Tag Archives: artificial intelligence

The Canadian science scene and the 2017 Canadian federal budget

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

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

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

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

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

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

Here are highlights from the 2017 federal budget:

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

You can find the entire 2017-18 budget here.

Science and the 2017-18 budget

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

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

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

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

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

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

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

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

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

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

Stem Cell Research

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

Space Exploration

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

Quantum Information

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

Social Innovation

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

International Research Collaborations

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

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

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

Canada a Pioneer in Deep Learning in Machines and Brains

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

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

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

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

Attracting brains from afar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thankfully, Bernstein calms down a bit,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Digital policy and intellectual property issues

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

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

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

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

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

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

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

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

New principles for AI (artificial intelligence) research along with some history and a plea for a democratic discussion

For almost a month I’ve been meaning to get to this Feb. 1, 2017 essay by Andrew Maynard (director of Risk Innovation Lab at Arizona State University) and Jack Stilgoe (science policy lecturer at University College London [UCL]) on the topic of artificial intelligence and principles (Note: Links have been removed). First, a walk down memory lane,

Today [Feb. 1, 2017] in Washington DC, leading US and UK scientists are meeting to share dispatches from the frontiers of machine learning – an area of research that is creating new breakthroughs in artificial intelligence (AI). Their meeting follows the publication of a set of principles for beneficial AI that emerged from a conference earlier this year at a place with an important history.

In February 1975, 140 people – mostly scientists, with a few assorted lawyers, journalists and others – gathered at a conference centre on the California coast. A magazine article from the time by Michael Rogers, one of the few journalists allowed in, reported that most of the four days’ discussion was about the scientific possibilities of genetic modification. Two years earlier, scientists had begun using recombinant DNA to genetically modify viruses. The Promethean nature of this new tool prompted scientists to impose a moratorium on such experiments until they had worked out the risks. By the time of the Asilomar conference, the pent-up excitement was ready to burst. It was only towards the end of the conference when a lawyer stood up to raise the possibility of a multimillion-dollar lawsuit that the scientists focussed on the task at hand – creating a set of principles to govern their experiments.

The 1975 Asilomar meeting is still held up as a beacon of scientific responsibility. However, the story told by Rogers, and subsequently by historians, is of scientists motivated by a desire to head-off top down regulation with a promise of self-governance. Geneticist Stanley Cohen said at the time, ‘If the collected wisdom of this group doesn’t result in recommendations, the recommendations may come from other groups less well qualified’. The mayor of Cambridge, Massachusetts was a prominent critic of the biotechnology experiments then taking place in his city. He said, ‘I don’t think these scientists are thinking about mankind at all. I think that they’re getting the thrills and the excitement and the passion to dig in and keep digging to see what the hell they can do’.

The concern in 1975 was with safety and containment in research, not with the futures that biotechnology might bring about. A year after Asilomar, Cohen’s colleague Herbert Boyer founded Genentech, one of the first biotechnology companies. Corporate interests barely figured in the conversations of the mainly university scientists.

Fast-forward 42 years and it is clear that machine learning, natural language processing and other technologies that come under the AI umbrella are becoming big business. The cast list of the 2017 Asilomar meeting included corporate wunderkinds from Google, Facebook and Tesla as well as researchers, philosophers, and other academics. The group was more intellectually diverse than their 1975 equivalents, but there were some notable absences – no public and their concerns, no journalists, and few experts in the responsible development of new technologies.

Maynard and Stilgoe offer a critique of the latest principles,

The principles that came out of the meeting are, at least at first glance, a comforting affirmation that AI should be ‘for the people’, and not to be developed in ways that could cause harm. They promote the idea of beneficial and secure AI, development for the common good, and the importance of upholding human values and shared prosperity.

This is good stuff. But it’s all rather Motherhood and Apple Pie: comforting and hard to argue against, but lacking substance. The principles are short on accountability, and there are notable absences, including the need to engage with a broader set of stakeholders and the public. At the early stages of developing new technologies, public concerns are often seen as an inconvenience. In a world in which populism appears to be trampling expertise into the dirt, it is easy to understand why scientists may be defensive.

I encourage you to read this thoughtful essay in its entirety although I do have one nit to pick:  Why only US and UK scientists? I imagine the answer may lie in funding and logistics issues but I find it surprising that the critique makes no mention of the international community as a nod to inclusion.

For anyone interested in the Asolimar AI principles (2017), you can find them here. You can also find videos of the two-day workshop (Jan. 31 – Feb. 1, 2017 workshop titled The Frontiers of Machine Learning (a Raymond and Beverly Sackler USA-UK Scientific Forum [US National Academy of Sciences]) here (videos for each session are available on Youtube).

Essays on Frankenstein

Slate.com is dedicating a month (January 2017) to Frankenstein. This means there were will be one or more essays each week on one aspect or another of Frankenstein and science. These essays are one of a series of initiatives jointly supported by Slate, Arizona State University, and an organization known as New America. It gets confusing since these essays are listed as part of two initiatives:  Futurography and Future Tense.

The really odd part, as far as I’m concerned, is that there is no mention of Arizona State University’s (ASU) The Frankenstein Bicentennial Project (mentioned in my Oct. 26, 2016 posting). Perhaps they’re concerned that people will think ASU is advertising the project?

Introductions

Getting back to the essays, a Jan. 3, 2017 article by Jacob Brogan explains, by means of a ‘Question and Answer’ format article, why the book and the monster maintain popular interest after two centuries (Note: We never do find out who or how many people are supplying the answers),

OK, fine. I get that this book is important, but why are we talking about it in a series about emerging technology?

Though people still tend to weaponize it as a simple anti-scientific screed, Frankenstein, which was first published in 1818, is much richer when we read it as a complex dialogue about our relationship to innovation—both our desire for it and our fear of the changes it brings. Mary Shelley was just a teenager when she began to compose Frankenstein, but she was already grappling with our complex relationship to new forces. Almost two centuries on, the book is just as propulsive and compelling as it was when it was first published. That’s partly because it’s so thick with ambiguity—and so resistant to easy interpretation.

Is it really ambiguous? I mean, when someone calls something frankenfood, they aren’t calling it “ethically ambiguous food.”

It’s a fair point. For decades, Frankenstein has been central to discussions in and about bioethics. Perhaps most notably, it frequently crops up as a reference point in discussions of genetically modified organisms, where the prefix Franken- functions as a sort of convenient shorthand for human attempts to meddle with the natural order. Today, the most prominent flashpoint for those anxieties is probably the clustered regularly interspaced short palindromic repeats, or CRISPR, gene-editing technique [emphasis mine]. But it’s really oversimplifying to suggest Frankenstein is a cautionary tale about monkeying with life.

As we’ll see throughout this month on Futurography, it’s become a lens for looking at the unintended consequences of things like synthetic biology, animal experimentation, artificial intelligence, and maybe even social networking. Facebook, for example, has arguably taken on a life of its own, as its algorithms seem to influence the course of elections. Mark Zuckerberg, who’s sometimes been known to disavow the power of his own platform, might well be understood as a Frankensteinian figure, amplifying his creation’s monstrosity by neglecting its practical needs.

But this book is almost 200 years old! Surely the actual science in it is bad.

Shelley herself would probably be the first to admit that the science in the novel isn’t all that accurate. Early in the novel, Victor Frankenstein meets with a professor who castigates him for having read the wrong works of “natural philosophy.” Shelley’s protagonist has mostly been studying alchemical tomes and otherwise fantastical works, the sort of things that were recognized as pseudoscience, even by the standards of the day. Near the start of the novel, Frankenstein attends a lecture in which the professor declaims on the promise of modern science. He observes that where the old masters “promised impossibilities and performed nothing,” the new scientists achieve far more in part because they “promise very little; they know that metals cannot be transmuted and that the elixir of life is a chimera.”

Is it actually about bad science, though?

Not exactly, but it has been read as a story about bad scientists.

Ultimately, Frankenstein outstrips his own teachers, of course, and pulls off the very feats they derided as mere fantasy. But Shelley never seems to confuse fact and fiction, and, in fact, she largely elides any explanation of how Frankenstein pulls off the miraculous feat of animating dead tissue. We never actually get a scene of the doctor awakening his creature. The novel spends far more dwelling on the broader reverberations of that act, showing how his attempt to create one life destroys countless others. Read in this light, Frankenstein isn’t telling us that we shouldn’t try to accomplish new things, just that we should take care when we do.

This speaks to why the novel has stuck around for so long. It’s not about particular scientific accomplishments but the vagaries of scientific progress in general.

Does that make it into a warning against playing God?

It’s probably a mistake to suggest that the novel is just a critique of those who would usurp the divine mantle. Instead, you can read it as a warning about the ways that technologists fall short of their ambitions, even in their greatest moments of triumph.

Look at what happens in the novel: After bringing his creature to life, Frankenstein effectively abandons it. Later, when it entreats him to grant it the rights it thinks it deserves, he refuses. Only then—after he reneges on his responsibilities—does his creation really go bad. We all know that Frankenstein is the doctor and his creation is the monster, but to some extent it’s the doctor himself who’s made monstrous by his inability to take responsibility for what he’s wrought.

I encourage you to read Brogan’s piece in its entirety and perhaps supplement the reading. Mary Shelley has a pretty interesting history. She ran off with Percy Bysshe Shelley who was married to another woman, in 1814  at the age of seventeen years. Her parents were both well known and respected intellectuals and philosophers, William Godwin and Mary Wollstonecraft. By the time Mary Shelley wrote her book, her first baby had died and she had given birth to a second child, a boy.  Percy Shelley was to die a few years later as was her son and a third child she’d given birth to. (Her fourth child born in 1819 did survive.) I mention the births because one analysis I read suggests the novel is also a commentary on childbirth. In fact, the Frankenstein narrative has been examined from many perspectives (other than science) including feminism and LGBTQ studies.

Getting back to the science fiction end of things, the next part of the Futurography series is titled “A Cheat-Sheet Guide to Frankenstein” and that too is written by Jacob Brogan with a publication date of Jan. 3, 2017,

Key Players

Marilyn Butler: Butler, a literary critic and English professor at the University of Cambridge, authored the seminal essay “Frankenstein and Radical Science.”

Jennifer Doudna: A professor of chemistry and biology at the University of California, Berkeley, Doudna helped develop the CRISPR gene-editing technique [emphasis mine].

Stephen Jay Gould: Gould is an evolutionary biologist and has written in defense of Frankenstein’s scientific ambitions, arguing that hubris wasn’t the doctor’s true fault.

Seán Ó hÉigeartaigh: As executive director of the Center for Existential Risk at the University of Cambridge, hÉigeartaigh leads research into technologies that threaten the existience of our species.

Jim Hightower: This columnist and activist helped popularize the term frankenfood to describe genetically modified crops.

Mary Shelley: Shelley, the author of Frankenstein, helped create science fiction as we now know it.

J. Craig Venter: A leading genomic researcher, Venter has pursued a variety of human biotechnology projects.

Lingo

….

Debates

Popular Culture

Further Reading

….

‘Franken’ and CRISPR

The first essay is in a Jan. 6, 2016 article by Kay Waldman focusing on the ‘franken’ prefix (Note: links have been removed),

In a letter to the New York Times on June 2, 1992, an English professor named Paul Lewis lopped off the top of Victor Frankenstein’s surname and sewed it onto a tomato. Railing against genetically modified crops, Lewis put a new generation of natural philosophers on notice: “If they want to sell us Frankenfood, perhaps it’s time to gather the villagers, light some torches and head to the castle,” he wrote.

William Safire, in a 2000 New York Times column, tracked the creation of the franken- prefix to this moment: an academic channeling popular distrust of science by invoking the man who tried to improve upon creation and ended up disfiguring it. “There’s no telling where or how it will end,” he wrote wryly, referring to the spread of the construction. “It has enhanced the sales of the metaphysical novel that Ms. Shelley’s husband, the poet Percy Bysshe Shelley, encouraged her to write, and has not harmed sales at ‘Frank’n’Stein,’ the fast-food chain whose hot dogs and beer I find delectably inorganic.” Safire went on to quote the American Dialect Society’s Laurence Horn, who lamented that despite the ’90s flowering of frankenfruits and frankenpigs, people hadn’t used Frankensense to describe “the opposite of common sense,” as in “politicians’ motivations for a creatively stupid piece of legislation.”

A year later, however, Safire returned to franken- in dead earnest. In an op-ed for the Times avowing the ethical value of embryonic stem cell research, the columnist suggested that a White House conference on bioethics would salve the fears of Americans concerned about “the real dangers of the slippery slope to Frankenscience.”

All of this is to say that franken-, the prefix we use to talk about human efforts to interfere with nature, flips between “funny” and “scary” with ease. Like Shelley’s monster himself, an ungainly patchwork of salvaged parts, it can seem goofy until it doesn’t—until it taps into an abiding anxiety that technology raises in us, a fear of overstepping.

Waldman’s piece hints at how language can shape discussions while retaining a rather playful quality.

This series looks to be a good introduction while being a bit problematic in spots, which roughly sums up my conclusion about their ‘nano’ series in my Oct. 7, 2016 posting titled: Futurography’s nanotechnology series: a digest.

By the way, I noted the mention of CRISPR as it brought up an issue that they don’t appear to be addressing in this series (perhaps they will do this elsewhere?): intellectual property.

There’s a patent dispute over CRISPR as noted in this American Chemical Society’s Chemistry and Engineering News Jan. 9, 2017 video,

Playing God

This series on Frankenstein is taking on other contentious issues. A perennial favourite is ‘playing God’ as noted in Bina Venkataraman’s Jan. 11, 2017 essay on the topic,

Since its publication nearly 200 years ago, Shelley’s gothic novel has been read as a cautionary tale of the dangers of creation and experimentation. James Whale’s 1931 film took the message further, assigning explicitly the hubris of playing God to the mad scientist. As his monster comes to life, Dr. Frankenstein, played by Colin Clive, triumphantly exclaims: “Now I know what it feels like to be God!”

The admonition against playing God has since been ceaselessly invoked as a rhetorical bogeyman. Secular and religious, critic and journalist alike have summoned the term to deride and outright dismiss entire areas of research and technology, including stem cells, genetically modified crops, recombinant DNA, geoengineering, and gene editing. As we near the two-century commemoration of Shelley’s captivating story, we would be wise to shed this shorthand lesson—and to put this part of the Frankenstein legacy to rest in its proverbial grave.

The trouble with the term arises first from its murkiness. What exactly does it mean to play God, and why should we find it objectionable on its face? All but zealots would likely agree that it’s fine to create new forms of life through selective breeding and grafting of fruit trees, or to use in-vitro fertilization to conceive life outside the womb to aid infertile couples. No one objects when people intervene in what some deem “acts of God,” such as earthquakes, to rescue victims and provide relief. People get fully behind treating patients dying of cancer with “unnatural” solutions like chemotherapy. Most people even find it morally justified for humans to mete out decisions as to who lives or dies in the form of organ transplant lists that prize certain people’s survival over others.

So what is it—if not the imitation of a deity or the creation of life—that inspires people to invoke the idea of “playing God” to warn against, or even stop, particular technologies? A presidential commission charged in the early 1980s with studying the ethics of genetic engineering of humans, in the wake of the recombinant DNA revolution, sheds some light on underlying motivations. The commission sought to understand the concerns expressed by leaders of three major religious groups in the United States—representing Protestants, Jews, and Catholics—who had used the phrase “playing God” in a 1980 letter to President Jimmy Carter urging government oversight. Scholars from the three faiths, the commission concluded, did not see a theological reason to flat-out prohibit genetic engineering. Their concerns, it turned out, weren’t exactly moral objections to scientists acting as God. Instead, they echoed those of the secular public; namely, they feared possible negative effects from creating new human traits or new species. In other words, the religious leaders who called recombinant DNA tools “playing God” wanted precautions taken against bad consequences but did not inherently oppose the use of the technology as an act of human hubris.

She presents an interesting argument and offers this as a solution,

The lesson for contemporary science, then, is not that we should cease creating and discovering at the boundaries of current human knowledge. It’s that scientists and technologists ought to steward their inventions into society, and to more rigorously participate in public debate about their work’s social and ethical consequences. Frankenstein’s proper legacy today would be to encourage researchers to address the unsavory implications of their technologies, whether it’s the cognitive and social effects of ubiquitous smartphone use or the long-term consequences of genetically engineered organisms on ecosystems and biodiversity.

Some will undoubtedly argue that this places an undue burden on innovators. Here, again, Shelley’s novel offers a lesson. Scientists who cloister themselves as Dr. Frankenstein did—those who do not fully contemplate the consequences of their work—risk later encounters with the horror of their own inventions.

At a guess, Venkataraman seems to be assuming that if scientists communicate and make their case that the public will cease to panic with reference moralistic and other concerns. My understanding is that social scientists have found this is not the case. Someone may understand the technology quite well and still oppose it.

Frankenstein and anti-vaxxers

The Jan. 16, 2017 essay by Charles Kenny is the weakest of the lot, so far (Note: Links have been removed),

In 1780, University of Bologna physician Luigi Galvani found something peculiar: When he applied an electric current to the legs of a dead frog, they twitched. Thirty-seven years later, Mary Shelley had Galvani’s experiments in mind as she wrote her fable of Faustian overreach, wherein Dr. Victor Frankenstein plays God by reanimating flesh.

And a little less than halfway between those two dates, English physician Edward Jenner demonstrated the efficacy of a vaccine against smallpox—one of the greatest killers of the age. Given the suspicion with which Romantic thinkers like Shelley regarded scientific progress, it is no surprise that many at the time damned the procedure as against the natural order. But what is surprising is how that suspicion continues to endure, even after two centuries of spectacular successes for vaccination. This anti-vaccination stance—which now infects even the White House—demonstrates the immense harm that can be done by excessive distrust of technological advance.

Kenny employs history as a framing device. Crudely, Galvani’s experiments led to Mary Shelley’s Frankenstein which is a fable about ‘playing God’. (Kenny seems unaware there are many other readings of and perspectives on the book.) As for his statement ” … the suspicion with which Romantic thinkers like Shelley regarded scientific progress … ,” I’m not sure how he arrived at his conclusion about Romantic thinkers. According to Richard Holmes (in his book, The Age of Wonder: How the Romantic Generation Discovered the Beauty and Terror of Science), their relationship to science was more complex. Percy Bysshe Shelley ran ballooning experiments and wrote poetry about science, which included footnotes for the literature and concepts he was referencing; John Keats was a medical student prior to his establishment as a poet; and Samuel Taylor Coleridge (The Rime of the Ancient Mariner, etc.) maintained a healthy correspondence with scientists of the day sometimes influencing their research. In fact, when you analyze the matter, you realize even scientists are, on occasion, suspicious of science.

As for the anti-vaccination wars, I wish this essay had been more thoughtful. Yes, Andrew Wakefield’s research showing a link between MMR (measles, mumps, and rubella) vaccinations and autism is a sham. However, having concerns and suspicions about technology does not render you a fool who hasn’t progressed from 18th/19th Century concerns and suspicions about science and technology. For example, vaccines are being touted for all kinds of things, the latest being a possible antidote to opiate addiction (see Susan Gados’ June 28, 2016 article for ScienceNews). Are we going to be vaccinated for everything? What happens when you keep piling vaccination on top of vaccination? Instead of a debate, the discussion has devolved to: “I’m right and you’re wrong.”

For the record, I’m grateful for the vaccinations I’ve had and the diminishment of diseases that were devastating and seem to be making a comeback with this current anti-vaccination fever. That said, I think there are some important questions about vaccines.

Kenny’s essay could have been a nuanced discussion of vaccines that have clearly raised the bar for public health and some of the concerns regarding the current pursuit of yet more vaccines. Instead, he’s been quite dismissive of anyone who questions vaccination orthodoxy.

The end of this piece

There will be more essays in Slate’s Frankenstein series but I don’t have time to digest and write commentary for all of them.

Please use this piece as a critical counterpoint to some of the series and, if I’ve done my job, you’ll critique this critique. Please do let me know if you find any errors or want to add an opinion or add your own critique in the Comments of this blog.

ETA Jan. 25, 2017: Here’s the Frankenstein webspace on Slate’s Futurography which lists all the essays in this series. It’s well worth looking at the list. There are several that were not covered here.

Spintronics-based artificial intelligence

Courtesy: Tohoku University

Japanese researchers have managed to mimic a synapse (artificial neural network) with a spintronics-based device according to a Dec. 19, 2016 Tohoku University press release (also on EurekAlert but dated Dec. 20, 2016),

Researchers at Tohoku University have, for the first time, successfully demonstrated the basic operation of spintronics-based artificial intelligence.

Artificial intelligence, which emulates the information processing function of the brain that can quickly execute complex and complicated tasks such as image recognition and weather prediction, has attracted growing attention and has already been partly put to practical use.

The currently-used artificial intelligence works on the conventional framework of semiconductor-based integrated circuit technology. However, this lacks the compactness and low-power feature of the human brain. To overcome this challenge, the implementation of a single solid-state device that plays the role of a synapse is highly promising.

The Tohoku University research group of Professor Hideo Ohno, Professor Shigeo Sato, Professor Yoshihiko Horio, Associate Professor Shunsuke Fukami and Assistant Professor Hisanao Akima developed an artificial neural network in which their recently-developed spintronic devices, comprising micro-scale magnetic material, are employed (Fig. 1). The used spintronic device is capable of memorizing arbitral values between 0 and 1 in an analogue manner unlike the conventional magnetic devices, and thus perform the learning function, which is served by synapses in the brain.

Using the developed network (Fig. 2), the researchers examined an associative memory operation, which is not readily executed by conventional computers. Through the multiple trials, they confirmed that the spintronic devices have a learning ability with which the developed artificial neural network can successfully associate memorized patterns (Fig. 3) from their input noisy versions just like the human brain can.

The proof-of-concept demonstration in this research is expected to open new horizons in artificial intelligence technology – one which is of a compact size, and which simultaneously achieves fast-processing capabilities and ultralow-power consumption. These features should enable the artificial intelligence to be used in a broad range of societal applications such as image/voice recognition, wearable terminals, sensor networks and nursing-care robots.

Here are Fig. 1 and Fig. 2, as mentioned in the press release,

Fig. 1. (a) Optical photograph of a fabricated spintronic device that serves as artificial synapse in the present demonstration. Measurement circuit for the resistance switching is also shown. (b) Measured relation between the resistance of the device and applied current, showing analogue-like resistance variation. (c) Photograph of spintronic device array mounted on a ceramic package, which is used for the developed artificial neural network. Courtesy: Tohoku University

Fig. 2. Block diagram of developed artificial neural network, consisting of PC, FPGA, and array of spintronics (spin-orbit torque; SOT) devices. Courtesy: Tohoku University

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

Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation by William A. Borders, Hisanao Akima1, Shunsuke Fukami, Satoshi Moriya, Shouta Kurihara, Yoshihiko Horio, Shigeo Sato, and Hideo Ohno. Applied Physics Express, Volume 10, Number 1 https://doi.org/10.7567/APEX.10.013007. Published 20 December 2016

© 2017 The Japan Society of Applied Physics

This is an open access paper.

For anyone interested in my other posts on memristors, artificial brains, and artificial intelligence, you can search this blog for those terms  and/or Neuromorphic Engineering in the Categories section.

Artificial intelligence and industrial applications

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How might artificial intelligence affect urban life in 2030? A study

Peering into the future is always a chancy business as anyone who’s seen those film shorts from the 1950’s and 60’s which speculate exuberantly as to what the future will bring knows.

A sober approach (appropriate to our times) has been taken in a study about the impact that artificial intelligence might have by 2030. From a Sept. 1, 2016 Stanford University news release (also on EurekAlert) by Tom Abate (Note: Links have been removed),

A panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city – in areas as diverse as transportation, health care and education ­– and to spur discussion about how to ensure the safe, fair and beneficial development of these rapidly emerging technologies.

Titled “Artificial Intelligence and Life in 2030,” this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines.

“We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life,” said Peter Stone, a computer scientist at the University of Texas at Austin and chair of the 17-member panel of international experts. “But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared.”

The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford. AI100 formed a standing committee of scientists and charged this body with commissioning periodic reports on different aspects of AI over the ensuing century.

“This process will be a marathon, not a sprint, but today we’ve made a good start,” said Russ Altman, a professor of bioengineering and the Stanford faculty director of AI100. “Stanford is excited to host this process of introspection. This work makes practical contribution to the public debate on the roles and implications of artificial intelligence.”

The AI100 standing committee first met in 2015, led by chairwoman and Harvard computer scientist Barbara Grosz. It sought to convene a panel of scientists with diverse professional and personal backgrounds and enlist their expertise to assess the technological, economic and policy implications of potential AI applications in a societally relevant setting.

“AI technologies can be reliable and broadly beneficial,” Grosz said. “Being transparent about their design and deployment challenges will build trust and avert unjustified fear and suspicion.”

The report investigates eight domains of human activity in which AI technologies are beginning to affect urban life in ways that will become increasingly pervasive and profound by 2030.

The 28,000-word report includes a glossary to help nontechnical readers understand how AI applications such as computer vision might help screen tissue samples for cancers or how natural language processing will allow computerized systems to grasp not simply the literal definitions, but the connotations and intent, behind words.

The report is broken into eight sections focusing on applications of AI. Five examine application arenas such as transportation where there is already buzz about self-driving cars. Three other sections treat technological impacts, like the section on employment and workplace trends which touches on the likelihood of rapid changes in jobs and incomes.

“It is not too soon for social debate on how the fruits of an AI-dominated economy should be shared,” the researchers write in the report, noting also the need for public discourse.

“Currently in the United States, at least sixteen separate agencies govern sectors of the economy related to AI technologies,” the researchers write, highlighting issues raised by AI applications: “Who is responsible when a self-driven car crashes or an intelligent medical device fails? How can AI applications be prevented from [being used for] racial discrimination or financial cheating?”

The eight sections discuss:

Transportation: Autonomous cars, trucks and, possibly, aerial delivery vehicles may alter how we commute, work and shop and create new patterns of life and leisure in cities.

Home/service robots: Like the robotic vacuum cleaners already in some homes, specialized robots will clean and provide security in live/work spaces that will be equipped with sensors and remote controls.

Health care: Devices to monitor personal health and robot-assisted surgery are hints of things to come if AI is developed in ways that gain the trust of doctors, nurses, patients and regulators.

Education: Interactive tutoring systems already help students learn languages, math and other skills. More is possible if technologies like natural language processing platforms develop to augment instruction by humans.

Entertainment: The conjunction of content creation tools, social networks and AI will lead to new ways to gather, organize and deliver media in engaging, personalized and interactive ways.

Low-resource communities: Investments in uplifting technologies like predictive models to prevent lead poisoning or improve food distributions could spread AI benefits to the underserved.

Public safety and security: Cameras, drones and software to analyze crime patterns should use AI in ways that reduce human bias and enhance safety without loss of liberty or dignity.

Employment and workplace: Work should start now on how to help people adapt as the economy undergoes rapid changes as many existing jobs are lost and new ones are created.

“Until now, most of what is known about AI comes from science fiction books and movies,” Stone said. “This study provides a realistic foundation to discuss how AI technologies are likely to affect society.”

Grosz said she hopes the AI 100 report “initiates a century-long conversation about ways AI-enhanced technologies might be shaped to improve life and societies.”

You can find the A100 website here, and the group’s first paper: “Artificial Intelligence and Life in 2030” here. Unfortunately, I don’t have time to read the report but I hope to do so soon.

The AI100 website’s About page offered a surprise,

This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.

Now, together with Russ Altman, a professor of bioengineering and computer science at Stanford, Horvitz has formed a committee that will select a panel to begin a series of periodic studies on how AI will affect automation, national security, psychology, ethics, law, privacy, democracy and other issues.

“Artificial intelligence is one of the most profound undertakings in science, and one that will affect every aspect of human life,” said Stanford President John Hennessy, who helped initiate the project. “Given’s Stanford’s pioneering role in AI and our interdisciplinary mindset, we feel obliged and qualified to host a conversation about how artificial intelligence will affect our children and our children’s children.”

Five leading academicians with diverse interests will join Horvitz and Altman in launching this effort. They are:

  • Barbara Grosz, the Higgins Professor of Natural Sciences at HarvardUniversity and an expert on multi-agent collaborative systems;
  • Deirdre K. Mulligan, a lawyer and a professor in the School of Information at the University of California, Berkeley, who collaborates with technologists to advance privacy and other democratic values through technical design and policy;

    This effort, called the One Hundred Year Study on Artificial Intelligence, or AI100, is the brainchild of computer scientist and Stanford alumnus Eric Horvitz who, among other credits, is a former president of the Association for the Advancement of Artificial Intelligence.

    In that capacity Horvitz convened a conference in 2009 at which top researchers considered advances in artificial intelligence and its influences on people and society, a discussion that illuminated the need for continuing study of AI’s long-term implications.

    Now, together with Russ Altman, a professor of bioengineering and computer science at Stanford, Horvitz has formed a committee that will select a panel to begin a series of periodic studies on how AI will affect automation, national security, psychology, ethics, law, privacy, democracy and other issues.

    “Artificial intelligence is one of the most profound undertakings in science, and one that will affect every aspect of human life,” said Stanford President John Hennessy, who helped initiate the project. “Given’s Stanford’s pioneering role in AI and our interdisciplinary mindset, we feel obliged and qualified to host a conversation about how artificial intelligence will affect our children and our children’s children.”

    Five leading academicians with diverse interests will join Horvitz and Altman in launching this effort. They are:

    • Barbara Grosz, the Higgins Professor of Natural Sciences at HarvardUniversity and an expert on multi-agent collaborative systems;
    • Deirdre K. Mulligan, a lawyer and a professor in the School of Information at the University of California, Berkeley, who collaborates with technologists to advance privacy and other democratic values through technical design and policy;
    • Yoav Shoham, a professor of computer science at Stanford, who seeks to incorporate common sense into AI;
    • Tom Mitchell, the E. Fredkin University Professor and chair of the machine learning department at Carnegie Mellon University, whose studies include how computers might learn to read the Web;
    • and Alan Mackworth, a professor of computer science at the University of British Columbia [emphases mine] and the Canada Research Chair in Artificial Intelligence, who built the world’s first soccer-playing robot.

    I wasn’t expecting to see a Canadian listed as a member of the AI100 standing committee and then I got another surprise (from the AI100 People webpage),

    Study Panels

    Study Panels are planned to convene every 5 years to examine some aspect of AI and its influences on society and the world. The first study panel was convened in late 2015 to study the likely impacts of AI on urban life by the year 2030, with a focus on typical North American cities.

    2015 Study Panel Members

    • Peter Stone, UT Austin, Chair
    • Rodney Brooks, Rethink Robotics
    • Erik Brynjolfsson, MIT
    • Ryan Calo, University of Washington
    • Oren Etzioni, Allen Institute for AI
    • Greg Hager, Johns Hopkins University
    • Julia Hirschberg, Columbia University
    • Shivaram Kalyanakrishnan, IIT Bombay
    • Ece Kamar, Microsoft
    • Sarit Kraus, Bar Ilan University
    • Kevin Leyton-Brown, [emphasis mine] UBC [University of British Columbia]
    • David Parkes, Harvard
    • Bill Press, UT Austin
    • AnnaLee (Anno) Saxenian, Berkeley
    • Julie Shah, MIT
    • Milind Tambe, USC
    • Astro Teller, Google[X]
  • [emphases mine] and the Canada Research Chair in Artificial Intelligence, who built the world’s first soccer-playing robot.

I wasn’t expecting to see a Canadian listed as a member of the AI100 standing committee and then I got another surprise (from the AI100 People webpage),

Study Panels

Study Panels are planned to convene every 5 years to examine some aspect of AI and its influences on society and the world. The first study panel was convened in late 2015 to study the likely impacts of AI on urban life by the year 2030, with a focus on typical North American cities.

2015 Study Panel Members

  • Peter Stone, UT Austin, Chair
  • Rodney Brooks, Rethink Robotics
  • Erik Brynjolfsson, MIT
  • Ryan Calo, University of Washington
  • Oren Etzioni, Allen Institute for AI
  • Greg Hager, Johns Hopkins University
  • Julia Hirschberg, Columbia University
  • Shivaram Kalyanakrishnan, IIT Bombay
  • Ece Kamar, Microsoft
  • Sarit Kraus, Bar Ilan University
  • Kevin Leyton-Brown, [emphasis mine] UBC [University of British Columbia]
  • David Parkes, Harvard
  • Bill Press, UT Austin
  • AnnaLee (Anno) Saxenian, Berkeley
  • Julie Shah, MIT
  • Milind Tambe, USC
  • Astro Teller, Google[X]

I see they have representation from Israel, India, and the private sector as well. Refreshingly, there’s more than one woman on the standing committee and in this first study group. It’s good to see these efforts at inclusiveness and I’m particularly delighted with the inclusion of an organization from Asia. All too often inclusiveness means Europe, especially the UK. So, it’s good (and I think important) to see a different range of representation.

As for the content of report, should anyone have opinions about it, please do let me know your thoughts in the blog comments.

Interactive chat with Amy Krouse Rosenthal’s memoir

It’s nice to see writers using technology in their literary work to create new forms although I do admit to a pang at the thought that this might have a deleterious effect on book clubs as the headline (Ditch Your Book Club: This AI-Powered Memoir Wants To Chat With You) for Claire Zulkey’s Sept. 1, 2016 article for Fast Company suggests,

Instead of attempting to write a book that would defeat the distractions of a smartphone, author Amy Krouse Rosenthal decided to make the two kiss and make up with her new memoir.

“I have this habit of doing interactive stuff,” says the Chicago writer and filmmaker, whose previous projects have enticed readers to communicate via email, website, or in person, and before all that, a P.O. box. As she pondered a logical follow-up to her 2005 memoir Encyclopedia of an Ordinary Life (which, among other prompts, offered readers a sample of her favorite perfume if they got in touch via her website), Rosenthal hit upon the concept of a textbook. The idea appealed to her, for its bibliographical elements and as a new way of conversing with her readers. And also, of course, because of the double meaning of the title. Textbook, which went on sale August 9 [2016], is a book readers can send texts to, and the book will text them back. “When I realized the wordplay opportunity, and that nobody had done that before, I loved it,” Rosenthal says. “Most people would probably be reading with a phone in their hands anyway.”

Rosenthal may be best known for the dozens of children’s books she’s published, but Encyclopedia was listed in Amazon’s top 10 memoirs of the decade for its alphabetized musings gathered together under the premise, “I have not survived against all odds. I have not lived to tell. I have not witnessed the extraordinary. This is my story.” Her writing often celebrates the serendipitous moment, the smallness of our world, the misheard sentence that was better than the real one—always in praise of the flashes of magic in our mundane lives. Textbook, Rosenthal says, is not a prequel or a sequel but “an equal” to Encyclopedia. It is organized by subject, and Rosenthal shares her favorite anagrams, admits a bias against people who sign emails with just their initials, and exhorts readers, next time they are at a party, to attempt to write a “group biography.” …

… when she sent the book out to publishers, Rosenthal explains, “Pretty much everybody got it. Nobody said, ‘We want to do this book but we don’t want to do that texting thing.’”

Zulkey also covers some of the nitty gritty elements of getting this book published and developed,

After she signed with Dutton, Rosenthal’s editors got in touch with OneReach, a Denver company that specializes in providing multichannel, conversational bot experiences, “This book is a great illustration of what we’re going to see a lot more of in the future,” says OneReach cofounder Robb Wilson. “It’s conversational and has some basic AI components in it.”

Textbook has nearly 20 interactive elements to it, some of which involve email or going to the book’s website, but many are purely text-message-based. One example is a prompt to send in good thoughts, which Rosenthal will then print and send out in a bottle to sea. Another asks readers to text photos of a rainbow they are witnessing in real time. The rainbow and its location are then posted on the book’s website in a live rainbow feed. And yet another puts out a call for suggestions for matching tattoos that at least one reader and Rosenthal will eventually get. Three weeks after its publication date, the book has received texts from over 600 readers.

Nearly anyone who has received a text from Walgreens saying a prescription is ready, gotten an appointment confirmation from a dentist, or even voted on American Idol has interacted with the type of technology OneReach handles. But behind the scenes of that technology were artistic quandaries that Rosenthal and the team had to solve or work around.

For instance, the reader has the option to pick and choose which prompts to engage with and in what order, which is not typically how text chains work. “Normally, with an automated text message you’re in kind of a lineal format,” says Justin Biel, who built Textbook’s system and made sure that if you skipped the best-wishes text, for instance, and go right to the rainbow, you wouldn’t get an error message. At one point Rosenthal and her assistant manually tried every possible permutation of text to confirm that there were no hitches jumping from one prompt to another.

Engineers also made lots of revisions so that the system felt like readers were having a realistic text conversation with a person, rather than a bot or someone who had obviously written out the messages ahead of time. “It’s a fine line between robotic and poetic,” Rosenthal says.

Unlike your Instacart shopper whom you hope doesn’t need to text to ask you about substitutions, Textbook readers will never receive a message alerting them to a new Rosenthal signing or a discount at Amazon. No promo or marketing messages, ever. “In a way, that’s a betrayal,” Wilson says. Texting, to him, is “a personal channel, and to try to use that channel for blatant reasons, I think, hurts you more than it helps you.

Zulkey’s piece is a good read and includes images and an embedded video.

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

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

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

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

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

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

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

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

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

Byte-size learning

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

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

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

Video games to the rescue

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

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

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

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

Korea Advanced Institute of Science and Technology (KAIST) at summer 2016 World Economic Forum in China

From the Ideas Lab at the 2016 World Economic Forum at Davos to offering expertise at the 2016 World Economic Forum in Tanjin, China that is taking place from June 26 – 28, 2016.

Here’s more from a June 24, 2016 KAIST news release on EurekAlert,

Scientific and technological breakthroughs are more important than ever as a key agent to drive social, economic, and political changes and advancements in today’s world. The World Economic Forum (WEF), an international organization that provides one of the broadest engagement platforms to address issues of major concern to the global community, will discuss the effects of these breakthroughs at its 10th Annual Meeting of the New Champions, a.k.a., the Summer Davos Forum, in Tianjin, China, June 26-28, 2016.

Three professors from the Korea Advanced Institute of Science and Technology (KAIST) will join the Annual Meeting and offer their expertise in the fields of biotechnology, artificial intelligence, and robotics to explore the conference theme, “The Fourth Industrial Revolution and Its Transformational Impact.” The Fourth Industrial Revolution, a term coined by WEF founder, Klaus Schwab, is characterized by a range of new technologies that fuse the physical, digital, and biological worlds, such as the Internet of Things, cloud computing, and automation.

Distinguished Professor Sang Yup Lee of the Chemical and Biomolecular Engineering Department will speak at the Experts Reception to be held on June 25, 2016 on the topic of “The Summer Davos Forum and Science and Technology in Asia.” On June 27, 2016, he will participate in two separate discussion sessions.

In the first session entitled “What If Drugs Are Printed from the Internet?” Professor Lee will discuss the future of medicine being impacted by advancements in biotechnology and 3D printing technology with Nita A. Farahany, a Duke University professor, under the moderation of Clare Matterson, the Director of Strategy at Wellcome Trust in the United Kingdom. The discussants will note recent developments made in the way patients receive their medicine, for example, downloading drugs directly from the internet and the production of yeast strains to make opioids for pain treatment through systems metabolic engineering, and predicting how these emerging technologies will transform the landscape of the pharmaceutical industry in the years to come.

In the second session, “Lessons for Life,” Professor Lee will talk about how to nurture life-long learning and creativity to support personal and professional growth necessary in an era of the new industrial revolution.

During the Annual Meeting, Professors Jong-Hwan Kim of the Electrical Engineering School and David Hyunchul Shim of the Aerospace Department will host, together with researchers from Carnegie Mellon University and AnthroTronix, an engineering research and development company, a technological exhibition on robotics. Professor Kim, the founder of the internally renowned Robot World Cup, will showcase his humanoid micro-robots that play soccer, displaying their various cutting-edge technologies such as imaging processing, artificial intelligence, walking, and balancing. Professor Shim will present a human-like robotic piloting system, PIBOT, which autonomously operates a simulated flight program, grabbing control sticks and guiding an airplane from take offs to landings.

In addition, the two professors will join Professor Lee, who is also a moderator, to host a KAIST-led session on June 26, 2016, entitled “Science in Depth: From Deep Learning to Autonomous Machines.” Professors Kim and Shim will explore new opportunities and challenges in their fields from machine learning to autonomous robotics including unmanned vehicles and drones.

Since 2011, KAIST has been participating in the World Economic Forum’s two flagship conferences, the January and June Davos Forums, to introduce outstanding talents, share their latest research achievements, and interact with global leaders.

KAIST President Steve Kang said, “It is important for KAIST to be involved in global talks that identify issues critical to humanity and seek answers to solve them, where our skills and knowledge in science and technology could play a meaningful role. The Annual Meeting in China will become another venue to accomplish this.”

I mentioned KAIST and the Ideas Lab at the 2016 Davos meeting in this Nov. 20, 2015 posting and was able to clear up my (and possible other people’s) confusion as to what the Fourth Industrial revolution might be in my Dec. 3, 2015 posting.