FrogHeart
Commentary about nanotech, science policy and communication, society, and the arts
Acknowledging the folks who supported my S.NET 2012 work
Donations and thank yous
Tag Archives: computation
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),
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),
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.
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.
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.
This entry was posted in intellectual property, nanotechnology, science, science policy, Technology and tagged 2012 S&T report, 2018 Canadian federal budget, aerospace, agriculture, AI, AI winter, AI-based fraud detection, AI-based risk assessment, Alberta, Alexandra Dean, and telecommunications) research, Apple, artificial intelligence, Artificial Intelligence Robotics and Society, astronomy, Atlantic Canada, automotive, bibliometrics, biotechnology, Blackberry, blockchain, Bluetooth, Bombshell: The Hedy Lamarr Story, branch plant economy, Brazil, Bret Victor, British Columbia, Calgary, Canada, Canadian entrepreneurs, Canadian Institute for Advanced Research (CIFAR), Canadian Intellectual Property Office, Canadian open science, Catalyzing Canada's Digital Economy (2010), chemistry, China, cities, civil engineering, cognitive sciences, Collective dynamics of 'small-world' networks, communications, Competing in a Global Innovation Economy: The Current State of R and D in Canada, computation, computer technology, Corel, Corel Corporation, Corel Draw 8 product packaging, Council of Canadian Academies (CCA), cryptocurrencies, data science packages, digital communication, Duncan Watts, earth sciences, electrical engineering, Elements of Math, enabling and strategic technologies, engineering, environment, environmental sciences, Ericsson, Facebook, financial services, FinTech, food chemistry, forestry, Fraser Mustard, future of computing, Genome Canada, Geoffrey Hinton, George Antheil, Google, Government of Canada, GPS, Gutenberg, Hamilton, health, health services, Hedy Lamarr, ICT, ICT (information, India, Information and Communication Technologies (ICT), Informing Research Choices: Indicators and Judgment (2012), Innovation and Business Strategy: Why Canada Falls Short (2009), Innovation Science and Economic Development Canada, Intellectual Property Strategy, inventor, Iran, Italian Research in the World Day, Italy, James Somers, John Lettice, Learning in Machines and Brain, machine learning, Maritime provinces, maritime resources, Maritimes, materials science, mathematics, McGill University, mechanical engineering, medical informatics, Microsoft, Mike Gorman, mining, Montréal, Montréal Neuro, Montréal Neurological Institute initiative, movie star, nanoscience and nanotechnology, Natural Philosophy, natural sciences, Natural Sciences and Engineering Research Council (NSERC), Nortel, Northwest Territories, OECD, oil and gas, Ontario, Ontario government, open access, open collaborative networks, open data, open science, optoelectronics, Organization for Economic Cooperation and Development, Oshawa, other special machines, Ottawa, Paradox Lost: Explaining Canada's Research Strength and Innovation Weakness (2013), patent collective, patents, performing arts, personalized medicine, pharmaceutical companies, Pharmaceuticals, philosophy, photonics, physical sciences, physics, Piano Straordinario Vivere all'Italiana - Giornata della ricerca Italiana nel mondo, plastics, Prairies, psychology, public health, Quebec, R&D, regenerative medicine, research and development, Rockstar consortium, s Montréal Neurological Institute and Hospital, science technology engineering and mathematics, Shahebaz Khan, shared intellectual property, Sony, South Korea, statistics, Statistics Canada, steel industries, STEM, Steven Strogatz, Structural Genomics Consortium (SGC), subregional R and D, technometrics, telecommunications, The Scientific Paper is Obsolete, The State of Industrial R&D in Canada (2013), The State of Science and Technology in Canada 2006, The State of Science and Technology in Canada 2012, theology, Third assessment of The State of Science and Technology and Industrial Research and Development in Canada, Third assessment on the State of S&T and IR&D, Toronto, Toronto Financial Services Alliance, United States, Universite de Montreal, University of Alberta, University of Toronto, US, US Navy, Vancouver, Vector Institute for Artificial Intelligence, visual, Watts-Strogatz paper, Wi-Fi, Windsor, World Intellectual Property Organization, Yoshua Bengio on April 12, 2018 by Maryse de la Giroday.
Intel's 14nm chip: architecture revealed and scientist discusses the limits to computers
Anxieties about how much longer we can design and manufacture smaller, faster computer chips are commonplace even as companies continue to announce new, faster, smaller chips. Just before the US National Science Foundation (NSF) issued a press release concerning a Nature (journal) essay on the limits of computation, Intel announced a new microarchitecture for its 14nm chips .
First, there's Intel. In an Aug. 12, 2014 news item on Azonano, Intel announced its newest microarchitecture optimization,
Intel today disclosed details of its newest microarchitecture that is optimized with Intel's industry-leading 14nm manufacturing process. Together these technologies will provide high-performance and low-power capabilities to serve a broad array of computing needs and products from the infrastructure of cloud computing and the Internet of Things to personal and mobile computing.
An Aug. 11, 2014 Intel news release, which originated the news item, lists key points,
Intel disclosed details of the microarchitecture of the Intel® Core™ M processor, the first product to be manufactured using 14nm.
The combination of the new microarchitecture and manufacturing process will usher in a wave of innovation in new form factors, experiences and systems that are thinner and run silent and cool.
Intel architects and chip designers have achieved greater than two times reduction in the thermal design point when compared to a previous generation of processor while providing similar performance and improved battery life.
The new microarchitecture was optimized to take advantage of the new capabilities of the 14nm manufacturing process.
Intel has delivered the world's first 14nm technology in volume production. It uses second-generation Tri-gate (FinFET) transistors with industry-leading performance, power, density and cost per transistor.
Intel's 14nm technology will be used to manufacture a wide range of high-performance to low-power products including servers, personal computing devices and Internet of Things.
The first systems based on the Intel® Core™ M processor will be on shelves for the holiday selling season followed by broader OEM availability in the first half of 2015.
Additional products based on the Broadwell microarchitecture and 14nm process technology will be introduced in the coming months.
The company has made available supporting materials including videos titled, 'Advancing Moore's Law in 2014', 'Microscopic Mark Bohr: 14nm Explained', and 'Intel 14nm Manufacturing Process' which can be found here. An earlier mention of Intel and its 14nm manufacturing process can be found in my July 9, 2014 posting.
Meanwhile, in a more contemplative mood, Igor Markov of the University of Michigan has written an essay for Nature questioning the limits of computation as per an Aug. 14, 2014 news item on Azonano,
From their origins in the 1940s as sequestered, room-sized machines designed for military and scientific use, computers have made a rapid march into the mainstream, radically transforming industry, commerce, entertainment and governance while shrinking to become ubiquitous handheld portals to the world.
This progress has been driven by the industry's ability to continually innovate techniques for packing increasing amounts of computational circuitry into smaller and denser microchips. But with miniature computer processors now containing millions of closely-packed transistor components of near atomic size, chip designers are facing both engineering and fundamental limits that have become barriers to the continued improvement of computer performance.
Have we reached the limits to computation?
In a review article in this week's issue of the journal Nature, Igor Markov of the University of Michigan reviews limiting factors in the development of computing systems to help determine what is achievable, identifying "loose" limits and viable opportunities for advancements through the use of emerging technologies. His research for this project was funded in part by the National Science Foundation (NSF).
An Aug. 13, 2014 NSF news release, which originated the news item, describes Markov's Nature essay in greater detail,
"Just as the second law of thermodynamics was inspired by the discovery of heat engines during the industrial revolution, we are poised to identify fundamental laws that could enunciate the limits of computation in the present information age," says Sankar Basu, a program director in NSF's Computer and Information Science and Engineering Directorate. "Markov's paper revolves around this important intellectual question of our time and briefly touches upon most threads of scientific work leading up to it."
The article summarizes and examines limitations in the areas of manufacturing and engineering, design and validation, power and heat, time and space, as well as information and computational complexity.​
"What are these limits, and are some of them negotiable? On which assumptions are they based? How can they be overcome?" asks Markov. "Given the wealth of knowledge about limits to computation and complicated relations between such limits, it is important to measure both dominant and emerging technologies against them."
Limits related to materials and manufacturing are immediately perceptible. In a material layer ten atoms thick, missing one atom due to imprecise manufacturing changes electrical parameters by ten percent or more. Shrinking designs of this scale further inevitably leads to quantum physics and associated limits.
Limits related to engineering are dependent upon design decisions, technical abilities and the ability to validate designs. While very real, these limits are difficult to quantify. However, once the premises of a limit are understood, obstacles to improvement can potentially be eliminated. One such breakthrough has been in writing software to automatically find, diagnose and fix bugs in hardware designs.
Limits related to power and energy have been studied for many years, but only recently have chip designers found ways to improve the energy consumption of processors by temporarily turning off parts of the chip. There are many other clever tricks for saving energy during computation. But moving forward, silicon chips will not maintain the pace of improvement without radical changes. Atomic physics suggests intriguing possibilities but these are far beyond modern engineering capabilities.
Limits relating to time and space can be felt in practice. The speed of light, while a very large number, limits how fast data can travel. Traveling through copper wires and silicon transistors, a signal can no longer traverse a chip in one clock cycle today. A formula limiting parallel computation in terms of device size, communication speed and the number of available dimensions has been known for more than 20 years, but only recently has it become important now that transistors are faster than interconnections. This is why alternatives to conventional wires are being developed, but in the meantime mathematical optimization can be used to reduce the length of wires by rearranging transistors and other components.
Several key limits related to information and computational complexity have been reached by modern computers. Some categories of computational tasks are conjectured to be so difficult to solve that no proposed technology, not even quantum computing, promises consistent advantage. But studying each task individually often helps reformulate it for more efficient computation.
When a specific limit is approached and obstructs progress, understanding the assumptions made is key to circumventing it. Chip scaling will continue for the next few years, but each step forward will meet serious obstacles, some too powerful to circumvent.
What about breakthrough technologies? New techniques and materials can be helpful in several ways and can potentially be "game changers" with respect to traditional limits. For example, carbon nanotube transistors provide greater drive strength and can potentially reduce delay, decrease energy consumption and shrink the footprint of an overall circuit. On the other hand, fundamental limits–sometimes not initially anticipated–tend to obstruct new and emerging technologies, so it is important to understand them before promising a new revolution in power, performance and other factors.
"Understanding these important limits," says Markov, "will help us to bet on the right new techniques and technologies."
Here's a link to and a citation for Markov's article,
Limits on fundamental limits to computation by Igor L. Markov. Nature 512, 147–154 (14 August 2014) doi:10.1038/nature13570 Published online 13 August 2014
This paper is behind a paywall but a free preview is available via ReadCube Access.
It's a fascinating question, what are the limits? It's one being asked not only with regard to computation but also to medicine, human enhancement, and artificial intelligence for just a few areas of endeavour.
This entry was posted in Technology and tagged 14nm computer chips, 14nm manufacturing process, computation, Igor L. Markov, Intel, Limits on fundamental limits to computation, NSF, Sankar Basu, University of Michigan, US National Science Foundation on August 19, 2014 by Maryse de la Giroday.
biomimcry
multimodal discourse
nanophotonics
neuromorphic engineering
public perceptions
science philosophy
social implications
sscience education