Tag Archives: Vector Institute for Artificial Intelligence

The Hedy Lamarr of international research: Canada’s Third assessment of The State of Science and Technology and Industrial Research and Development in Canada (2 of 2)

Taking up from where I left off with my comments on Competing in a Global Innovation Economy: The Current State of R and D in Canada or as I prefer to call it the Third assessment of Canadas S&T (science and technology) and R&D (research and development). (Part 1 for anyone who missed it).

Is it possible to get past Hedy?

Interestingly (to me anyway), one of our R&D strengths, the visual and performing arts, features sectors where a preponderance of people are dedicated to creating culture in Canada and don’t spend a lot of time trying to make money so they can retire before the age of 40 as so many of our start-up founders do. (Retiring before the age of 40 just reminded me of Hollywood actresses {Hedy] who found and still do find that work was/is hard to come by after that age. You may be able but I’m not sure I can get past Hedy.) Perhaps our business people (start-up founders) could take a leaf out of the visual and performing arts handbook? Or, not. There is another question.

Does it matter if we continue to be a ‘branch plant’ economy? Somebody once posed that question to me when I was grumbling that our start-ups never led to larger businesses and acted more like incubators (which could describe our R&D as well),. He noted that Canadians have a pretty good standard of living and we’ve been running things this way for over a century and it seems to work for us. Is it that bad? I didn’t have an  answer for him then and I don’t have one now but I think it’s a useful question to ask and no one on this (2018) expert panel or the previous expert panel (2013) seems to have asked.

I appreciate that the panel was constrained by the questions given by the government but given how they snuck in a few items that technically speaking were not part of their remit, I’m thinking they might have gone just a bit further. The problem with answering the questions as asked is that if you’ve got the wrong questions, your answers will be garbage (GIGO; garbage in, garbage out) or, as is said, where science is concerned, it’s the quality of your questions.

On that note, I would have liked to know more about the survey of top-cited researchers. I think looking at the questions could have been quite illuminating and I would have liked some information on from where (geographically and area of specialization) they got most of their answers. In keeping with past practice (2012 assessment published in 2013), there is no additional information offered about the survey questions or results. Still, there was this (from the report released April 10, 2018; Note: There may be some difference between the formatting seen here and that seen in the document),

3.1.2 International Perceptions of Canadian Research
As with the 2012 S&T report, the CCA commissioned a survey of top-cited researchers’ perceptions of Canada’s research strength in their field or subfield relative to that of other countries (Section 1.3.2). Researchers were asked to identify the top five countries in their field and subfield of expertise: 36% of respondents (compared with 37% in the 2012 survey) from across all fields of research rated Canada in the top five countries in their field (Figure B.1 and Table B.1 in the appendix). Canada ranks fourth out of all countries, behind the United States, United Kingdom, and Germany, and ahead of France. This represents a change of about 1 percentage point from the overall results of the 2012 S&T survey. There was a 4 percentage point decrease in how often France is ranked among the top five countries; the ordering of the top five countries, however, remains the same.

When asked to rate Canada’s research strength among other advanced countries in their field of expertise, 72% (4,005) of respondents rated Canadian research as “strong” (corresponding to a score of 5 or higher on a 7-point scale) compared with 68% in the 2012 S&T survey (Table 3.4). [pp. 40-41 Print; pp. 78-70 PDF]

Before I forget, there was mention of the international research scene,

Growth in research output, as estimated by number of publications, varies considerably for the 20 top countries. Brazil, China, India, Iran, and South Korea have had the most significant increases in publication output over the last 10 years. [emphases mine] In particular, the dramatic increase in China’s output means that it is closing the gap with the United States. In 2014, China’s output was 95% of that of the United States, compared with 26% in 2003. [emphasis mine]

Table 3.2 shows the Growth Index (GI), a measure of the rate at which the research output for a given country changed between 2003 and 2014, normalized by the world growth rate. If a country’s growth in research output is higher than the world average, the GI score is greater than 1.0. For example, between 2003 and 2014, China’s GI score was 1.50 (i.e., 50% greater than the world average) compared with 0.88 and 0.80 for Canada and the United States, respectively. Note that the dramatic increase in publication production of emerging economies such as China and India has had a negative impact on Canada’s rank and GI score (see CCA, 2016).

As long as I’ve been blogging (10 years), the international research community (in particular the US) has been looking over its shoulder at China.

Patents and intellectual property

As an inventor, Hedy got more than one patent. Much has been made of the fact that  despite an agreement, the US Navy did not pay her or her partner (George Antheil) for work that would lead to significant military use (apparently, it was instrumental in the Bay of Pigs incident, for those familiar with that bit of history), GPS, WiFi, Bluetooth, and more.

Some comments about patents. They are meant to encourage more innovation by ensuring that creators/inventors get paid for their efforts .This is true for a set time period and when it’s over, other people get access and can innovate further. It’s not intended to be a lifelong (or inheritable) source of income. The issue in Lamarr’s case is that the navy developed the technology during the patent’s term without telling either her or her partner so, of course, they didn’t need to compensate them despite the original agreement. They really should have paid her and Antheil.

The current patent situation, particularly in the US, is vastly different from the original vision. These days patents are often used as weapons designed to halt innovation. One item that should be noted is that the Canadian federal budget indirectly addressed their misuse (from my March 16, 2018 posting),

Surprisingly, no one else seems to have mentioned a new (?) intellectual property strategy introduced in the document (from Chapter 2: Progress; scroll down about 80% of the way, Note: The formatting has been changed),

Budget 2018 proposes measures in support of a new Intellectual Property Strategy to help Canadian entrepreneurs better understand and protect intellectual property, and get better access to shared intellectual property.

What Is a Patent Collective?
A Patent Collective is a way for firms to share, generate, and license or purchase intellectual property. The collective approach is intended to help Canadian firms ensure a global “freedom to operate”, mitigate the risk of infringing a patent, and aid in the defence of a patent infringement suit.

Budget 2018 proposes to invest $85.3 million over five years, starting in 2018–19, with $10 million per year ongoing, in support of the strategy. The Minister of Innovation, Science and Economic Development will bring forward the full details of the strategy in the coming months, including the following initiatives to increase the intellectual property literacy of Canadian entrepreneurs, and to reduce costs and create incentives for Canadian businesses to leverage their intellectual property:

  • To better enable firms to access and share intellectual property, the Government proposes to provide $30 million in 2019–20 to pilot a Patent Collective. This collective will work with Canada’s entrepreneurs to pool patents, so that small and medium-sized firms have better access to the critical intellectual property they need to grow their businesses.
  • To support the development of intellectual property expertise and legal advice for Canada’s innovation community, the Government proposes to provide $21.5 million over five years, starting in 2018–19, to Innovation, Science and Economic Development Canada. This funding will improve access for Canadian entrepreneurs to intellectual property legal clinics at universities. It will also enable the creation of a team in the federal government to work with Canadian entrepreneurs to help them develop tailored strategies for using their intellectual property and expanding into international markets.
  • To support strategic intellectual property tools that enable economic growth, Budget 2018 also proposes to provide $33.8 million over five years, starting in 2018–19, to Innovation, Science and Economic Development Canada, including $4.5 million for the creation of an intellectual property marketplace. This marketplace will be a one-stop, online listing of public sector-owned intellectual property available for licensing or sale to reduce transaction costs for businesses and researchers, and to improve Canadian entrepreneurs’ access to public sector-owned intellectual property.

The Government will also consider further measures, including through legislation, in support of the new intellectual property strategy.

Helping All Canadians Harness Intellectual Property
Intellectual property is one of our most valuable resources, and every Canadian business owner should understand how to protect and use it.

To better understand what groups of Canadians are benefiting the most from intellectual property, Budget 2018 proposes to provide Statistics Canada with $2 million over three years to conduct an intellectual property awareness and use survey. This survey will help identify how Canadians understand and use intellectual property, including groups that have traditionally been less likely to use intellectual property, such as women and Indigenous entrepreneurs. The results of the survey should help the Government better meet the needs of these groups through education and awareness initiatives.

The Canadian Intellectual Property Office will also increase the number of education and awareness initiatives that are delivered in partnership with business, intermediaries and academia to ensure Canadians better understand, integrate and take advantage of intellectual property when building their business strategies. This will include targeted initiatives to support underrepresented groups.

Finally, Budget 2018 also proposes to invest $1 million over five years to enable representatives of Canada’s Indigenous Peoples to participate in discussions at the World Intellectual Property Organization related to traditional knowledge and traditional cultural expressions, an important form of intellectual property.

It’s not wholly clear what they mean by ‘intellectual property’. The focus seems to be on  patents as they are the only intellectual property (as opposed to copyright and trademarks) singled out in the budget. As for how the ‘patent collective’ is going to meet all its objectives, this budget supplies no clarity on the matter. On the plus side, I’m glad to see that indigenous peoples’ knowledge is being acknowledged as “an important form of intellectual property” and I hope the discussions at the World Intellectual Property Organization are fruitful.

As for the patent situation in Canada (from the report released April 10, 2018),

Over the past decade, the Canadian patent flow in all technical sectors has consistently decreased. Patent flow provides a partial picture of how patents in Canada are exploited. A negative flow represents a deficit of patented inventions owned by Canadian assignees versus the number of patented inventions created by Canadian inventors. The patent flow for all Canadian patents decreased from about −0.04 in 2003 to −0.26 in 2014 (Figure 4.7). This means that there is an overall deficit of 26% of patent ownership in Canada. In other words, fewer patents were owned by Canadian institutions than were invented in Canada.

This is a significant change from 2003 when the deficit was only 4%. The drop is consistent across all technical sectors in the past 10 years, with Mechanical Engineering falling the least, and Electrical Engineering the most (Figure 4.7). At the technical field level, the patent flow dropped significantly in Digital Communication and Telecommunications. For example, the Digital Communication patent flow fell from 0.6 in 2003 to −0.2 in 2014. This fall could be partially linked to Nortel’s US$4.5 billion patent sale [emphasis mine] to the Rockstar consortium (which included Apple, BlackBerry, Ericsson, Microsoft, and Sony) (Brickley, 2011). Food Chemistry and Microstructural [?] and Nanotechnology both also showed a significant drop in patent flow. [p. 83 Print; p. 121 PDF]

Despite a fall in the number of parents for ‘Digital Communication’, we’re still doing well according to statistics elsewhere in this report. Is it possible that patents aren’t that big a deal? Of course, it’s also possible that we are enjoying the benefits of past work and will miss out on future work. (Note: A video of the April 10, 2018 report presentation by Max Blouw features him saying something like that.)

One last note, Nortel died many years ago. Disconcertingly, this report, despite more than one reference to Nortel, never mentions the company’s demise.

Boxed text

While the expert panel wasn’t tasked to answer certain types of questions, as I’ve noted earlier they managed to sneak in a few items.  One of the strategies they used was putting special inserts into text boxes including this (from the report released April 10, 2018),

Box 4.2
The FinTech Revolution

Financial services is a key industry in Canada. In 2015, the industry accounted for 4.4%

of Canadia jobs and about 7% of Canadian GDP (Burt, 2016). Toronto is the second largest financial services hub in North America and one of the most vibrant research hubs in FinTech. Since 2010, more than 100 start-up companies have been founded in Canada, attracting more than $1 billion in investment (Moffatt, 2016). In 2016 alone, venture-backed investment in Canadian financial technology companies grew by 35% to $137.7 million (Ho, 2017). The Toronto Financial Services Alliance estimates that there are approximately 40,000 ICT specialists working in financial services in Toronto alone.

AI, blockchain, [emphasis mine] and other results of ICT research provide the basis for several transformative FinTech innovations including, for example, decentralized transaction ledgers, cryptocurrencies (e.g., bitcoin), and AI-based risk assessment and fraud detection. These innovations offer opportunities to develop new markets for established financial services firms, but also provide entry points for technology firms to develop competing service offerings, increasing competition in the financial services industry. In response, many financial services companies are increasing their investments in FinTech companies (Breznitz et al., 2015). By their own account, the big five banks invest more than $1 billion annually in R&D of advanced software solutions, including AI-based innovations (J. Thompson, personal communication, 2016). The banks are also increasingly investing in university research and collaboration with start-up companies. For instance, together with several large insurance and financial management firms, all big five banks have invested in the Vector Institute for Artificial Intelligence (Kolm, 2017).

I’m glad to see the mention of blockchain while AI (artificial intelligence) is an area where we have innovated (from the report released April 10, 2018),

AI has attracted researchers and funding since the 1960s; however, there were periods of stagnation in the 1970s and 1980s, sometimes referred to as the “AI winter.” During this period, the Canadian Institute for Advanced Research (CIFAR), under the direction of Fraser Mustard, started supporting AI research with a decade-long program called Artificial Intelligence, Robotics and Society, [emphasis mine] which was active from 1983 to 1994. In 2004, a new program called Neural Computation and Adaptive Perception was initiated and renewed twice in 2008 and 2014 under the title, Learning in Machines and Brains. Through these programs, the government provided long-term, predictable support for high- risk research that propelled Canadian researchers to the forefront of global AI development. In the 1990s and early 2000s, Canadian research output and impact on AI were second only to that of the United States (CIFAR, 2016). NSERC has also been an early supporter of AI. According to its searchable grant database, NSERC has given funding to research projects on AI since at least 1991–1992 (the earliest searchable year) (NSERC, 2017a).

The University of Toronto, the University of Alberta, and the Université de Montréal have emerged as international centres for research in neural networks and deep learning, with leading experts such as Geoffrey Hinton and Yoshua Bengio. Recently, these locations have expanded into vibrant hubs for research in AI applications with a diverse mix of specialized research institutes, accelerators, and start-up companies, and growing investment by major international players in AI development, such as Microsoft, Google, and Facebook. Many highly influential AI researchers today are either from Canada or have at some point in their careers worked at a Canadian institution or with Canadian scholars.

As international opportunities in AI research and the ICT industry have grown, many of Canada’s AI pioneers have been drawn to research institutions and companies outside of Canada. According to the OECD, Canada’s share of patents in AI declined from 2.4% in 2000 to 2005 to 2% in 2010 to 2015. Although Canada is the sixth largest producer of top-cited scientific publications related to machine learning, firms headquartered in Canada accounted for only 0.9% of all AI-related inventions from 2012 to 2014 (OECD, 2017c). Canadian AI researchers, however, remain involved in the core nodes of an expanding international network of AI researchers, most of whom continue to maintain ties with their home institutions. Compared with their international peers, Canadian AI researchers are engaged in international collaborations far more often than would be expected by Canada’s level of research output, with Canada ranking fifth in collaboration. [p. 97-98 Print; p. 135-136 PDF]

The only mention of robotics seems to be here in this section and it’s only in passing. This is a bit surprising given its global importance. I wonder if robotics has been somehow hidden inside the term artificial intelligence, although sometimes it’s vice versa with robot being used to describe artificial intelligence. I’m noticing this trend of assuming the terms are synonymous or interchangeable not just in Canadian publications but elsewhere too.  ’nuff said.

Getting back to the matter at hand, t he report does note that patenting (technometric data) is problematic (from the report released April 10, 2018),

The limitations of technometric data stem largely from their restricted applicability across areas of R&D. Patenting, as a strategy for IP management, is similarly limited in not being equally relevant across industries. Trends in patenting can also reflect commercial pressures unrelated to R&D activities, such as defensive or strategic patenting practices. Finally, taxonomies for assessing patents are not aligned with bibliometric taxonomies, though links can be drawn to research publications through the analysis of patent citations. [p. 105 Print; p. 143 PDF]

It’s interesting to me that they make reference to many of the same issues that I mention but they seem to forget and don’t use that information in their conclusions.

There is one other piece of boxed text I want to highlight (from the report released April 10, 2018),

Box 6.3
Open Science: An Emerging Approach to Create New Linkages

Open Science is an umbrella term to describe collaborative and open approaches to
undertaking science, which can be powerful catalysts of innovation. This includes
the development of open collaborative networks among research performers, such
as the private sector, and the wider distribution of research that usually results when
restrictions on use are removed. Such an approach triggers faster translation of ideas
among research partners and moves the boundaries of pre-competitive research to
later, applied stages of research. With research results freely accessible, companies
can focus on developing new products and processes that can be commercialized.

Two Canadian organizations exemplify the development of such models. In June
2017, Genome Canada, the Ontario government, and pharmaceutical companies
invested $33 million in the Structural Genomics Consortium (SGC) (Genome Canada,
2017). Formed in 2004, the SGC is at the forefront of the Canadian open science
movement and has contributed to many key research advancements towards new
treatments (SGC, 2018). McGill University’s Montréal Neurological Institute and
Hospital has also embraced the principles of open science. Since 2016, it has been
sharing its research results with the scientific community without restriction, with
the objective of expanding “the impact of brain research and accelerat[ing] the
discovery of ground-breaking therapies to treat patients suffering from a wide range
of devastating neurological diseases” (neuro, n.d.).

This is exciting stuff and I’m happy the panel featured it. (I wrote about the Montréal Neurological Institute initiative in a Jan. 22, 2016 posting.)

More than once, the report notes the difficulties with using bibliometric and technometric data as measures of scientific achievement and progress and open science (along with its cousins, open data and open access) are contributing to the difficulties as James Somers notes in his April 5, 2018 article ‘The Scientific Paper is Obsolete’ for The Atlantic (Note: Links have been removed),

The scientific paper—the actual form of it—was one of the enabling inventions of modernity. Before it was developed in the 1600s, results were communicated privately in letters, ephemerally in lectures, or all at once in books. There was no public forum for incremental advances. By making room for reports of single experiments or minor technical advances, journals made the chaos of science accretive. Scientists from that point forward became like the social insects: They made their progress steadily, as a buzzing mass.

The earliest papers were in some ways more readable than papers are today. They were less specialized, more direct, shorter, and far less formal. Calculus had only just been invented. Entire data sets could fit in a table on a single page. What little “computation” contributed to the results was done by hand and could be verified in the same way.

The more sophisticated science becomes, the harder it is to communicate results. Papers today are longer than ever and full of jargon and symbols. They depend on chains of computer programs that generate data, and clean up data, and plot data, and run statistical models on data. These programs tend to be both so sloppily written and so central to the results that it’s [sic] contributed to a replication crisis, or put another way, a failure of the paper to perform its most basic task: to report what you’ve actually discovered, clearly enough that someone else can discover it for themselves.

Perhaps the paper itself is to blame. Scientific methods evolve now at the speed of software; the skill most in demand among physicists, biologists, chemists, geologists, even anthropologists and research psychologists, is facility with programming languages and “data science” packages. And yet the basic means of communicating scientific results hasn’t changed for 400 years. Papers may be posted online, but they’re still text and pictures on a page.

What would you get if you designed the scientific paper from scratch today? A little while ago I spoke to Bret Victor, a researcher who worked at Apple on early user-interface prototypes for the iPad and now runs his own lab in Oakland, California, that studies the future of computing. Victor has long been convinced that scientists haven’t yet taken full advantage of the computer. “It’s not that different than looking at the printing press, and the evolution of the book,” he said. After Gutenberg, the printing press was mostly used to mimic the calligraphy in bibles. It took nearly 100 years of technical and conceptual improvements to invent the modern book. “There was this entire period where they had the new technology of printing, but they were just using it to emulate the old media.”Victor gestured at what might be possible when he redesigned a journal article by Duncan Watts and Steven Strogatz, “Collective dynamics of ‘small-world’ networks.” He chose it both because it’s one of the most highly cited papers in all of science and because it’s a model of clear exposition. (Strogatz is best known for writing the beloved “Elements of Math” column for The New York Times.)

The Watts-Strogatz paper described its key findings the way most papers do, with text, pictures, and mathematical symbols. And like most papers, these findings were still hard to swallow, despite the lucid prose. The hardest parts were the ones that described procedures or algorithms, because these required the reader to “play computer” in their head, as Victor put it, that is, to strain to maintain a fragile mental picture of what was happening with each step of the algorithm.Victor’s redesign interleaved the explanatory text with little interactive diagrams that illustrated each step. In his version, you could see the algorithm at work on an example. You could even control it yourself….

For anyone interested in the evolution of how science is conducted and communicated, Somers’ article is a fascinating and in depth look at future possibilities.

Subregional R&D

I didn’t find this quite as compelling as the last time and that may be due to the fact that there’s less information and I think the 2012 report was the first to examine the Canadian R&D scene with a subregional (in their case, provinces) lens. On a high note, this report also covers cities (!) and regions, as well as, provinces.

Here’s the conclusion (from the report released April 10, 2018),

Ontario leads Canada in R&D investment and performance. The province accounts for almost half of R&D investment and personnel, research publications and collaborations, and patents. R&D activity in Ontario produces high-quality publications in each of Canada’s five R&D strengths, reflecting both the quantity and quality of universities in the province. Quebec lags Ontario in total investment, publications, and patents, but performs as well (citations) or better (R&D intensity) by some measures. Much like Ontario, Quebec researchers produce impactful publications across most of Canada’s five R&D strengths. Although it invests an amount similar to that of Alberta, British Columbia does so at a significantly higher intensity. British Columbia also produces more highly cited publications and patents, and is involved in more international research collaborations. R&D in British Columbia and Alberta clusters around Vancouver and Calgary in areas such as physics and ICT and in clinical medicine and energy, respectively. [emphasis mine] Smaller but vibrant R&D communities exist in the Prairies and Atlantic Canada [also referred to as the Maritime provinces or Maritimes] (and, to a lesser extent, in the Territories) in natural resource industries.

Globally, as urban populations expand exponentially, cities are likely to drive innovation and wealth creation at an increasing rate in the future. In Canada, R&D activity clusters around five large cities: Toronto, Montréal, Vancouver, Ottawa, and Calgary. These five cities create patents and high-tech companies at nearly twice the rate of other Canadian cities. They also account for half of clusters in the services sector, and many in advanced manufacturing.

Many clusters relate to natural resources and long-standing areas of economic and research strength. Natural resource clusters have emerged around the location of resources, such as forestry in British Columbia, oil and gas in Alberta, agriculture in Ontario, mining in Quebec, and maritime resources in Atlantic Canada. The automotive, plastics, and steel industries have the most individual clusters as a result of their economic success in Windsor, Hamilton, and Oshawa. Advanced manufacturing industries tend to be more concentrated, often located near specialized research universities. Strong connections between academia and industry are often associated with these clusters. R&D activity is distributed across the country, varying both between and within regions. It is critical to avoid drawing the wrong conclusion from this fact. This distribution does not imply the existence of a problem that needs to be remedied. Rather, it signals the benefits of diverse innovation systems, with differentiation driven by the needs of and resources available in each province. [pp.  132-133 Print; pp. 170-171 PDF]

Intriguingly, there’s no mention that in British Columbia (BC), there are leading areas of research: Visual & Performing Arts, Psychology & Cognitive Sciences, and Clinical Medicine (according to the table on p. 117 Print, p. 153 PDF).

As I said and hinted earlier, we’ve got brains; they’re just not the kind of brains that command respect.

Final comments

My hat’s off to the expert panel and staff of the Council of Canadian Academies. Combining two previous reports into one could not have been easy. As well, kudos to their attempts to broaden the discussion by mentioning initiative such as open science and for emphasizing the problems with bibliometrics, technometrics, and other measures. I have covered only parts of this assessment, (Competing in a Global Innovation Economy: The Current State of R&D in Canada), there’s a lot more to it including a substantive list of reference materials (bibliography).

While I have argued that perhaps the situation isn’t quite as bad as the headlines and statistics may suggest, there are some concerning trends for Canadians but we have to acknowledge that many countries have stepped up their research game and that’s good for all of us. You don’t get better at anything unless you work with and play with others who are better than you are. For example, both India and Italy surpassed us in numbers of published research papers. We slipped from 7th place to 9th. Thank you, Italy and India. (And, Happy ‘Italian Research in the World Day’ on April 15, 2018, the day’s inaugural year. In Italian: Piano Straordinario “Vivere all’Italiana” – Giornata della ricerca Italiana nel mondo.)

Unfortunately, the reading is harder going than previous R&D assessments in the CCA catalogue. And in the end, I can’t help thinking we’re just a little bit like Hedy Lamarr. Not really appreciated in all of our complexities although the expert panel and staff did try from time to time. Perhaps the government needs to find better ways of asking the questions.

***ETA April 12, 2018 at 1500 PDT: Talking about missing the obvious! I’ve been ranting on about how research strength in visual and performing arts and in philosophy and theology, etc. is perfectly fine and could lead to ‘traditional’ science breakthroughs without underlining the point by noting that Antheil was a musician, Lamarr was as an actress and they set the foundation for work by electrical engineers (or people with that specialty) for their signature work leading to WiFi, etc.***

There is, by the way, a Hedy-Canada connection. In 1998, she sued Canadian software company Corel, for its unauthorized use of her image on their Corel Draw 8 product packaging. She won.

More stuff

For those who’d like to see and hear the April 10, 2017 launch for “Competing in a Global Innovation Economy: The Current State of R&D in Canada” or the Third Assessment as I think of it, go here.

The report can be found here.

For anyone curious about ‘Bombshell: The Hedy Lamarr Story’ to be broadcast on May 18, 2018 as part of PBS’s American Masters series, there’s this trailer,

For the curious, I did find out more about the Hedy Lamarr and Corel Draw. John Lettice’s December 2, 1998 article The Rgister describes the suit and her subsequent victory in less than admiring terms,

Our picture doesn’t show glamorous actress Hedy Lamarr, who yesterday [Dec. 1, 1998] came to a settlement with Corel over the use of her image on Corel’s packaging. But we suppose that following the settlement we could have used a picture of Corel’s packaging. Lamarr sued Corel earlier this year over its use of a CorelDraw image of her. The picture had been produced by John Corkery, who was 1996 Best of Show winner of the Corel World Design Contest. Corel now seems to have come to an undisclosed settlement with her, which includes a five-year exclusive (oops — maybe we can’t use the pack-shot then) licence to use “the lifelike vector illustration of Hedy Lamarr on Corel’s graphic software packaging”. Lamarr, bless ‘er, says she’s looking forward to the continued success of Corel Corporation,  …

There’s this excerpt from a Sept. 21, 2015 posting (a pictorial essay of Lamarr’s life) by Shahebaz Khan on The Blaze Blog,

6. CorelDRAW:
For several years beginning in 1997, the boxes of Corel DRAW’s software suites were graced by a large Corel-drawn image of Lamarr. The picture won Corel DRAW’s yearly software suite cover design contest in 1996. Lamarr sued Corel for using the image without her permission. Corel countered that she did not own rights to the image. The parties reached an undisclosed settlement in 1998.

There’s also a Nov. 23, 1998 Corel Draw 8 product review by Mike Gorman on mymac.com, which includes a screenshot of the packaging that precipitated the lawsuit. Once they settled, it seems Corel used her image at least one more time.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.