downtown Victoria.

That was more than the Leave side paid any other company or individual during the campaign and about 40 per cent of its spending ahead of the June referendum that saw Britons narrowly vote to exit the European Union.

According to media reports, Aggregate develops advertising to be used on sites including Facebook, Twitter and YouTube, then targets messages to audiences who are likely to be receptive.

The Telegraph story described Victoria as “provincial” and “picturesque” and AggregateIQ as “secretive” and “low-profile.”

Canadian media also expressed surprise at AggregateIQ’s outsized role in the Brexit vote.

The Globe and Mail’s Paul Waldie wrote “It’s quite a coup for Mr. Massingham, who has only been involved in politics for six years and started AggregateIQ in 2013.”

Victoria Times Colonist columnist Jack Knox wrote “If you have never heard of AIQ, join the club.”

The Victoria company, however, appears to be connected to the much larger SCL Group, which describes itself on its website as “the global leader in data-driven communications.”

In the United States it works through related company Cambridge Analytica and has been involved in elections since 2012. Politico reported in 2015 that the firm was working on Ted Cruz’s presidential primary campaign.

And NBC and other media outlets reported that the Trump campaign paid Cambridge Analytica millions to crunch data on 230 million U.S. adults, using information from loyalty cards, club and gym memberships and charity donations [emphasis mine] to predict how an individual might vote and to shape targeted political messages.

That’s quite a chunk of change and I don’t believe that gym memberships, charity donations, etc. were the only sources of information (in the US, there’s voter registration, credit card information, and more) but the list did raise my eyebrows. It would seem we are under surveillance at all times, even in the gym.

In any event, I hope that Hirsh’s call for discussion is successful and that the discussion includes more critical thinking about the implications of Hirsh’s ‘Brave New World’.

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.

Atomic force microscope (AFM) shrunk down to a dime-sized device?

Before getting to the announcement, here’s a little background from Dexter Johnson’s Feb. 21, 2017 posting on his NanoClast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website; Note: Links have been removed),

Ever since the 1980s, when Gerd Binnig of IBM first heard that “beautiful noise” made by the tip of the first scanning tunneling microscope (STM) dragging across the surface of an atom, and he later developed the atomic force microscope (AFM), these microscopy tools have been the bedrock of nanotechnology research and development.

AFMs have continued to evolve over the years, and at one time, IBM even looked into using them as the basis of a memory technology in the company’s Millipede project. Despite all this development, AFMs have remained bulky and expensive devices, costing as much as $50,000 [or more].

Now, here’s the announcement in a Feb. 15, 2017 news item on Nanowerk,

Researchers at The University of Texas at Dallas have created an atomic force microscope on a chip, dramatically shrinking the size — and, hopefully, the price tag — of a high-tech device commonly used to characterize material properties.

“A standard atomic force microscope is a large, bulky instrument, with multiple control loops, electronics and amplifiers,” said Dr. Reza Moheimani, professor of mechanical engineering at UT Dallas. “We have managed to miniaturize all of the electromechanical components down onto a single small chip.”

A Feb. 15, 2017 University of Texas at Dallas news release, which originated the news item, provides more detail,

An atomic force microscope (AFM) is a scientific tool that is used to create detailed three-dimensional images of the surfaces of materials, down to the nanometer scale — that’s roughly on the scale of individual molecules.

The basic AFM design consists of a tiny cantilever, or arm, that has a sharp tip attached to one end. As the apparatus scans back and forth across the surface of a sample, or the sample moves under it, the interactive forces between the sample and the tip cause the cantilever to move up and down as the tip follows the contours of the surface. Those movements are then translated into an image.

“An AFM is a microscope that ‘sees’ a surface kind of the way a visually impaired person might, by touching. You can get a resolution that is well beyond what an optical microscope can achieve,” said Moheimani, who holds the James Von Ehr Distinguished Chair in Science and Technology in the Erik Jonsson School of Engineering and Computer Science. “It can capture features that are very, very small.”

The UT Dallas team created its prototype on-chip AFM using a microelectromechanical systems (MEMS) approach.

“A classic example of MEMS technology are the accelerometers and gyroscopes found in smartphones,” said Dr. Anthony Fowler, a research scientist in Moheimani’s Laboratory for Dynamics and Control of Nanosystems and one of the article’s co-authors. “These used to be big, expensive, mechanical devices, but using MEMS technology, accelerometers have shrunk down onto a single chip, which can be manufactured for just a few dollars apiece.”

The MEMS-based AFM is about 1 square centimeter in size, or a little smaller than a dime. It is attached to a small printed circuit board, about half the size of a credit card, which contains circuitry, sensors and other miniaturized components that control the movement and other aspects of the device.

Conventional AFMs operate in various modes. Some map out a sample’s features by maintaining a constant force as the probe tip drags across the surface, while others do so by maintaining a constant distance between the two.

“The problem with using a constant height approach is that the tip is applying varying forces on a sample all the time, which can damage a sample that is very soft,” Fowler said. “Or, if you are scanning a very hard surface, you could wear down the tip,”

The MEMS-based AFM operates in “tapping mode,” which means the cantilever and tip oscillate up and down perpendicular to the sample, and the tip alternately contacts then lifts off from the surface. As the probe moves back and forth across a sample material, a feedback loop maintains the height of that oscillation, ultimately creating an image.

“In tapping mode, as the oscillating cantilever moves across the surface topography, the amplitude of the oscillation wants to change as it interacts with sample,” said Dr. Mohammad Maroufi, a research associate in mechanical engineering and co-author of the paper. “This device creates an image by maintaining the amplitude of oscillation.”

Because conventional AFMs require lasers and other large components to operate, their use can be limited. They’re also expensive.

“An educational version can cost about $30,000 or $40,000, and a laboratory-level AFM can run $500,000 or more,” Moheimani said. “Our MEMS approach to AFM design has the potential to significantly reduce the complexity and cost of the instrument.

“One of the attractive aspects about MEMS is that you can mass produce them, building hundreds or thousands of them in one shot, so the price of each chip would only be a few dollars. As a result, you might be able to offer the whole miniature AFM system for a few thousand dollars.”

A reduced size and price tag also could expand the AFMs’ utility beyond current scientific applications.

“For example, the semiconductor industry might benefit from these small devices, in particular companies that manufacture the silicon wafers from which computer chips are made,” Moheimani said. “With our technology, you might have an array of AFMs to characterize the wafer’s surface to find micro-faults before the product is shipped out.”

The lab prototype is a first-generation device, Moheimani said, and the group is already working on ways to improve and streamline the fabrication of the device.

“This is one of those technologies where, as they say, ‘If you build it, they will come.’ We anticipate finding many applications as the technology matures,” Moheimani said.

In addition to the UT Dallas researchers, Michael Ruppert, a visiting graduate student from the University of Newcastle in Australia, was a co-author of the journal article. Moheimani was Ruppert’s doctoral advisor.

So, an AFM that could cost as much as $500,000 for a laboratory has been shrunk to this size and become far less expensive,

A MEMS-based atomic force microscope developed by engineers at UT Dallas is about 1 square centimeter in size (top center). Here it is attached to a small printed circuit board that contains circuitry, sensors and other miniaturized components that control the movement and other aspects of the device. Courtesy: University of Texas at Dallas

Of course, there’s still more work to be done as you’ll note when reading Dexter’s Feb. 21, 2017 posting where he features answers to questions he directed to the researchers.

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

On-Chip Dynamic Mode Atomic Force Microscopy: A Silicon-on-Insulator MEMS Approach by  Michael G. Ruppert, Anthony G. Fowler, Mohammad Maroufi, S. O. Reza Moheimani. IEEE Journal of Microelectromechanical Systems Volume: 26 Issue: 1  Feb. 2017 DOI: 10.1109/JMEMS.2016.2628890 Date of Publication: 06 December 2016

This paper is behind a paywall.

Keeping up with science is impossible: ruminations on a nanotechnology talk

I think it’s time to give this suggestion again. Always hold a little doubt about the science information you read and hear. Everybody makes mistakes.

Here’s an example of what can happen. George Tulevski who gave a talk about nanotechnology in Nov. 2016 for TED@IBM is an accomplished scientist who appears to have made an error during his TED talk. From Tulevski’s The Next Step in Nanotechnology talk transcript page,

When I was a graduate student, it was one of the most exciting times to be working in nanotechnology. There were scientific breakthroughs happening all the time. The conferences were buzzing, there was tons of money pouring in from funding agencies. And the reason is when objects get really small, they’re governed by a different set of physics that govern ordinary objects, like the ones we interact with. We call this physics quantum mechanics. [emphases mine] And what it tells you is that you can precisely tune their behavior just by making seemingly small changes to them, like adding or removing a handful of atoms, or twisting the material. It’s like this ultimate toolkit. You really felt empowered; you felt like you could make anything.

In September 2016, scientists at Cambridge University (UK) announced they had concrete proof that the physics governing materials at the nanoscale is unique, i.e., it does not follow the rules of either classical or quantum physics. From my Oct. 27, 2016 posting,

A Sept. 29, 2016 University of Cambridge press release, which originated the news item, hones in on the peculiarities of the nanoscale,

In the middle, on the order of around 10–100,000 molecules, something different is going on. Because it’s such a tiny scale, the particles have a really big surface-area-to-volume ratio. This means the energetics of what goes on at the surface become very important, much as they do on the atomic scale, where quantum mechanics is often applied.

Classical thermodynamics breaks down. But because there are so many particles, and there are many interactions between them, the quantum model doesn’t quite work either.

It is very, very easy to miss new developments no matter how tirelessly you scan for information.

Tulevski is a good, interesting, and informed speaker but I do have one other hesitation regarding his talk. He seems to think that over the last 15 years there should have been more practical applications arising from the field of nanotechnology. There are two aspects here. First, he seems to be dating the ‘nanotechnology’ effort from the beginning of the US National Nanotechnology Initiative and there are many scientists who would object to that as the starting point. Second, 15 or even 30 or more years is a brief period of time especially when you are investigating that which hasn’t been investigated before. For example, you might want to check out the book, “Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life” (published 1985) is a book by Steven Shapin and Simon Schaffer (Wikipedia entry for the book). The amount of time (years) spent on how to make just the glue which held the various experimental apparatuses together was a revelation to me. Of  course, it makes perfect sense that if you’re trying something new, you’re going to have figure out everything.

By the way, I include my blog as one of the sources of information that can be faulty despite efforts to make corrections and to keep up with the latest. Even the scientists at Cambridge University can run into some problems as I noted in my Jan. 28, 2016 posting.

Getting back to Tulevsk, herei’s a link to his lively, informative talk :

ETA Jan. 24, 2017: For some insight into how uncertain, tortuous, and expensive commercializing technology can be read Dexter Johnson’s Jan. 23, 2017 posting on his Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website). Here’s an excerpt (Note: Links have been removed),

The brief description of this odyssey includes US $78 million in financing over 15 years and $50 million in revenues over that period through licensing of its technology and patents. That revenue includes a back-against-the-wall sell-off of a key business unit to Lockheed Martin in 2008.  Another key moment occured back in 2012 when Belgian-based nanoelectronics powerhouse Imec took on the job of further developing Nantero’s carbon-nanotube-based memory back in 2012. Despite the money and support from major electronics players, the big commercial breakout of their NRAM technology seemed ever less likely to happen with the passage of time.

Artificial intelligence and industrial applications

This is take on artificial intelligence that I haven’t encountered before. Sean Captain’s Nov. 15, 2016 article for Fast Company profiles industry giant GE (General Electric) and its foray into that world (Note: Links have been removed),

When you hear the term “artificial intelligence,” you may think of tech giants Amazon, Google, IBM, Microsoft, or Facebook. Industrial powerhouse General Electric is now aiming to be included on that short list. It may not have a chipper digital assistant like Cortana or Alexa. It won’t sort through selfies, but it will look through X-rays. It won’t recommend movies, but it will suggest how to care for a diesel locomotive. Today, GE announced a pair of acquisitions and new services that will bring machine learning AI to the kinds of products it’s known for, including planes, trains, X-ray machines, and power plants.

The effort started in 2015 when GE announced Predix Cloud—an online platform to network and collect data from sensors on industrial machinery such as gas turbines or windmills. At the time, GE touted the benefits of using machine learning to find patterns in sensor data that could lead to energy savings or preventative maintenance before a breakdown. Predix Cloud opened up to customers in February [2016?], but GE is still building up the AI capabilities to fulfill the promise. “We were using machine learning, but I would call it in a custom way,” says Bill Ruh, GE’s chief digital officer and CEO of its GE Digital business (GE calls its division heads CEOs). “And we hadn’t gotten to a general-purpose framework in machine learning.”

Today [Nov. 15, 2016] GE revealed the purchase of two AI companies that Ruh says will get them there. Bit Stew Systems, founded in 2005, was already doing much of what Predix Cloud promises—collecting and analyzing sensor data from power utilities, oil and gas companies, aviation, and factories. (GE Ventures has funded the company.) Customers include BC Hydro, Pacific Gas & Electric, and Scottish & Southern Energy.

The second purchase, Wise.io is a less obvious purchase. Founded by astrophysics and AI experts using machine learning to study the heavens, the company reapplied the tech to streamlining a company’s customer support systems, picking up clients like Pinterest, Twilio, and TaskRabbit. GE believes the technology will transfer yet again, to managing industrial machines. “I think by the middle of next year we will have a full machine learning stack,” says Ruh.

Though young, Predix is growing fast, with 270 partner companies using the platform, according to GE, which expects revenue on software and services to grow over 25% this year, to more than $7 billion. Ruh calls Predix a “significant part” of that extra money. And he’s ready to brag, taking a jab at IBM Watson for being a “general-purpose” machine-learning provider without the deep knowledge of the industries it serves. “We have domain algorithms, on machine learning, that’ll know what a power plant is and all the depth of that, that a general-purpose machine learning will never really understand,” he says.

One especially dull-sounding new Predix service—Predictive Corrosion Management—touches on a very hot political issue: giant oil and gas pipeline projects. Over 400 people have been arrested in months of protests against the Dakota Access Pipeline, which would carry crude oil from North Dakota to Illinois. The issue is very complicated, but one concern of protestors is that a pipeline rupture would contaminate drinking water for the Standing Rock Sioux reservation.

“I think absolutely this is aimed at that problem. If you look at why pipelines spill, it’s corrosion,” says Ruh. “We believe that 10 years from now, we can detect a leak before it occurs and fix it before you see it happen.” Given how political battles over pipelines drag on, 10 years might not be so long to wait.

I recommend reading the article in its entirety if you have the time. And, for those of us in British Columbia, Canada, it was a surprise to see BC Hydro on the list of customers for one of GE’s new acquisitions. As well, that business about the pipelines hits home hard given the current debates (Enbridge Northern Gateway Pipelines) here. *ETA Dec. 27, 2016: This was originally edited just prior to publication to include information about the announcement by the Trudeau cabinet approving two pipelines for TransMountain  and Enbridge respectively while rejecting the Northern Gateway pipeline (Canadian Broadcasting Corporation [CBC] online news Nov. 29, 2016).  I trust this second edit will stick.*

It seems GE is splashing out in a big way. There’s a second piece on Fast Company, a Nov. 16, 2016 article by Sean Captain (again) this time featuring a chat between an engineer and a robotic power plant,

We are entering the era of talking machines—and it’s about more than just asking Amazon’s Alexa to turn down the music. General Electric has built a digital assistant into its cloud service for managing power plants, jet engines, locomotives, and the other heavy equipment it builds. Over the internet, an engineer can ask a machine—even one hundreds of miles away—how it’s doing and what it needs. …

Voice controls are built on top of GE’s Digital Twin program, which uses sensor readings from machinery to create virtual models in cyberspace. “That model is constantly getting a stream of data, both operational and environmental,” says Colin Parris, VP at GE Software Research. “So it’s adapting itself to that type of data.” The machines live virtual lives online, allowing engineers to see how efficiently each is running and if they are wearing down.

GE partnered with Microsoft on the interface, using the Bing Speech API (the same tech powering the Cortana digital assistant), with special training on key terms like “rotor.” The twin had little trouble understanding the Mandarin Chinese accent of Bo Yu, one of the researchers who built the system; nor did it stumble on Parris’s Trinidad accent. Digital Twin will also work with Microsoft’s HoloLens mixed reality goggles, allowing someone to step into a 3D image of the equipment.

I can’t help wondering if there are some jobs that were eliminated with this technology.

The State of Science and Technology (S&T) and Industrial Research and Development (IR&D) in Canada

Earlier this year I featured (in a July 1, 2016 posting) the announcement of a third assessment of science and technology in Canada by the Council of Canadian Academies. At the time I speculated as to the size of the ‘expert panel’ making the assessment as they had rolled a second assessment (Industrial Research and Development) into this one on the state of science and technology. I now have my answer thanks to an Oct. 17, 2016 Council of Canadian Academies news release announcing the chairperson (received via email; Note: Links have been removed and emphases added for greater readability),

The Council of Canadian Academies (CCA) is pleased to announce Dr. Max Blouw, President and Vice-Chancellor of Wilfrid Laurier University, as Chair of the newly appointed Expert Panel on the State of Science and Technology (S&T) and Industrial Research and Development (IR&D) in Canada.

“Dr. Blouw is a widely respected leader with a strong background in research and academia,” said Eric M. Meslin, PhD, FCAHS, President and CEO of the CCA. “I am delighted he has agreed to serve as Chair for an assessment that will contribute to the current policy discussion in Canada.”

As Chair of the Expert Panel, Dr. Blouw will work with the multidisciplinary, multi-sectoral Expert Panel to address the following assessment question, referred to the CCA by Innovation, Science and Economic Development Canada (ISED):

What is the current state of science and technology and industrial research and development in Canada?

Dr. Blouw will lead the CCA Expert Panel to assess the available evidence and deliver its final report by late 2017. Members of the panel include experts from different fields of academic research, R&D, innovation, and research administration. The depth of the Panel’s experience and expertise, paired with the CCA’s rigorous assessment methodology, will ensure the most authoritative, credible, and independent response to the question.

“I am very pleased to accept the position of Chair for this assessment and I consider myself privileged to be working with such an eminent group of experts,” said Dr. Blouw. “The CCA’s previous reports on S&T and IR&D provided crucial insights into Canada’s strengths and weaknesses in these areas. I look forward to contributing to this important set of reports with new evidence and trends.”

Dr. Blouw was Vice-President Research, Associate Vice-President Research, and Professor of Biology, at the University of Northern British Columbia, before joining Wilfrid Laurier as President. Dr. Blouw served two terms as the chair of the university advisory group to Industry Canada and was a member of the adjudication panel for the Ontario Premier’s Discovery Awards, which recognize the province’s finest senior researchers. He recently chaired the International Review Committee of the NSERC Discovery Grants Program.

For a complete list of Expert Panel members, their biographies, and details on the assessment, please visit the assessment page. The CCA’s Member Academies – the Royal Society of Canada, the Canadian Academy of Engineering, and the Canadian Academy of Health Sciences – are a key source of membership for expert panels. Many experts are also Fellows of the Academies.

The Expert Panel on the State of S&T and IR&D
Max Blouw, (Chair) President and Vice-Chancellor of Wilfrid Laurier University
Luis Barreto, President, Dr. Luis Barreto & Associates and Special Advisor, NEOMED-LABS
Catherine Beaudry, Professor, Department of Mathematical and Industrial Engineering, Polytechnique Montréal
Donald Brooks, FCAHS, Professor, Pathology and Laboratory Medicine, and Chemistry, University of British Columbia
Madeleine Jean, General Manager, Prompt
Philip Jessop, FRSC, Professor, Inorganic Chemistry and Canada Research Chair in Green Chemistry, Department of Chemistry, Queen’s University; Technical Director, GreenCentre Canada
Claude Lajeunesse, FCAE, Corporate Director and Interim Chair of the Board of Directors, Atomic Energy of Canada Ltd.
Steve Liang, Associate Professor, Geomatics Engineering, University of Calgary; Director, GeoSensorWeb Laboratory; CEO, SensorUp Inc.
Robert Luke, Vice-President, Research and Innovation, OCAD University
Douglas Peers, Professor, Dean of Arts, Department of History, University of Waterloo
John M. Thompson, O.C., FCAE, Retired Executive Vice-Chairman, IBM Corporation
Anne Whitelaw, Associate Dean Research, Faculty of Fine Arts and Associate Professor, Department of Art History, Concordia University
David A. Wolfe, Professor, Political Science and Co-Director, Innovation Policy Lab, Munk School of Global Affairs, University of Toronto

You can find more information about the expert panel here and about this assessment and its predecesors here.

A few observations, given the size of the task this panel is lean. As well, there are three women in a group of 13 (less than 25% representation) in 2016? It’s Ontario and Québec-dominant; only BC and Alberta rate a representative on the panel. I hope they will find ways to better balance this panel and communicate that ‘balanced story’ to the rest of us. On the plus side, the panel has representatives from the humanities, arts, and industry in addition to the expected representatives from the sciences.

Westworld: a US television programme investigating AI (artificial intelligence) and consciousness

The US television network, Home Box Office (HBO) is getting ready to première Westworld, a new series based on a movie first released in 1973. Here’s more about the movie from its Wikipedia entry (Note: Links have been removed),

Westworld is a 1973 science fiction Western thriller film written and directed by novelist Michael Crichton and produced by Paul Lazarus III about amusement park robots that malfunction and begin killing visitors. It stars Yul Brynner as an android in a futuristic Western-themed amusement park, and Richard Benjamin and James Brolin as guests of the park.

Westworld was the first theatrical feature directed by Michael Crichton.[3] It was also the first feature film to use digital image processing, to pixellate photography to simulate an android point of view.[4] The film was nominated for Hugo, Nebula and Saturn awards, and was followed by a sequel film, Futureworld, and a short-lived television series, Beyond Westworld. In August 2013, HBO announced plans for a television series based on the original film.

The latest version is due to start broadcasting in the US on Sunday, Oct. 2, 2016 and as part of the publicity effort the producers are profiled by Sean Captain for Fast Company in a Sept. 30, 2016 article,

As Game of Thrones marches into its final seasons, HBO is debuting this Sunday what it hopes—and is betting millions of dollars on—will be its new blockbuster series: Westworld, a thorough reimagining of Michael Crichton’s 1973 cult classic film about a Western theme park populated by lifelike robot hosts. A philosophical prelude to Jurassic Park, Crichton’s Westworld is a cautionary tale about technology gone very wrong: the classic tale of robots that rise up and kill the humans. HBO’s new series, starring Evan Rachel Wood, Anthony Hopkins, and Ed Harris, is subtler and also darker: The humans are the scary ones.

“We subverted the entire premise of Westworld in that our sympathies are meant to be with the robots, the hosts,” says series co-creator Lisa Joy. She’s sitting on a couch in her Burbank office next to her partner in life and on the show—writer, director, producer, and husband Jonathan Nolan—who goes by Jonah. …

Their Westworld, which runs in the revered Sunday-night 9 p.m. time slot, combines present-day production values and futuristic technological visions—thoroughly revamping Crichton’s story with hybrid mechanical-biological robots [emphasis mine] fumbling along the blurry line between simulated and actual consciousness.

Captain never does explain the “hybrid mechanical-biological robots.” For example, do they have human skin or other organs grown for use in a robot? In other words, how are they hybrid?

That nitpick aside, the article provides some interesting nuggets of information and insight into the themes and ideas 2016 Westworld’s creators are exploring (Note: A link has been removed),

… Based on the four episodes I previewed (which get progressively more interesting), Westworld does a good job with the trope—which focused especially on the awakening of Dolores, an old soul of a robot played by Evan Rachel Wood. Dolores is also the catchall Spanish word for suffering, pain, grief, and other displeasures. “There are no coincidences in Westworld,” says Joy, noting that the name is also a play on Dolly, the first cloned mammal.

The show operates on a deeper, though hard-to-define level, that runs beneath the shoot-em and screw-em frontier adventure and robotic enlightenment narratives. It’s an allegory of how even today’s artificial intelligence is already taking over, by cataloging and monetizing our lives and identities. “Google and Facebook, their business is reading your mind in order to advertise shit to you,” says Jonah Nolan. …

“Exist free of rules, laws or judgment. No impulse is taboo,” reads a spoof home page for the resort that HBO launched a few weeks ago. That’s lived to the fullest by the park’s utterly sadistic loyal guest, played by Ed Harris and known only as the Man in Black.

The article also features some quotes from scientists on the topic of artificial intelligence (Note: Links have been removed),

“In some sense, being human, but less than human, it’s a good thing,” says Jon Gratch, professor of computer science and psychology at the University of Southern California [USC]. Gratch directs research at the university’s Institute for Creative Technologies on “virtual humans,” AI-driven onscreen avatars used in military-funded training programs. One of the projects, SimSensei, features an avatar of a sympathetic female therapist, Ellie. It uses AI and sensors to interpret facial expressions, posture, tension in the voice, and word choices by users in order to direct a conversation with them.

“One of the things that we’ve found is that people don’t feel like they’re being judged by this character,” says Gratch. In work with a National Guard unit, Ellie elicited more honest responses about their psychological stresses than a web form did, he says. Other data show that people are more honest when they know the avatar is controlled by an AI versus being told that it was controlled remotely by a human mental health clinician.

“If you build it like a human, and it can interact like a human. That solves a lot of the human-computer or human-robot interaction issues,” says professor Paul Rosenbloom, also with USC’s Institute for Creative Technologies. He works on artificial general intelligence, or AGI—the effort to create a human-like or human level of intellect.

Rosenbloom is building an AGI platform called Sigma that models human cognition, including emotions. These could make a more effective robotic tutor, for instance, “There are times you want the person to know you are unhappy with them, times you want them to know that you think they’re doing great,” he says, where “you” is the AI programmer. “And there’s an emotional component as well as the content.”

Achieving full AGI could take a long time, says Rosenbloom, perhaps a century. Bernie Meyerson, IBM’s chief innovation officer, is also circumspect in predicting if or when Watson could evolve into something like HAL or Her. “Boy, we are so far from that reality, or even that possibility, that it becomes ludicrous trying to get hung up there, when we’re trying to get something to reasonably deal with fact-based data,” he says.

Gratch, Rosenbloom, and Meyerson are talking about screen-based entities and concepts of consciousness and emotions. Then, there’s a scientist who’s talking about the difficulties with robots,

… Ken Goldberg, an artist and professor of engineering at UC [University of California] Berkeley, calls the notion of cyborg robots in Westworld “a pretty common trope in science fiction.” (Joy will take up the theme again, as the screenwriter for a new Battlestar Galactica movie.) Goldberg’s lab is struggling just to build and program a robotic hand that can reliably pick things up. But a sympathetic, somewhat believable Dolores in a virtual setting is not so farfetched.

Captain delves further into a thorny issue,

“Can simulations, at some point, become the real thing?” asks Patrick Lin, director of the Ethics + Emerging Sciences Group at California Polytechnic State University. “If we perfectly simulate a rainstorm on a computer, it’s still not a rainstorm. We won’t get wet. But is the mind or consciousness different? The jury is still out.”

While artificial consciousness is still in the dreamy phase, today’s level of AI is serious business. “What was sort of a highfalutin philosophical question a few years ago has become an urgent industrial need,” says Jonah Nolan. It’s not clear yet how the Delos management intends, beyond entrance fees, to monetize Westworld, although you get a hint when Ford tells Theresa Cullen “We know everything about our guests, don’t we? As we know everything about our employees.”

AI has a clear moneymaking model in this world, according to Nolan. “Facebook is monetizing your social graph, and Google is advertising to you.” Both companies (and others) are investing in AI to better understand users and find ways to make money off this knowledge. …

As my colleague David Bruggeman has often noted on his Pasco Phronesis blog, there’s a lot of science on television.

For anyone who’s interested in artificial intelligence and the effects it may have on urban life, see my Sept. 27, 2016 posting featuring the ‘One Hundred Year Study on Artificial Intelligence (AI100)’, hosted by Stanford University.

Points to anyone who recognized Jonah (Jonathan) Nolan as the producer for the US television series, Person of Interest, a programme based on the concept of a supercomputer with intelligence and personality and the ability to continuously monitor the population 24/7.

Memristive capabilities from IBM (International Business Machines)

Does memristive mean it’s like a memristor but it’s not one? In any event, IBM is claiming some new ground in the world of cognitive computing (also known as, neuromorphic computing).

An artistic rendering of a population of stochastic phase-change neurons which appears on the cover of Nature Nanotechnology, 3 August 2016. (Credit: IBM Research)

An artistic rendering of a population of stochastic phase-change neurons which appears on the cover of Nature Nanotechnology, 3 August 2016. (Credit: IBM Research)

From an Aug. 3, 2016 news item on phys.org,

IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing.

Inspired by the way the biological brain functions, scientists have theorized for decades that it should be possible to imitate the versatile computational capabilities of large populations of neurons. However, doing so at densities and with a power budget that would be comparable to those seen in biology has been a significant challenge, until now.

“We have been researching phase-change materials for memory applications for over a decade, and our progress in the past 24 months has been remarkable,” said IBM Fellow Evangelos Eleftheriou. “In this period, we have discovered and published new memory techniques, including projected memory, stored 3 bits per cell in phase-change memory for the first time, and now are demonstrating the powerful capabilities of phase-change-based artificial neurons, which can perform various computational primitives such as data-correlation detection and unsupervised learning at high speeds using very little energy.”

An Aug. 3, 2016 IBM news release, which originated the news item, expands on the theme,

The artificial neurons designed by IBM scientists in Zurich consist of phase-change materials, including germanium antimony telluride, which exhibit two stable states, an amorphous one (without a clearly defined structure) and a crystalline one (with structure). These materials are the basis of re-writable Blu-ray discs. However, the artificial neurons do not store digital information; they are analog, just like the synapses and neurons in our biological brain.

In the published demonstration, the team applied a series of electrical pulses to the artificial neurons, which resulted in the progressive crystallization of the phase-change material, ultimately causing the neuron to fire. In neuroscience, this function is known as the integrate-and-fire property of biological neurons. This is the foundation for event-based computation and, in principle, is similar to how our brain triggers a response when we touch something hot.

Exploiting this integrate-and-fire property, even a single neuron can be used to detect patterns and discover correlations in real-time streams of event-based data. For example, in the Internet of Things, sensors can collect and analyze volumes of weather data collected at the edge for faster forecasts. The artificial neurons could be used to detect patterns in financial transactions to find discrepancies or use data from social media to discover new cultural trends in real time. Large populations of these high-speed, low-energy nano-scale neurons could also be used in neuromorphic coprocessors with co-located memory and processing units.

IBM scientists have organized hundreds of artificial neurons into populations and used them to represent fast and complex signals. Moreover, the artificial neurons have been shown to sustain billions of switching cycles, which would correspond to multiple years of operation at an update frequency of 100 Hz. The energy required for each neuron update was less than five picojoule and the average power less than 120 microwatts — for comparison, 60 million microwatts power a 60 watt lightbulb.

“Populations of stochastic phase-change neurons, combined with other nanoscale computational elements such as artificial synapses, could be a key enabler for the creation of a new generation of extremely dense neuromorphic computing systems,” said Tomas Tuma, a co-author of the paper.

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

Stochastic phase-change neurons by Tomas Tuma, Angeliki Pantazi, Manuel Le Gallo, Abu Sebastian, & Evangelos Eleftheriou. Nature Nanotechnology  11, 693–699 (2016) doi:10.1038/nnano.2016.70 Published online 16 May 2016

I gather IBM waited for the print version of the paper before publicizing the work. The online version is behind paper. For those who can’t get past the paywall, there is a video offering a demonstration of sorts,

For the interested, the US government recently issued a white paper on neuromorphic computing (my Aug. 22, 2016 post).

This team has published a paper that has a similar theme to the one in Nature Nanotechnology,

All-memristive neuromorphic computing with level-tuned neurons by Angeliki Pantazi, Stanisław Woźniak, Tomas Tuma, and Evangelos Eleftheriou. Nanotechnology, Volume 27, Number 35  DOI: 10.1088/0957-4484/27/35/355205 Published 26 July 2016

© 2016 IOP Publishing Ltd

This paper is open access.

An Aug. 18, 2016 news piece by Lisa Zyga for phys.org provides a summary of the research in the July 2016 published paper.