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Quantum computing and more at SXSW (South by Southwest) 2018

It’s that time of year again. The entertainment conference such as South by South West (SXSW) is being held from March 9-18, 2018. The science portion of the conference can be found in the Intelligent Future sessions, from the description,

AI and new technologies embody the realm of possibilities where intelligence empowers and enables technology while sparking legitimate concerns about its uses. Highlighted Intelligent Future sessions include New Mobility and the Future of Our Cities, Mental Work: Moving Beyond Our Carbon Based Minds, Can We Create Consciousness in a Machine?, and more.

Intelligent Future Track sessions are held March 9-15 at the Fairmont.

Last year I focused on the conference sessions on robots, Hiroshi Ishiguro’s work, and artificial intelligence in a  March 27, 2017 posting. This year I’m featuring one of the conference’s quantum computing session, from a March 9, 2018 University of Texas at Austin news release  (also on EurekAlert),

Imagine a new kind of computer that can quickly solve problems that would stump even the world’s most powerful supercomputers. Quantum computers are fundamentally different. They can store information as not only just ones and zeros, but in all the shades of gray in-between. Several companies and government agencies are investing billions of dollars in the field of quantum information. But what will quantum computers be used for?

South by Southwest 2018 hosts a panel on March 10th [2018] called Quantum Computing: Science Fiction to Science Fact. Experts on quantum computing make up the panel, including Jerry Chow of IBM; Bo Ewald of D-Wave Systems; Andrew Fursman of 1QBit; and Antia Lamas-Linares of the Texas Advanced Computing Center at UT Austin.

Antia Lamas-Linares is a Research Associate in the High Performance Computing group at TACC. Her background is as an experimentalist with quantum computing systems, including work done with them at the Centre for Quantum Technologies in Singapore. She joins podcast host Jorge Salazar to talk about her South by Southwest panel and about some of her latest research on quantum information.

Lamas-Linares co-authored a study (doi: 10.1117/12.2290561) in the Proceedings of the SPIE, The International Society for Optical Engineering, that published in February of 2018. The study, “Secure Quantum Clock Synchronization,” proposed a protocol to verify and secure time synchronization of distant atomic clocks, such as those used for GPS signals in cell phone towers and other places. “It’s important work,” explained Lamas-Linares, “because people are worried about malicious parties messing with the channels of GPS. What James Troupe (Applied Research Laboratories, UT Austin) and I looked at was whether we can use techniques from quantum cryptography and quantum information to make something that is inherently unspoofable.”

Antia Lamas-Linares: The most important thing is that quantum technologies is a really exciting field. And it’s exciting in a fundamental sense. We don’t quite know what we’re going to get out of it. We know a few things, and that’s good enough to drive research. But the things we don’t know are much broader than the things we know, and it’s going to be really interesting. Keep your eyes open for this.

Quantum Computing: Science Fiction to Science Fact, March 10, 2018 | 11:00AM – 12:00PM, Fairmont Manchester EFG, SXSW 2018, Austin, TX.

If you look up the session, you will find,

Quantum Computing: Science Fiction to Science Fact

Quantum Computing: Science Fiction to Science Fact


Bo Ewald

D-Wave Systems

Antia Lamas-Linares

Texas Advanced Computing Center at University of Texas

Startups and established players have sold 2000 Qubit systems, made freely available cloud access to quantum computer processors, and created large scale open source initiatives, all taking quantum computing from science fiction to science fact. Government labs and others like IBM, Microsoft, Google are developing software for quantum computers. What problems will be solved with this quantum leap in computing power that cannot be solved today with the world’s most powerful supercomputers?

[Programming descriptions are generated by participants and do not necessarily reflect the opinions of SXSW.]

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Primary Entry: Platinum Badge, Interactive Badge

Secondary Entry: Music Badge, Film Badge

Format: Panel

Event Type: Session

Track: Intelligent Future

Level: Intermediate


I wonder what ‘level’ means? I was not able to find an answer (quickly).

It’s was a bit surprising to find someone from D-Wave Systems (a Vancouver-based quantum computing based enterprise) at an entertainment conference. Still, it shouldn’t have been. Two other examples immediately come to mind, the TED (technology, entertainment, and design) conferences have been melding technology, if not science, with creative activities of all kinds for many years (TED 2018: The Age of Amazement, April 10 -14, 2018 in Vancouver [Canada]) and Beakerhead (2018 dates: Sept. 19 – 23) has been melding art, science, and engineering in a festival held in Calgary (Canada) since 2013. One comment about TED, it was held for several years in California (1984, 1990 – 2013) and moved to Vancouver in 2014.

For anyone wanting to browse the 2018 SxSW Intelligent Future sessions online, go here. or wanting to hear Antia Lamas-Linares talk about quantum computing, there’s the interview with Jorge Salazar (mentioned in the news release),

Machine learning, neural networks, and knitting

In a recent (Tuesday, March 6, 2018) live stream ‘conversation’ (‘Science in Canada; Investing in Canadian Innovation’ now published on YouTube) between Canadian Prime Minister, Justin Trudeau, and US science communicator, Bill Nye, at the University of Ottawa, they discussed, amongst many other topics, what AI (artificial intelligence) can and can’t do. They seemed to agree that AI can’t be creative, i.e., write poetry, create works of art, make jokes, etc. A conclusion which is both (in my opinion) true and not true.

There are times when I think the joke may be on us (humans). Take for example this March 6, 2018 story by Alexis Madrigal for The Atlantic magazine (Note: Links have been removed),

SkyKnit: How an AI Took Over an Adult Knitting Community

Ribald knitters teamed up with a neural-network creator to generate new types of tentacled, cozy shapes.

Janelle Shane is a humorist [Note: She describes herself as a “Research Scientist in optics. Plays with neural networks. …” in her Twitter bio.] who creates and mines her material from neural networks, the form of machine learning that has come to dominate the field of artificial intelligence over the last half-decade.

Perhaps you’ve seen the candy-heart slogans she generated for Valentine’s Day: DEAR ME, MY MY, LOVE BOT, CUTE KISS, MY BEAR, and LOVE BUN.

Or her new paint-color names: Parp Green, Shy Bather, Farty Red, and Bull Cream.

Or her neural-net-generated Halloween costumes: Punk Tree, Disco Monster, Spartan Gandalf, Starfleet Shark, and A Masked Box.

Her latest project, still ongoing, pushes the joke into a new, physical realm. Prodded by a knitter on the knitting forum Ravelry, Shane trained a type of neural network on a series of over 500 sets of knitting instructions. Then, she generated new instructions, which members of the Ravelry community have actually attempted to knit.

“The knitting project has been a particularly fun one so far just because it ended up being a dialogue between this computer program and these knitters that went over my head in a lot of ways,” Shane told me. “The computer would spit out a whole bunch of instructions that I couldn’t read and the knitters would say, this is the funniest thing I’ve ever read.”

It appears that the project evolved,

The human-machine collaboration created configurations of yarn that you probably wouldn’t give to your in-laws for Christmas, but they were interesting. The user citikas was the first to post a try at one of the earliest patterns, “reverss shawl.” It was strange, but it did have some charisma.

Shane nicknamed the whole effort “Project Hilarious Disaster.” The community called it SkyKnit.

I’m not sure what’s meant by “community” as mentioned in the previous excerpt. Are we talking about humans only, AI only, or both humans and AI?

Here’s some of what underlies Skyknit (Note: Links have been removed),

The different networks all attempt to model the data they’ve been fed by tuning a vast, funky flowchart. After you’ve created a statistical model that describes your real data, you can also roll the dice and generate new, never-before-seen data of the same kind.

How this works—like, the math behind it—is very hard to visualize because values inside the model can have hundreds of dimensions and we are humble three-dimensional creatures moving through time. But as the neural-network enthusiast Robin Sloan puts it, “So what? It turns out imaginary spaces are useful even if you can’t, in fact, imagine them.”

Out of that ferment, a new kind of art has emerged. Its practitioners use neural networks not to attain practical results, but to see what’s lurking in the these vast, opaque systems. What did the machines learn about the world as they attempted to understand the data they’d been fed? Famously, Google released DeepDream, which produced trippy visualizations that also demonstrated how that type of neural network processed the textures and objects in its source imagery.

Madrigal’s article is well worth reading if you have the time. You can also supplement Madrigal’s piece with an August 9, 2017 article about Janelle Shane’s algorithmic experiments by Jacob Brogan for slate.com.

I found some SkyKnit examples on Ravelry including this one from the Dollybird Workshop,

© Chatelaine

SkyKnit fancy addite rifopshent
by SkyKnit
Published in
Dollybird Workshop
Stitch pattern
February 2018
Suggested yarn
Yarn weight
Fingering (14 wpi) ?
24 stitches and 30 rows = 4 inches
in stockinette stitch
Needle size
US 4 – 3.5 mm


This pattern is available as a free Ravelry download

SkyKnit is a type of machine learning algorithm called an artificial neural network. Its creator, Janelle Shane of AIweirdness.com, gave it 88,000 lines of knitting instructions from Stitch-Maps.com and Ravelry, and it taught itself how to make new patterns. Join the discussion!

SkyKnit seems to have created something that has paralell columns, and is reversible. Perhaps a scarf?

Test-knitting & image courtesy of Chatelaine

Patterns may include notes from testknitters; yarn, needles, and gauge are totally at your discretion.

About the designer
SkyKnit’s favorites include lace, tentacles, and totally not the elimination of the human race.
For more information, see: http://aiweirdness.com/

Shane’s website, aiweirdness.com, is where she posts musings such as this (from a March 2, [?] 2018 posting), Note: A link has been removed,

If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies – including Google, Microsoft, IBM, and Facebook – have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.


Microsoft Azure’s computer vision API [application programming interface] added the above caption and tags. But there are no sheep in the image of above. None. I zoomed all the way in and inspected every speck.


I have become quite interested in Shane’s self descriptions such as this one from the aiweirdness.com website,



I train neural networks, a type of machine learning algorithm, to write unintentional humor as they struggle to imitate human datasets. Well, I intend the humor. The neural networks are just doing their best to understand what’s going on. Currently located on the occupied land of the Arapahoe Nation.

As for the joke being on us, I can’t help remembering the Facebook bots that developed their own language (Facebotlish), and were featured in my June 30, 2017 posting, There’s a certain eerieness to it all, which seems an appropriate response in a year celebrating the 200th anniversary of Mary Shelley’s 1818 book, Frankenstein; or, the Modern Prometheus. I’m closing with a video clip from the 1931 movie,

Happy Weekend!

New nanomapping technology: CRISPR-CAS9 as a programmable nanoparticle

A November 21, 2017 news item on Nanowerk describes a rather extraordinary (to me, anyway) approach to using CRRISP ( Clustered Regularly Interspaced Short Palindromic Repeats)-CAS9 (Note: A link has been removed),

A team of scientists led by Virginia Commonwealth University physicist Jason Reed, Ph.D., have developed new nanomapping technology that could transform the way disease-causing genetic mutations are diagnosed and discovered. Described in a study published today [November 21, 2017] in the journal Nature Communications (“DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle”), this novel approach uses high-speed atomic force microscopy (AFM) combined with a CRISPR-based chemical barcoding technique to map DNA nearly as accurately as DNA sequencing while processing large sections of the genome at a much faster rate. What’s more–the technology can be powered by parts found in your run-of-the-mill DVD player.

A November 21, 2017 Virginia Commonwealth University news release by John Wallace, which originated the news item, provides more detail,

The human genome is made up of billions of DNA base pairs. Unraveled, it stretches to a length of nearly six feet long. When cells divide, they must make a copy of their DNA for the new cell. However, sometimes various sections of the DNA are copied incorrectly or pasted together at the wrong location, leading to genetic mutations that cause diseases such as cancer. DNA sequencing is so precise that it can analyze individual base pairs of DNA. But in order to analyze large sections of the genome to find genetic mutations, technicians must determine millions of tiny sequences and then piece them together with computer software. In contrast, biomedical imaging techniques such as fluorescence in situ hybridization, known as FISH, can only analyze DNA at a resolution of several hundred thousand base pairs.

Reed’s new high-speed AFM method can map DNA to a resolution of tens of base pairs while creating images up to a million base pairs in size. And it does it using a fraction of the amount of specimen required for DNA sequencing.

“DNA sequencing is a powerful tool, but it is still quite expensive and has several technological and functional limitations that make it difficult to map large areas of the genome efficiently and accurately,” said Reed, principal investigator on the study. Reed is a member of the Cancer Molecular Genetics research program at VCU Massey Cancer Center and an associate professor in the Department of Physics in the College of Humanities and Sciences.

“Our approach bridges the gap between DNA sequencing and other physical mapping techniques that lack resolution,” he said. “It can be used as a stand-alone method or it can complement DNA sequencing by reducing complexity and error when piecing together the small bits of genome analyzed during the sequencing process.”

IBM scientists made headlines in 1989 when they developed AFM technology and used a related technique to rearrange molecules at the atomic level to spell out “IBM.” AFM achieves this level of detail by using a microscopic stylus — similar to a needle on a record player — that barely makes contact with the surface of the material being studied. The interaction between the stylus and the molecules creates the image. However, traditional AFM is too slow for medical applications and so it is primarily used by engineers in materials science.

“Our device works in the same fashion as AFM but we move the sample past the stylus at a much greater velocity and use optical instruments to detect the interaction between the stylus and the molecules. We can achieve the same level of detail as traditional AFM but can process material more than a thousand times faster,” said Reed, whose team proved the technology can be mainstreamed by using optical equipment found in DVD players. “High-speed AFM is ideally suited for some medical applications as it can process materials quickly and provide hundreds of times more resolution than comparable imaging methods.”

Increasing the speed of AFM was just one hurdle Reed and his colleagues had to overcome. In order to actually identify genetic mutations in DNA, they had to develop a way to place markers or labels on the surface of the DNA molecules so they could recognize patterns and irregularities. An ingenious chemical barcoding solution was developed using a form of CRISPR technology.

CRISPR has made a lot of headlines recently in regard to gene editing. CRISPR is an enzyme that scientists have been able to “program” using targeting RNA in order to cut DNA at precise locations that the cell then repairs on its own. Reed’s team altered the chemical reaction conditions of the CRISPR enzyme so that it only sticks to the DNA and does not actually cut it.

“Because the CRISPR enzyme is a protein that’s physically bigger than the DNA molecule, it’s perfect for this barcoding application,” Reed said. “We were amazed to discover this method is nearly 90 percent efficient at bonding to the DNA molecules. And because it’s easy to see the CRISPR proteins, you can spot genetic mutations among the patterns in DNA.”

To demonstrate the technique’s effectiveness, the researchers mapped genetic translocations present in lymph node biopsies of lymphoma patients. Translocations occur when one section of the DNA gets copied and pasted to the wrong place in the genome. They are especially prevalent in blood cancers such as lymphoma but occur in other cancers as well.

While there are many potential uses for this technology, Reed and his team are focusing on medical applications. They are currently developing software based on existing algorithms that can analyze patterns in sections of DNA up to and over a million base pairs in size. Once completed, it would not be hard to imagine this shoebox-sized instrument in pathology labs assisting in the diagnosis and treatment of diseases linked to genetic mutations.

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

DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle by Andrey Mikheikin, Anita Olsen, Kevin Leslie, Freddie Russell-Pavier, Andrew Yacoot, Loren Picco, Oliver Payton, Amir Toor, Alden Chesney, James K. Gimzewski, Bud Mishra, & Jason Reed. Nature Communications 8, Article number: 1665 (2017) doi:10.1038/s41467-017-01891-9 Published online: 21 November 2017

This paper is open access.

Alberta adds a newish quantum nanotechnology research hub to the Canada’s quantum computing research scene

One of the winners in Canada’s 2017 federal budget announcement of the Pan-Canadian Artificial Intelligence Strategy was Edmonton, Alberta. It’s a fact which sometimes goes unnoticed while Canadians marvel at the wonderfulness found in Toronto and Montréal where it seems new initiatives and monies are being announced on a weekly basis (I exaggerate) for their AI (artificial intelligence) efforts.

Alberta’s quantum nanotechnology hub (graduate programme)

Intriguingly, it seems that Edmonton has higher aims than (an almost unnoticed) leadership in AI. Physicists at the University of Alberta have announced hopes to be just as successful as their AI brethren in a Nov. 27, 2017 article by Juris Graney for the Edmonton Journal,

Physicists at the University of Alberta [U of A] are hoping to emulate the success of their artificial intelligence studying counterparts in establishing the city and the province as the nucleus of quantum nanotechnology research in Canada and North America.

Google’s artificial intelligence research division DeepMind announced in July [2017] it had chosen Edmonton as its first international AI research lab, based on a long-running partnership with the U of A’s 10-person AI lab.

Retaining the brightest minds in the AI and machine-learning fields while enticing a global tech leader to Alberta was heralded as a coup for the province and the university.

It is something U of A physics professor John Davis believes the university’s new graduate program, Quanta, can help achieve in the world of quantum nanotechnology.

The field of quantum mechanics had long been a realm of theoretical science based on the theory that atomic and subatomic material like photons or electrons behave both as particles and waves.

“When you get right down to it, everything has both behaviours (particle and wave) and we can pick and choose certain scenarios which one of those properties we want to use,” he said.

But, Davis said, physicists and scientists are “now at the point where we understand quantum physics and are developing quantum technology to take to the marketplace.”

“Quantum computing used to be realm of science fiction, but now we’ve figured it out, it’s now a matter of engineering,” he said.

Quantum computing labs are being bought by large tech companies such as Google, IBM and Microsoft because they realize they are only a few years away from having this power, he said.

Those making the groundbreaking developments may want to commercialize their finds and take the technology to market and that is where Quanta comes in.

East vs. West—Again?

Ivan Semeniuk in his article, Quantum Supremacy, ignores any quantum research effort not located in either Waterloo, Ontario or metro Vancouver, British Columbia to describe a struggle between the East and the West (a standard Canadian trope). From Semeniuk’s Oct. 17, 2017 quantum article [link follows the excerpts] for the Globe and Mail’s October 2017 issue of the Report on Business (ROB),

 Lazaridis [Mike], of course, has experienced lost advantage first-hand. As co-founder and former co-CEO of Research in Motion (RIM, now called Blackberry), he made the smartphone an indispensable feature of the modern world, only to watch rivals such as Apple and Samsung wrest away Blackberry’s dominance. Now, at 56, he is engaged in a high-stakes race that will determine who will lead the next technology revolution. In the rolling heartland of southwestern Ontario, he is laying the foundation for what he envisions as a new Silicon Valley—a commercial hub based on the promise of quantum technology.

Semeniuk skips over the story of how Blackberry lost its advantage. I came onto that story late in the game when Blackberry was already in serious trouble due to a failure to recognize that the field they helped to create was moving in a new direction. If memory serves, they were trying to keep their technology wholly proprietary which meant that developers couldn’t easily create apps to extend the phone’s features. Blackberry also fought a legal battle in the US with a patent troll draining company resources and energy in proved to be a futile effort.

Since then Lazaridis has invested heavily in quantum research. He gave the University of Waterloo a serious chunk of money as they named their Quantum Nano Centre (QNC) after him and his wife, Ophelia (you can read all about it in my Sept. 25, 2012 posting about the then new centre). The best details for Lazaridis’ investments in Canada’s quantum technology are to be found on the Quantum Valley Investments, About QVI, History webpage,

History-bannerHistory has repeatedly demonstrated the power of research in physics to transform society.  As a student of history and a believer in the power of physics, Mike Lazaridis set out in 2000 to make real his bold vision to establish the Region of Waterloo as a world leading centre for physics research.  That is, a place where the best researchers in the world would come to do cutting-edge research and to collaborate with each other and in so doing, achieve transformative discoveries that would lead to the commercialization of breakthrough  technologies.

Establishing a World Class Centre in Quantum Research:

The first step in this regard was the establishment of the Perimeter Institute for Theoretical Physics.  Perimeter was established in 2000 as an independent theoretical physics research institute.  Mike started Perimeter with an initial pledge of $100 million (which at the time was approximately one third of his net worth).  Since that time, Mike and his family have donated a total of more than $170 million to the Perimeter Institute.  In addition to this unprecedented monetary support, Mike also devotes his time and influence to help lead and support the organization in everything from the raising of funds with government and private donors to helping to attract the top researchers from around the globe to it.  Mike’s efforts helped Perimeter achieve and grow its position as one of a handful of leading centres globally for theoretical research in fundamental physics.

Stephen HawkingPerimeter is located in a Governor-General award winning designed building in Waterloo.  Success in recruiting and resulting space requirements led to an expansion of the Perimeter facility.  A uniquely designed addition, which has been described as space-ship-like, was opened in 2011 as the Stephen Hawking Centre in recognition of one of the most famous physicists alive today who holds the position of Distinguished Visiting Research Chair at Perimeter and is a strong friend and supporter of the organization.

Recognizing the need for collaboration between theorists and experimentalists, in 2002, Mike applied his passion and his financial resources toward the establishment of The Institute for Quantum Computing at the University of Waterloo.  IQC was established as an experimental research institute focusing on quantum information.  Mike established IQC with an initial donation of $33.3 million.  Since that time, Mike and his family have donated a total of more than $120 million to the University of Waterloo for IQC and other related science initiatives.  As in the case of the Perimeter Institute, Mike devotes considerable time and influence to help lead and support IQC in fundraising and recruiting efforts.  Mike’s efforts have helped IQC become one of the top experimental physics research institutes in the world.

Quantum ComputingMike and Doug Fregin have been close friends since grade 5.  They are also co-founders of BlackBerry (formerly Research In Motion Limited).  Doug shares Mike’s passion for physics and supported Mike’s efforts at the Perimeter Institute with an initial gift of $10 million.  Since that time Doug has donated a total of $30 million to Perimeter Institute.  Separately, Doug helped establish the Waterloo Institute for Nanotechnology at the University of Waterloo with total gifts for $29 million.  As suggested by its name, WIN is devoted to research in the area of nanotechnology.  It has established as an area of primary focus the intersection of nanotechnology and quantum physics.

With a donation of $50 million from Mike which was matched by both the Government of Canada and the province of Ontario as well as a donation of $10 million from Doug, the University of Waterloo built the Mike & Ophelia Lazaridis Quantum-Nano Centre, a state of the art laboratory located on the main campus of the University of Waterloo that rivals the best facilities in the world.  QNC was opened in September 2012 and houses researchers from both IQC and WIN.

Leading the Establishment of Commercialization Culture for Quantum Technologies in Canada:

In the Research LabFor many years, theorists have been able to demonstrate the transformative powers of quantum mechanics on paper.  That said, converting these theories to experimentally demonstrable discoveries has, putting it mildly, been a challenge.  Many naysayers have suggested that achieving these discoveries was not possible and even the believers suggested that it could likely take decades to achieve these discoveries.  Recently, a buzz has been developing globally as experimentalists have been able to achieve demonstrable success with respect to Quantum Information based discoveries.  Local experimentalists are very much playing a leading role in this regard.  It is believed by many that breakthrough discoveries that will lead to commercialization opportunities may be achieved in the next few years and certainly within the next decade.

Recognizing the unique challenges for the commercialization of quantum technologies (including risk associated with uncertainty of success, complexity of the underlying science and high capital / equipment costs) Mike and Doug have chosen to once again lead by example.  The Quantum Valley Investment Fund will provide commercialization funding, expertise and support for researchers that develop breakthroughs in Quantum Information Science that can reasonably lead to new commercializable technologies and applications.  Their goal in establishing this Fund is to lead in the development of a commercialization infrastructure and culture for Quantum discoveries in Canada and thereby enable such discoveries to remain here.

Semeniuk goes on to set the stage for Waterloo/Lazaridis vs. Vancouver (from Semeniuk’s 2017 ROB article),

… as happened with Blackberry, the world is once again catching up. While Canada’s funding of quantum technology ranks among the top five in the world, the European Union, China, and the US are all accelerating their investments in the field. Tech giants such as Google [also known as Alphabet], Microsoft and IBM are ramping up programs to develop companies and other technologies based on quantum principles. Meanwhile, even as Lazaridis works to establish Waterloo as the country’s quantum hub, a Vancouver-area company has emerged to challenge that claim. The two camps—one methodically focused on the long game, the other keen to stake an early commercial lead—have sparked an East-West rivalry that many observers of the Canadian quantum scene are at a loss to explain.

Is it possible that some of the rivalry might be due to an influential individual who has invested heavily in a ‘quantum valley’ and has a history of trying to ‘own’ a technology?

Getting back to D-Wave Systems, the Vancouver company, I have written about them a number of times (particularly in 2015; for the full list: input D-Wave into the blog search engine). This June 26, 2015 posting includes a reference to an article in The Economist magazine about D-Wave’s commercial opportunities while the bulk of the posting is focused on a technical breakthrough.

Semeniuk offers an overview of the D-Wave Systems story,

D-Wave was born in 1999, the same year Lazaridis began to fund quantum science in Waterloo. From the start, D-Wave had a more immediate goal: to develop a new computer technology to bring to market. “We didn’t have money or facilities,” says Geordie Rose, a physics PhD who co0founded the company and served in various executive roles. …

The group soon concluded that the kind of machine most scientists were pursing based on so-called gate-model architecture was decades away from being realized—if ever. …

Instead, D-Wave pursued another idea, based on a principle dubbed “quantum annealing.” This approach seemed more likely to produce a working system, even if the application that would run on it were more limited. “The only thing we cared about was building the machine,” says Rose. “Nobody else was trying to solve the same problem.”

D-Wave debuted its first prototype at an event in California in February 2007 running it through a few basic problems such as solving a Sudoku puzzle and finding the optimal seating plan for a wedding reception. … “They just assumed we were hucksters,” says Hilton [Jeremy Hilton, D.Wave senior vice-president of systems]. Federico Spedalieri, a computer scientist at the University of Southern California’s [USC} Information Sciences Institute who has worked with D-Wave’s system, says the limited information the company provided about the machine’s operation provoked outright hostility. “I think that played against them a lot in the following years,” he says.

It seems Lazaridis is not the only one who likes to hold company information tightly.

Back to Semeniuk and D-Wave,

Today [October 2017], the Los Alamos National Laboratory owns a D-Wave machine, which costs about $15million. Others pay to access D-Wave systems remotely. This year , for example, Volkswagen fed data from thousands of Beijing taxis into a machine located in Burnaby [one of the municipalities that make up metro Vancouver] to study ways to optimize traffic flow.

But the application for which D-Wave has the hights hope is artificial intelligence. Any AI program hings on the on the “training” through which a computer acquires automated competence, and the 2000Q [a D-Wave computer] appears well suited to this task. …

Yet, for all the buzz D-Wave has generated, with several research teams outside Canada investigating its quantum annealing approach, the company has elicited little interest from the Waterloo hub. As a result, what might seem like a natural development—the Institute for Quantum Computing acquiring access to a D-Wave machine to explore and potentially improve its value—has not occurred. …

I am particularly interested in this comment as it concerns public funding (from Semeniuk’s article),

Vern Brownell, a former Goldman Sachs executive who became CEO of D-Wave in 2009, calls the lack of collaboration with Waterloo’s research community “ridiculous,” adding that his company’s efforts to establish closer ties have proven futile, “I’ll be blunt: I don’t think our relationship is good enough,” he says. Brownell also point out that, while  hundreds of millions in public funds have flowed into Waterloo’s ecosystem, little funding is available for  Canadian scientists wishing to make the most of D-Wave’s hardware—despite the fact that it remains unclear which core quantum technology will prove the most profitable.

There’s a lot more to Semeniuk’s article but this is the last excerpt,

The world isn’t waiting for Canada’s quantum rivals to forge a united front. Google, Microsoft, IBM, and Intel are racing to develop a gate-model quantum computer—the sector’s ultimate goal. (Google’s researchers have said they will unveil a significant development early next year.) With the U.K., Australia and Japan pouring money into quantum, Canada, an early leader, is under pressure to keep up. The federal government is currently developing  a strategy for supporting the country’s evolving quantum sector and, ultimately, getting a return on its approximately $1-billion investment over the past decade [emphasis mine].

I wonder where the “approximately $1-billion … ” figure came from. I ask because some years ago MP Peter Julian asked the government for information about how much Canadian federal money had been invested in nanotechnology. The government replied with sheets of paper (a pile approximately 2 inches high) that had funding disbursements from various ministries. Each ministry had its own method with different categories for listing disbursements and the titles for the research projects were not necessarily informative for anyone outside a narrow specialty. (Peter Julian’s assistant had kindly sent me a copy of the response they had received.) The bottom line is that it would have been close to impossible to determine the amount of federal funding devoted to nanotechnology using that data. So, where did the $1-billion figure come from?

In any event, it will be interesting to see how the Council of Canadian Academies assesses the ‘quantum’ situation in its more academically inclined, “The State of Science and Technology and Industrial Research and Development in Canada,” when it’s released later this year (2018).

Finally, you can find Semeniuk’s October 2017 article here but be aware it’s behind a paywall.

Whither we goest?

Despite any doubts one might have about Lazaridis’ approach to research and technology, his tremendous investment and support cannot be denied. Without him, Canada’s quantum research efforts would be substantially less significant. As for the ‘cowboys’ in Vancouver, it takes a certain temperament to found a start-up company and it seems the D-Wave folks have more in common with Lazaridis than they might like to admit. As for the Quanta graduate  programme, it’s early days yet and no one should ever count out Alberta.

Meanwhile, one can continue to hope that a more thoughtful approach to regional collaboration will be adopted so Canada can continue to blaze trails in the field of quantum research.

A customized cruise experience with wearable technology (and decreased personal agency?)

The days when you went cruising to ‘get away from it all’ seem to have passed (if they ever really existed) with the introduction of wearable technology that will register your every preference and make life easier according to Cliff Kuang’s Oct. 19, 2017 article for Fast Company,

This month [October 2017], the 141,000-ton Regal Princess will push out to sea after a nine-figure revamp of mind-boggling scale. Passengers won’t be greeted by new restaurants, swimming pools, or onboard activities, but will instead step into a future augured by the likes of Netflix and Uber, where nearly everything is on demand and personally tailored. An ambitious new customization platform has been woven into the ship’s 19 passenger decks: some 7,000 onboard sensors and 4,000 “guest portals” (door-access panels and touch-screen TVs), all of them connected by 75 miles of internal cabling. As the Carnival-owned ship cruises to Nassau, Bahamas, and Grand Turk, its 3,500 passengers will have the option of carrying a quarter-size device, called the Ocean Medallion, which can be slipped into a pocket or worn on the wrist and is synced with a companion app.

The platform will provide a new level of service for passengers; the onboard sensors record their tastes and respond to their movements, and the app guides them around the ship and toward activities aligned with their preferences. Carnival plans to roll out the platform to another seven ships by January 2019. Eventually, the Ocean Medallion could be opening doors, ordering drinks, and scheduling activities for passengers on all 102 of Carnival’s vessels across 10 cruise lines, from the mass-market Princess ships to the legendary ocean liners of Cunard.

Kuang goes on to explain the reasoning behind this innovation,

The Ocean Medallion is Carnival’s attempt to address a problem that’s become increasingly vexing to the $35.5 billion cruise industry. Driven by economics, ships have exploded in size: In 1996, Carnival Destiny was the world’s largest cruise ship, carrying 2,600 passengers. Today, Royal Caribbean’s MS Harmony of the Seas carries up to 6,780 passengers and 2,300 crew. Larger ships expend less fuel per passenger; the money saved can then go to adding more amenities—which, in turn, are geared to attracting as many types of people as possible. Today on a typical ship you can do practically anything—from attending violin concertos to bungee jumping. And that’s just onboard. Most of a cruise is spent in port, where each day there are dozens of experiences available. This avalanche of choice can bury a passenger. It has also made personalized service harder to deliver. …

Kuang also wrote this brief description of how the technology works from the passenger’s perspective in an Oct. 19, 2017 item for Fast Company,

1. Pre-trip

On the web or on the app, you can book experiences, log your tastes and interests, and line up your days. That data powers the recommendations you’ll see. The Ocean Medallion arrives by mail and becomes the key to ship access.

2. Stateroom

When you draw near, your cabin-room door unlocks without swiping. The room’s unique 43-inch TV, which doubles as a touch screen, offers a range of Carnival’s bespoke travel shows. Whatever you watch is fed into your excursion suggestions.

3. Food

When you order something, sensors detect where you are, allowing your server to find you. Your allergies and preferences are also tracked, and shape the choices you’re offered. In all, the back-end data has 45,000 allergens tagged and manages 250,000 drink combinations.

4. Activities

The right algorithms can go beyond suggesting wines based on previous orders. Carnival is creating a massive semantic database, so if you like pricey reds, you’re more apt to be guided to a violin concerto than a limbo competition. Your onboard choices—the casino, the gym, the pool—inform your excursion recommendations.

In Kuang’s Oct. 19, 2017 article he notes that the cruise ship line is putting a lot of effort into retraining their staff and emphasizing the ‘soft’ skills that aren’t going to be found in this iteration of the technology. No mention is made of whether or not there will be reductions in the number of staff members on this cruise ship nor is the possibility that ‘soft’ skills may in the future be incorporated into this technological marvel.

Personalization/customization is increasingly everywhere

How do you feel about customized news feeds? As it turns out, this is not a rhetorical question as Adrienne LaFrance notes in her Oct. 19, 2017 article for The Atlantic (Note: Links have been removed),

Today, a Google search for news runs through the same algorithmic filtration system as any other Google search: A person’s individual search history, geographic location, and other demographic information affects what Google shows you. Exactly how your search results differ from any other person’s is a mystery, however. Not even the computer scientists who developed the algorithm could precisely reverse engineer it, given the fact that the same result can be achieved through numerous paths, and that ranking factors—deciding which results show up first—are constantly changing, as are the algorithms themselves.

We now get our news in real time, on demand, tailored to our interests, across multiple platforms, without knowing just how much is actually personalized. It was technology companies like Google and Facebook, not traditional newsrooms, that made it so. But news organizations are increasingly betting that offering personalized content can help them draw audiences to their sites—and keep them coming back.

Personalization extends beyond how and where news organizations meet their readers. Already, smartphone users can subscribe to push notifications for the specific coverage areas that interest them. On Facebook, users can decide—to some extent—which organizations’ stories they would like to appear in their news feeds. At the same time, devices and platforms that use machine learning to get to know their users will increasingly play a role in shaping ultra-personalized news products. Meanwhile, voice-activated artificially intelligent devices, such as Google Home and Amazon Echo, are poised to redefine the relationship between news consumers and the news [emphasis mine].

While news personalization can help people manage information overload by making individuals’ news diets unique, it also threatens to incite filter bubbles and, in turn, bias [emphasis mine]. This “creates a bit of an echo chamber,” says Judith Donath, author of The Social Machine: Designs for Living Online and a researcher affiliated with Harvard University ’s Berkman Klein Center for Internet and Society. “You get news that is designed to be palatable to you. It feeds into people’s appetite of expecting the news to be entertaining … [and] the desire to have news that’s reinforcing your beliefs, as opposed to teaching you about what’s happening in the world and helping you predict the future better.”

Still, algorithms have a place in responsible journalism. “An algorithm actually is the modern editorial tool,” says Tamar Charney, the managing editor of NPR One, the organization’s customizable mobile-listening app. A handcrafted hub for audio content from both local and national programs as well as podcasts from sources other than NPR, NPR One employs an algorithm to help populate users’ streams with content that is likely to interest them. But Charney assures there’s still a human hand involved: “The whole editorial vision of NPR One was to take the best of what humans do and take the best of what algorithms do and marry them together.” [emphasis mine]

The skimming and diving Charney describes sounds almost exactly like how Apple and Google approach their distributed-content platforms. With Apple News, users can decide which outlets and topics they are most interested in seeing, with Siri offering suggestions as the algorithm gets better at understanding your preferences. Siri now has have help from Safari. The personal assistant can now detect browser history and suggest news items based on what someone’s been looking at—for example, if someone is searching Safari for Reykjavík-related travel information, they will then see Iceland-related news on Apple News. But the For You view of Apple News isn’t 100 percent customizable, as it still spotlights top stories of the day, and trending stories that are popular with other users, alongside those curated just for you.

Similarly, with Google’s latest update to Google News, readers can scan fixed headlines, customize sidebars on the page to their core interests and location—and, of course, search. The latest redesign of Google News makes it look newsier than ever, and adds to many of the personalization features Google first introduced in 2010. There’s also a place where you can preprogram your own interests into the algorithm.

Google says this isn’t an attempt to supplant news organizations, nor is it inspired by them. The design is rather an embodiment of Google’s original ethos, the product manager for Google News Anand Paka says: “Just due to the deluge of information, users do want ways to control information overload. In other words, why should I read the news that I don’t care about?” [emphasis mine]

Meanwhile, in May [2017?], Google briefly tested a personalized search filter that would dip into its trove of data about users with personal Google and Gmail accounts and include results exclusively from their emails, photos, calendar items, and other personal data related to their query. [emphasis mine] The “personal” tab was supposedly “just an experiment,” a Google spokesperson said, and the option was temporarily removed, but seems to have rolled back out for many users as of August [2017?].

Now, Google, in seeking to settle a class-action lawsuit alleging that scanning emails to offer targeted ads amounts to illegal wiretapping, is promising that for the next three years it won’t use the content of its users’ emails to serve up targeted ads in Gmail. The move, which will go into effect at an unspecified date, doesn’t mean users won’t see ads, however. Google will continue to collect data from users’ search histories, YouTube, and Chrome browsing habits, and other activity.

The fear that personalization will encourage filter bubbles by narrowing the selection of stories is a valid one, especially considering that the average internet user or news consumer might not even be aware of such efforts. Elia Powers, an assistant professor of journalism and news media at Towson University in Maryland, studied the awareness of news personalization among students after he noticed those in his own classes didn’t seem to realize the extent to which Facebook and Google customized users’ results. “My sense is that they didn’t really understand … the role that people that were curating the algorithms [had], how influential that was. And they also didn’t understand that they could play a pretty active role on Facebook in telling Facebook what kinds of news they want them to show and how to prioritize [content] on Google,” he says.

The results of Powers’s study, which was published in Digital Journalism in February [2017], showed that the majority of students had no idea that algorithms were filtering the news content they saw on Facebook and Google. When asked if Facebook shows every news item, posted by organizations or people, in a users’ newsfeed, only 24 percent of those surveyed were aware that Facebook prioritizes certain posts and hides others. Similarly, only a quarter of respondents said Google search results would be different for two different people entering the same search terms at the same time. [emphasis mine; Note: Respondents in this study were students.]

This, of course, has implications beyond the classroom, says Powers: “People as news consumers need to be aware of what decisions are being made [for them], before they even open their news sites, by algorithms and the people behind them, and also be able to understand how they can counter the effects or maybe even turn off personalization or make tweaks to their feeds or their news sites so they take a more active role in actually seeing what they want to see in their feeds.”

On Google and Facebook, the algorithm that determines what you see is invisible. With voice-activated assistants, the algorithm suddenly has a persona. “We are being trained to have a relationship with the AI,” says Amy Webb, founder of the Future Today Institute and an adjunct professor at New York University Stern School of Business. “This is so much more catastrophically horrible for news organizations than the internet. At least with the internet, I have options. The voice ecosystem is not built that way. It’s being built so I just get the information I need in a pleasing way.”

LaFrance’s article is thoughtful and well worth reading in its entirety. Now, onto some commentary.

Loss of personal agency

I have been concerned for some time about the increasingly dull results I get from a Google search and while I realize the company has been gathering information about me via my searches , supposedly in service of giving me better searches, I had no idea how deeply the company can mine for personal data. It makes me wonder what would happen if Google and Facebook attempted a merger.

More cogently, I rather resent the search engines and artificial intelligence agents (e.g. Facebook bots) which have usurped my role as the arbiter of what interests me, in short, my increasing loss of personal agency.

I’m also deeply suspicious of what these companies are going to do with my data. Will it be used to manipulate me in some way? Presumably, the data will be sold and used for some purpose. In the US, they have married electoral data with consumer data as Brent Bambury notes in an Oct. 13, 2017 article for his CBC (Canadian Broadcasting Corporation) Radio show,

How much of your personal information circulates in the free-market ether of metadata? It could be more than you imagine, and it might be enough to let others change the way you vote.

A data firm that specializes in creating psychological profiles of voters claims to have up to 5,000 data points on 220 million Americans. Cambridge Analytica has deep ties to the American right and was hired by the campaigns of Ben Carson, Ted Cruz and Donald Trump.

During the U.S. election, CNN called them “Donald Trump’s mind readers” and his secret weapon.

David Carroll is a Professor at the Parsons School of Design in New York City. He is one of the millions of Americans profiled by Cambridge Analytica and he’s taking legal action to find out where the company gets its masses of data and how they use it to create their vaunted psychographic profiles of voters.

On Day 6 [Banbury’s CBC radio programme], he explained why that’s important.

“They claim to have figured out how to project our voting behavior based on our consumer behavior. So it’s important for citizens to be able to understand this because it would affect our ability to understand how we’re being targeted by campaigns and how the messages that we’re seeing on Facebook and television are being directed at us to manipulate us.” [emphasis mine]

The parent company of Cambridge Analytica, SCL Group, is a U.K.-based data operation with global ties to military and political activities. David Carroll says the potential for sharing personal data internationally is a cause for concern.

“It’s the first time that this kind of data is being collected and transferred across geographic boundaries,” he says.

But that also gives Carroll an opening for legal action. An individual has more rights to access their personal information in the U.K., so that’s where he’s launching his lawsuit.

Reports link Michael Flynn, briefly Trump’s National Security Adviser, to SCL Group and indicate that former White House strategist Steve Bannon is a board member of Cambridge Analytica. Billionaire Robert Mercer, who has underwritten Bannon’s Breitbart operations and is a major Trump donor, also has a significant stake in Cambridge Analytica.

In the world of data, Mercer’s credentials are impeccable.

“He is an important contributor to the field of artificial intelligence,” says David Carroll.

“His work at IBM is seminal and really important in terms of the foundational ideas that go into big data analytics, so the relationship between AI and big data analytics. …

Banbury’s piece offers a lot more, including embedded videos, than I’ve not included in that excerpt but I also wanted to include some material from Carole Cadwalladr’s Oct. 1, 2017 Guardian article about Carroll and his legal fight in the UK,

“There are so many disturbing aspects to this. One of the things that really troubles me is how the company can buy anonymous data completely legally from all these different sources, but as soon as it attaches it to voter files, you are re-identified. It means that every privacy policy we have ignored in our use of technology is a broken promise. It would be one thing if this information stayed in the US, if it was an American company and it only did voter data stuff.”

But, he [Carroll] argues, “it’s not just a US company and it’s not just a civilian company”. Instead, he says, it has ties with the military through SCL – “and it doesn’t just do voter targeting”. Carroll has provided information to the Senate intelligence committee and believes that the disclosures mandated by a British court could provide evidence helpful to investigators.

Frank Pasquale, a law professor at the University of Maryland, author of The Black Box Society and a leading expert on big data and the law, called the case a “watershed moment”.

“It really is a David and Goliath fight and I think it will be the model for other citizens’ actions against other big corporations. I think we will look back and see it as a really significant case in terms of the future of algorithmic accountability and data protection. …

Nobody is discussing personal agency directly but if you’re only being exposed to certain kinds of messages then your personal agency has been taken from you. Admittedly we don’t have complete personal agency in our lives but AI along with the data gathering done online and increasingly with wearable and smart technology means that another layer of control has been added to your life and it is largely invisible. After all, the students in Elia Powers’ study didn’t realize their news feeds were being pre-curated.

IBM and a 5 nanometre chip

If this continues, they’re going to have change the scale from nano to pico. IBM has announced work on a 5 nanometre (5nm) chip in a June 5, 2017 news item on Nanotechnology Now,

IBM (NYSE: IBM), its Research Alliance partners GLOBALFOUNDRIES and Samsung, and equipment suppliers have developed an industry-first process to build silicon nanosheet transistors that will enable 5 nanometer (nm) chips. The details of the process will be presented at the 2017 Symposia on VLSI Technology and Circuits conference in Kyoto, Japan. In less than two years since developing a 7nm test node chip with 20 billion transistors, scientists have paved the way for 30 billion switches on a fingernail-sized chip.

A June 5, 2017 IBM news release, which originated the news item, spells out some of the details about IBM’s latest breakthrough,

The resulting increase in performance will help accelerate cognitive computing [emphasis mine], the Internet of Things (IoT), and other data-intensive applications delivered in the cloud. The power savings could also mean that the batteries in smartphones and other mobile products could last two to three times longer than today’s devices, before needing to be charged.

Scientists working as part of the IBM-led Research Alliance at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering’s NanoTech Complex in Albany, NY achieved the breakthrough by using stacks of silicon nanosheets as the device structure of the transistor, instead of the standard FinFET architecture, which is the blueprint for the semiconductor industry up through 7nm node technology.

“For business and society to meet the demands of cognitive and cloud computing in the coming years, advancement in semiconductor technology is essential,” said Arvind Krishna, senior vice president, Hybrid Cloud, and director, IBM Research. “That’s why IBM aggressively pursues new and different architectures and materials that push the limits of this industry, and brings them to market in technologies like mainframes and our cognitive systems.”

The silicon nanosheet transistor demonstration, as detailed in the Research Alliance paper Stacked Nanosheet Gate-All-Around Transistor to Enable Scaling Beyond FinFET, and published by VLSI, proves that 5nm chips are possible, more powerful, and not too far off in the future.

Compared to the leading edge 10nm technology available in the market, a nanosheet-based 5nm technology can deliver 40 percent performance enhancement at fixed power, or 75 percent power savings at matched performance. This improvement enables a significant boost to meeting the future demands of artificial intelligence (AI) systems, virtual reality and mobile devices.

Building a New Switch

“This announcement is the latest example of the world-class research that continues to emerge from our groundbreaking public-private partnership in New York,” said Gary Patton, CTO and Head of Worldwide R&D at GLOBALFOUNDRIES. “As we make progress toward commercializing 7nm in 2018 at our Fab 8 manufacturing facility, we are actively pursuing next-generation technologies at 5nm and beyond to maintain technology leadership and enable our customers to produce a smaller, faster, and more cost efficient generation of semiconductors.”

IBM Research has explored nanosheet semiconductor technology for more than 10 years. This work is the first in the industry to demonstrate the feasibility to design and fabricate stacked nanosheet devices with electrical properties superior to FinFET architecture.

This same Extreme Ultraviolet (EUV) lithography approach used to produce the 7nm test node and its 20 billion transistors was applied to the nanosheet transistor architecture. Using EUV lithography, the width of the nanosheets can be adjusted continuously, all within a single manufacturing process or chip design. This adjustability permits the fine-tuning of performance and power for specific circuits – something not possible with today’s FinFET transistor architecture production, which is limited by its current-carrying fin height. Therefore, while FinFET chips can scale to 5nm, simply reducing the amount of space between fins does not provide increased current flow for additional performance.

“Today’s announcement continues the public-private model collaboration with IBM that is energizing SUNY-Polytechnic’s, Albany’s, and New York State’s leadership and innovation in developing next generation technologies,” said Dr. Bahgat Sammakia, Interim President, SUNY Polytechnic Institute. “We believe that enabling the first 5nm transistor is a significant milestone for the entire semiconductor industry as we continue to push beyond the limitations of our current capabilities. SUNY Poly’s partnership with IBM and Empire State Development is a perfect example of how Industry, Government and Academia can successfully collaborate and have a broad and positive impact on society.”

Part of IBM’s $3 billion, five-year investment in chip R&D (announced in 2014), the proof of nanosheet architecture scaling to a 5nm node continues IBM’s legacy of historic contributions to silicon and semiconductor innovation. They include the invention or first implementation of the single cell DRAM, the Dennard Scaling Laws, chemically amplified photoresists, copper interconnect wiring, Silicon on Insulator, strained engineering, multi core microprocessors, immersion lithography, high speed SiGe, High-k gate dielectrics, embedded DRAM, 3D chip stacking and Air gap insulators.

I last wrote about IBM and computer chips in a July 15, 2015 posting regarding their 7nm chip. You may want to scroll down approximately 55% of the way where I note research from MIT (Massachusetts Institute of Technology) about metal nanoparticles with unexpected properties possibly having an impact on nanoelectronics.

Getting back to IBM, they have produced a slick video about their 5nm chip breakthrough,

Meanwhile, Katherine Bourzac provides technical detail in a June 5, 2017 posting on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website), Note: A link has been removed,

Researchers at IBM believe the future of the transistor is in stacked nanosheets. …

Today’s state-of-the-art transistor is the finFET, named for the fin-like ridges of current-carrying silicon that project from the chip’s surface. The silicon fins are surrounded on their three exposed sides by a structure called the gate. The gate switches the flow of current on, and prevents electrons from leaking out when the transistor is off. This design is expected to last from this year’s bleeding-edge process technology, the “10-nanometer” node, through the next node, 7 nanometers. But any smaller, and these transistors will become difficult to switch off: electrons will leak out, even with the three-sided gates.

So the semiconductor industry has been working on alternatives for the upcoming 5 nanometer node. One popular idea is to use lateral silicon nanowires that are completely surrounded by the gate, preventing electron leaks and saving power. This design is called “gate all around.” IBM’s new design is a variation on this. In their test chips, each transistor is made up of three stacked horizontal sheets of silicon, each only a few nanometers thick and completely surrounded by a gate.

Why a sheet instead of a wire? Huiming Bu, director of silicon integration and devices at IBM, says nanosheets can bring back one of the benefits of pre-finFET, planar designs. Designers used to be able to vary the width of a transistor to prioritize fast operations or energy efficiency. Varying the amount of silicon in a finFET transistor is not practicable because it would mean making some fins taller and other shorter. Fins must all be the same height due to manufacturing constraints, says Bu.

IBM’s nanosheets can range from 8 to 50 nanometers in width. “Wider gives you better performance but takes more power, smaller width relaxes performance but reduces power use,” says Bu. This will allow circuit designers to pick and choose what they need, whether they are making a power efficient mobile chip processor or designing a bank of SRAM memory. “We are bringing flexibility back to the designers,” he says.

The test chips have 30 billion transistors. …

It was a struggle trying to edit Bourzac’s posting with its good detail and clear writing. I encourage you to read it (June 5, 2017 posting) in its entirety.

As for where this drive downwards to the ‘ever smaller’ is going, there’s Dexter’s Johnson’s June 29, 2017 posting about another IBM team’s research on his Nanoclast blog on the IEEE website (Note: Links have been removed),

There have been increasing signs coming from the research community that carbon nanotubes are beginning to step up to the challenge of offering a real alternative to silicon-based complementary metal-oxide semiconductor (CMOS) transistors.

Now, researchers at IBM Thomas J. Watson Research Center have advanced carbon nanotube-based transistors another step toward meeting the demands of the International Technology Roadmap for Semiconductors (ITRS) for the next decade. The IBM researchers have fabricated a p-channel transistor based on carbon nanotubes that takes up less than half the space of leading silicon technologies while operating at a lower voltage.

In research described in the journal Science, the IBM scientists used a carbon nanotube p-channel to reduce the transistor footprint; their transistor contains all components to 40 square nanometers [emphasis mine], an ITRS roadmap benchmark for ten years out.

One of the keys to being able to reduce the transistor to such a small size is the use of the carbon nanotube as the channel in place of silicon. The nanotube is only 1 nanometer thick. Such thinness offers a significant advantage in electrostatics, so that it’s possible to reduce the device gate length to 10 nanometers without seeing the device performance adversely affected by short-channel effects. An additional benefit of the nanotubes is that the electrons travel much faster, which contributes to a higher level of device performance.

Happy reading!

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.