Tag Archives: big data

I found it at the movies: a commentary on/review of “Films from the Future”

Kudos to anyone who recognized the reference to Pauline Kael (she changed film criticism forever) and her book “I Lost it at the Movies.” Of course, her book title was a bit of sexual innuendo, quite risqué for an important film critic in 1965 but appropriate for a period (the 1960s) associated with a sexual revolution. (There’s more about the 1960’s sexual revolution in the US along with mention of a prior sexual revolution in the 1920s in this Wikipedia entry.)

The title for this commentary is based on an anecdote from Dr. Andrew Maynard’s (director of the Arizona State University [ASU] Risk Innovation Lab) popular science and technology book, “Films from the Future: The Technology and Morality of Sci-Fi Movies.”

The ‘title-inspiring’ anecdote concerns Maynard’s first viewing of ‘2001: A Space Odyssey, when as a rather “bratty” 16-year-old who preferred to read science fiction, he discovered new ways of seeing and imaging the world. Maynard isn’t explicit about when he became a ‘techno nerd’ or how movies gave him an experience books couldn’t but presumably at 16 he was already gearing up for a career in the sciences. That ‘movie’ revelation received in front of a black and white television on January 1,1982 eventually led him to write, “Films from the Future.” (He has a PhD in physics which he is now applying to the field of risk innovation. For a more detailed description of Dr. Maynard and his work, there’s his ASU profile webpage and, of course, the introduction to his book.)

The book is quite timely. I don’t know how many people have noticed but science and scientific innovation is being covered more frequently in the media than it has been in many years. Science fairs and festivals are being founded on what seems to be a daily basis and you can now find science in art galleries. (Not to mention the movies and television where science topics are covered in comic book adaptations, in comedy, and in standard science fiction style.) Much of this activity is centered on what’s called ’emerging technologies’. These technologies are why people argue for what’s known as ‘blue sky’ or ‘basic’ or ‘fundamental’ science for without that science there would be no emerging technology.

Films from the Future

Isn’t reading the Table of Contents (ToC) the best way to approach a book? (From Films from the Future; Note: The formatting has been altered),

Table of Contents
Chapter One
In the Beginning 14
Beginnings 14
Welcome to the Future 16
The Power of Convergence 18
Socially Responsible Innovation 21
A Common Point of Focus 25
Spoiler Alert 26
Chapter Two
Jurassic Park: The Rise of Resurrection Biology 27
When Dinosaurs Ruled the World 27
De-Extinction 31
Could We, Should We? 36
The Butterfly Effect 39
Visions of Power 43
Chapter Three
Never Let Me Go: A Cautionary Tale of Human Cloning 46
Sins of Futures Past 46
Cloning 51
Genuinely Human? 56
Too Valuable to Fail? 62
Chapter Four
Minority Report: Predicting Criminal Intent 64
Criminal Intent 64
The “Science” of Predicting Bad Behavior 69
Criminal Brain Scans 74
Machine Learning-Based Precognition 77
Big Brother, Meet Big Data 79
Chapter Five
Limitless: Pharmaceutically-enhanced Intelligence 86
A Pill for Everything 86
The Seduction of Self-Enhancement 89
Nootropics 91
If You Could, Would You? 97
Privileged Technology 101
Our Obsession with Intelligence 105
Chapter Six
Elysium: Social Inequity in an Age of Technological
Extremes 110
The Poor Shall Inherit the Earth 110
Bioprinting Our Future Bodies 115
The Disposable Workforce 119
Living in an Automated Future 124
Chapter Seven
Ghost in the Shell: Being Human in an
Augmented Future 129
Through a Glass Darkly 129
Body Hacking 135
More than “Human”? 137
Plugged In, Hacked Out 142
Your Corporate Body 147
Chapter Eight
Ex Machina: AI and the Art of Manipulation 154
Plato’s Cave 154
The Lure of Permissionless Innovation 160
Technologies of Hubris 164
Superintelligence 169
Defining Artificial Intelligence 172
Artificial Manipulation 175
Chapter Nine
Transcendence: Welcome to the Singularity 180
Visions of the Future 180
Technological Convergence 184
Enter the Neo-Luddites 190
Techno-Terrorism 194
Exponential Extrapolation 200
Make-Believe in the Age of the Singularity 203
Chapter Ten
The Man in the White Suit: Living in a Material World 208
There’s Plenty of Room at the Bottom 208
Mastering the Material World 213
Myopically Benevolent Science 220
Never Underestimate the Status Quo 224
It’s Good to Talk 227
Chapter Eleven
Inferno: Immoral Logic in an Age of
Genetic Manipulation 231
Decoding Make-Believe 231
Weaponizing the Genome 234
Immoral Logic? 238
The Honest Broker 242
Dictating the Future 248
Chapter Twelve
The Day After Tomorrow: Riding the Wave of
Climate Change 251
Our Changing Climate 251
Fragile States 255
A Planetary “Microbiome” 258
The Rise of the Anthropocene 260
Building Resiliency 262
Geoengineering the Future 266
Chapter Thirteen
Contact: Living by More than Science Alone 272
An Awful Waste of Space 272
More than Science Alone 277
Occam’s Razor 280
What If We’re Not Alone? 283
Chapter Fourteen
Looking to the Future 288
Acknowledgments 293

The ToC gives the reader a pretty clue as to where the author is going with their book and Maynard explains how he chose his movies in his introductory chapter (from Films from the Future),

“There are some quite wonderful science fiction movies that didn’t make the cut because they didn’t fit the overarching narrative (Blade Runner and its sequel Blade Runner 2049, for instance, and the first of the Matrix trilogy). There are also movies that bombed with the critics, but were included because they ably fill a gap in the bigger story around emerging and converging technologies. Ultimately, the movies that made the cut were chosen because, together, they create an overarching narrative around emerging trends in biotechnologies, cybertechnologies, and materials-based technologies, and they illuminate a broader landscape around our evolving relationship with science and technology. And, to be honest, they are all movies that I get a kick out of watching.” (p. 17)

Jurassic Park (Chapter Two)

Dinosaurs do not interest me—they never have. Despite my profound indifference I did see the movie, Jurassic Park, when it was first released (someone talked me into going). And, I am still profoundly indifferent. Thankfully, Dr. Maynard finds meaning and a connection to current trends in biotechnology,

Jurassic Park is unabashedly a movie about dinosaurs. But it’s also a movie about greed, ambition, genetic engineering, and human folly—all rich pickings for thinking about the future, and what could possibly go wrong. (p. 28)

What really stands out with Jurassic Park, over twenty-five years later, is how it reveals a very human side of science and technology. This comes out in questions around when we should tinker with technology and when we should leave well enough alone. But there is also a narrative here that appears time and time again with the movies in this book, and that is how we get our heads around the sometimes oversized roles mega-entrepreneurs play in dictating how new tech is used, and possibly abused. These are all issues that are just as relevant now as they were in 1993, and are front and center of ensuring that the technologyenabled future we’re building is one where we want to live, and not one where we’re constantly fighting for our lives.  (pp. 30-1)

He also describes a connection to current trends in biotechnology,

De-Extinction

In a far corner of Siberia, two Russians—Sergey Zimov and his son Nikita—are attempting to recreate the Ice Age. More precisely, their vision is to reconstruct the landscape and ecosystem of northern Siberia in the Pleistocene, a period in Earth’s history that stretches from around two and a half million years ago to eleven thousand years ago. This was a time when the environment was much colder than now, with huge glaciers and ice sheets flowing over much of the Earth’s northern hemisphere. It was also a time when humans
coexisted with animals that are long extinct, including saber-tooth cats, giant ground sloths, and woolly mammoths.

The Zimovs’ ambitions are an extreme example of “Pleistocene rewilding,” a movement to reintroduce relatively recently extinct large animals, or their close modern-day equivalents, to regions where they were once common. In the case of the Zimovs, the
father-and-son team believe that, by reconstructing the Pleistocene ecosystem in the Siberian steppes and elsewhere, they can slow down the impacts of climate change on these regions. These areas are dominated by permafrost, ground that never thaws through
the year. Permafrost ecosystems have developed and survived over millennia, but a warming global climate (a theme we’ll come back to in chapter twelve and the movie The Day After Tomorrow) threatens to catastrophically disrupt them, and as this happens, the impacts
on biodiversity could be devastating. But what gets climate scientists even more worried is potentially massive releases of trapped methane as the permafrost disappears.

Methane is a powerful greenhouse gas—some eighty times more effective at exacerbating global warming than carbon dioxide— and large-scale releases from warming permafrost could trigger catastrophic changes in climate. As a result, finding ways to keep it in the ground is important. And here the Zimovs came up with a rather unusual idea: maintaining the stability of the environment by reintroducing long-extinct species that could help prevent its destruction, even in a warmer world. It’s a wild idea, but one that has some merit.8 As a proof of concept, though, the Zimovs needed somewhere to start. And so they set out to create a park for deextinct Siberian animals: Pleistocene Park.9

Pleistocene Park is by no stretch of the imagination a modern-day Jurassic Park. The dinosaurs in Hammond’s park date back to the Mesozoic period, from around 250 million years ago to sixty-five million years ago. By comparison, the Pleistocene is relatively modern history, ending a mere eleven and a half thousand years ago. And the vision behind Pleistocene Park is not thrills, spills, and profit, but the serious use of science and technology to stabilize an increasingly unstable environment. Yet there is one thread that ties them together, and that’s using genetic engineering to reintroduce extinct species. In this case, the species in question is warm-blooded and furry: the woolly mammoth.

The idea of de-extinction, or bringing back species from extinction (it’s even called “resurrection biology” in some circles), has been around for a while. It’s a controversial idea, and it raises a lot of tough ethical questions. But proponents of de-extinction argue
that we’re losing species and ecosystems at such a rate that we can’t afford not to explore technological interventions to help stem the flow.

Early approaches to bringing species back from the dead have involved selective breeding. The idea was simple—if you have modern ancestors of a recently extinct species, selectively breeding specimens that have a higher genetic similarity to their forebears can potentially help reconstruct their genome in living animals. This approach is being used in attempts to bring back the aurochs, an ancestor of modern cattle.10 But it’s slow, and it depends on
the fragmented genome of the extinct species still surviving in its modern-day equivalents.

An alternative to selective breeding is cloning. This involves finding a viable cell, or cell nucleus, in an extinct but well-preserved animal and growing a new living clone from it. It’s definitely a more appealing route for impatient resurrection biologists, but it does mean getting your hands on intact cells from long-dead animals and devising ways to “resurrect” these, which is no mean feat. Cloning has potential when it comes to recently extinct species whose cells have been well preserved—for instance, where the whole animal has become frozen in ice. But it’s still a slow and extremely limited option.

Which is where advances in genetic engineering come in.

The technological premise of Jurassic Park is that scientists can reconstruct the genome of long-dead animals from preserved DNA fragments. It’s a compelling idea, if you think of DNA as a massively long and complex instruction set that tells a group of biological molecules how to build an animal. In principle, if we could reconstruct the genome of an extinct species, we would have the basic instruction set—the biological software—to reconstruct
individual members of it.

The bad news is that DNA-reconstruction-based de-extinction is far more complex than this. First you need intact fragments of DNA, which is not easy, as DNA degrades easily (and is pretty much impossible to obtain, as far as we know, for dinosaurs). Then you
need to be able to stitch all of your fragments together, which is akin to completing a billion-piece jigsaw puzzle without knowing what the final picture looks like. This is a Herculean task, although with breakthroughs in data manipulation and machine learning,
scientists are getting better at it. But even when you have your reconstructed genome, you need the biological “wetware”—all the stuff that’s needed to create, incubate, and nurture a new living thing, like eggs, nutrients, a safe space to grow and mature, and so on. Within all this complexity, it turns out that getting your DNA sequence right is just the beginning of translating that genetic code into a living, breathing entity. But in some cases, it might be possible.

In 2013, Sergey Zimov was introduced to the geneticist George Church at a conference on de-extinction. Church is an accomplished scientist in the field of DNA analysis and reconstruction, and a thought leader in the field of synthetic biology (which we’ll come
back to in chapter nine). It was a match made in resurrection biology heaven. Zimov wanted to populate his Pleistocene Park with mammoths, and Church thought he could see a way of
achieving this.

What resulted was an ambitious project to de-extinct the woolly mammoth. Church and others who are working on this have faced plenty of hurdles. But the technology has been advancing so fast that, as of 2017, scientists were predicting they would be able to reproduce the woolly mammoth within the next two years.

One of those hurdles was the lack of solid DNA sequences to work from. Frustratingly, although there are many instances of well preserved woolly mammoths, their DNA rarely survives being frozen for tens of thousands of years. To overcome this, Church and others
have taken a different tack: Take a modern, living relative of the mammoth, and engineer into it traits that would allow it to live on the Siberian tundra, just like its woolly ancestors.

Church’s team’s starting point has been the Asian elephant. This is their source of base DNA for their “woolly mammoth 2.0”—their starting source code, if you like. So far, they’ve identified fifty plus gene sequences they think they can play with to give their modern-day woolly mammoth the traits it would need to thrive in Pleistocene Park, including a coat of hair, smaller ears, and a constitution adapted to cold.

The next hurdle they face is how to translate the code embedded in their new woolly mammoth genome into a living, breathing animal. The most obvious route would be to impregnate a female Asian elephant with a fertilized egg containing the new code. But Asian elephants are endangered, and no one’s likely to allow such cutting edge experimentation on the precious few that are still around, so scientists are working on an artificial womb for their reinvented woolly mammoth. They’re making progress with mice and hope to crack the motherless mammoth challenge relatively soon.

It’s perhaps a stretch to call this creative approach to recreating a species (or “reanimation” as Church refers to it) “de-extinction,” as what is being formed is a new species. … (pp. 31-4)

This selection illustrates what Maynard does so very well throughout the book where he uses each film as a launching pad for a clear, readable description of relevant bits of science so you understand why the premise was likely, unlikely, or pure fantasy while linking it to contemporary practices, efforts, and issues. In the context of Jurassic Park, Maynard goes on to raise some fascinating questions such as: Should we revive animals rendered extinct (due to obsolescence or inability to adapt to new conditions) when we could develop new animals?

General thoughts

‘Films for the Future’ offers readable (to non-scientific types) science, lively writing, and the occasional ‘memorish’ anecdote. As well, Dr. Maynard raises the curtain on aspects of the scientific enterprise that most of us do not get to see.  For example, the meeting  between Sergey Zimov and George Church and how it led to new ‘de-extinction’ work’. He also describes the problems that the scientists encountered and are encountering. This is in direct contrast to how scientific work is usually presented in the news media as one glorious breakthrough after the next.

Maynard does discuss the issues of social inequality and power and ownership. For example, who owns your transplant or data? Puzzlingly, he doesn’t touch on the current environment where scientists in the US and elsewhere are encouraged/pressured to start up companies commercializing their work.

Nor is there any mention of how universities are participating in this grand business experiment often called ‘innovation’. (My March 15, 2017 posting describes an outcome for the CRISPR [gene editing system] patent fight taking place between Harvard University’s & MIT’s [Massachusetts Institute of Technology] Broad Institute vs the University of California at Berkeley and my Sept. 11, 2018 posting about an art/science exhibit in Vancouver [Canada] provides an update for round 2 of the Broad Institute vs. UC Berkeley patent fight [scroll down about 65% of the way.) *To read about how my ‘cultural blindness’ shows up here scroll down to the single asterisk at the end.*

There’s a foray through machine-learning and big data as applied to predictive policing in Maynard’s ‘Minority Report’ chapter (my November 23, 2017 posting describes Vancouver’s predictive policing initiative [no psychics involved], the first such in Canada). There’s no mention of surveillance technology, which if I recall properly was part of the future environment, both by the state and by corporations. (Mia Armstrong’s November 15, 2018 article for Slate on Chinese surveillance being exported to Venezuela provides interesting insight.)

The gaps are interesting and various. This of course points to a problem all science writers have when attempting an overview of science. (Carl Zimmer’s latest, ‘She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity’] a doorstopping 574 pages, also has some gaps despite his focus on heredity,)

Maynard has worked hard to give an comprehensive overview in a remarkably compact 279 pages while developing his theme about science and the human element. In other words, science is not monolithic; it’s created by human beings and subject to all the flaws and benefits that humanity’s efforts are always subject to—scientists are people too.

The readership for ‘Films from the Future’ spans from the mildly interested science reader to someone like me who’s been writing/blogging about these topics (more or less) for about 10 years. I learned a lot reading this book.

Next time, I’m hopeful there’ll be a next time, Maynard might want to describe the parameters he’s set for his book in more detail that is possible in his chapter headings. He could have mentioned that he’s not a cinéaste so his descriptions of the movies are very much focused on the story as conveyed through words. He doesn’t mention colour palates, camera angles, or, even, cultural lenses.

Take for example, his chapter on ‘Ghost in the Shell’. Focused on the Japanese animation film and not the live action Hollywood version he talks about human enhancement and cyborgs. The Japanese have a different take on robots, inanimate objects, and, I assume, cyborgs than is found in Canada or the US or Great Britain, for that matter (according to a colleague of mine, an Englishwoman who lived in Japan for ten or more years). There’s also the chapter on the Ealing comedy, The Man in The White Suit, an English film from the 1950’s. That too has a cultural (as well as, historical) flavour but since Maynard is from England, he may take that cultural flavour for granted. ‘Never let me go’ in Chapter Two was also a UK production, albeit far more recent than the Ealing comedy and it’s interesting to consider how a UK production about cloning might differ from a US or Chinese or … production on the topic. I am hearkening back to Maynard’s anecdote about movies giving him new ways of seeing and imagining the world.

There’s a corrective. A couple of sentences in Maynard’s introductory chapter cautioning that in depth exploration of ‘cultural lenses’ was not possible without expanding the book to an unreadable size followed by a sentence in each of the two chapters that there are cultural differences.

One area where I had a significant problem was with regard to being “programmed” and having  “instinctual” behaviour,

As a species, we are embarrassingly programmed to see “different” as “threatening,” and to take instinctive action against it. It’s a trait that’s exploited in many science fiction novels and movies, including those in this book. If we want to see the rise of increasingly augmented individuals, we need to be prepared for some social strife. (p. 136)

These concepts are much debated in the social sciences and there are arguments for and against ‘instincts regarding strangers and their possible differences’. I gather Dr. Maynard hies to the ‘instinct to defend/attack’ school of thought.

One final quandary, there was no sex and I was expecting it in the Ex Machina chapter, especially now that sexbots are about to take over the world (I exaggerate). Certainly, if you’re talking about “social strife,” then sexbots would seem to be fruitful line of inquiry, especially when there’s talk of how they could benefit families (my August 29, 2018 posting). Again, there could have been a sentence explaining why Maynard focused almost exclusively in this chapter on the discussions about artificial intelligence and superintelligence.

Taken in the context of the book, these are trifling issues and shouldn’t stop you from reading Films from the Future. What Maynard has accomplished here is impressive and I hope it’s just the beginning.

Final note

Bravo Andrew! (Note: We’ve been ‘internet acquaintances/friends since the first year I started blogging. When I’m referring to him in his professional capacity, he’s Dr. Maynard and when it’s not strictly in his professional capacity, it’s Andrew. For this commentary/review I wanted to emphasize his professional status.)

If you need to see a few more samples of Andrew’s writing, there’s a Nov. 15, 2018 essay on The Conversation, Sci-fi movies are the secret weapon that could help Silicon Valley grow up and a Nov. 21, 2018 article on slate.com, The True Cost of Stain-Resistant Pants; The 1951 British comedy The Man in the White Suit anticipated our fears about nanotechnology. Enjoy.

****Added at 1700 hours on Nov. 22, 2018: You can purchase Films from the Future here.

*Nov. 23, 2018: I should have been more specific and said ‘academic scientists’. In Canada, the great percentage of scientists are academic. It’s to the point where the OECD (Organization for Economic Cooperation and Development) has noted that amongst industrialized countries, Canada has very few industrial scientists in comparison to the others.

Socially responsible AI—it’s time says University of Manchester (UK) researchers

A May 10, 2018 news item on ScienceDaily describes a report on the ‘fourth industrial revolution’ being released by the University of Manchester,

The development of new Artificial Intelligence (AI) technology is often subject to bias, and the resulting systems can be discriminatory, meaning more should be done by policymakers to ensure its development is democratic and socially responsible.

This is according to Dr Barbara Ribeiro of Manchester Institute of Innovation Research at The University of Manchester, in On AI and Robotics: Developing policy for the Fourth Industrial Revolution, a new policy report on the role of AI and Robotics in society, being published today [May 10, 2018].

Interestingly, the US White House is hosting a summit on AI today, May 10, 2018, according to a May 8, 2018 article by Danny Crichton for TechCrunch (Note: Links have been removed),

Now, it appears the White House itself is getting involved in bringing together key American stakeholders to discuss AI and those opportunities and challenges. …

Among the confirmed guests are Facebook’s Jerome Pesenti, Amazon’s Rohit Prasad, and Intel’s CEO Brian Krzanich. While the event has many tech companies present, a total of 38 companies are expected to be in attendance including United Airlines and Ford.

AI policy has been top-of-mind for many policymakers around the world. French President Emmanuel Macron has announced a comprehensive national AI strategy, as has Canada, which has put together a research fund and a set of programs to attempt to build on the success of notable local AI researchers such as University of Toronto professor George Hinton, who is a major figure in deep learning.

But it is China that has increasingly drawn the attention and concern of U.S. policymakers. The country and its venture capitalists are outlaying billions of dollars to invest in the AI industry, and it has made leading in artificial intelligence one of the nation’s top priorities through its Made in China 2025 program and other reports. …

In comparison, the United States has been remarkably uncoordinated when it comes to AI. …

That lack of engagement from policymakers has been fine — after all, the United States is the world leader in AI research. But with other nations pouring resources and talent into the space, DC policymakers are worried that the U.S. could suddenly find itself behind the frontier of research in the space, with particular repercussions for the defense industry.

Interesting contrast: do we take time to consider the implications or do we engage in a race?

While it’s becoming fashionable to dismiss dichotomous questions of this nature, the two approaches (competition and reflection) are not that compatible and it does seem to be an either/or proposition.

A May 10, 2018 University of Manchester press release (also on EurekAlert), which originated the news item, expands on the theme of responsibility and AI,

Dr Ribeiro adds because investment into AI will essentially be paid for by tax-payers in the long-term, policymakers need to make sure that the benefits of such technologies are fairly distributed throughout society.

She says: “Ensuring social justice in AI development is essential. AI technologies rely on big data and the use of algorithms, which influence decision-making in public life and on matters such as social welfare, public safety and urban planning.”

“In these ‘data-driven’ decision-making processes some social groups may be excluded, either because they lack access to devices necessary to participate or because the selected datasets do not consider the needs, preferences and interests of marginalised and disadvantaged people.”

On AI and Robotics: Developing policy for the Fourth Industrial Revolution is a comprehensive report written, developed and published by Policy@Manchester with leading experts and academics from across the University.

The publication is designed to help employers, regulators and policymakers understand the potential effects of AI in areas such as industry, healthcare, research and international policy.

However, the report doesn’t just focus on AI. It also looks at robotics, explaining the differences and similarities between the two separate areas of research and development (R&D) and the challenges policymakers face with each.

Professor Anna Scaife, Co-Director of the University’s Policy@Manchester team, explains: “Although the challenges that companies and policymakers are facing with respect to AI and robotic systems are similar in many ways, these are two entirely separate technologies – something which is often misunderstood, not just by the general public, but policymakers and employers too. This is something that has to be addressed.”

One particular area the report highlights where robotics can have a positive impact is in the world of hazardous working environments, such a nuclear decommissioning and clean-up.

Professor Barry Lennox, Professor of Applied Control and Head of the UOM Robotics Group, adds: “The transfer of robotics technology into industry, and in particular the nuclear industry, requires cultural and societal changes as well as technological advances.

“It is really important that regulators are aware of what robotic technology is and is not capable of doing today, as well as understanding what the technology might be capable of doing over the next -5 years.”

The report also highlights the importance of big data and AI in healthcare, for example in the fight against antimicrobial resistance (AMR).

Lord Jim O’Neill, Honorary Professor of Economics at The University of Manchester and Chair of the Review on Antimicrobial Resistance explains: “An important example of this is the international effort to limit the spread of antimicrobial resistance (AMR). The AMR Review gave 27 specific recommendations covering 10 broad areas, which became known as the ‘10 Commandments’.

“All 10 are necessary, and none are sufficient on their own, but if there is one that I find myself increasingly believing is a permanent game-changer, it is state of the art diagnostics. We need a ‘Google for doctors’ to reduce the rate of over prescription.”

The versatile nature of AI and robotics is leading many experts to predict that the technologies will have a significant impact on a wide variety of fields in the coming years. Policy@Manchester hopes that the On AI and Robotics report will contribute to helping policymakers, industry stakeholders and regulators better understand the range of issues they will face as the technologies play ever greater roles in our everyday lives.

As far as I can tell, the report has been designed for online viewing only. There are none of the markers (imprint date, publisher, etc.) that I expect to see on a print document. There is no bibliography or list of references but there are links to outside sources throughout the document.

It’s an interesting approach to publishing a report that calls for social justice, especially since the issue of ‘trust’ is increasingly being emphasized where all AI is concerned. With regard to this report, I’m not sure I can trust it. With a print document or a PDF I have markers. I can examine the index, the bibliography, etc. and determine if this material has covered the subject area with reference to well known authorities. It’s much harder to do that with this report. As well, this ‘souped up’ document also looks like it might be easy to change something without my knowledge. With a print or PDF version, I can compare the documents but not with this one.

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

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

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

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

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

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

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

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

Our incubator

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

OUR SERVICES

IBM Bluemix
IBM Global Entrepreneur
Softlayer – an IBM Company
Watson

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

Collaborative workspace in the heart of Downtown Toronto
Mentorship opportunities available with leading experts
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Final comments

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

GIGO, bias, and de-skilling

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

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

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

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

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

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

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

Who owns your data?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Emerging technology and the law

I have three news bits about legal issues that are arising as a consequence of emerging technologies.

Deep neural networks, art, and copyright

Caption: The rise of automated art opens new creative avenues, coupled with new problems for copyright protection. Credit: Provided by: Alexander Mordvintsev, Christopher Olah and Mike Tyka

Presumably this artwork is a demonstration of automated art although they never really do explain how in the news item/news release. An April 26, 2017 news item on ScienceDaily announces research into copyright and the latest in using neural networks to create art,

In 1968, sociologist Jean Baudrillard wrote on automatism that “contained within it is the dream of a dominated world […] that serves an inert and dreamy humanity.”

With the growing popularity of Deep Neural Networks (DNN’s), this dream is fast becoming a reality.

Dr. Jean-Marc Deltorn, researcher at the Centre d’études internationales de la propriété intellectuelle in Strasbourg, argues that we must remain a responsive and responsible force in this process of automation — not inert dominators. As he demonstrates in a recent Frontiers in Digital Humanities paper, the dream of automation demands a careful study of the legal problems linked to copyright.

An April 26, 2017 Frontiers (publishing) news release on EurekAlert, which originated the news item, describes the research in more detail,

For more than half a century, artists have looked to computational processes as a way of expanding their vision. DNN’s are the culmination of this cross-pollination: by learning to identify a complex number of patterns, they can generate new creations.

These systems are made up of complex algorithms modeled on the transmission of signals between neurons in the brain.

DNN creations rely in equal measure on human inputs and the non-human algorithmic networks that process them.

Inputs are fed into the system, which is layered. Each layer provides an opportunity for a more refined knowledge of the inputs (shape, color, lines). Neural networks compare actual outputs to expected ones, and correct the predictive error through repetition and optimization. They train their own pattern recognition, thereby optimizing their learning curve and producing increasingly accurate outputs.

The deeper the layers are, the higher the level of abstraction. The highest layers are able to identify the contents of a given input with reasonable accuracy, after extended periods of training.

Creation thus becomes increasingly automated through what Deltorn calls “the arcane traceries of deep architecture”. The results are sufficiently abstracted from their sources to produce original creations that have been exhibited in galleries, sold at auction and performed at concerts.

The originality of DNN’s is a combined product of technological automation on one hand, human inputs and decisions on the other.

DNN’s are gaining popularity. Various platforms (such as DeepDream) now allow internet users to generate their very own new creations . This popularization of the automation process calls for a comprehensive legal framework that ensures a creator’s economic and moral rights with regards to his work – copyright protection.

Form, originality and attribution are the three requirements for copyright. And while DNN creations satisfy the first of these three, the claim to originality and attribution will depend largely on a given country legislation and on the traceability of the human creator.

Legislation usually sets a low threshold to originality. As DNN creations could in theory be able to create an endless number of riffs on source materials, the uncurbed creation of original works could inflate the existing number of copyright protections.

Additionally, a small number of national copyright laws confers attribution to what UK legislation defines loosely as “the person by whom the arrangements necessary for the creation of the work are undertaken.” In the case of DNN’s, this could mean anybody from the programmer to the user of a DNN interface.

Combined with an overly supple take on originality, this view on attribution would further increase the number of copyrightable works.

The risk, in both cases, is that artists will be less willing to publish their own works, for fear of infringement of DNN copyright protections.

In order to promote creativity – one seminal aim of copyright protection – the issue must be limited to creations that manifest a personal voice “and not just the electric glint of a computational engine,” to quote Deltorn. A delicate act of discernment.

DNN’s promise new avenues of creative expression for artists – with potential caveats. Copyright protection – a “catalyst to creativity” – must be contained. Many of us gently bask in the glow of an increasingly automated form of technology. But if we want to safeguard the ineffable quality that defines much art, it might be a good idea to hone in more closely on the differences between the electric and the creative spark.

This research is and be will part of a broader Frontiers Research Topic collection of articles on Deep Learning and Digital Humanities.

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

Deep Creations: Intellectual Property and the Automata by Jean-Marc Deltorn. Front. Digit. Humanit., 01 February 2017 | https://doi.org/10.3389/fdigh.2017.00003

This paper is open access.

Conference on governance of emerging technologies

I received an April 17, 2017 notice via email about this upcoming conference. Here’s more from the Fifth Annual Conference on Governance of Emerging Technologies: Law, Policy and Ethics webpage,

The Fifth Annual Conference on Governance of Emerging Technologies:

Law, Policy and Ethics held at the new

Beus Center for Law & Society in Phoenix, AZ

May 17-19, 2017!

Call for Abstracts – Now Closed

The conference will consist of plenary and session presentations and discussions on regulatory, governance, legal, policy, social and ethical aspects of emerging technologies, including (but not limited to) nanotechnology, synthetic biology, gene editing, biotechnology, genomics, personalized medicine, human enhancement technologies, telecommunications, information technologies, surveillance technologies, geoengineering, neuroscience, artificial intelligence, and robotics. The conference is premised on the belief that there is much to be learned and shared from and across the governance experience and proposals for these various emerging technologies.

Keynote Speakers:

Gillian HadfieldRichard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics USC [University of Southern California] Gould School of Law

Shobita Parthasarathy, Associate Professor of Public Policy and Women’s Studies, Director, Science, Technology, and Public Policy Program University of Michigan

Stuart Russell, Professor at [University of California] Berkeley, is a computer scientist known for his contributions to artificial intelligence

Craig Shank, Vice President for Corporate Standards Group in Microsoft’s Corporate, External and Legal Affairs (CELA)

Plenary Panels:

Innovation – Responsible and/or Permissionless

Ellen-Marie Forsberg, Senior Researcher/Research Manager at Oslo and Akershus University College of Applied Sciences

Adam Thierer, Senior Research Fellow with the Technology Policy Program at the Mercatus Center at George Mason University

Wendell Wallach, Consultant, ethicist, and scholar at Yale University’s Interdisciplinary Center for Bioethics

 Gene Drives, Trade and International Regulations

Greg Kaebnick, Director, Editorial Department; Editor, Hastings Center Report; Research Scholar, Hastings Center

Jennifer Kuzma, Goodnight-North Carolina GlaxoSmithKline Foundation Distinguished Professor in Social Sciences in the School of Public and International Affairs (SPIA) and co-director of the Genetic Engineering and Society (GES) Center at North Carolina State University

Andrew Maynard, Senior Sustainability Scholar, Julie Ann Wrigley Global Institute of Sustainability Director, Risk Innovation Lab, School for the Future of Innovation in Society Professor, School for the Future of Innovation in Society, Arizona State University

Gary Marchant, Regents’ Professor of Law, Professor of Law Faculty Director and Faculty Fellow, Center for Law, Science & Innovation, Arizona State University

Marc Saner, Inaugural Director of the Institute for Science, Society and Policy, and Associate Professor, University of Ottawa Department of Geography

Big Data

Anupam Chander, Martin Luther King, Jr. Professor of Law and Director, California International Law Center, UC Davis School of Law

Pilar Ossorio, Professor of Law and Bioethics, University of Wisconsin, School of Law and School of Medicine and Public Health; Morgridge Institute for Research, Ethics Scholar-in-Residence

George Poste, Chief Scientist, Complex Adaptive Systems Initiative (CASI) (http://www.casi.asu.edu/), Regents’ Professor and Del E. Webb Chair in Health Innovation, Arizona State University

Emily Shuckburgh, climate scientist and deputy head of the Polar Oceans Team at the British Antarctic Survey, University of Cambridge

 Responsible Development of AI

Spring Berman, Ira A. Fulton Schools of Engineering, Arizona State University

John Havens, The IEEE [Institute of Electrical and Electronics Engineers] Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Subbarao Kambhampati, Senior Sustainability Scientist, Julie Ann Wrigley Global Institute of Sustainability, Professor, School of Computing, Informatics and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University

Wendell Wallach, Consultant, Ethicist, and Scholar at Yale University’s Interdisciplinary Center for Bioethics

Existential and Catastrophic Ricks [sic]

Tony Barrett, Co-Founder and Director of Research of the Global Catastrophic Risk Institute

Haydn Belfield,  Academic Project Administrator, Centre for the Study of Existential Risk at the University of Cambridge

Margaret E. Kosal Associate Director, Sam Nunn School of International Affairs, Georgia Institute of Technology

Catherine Rhodes,  Academic Project Manager, Centre for the Study of Existential Risk at CSER, University of Cambridge

These were the panels that are of interest to me; there are others on the homepage.

Here’s some information from the Conference registration webpage,

Early Bird Registration – $50 off until May 1! Enter discount code: earlybirdGETs50

New: Group Discount – Register 2+ attendees together and receive an additional 20% off for all group members!

Click Here to Register!

Conference registration fees are as follows:

  • General (non-CLE) Registration: $150.00
  • CLE Registration: $350.00
  • *Current Student / ASU Law Alumni Registration: $50.00
  • ^Cybsersecurity sessions only (May 19): $100 CLE / $50 General / Free for students (registration info coming soon)

There you have it.

Neuro-techno future laws

I’m pretty sure this isn’t the first exploration of potential legal issues arising from research into neuroscience although it’s the first one I’ve stumbled across. From an April 25, 2017 news item on phys.org,

New human rights laws to prepare for advances in neurotechnology that put the ‘freedom of the mind’ at risk have been proposed today in the open access journal Life Sciences, Society and Policy.

The authors of the study suggest four new human rights laws could emerge in the near future to protect against exploitation and loss of privacy. The four laws are: the right to cognitive liberty, the right to mental privacy, the right to mental integrity and the right to psychological continuity.

An April 25, 2017 Biomed Central news release on EurekAlert, which originated the news item, describes the work in more detail,

Marcello Ienca, lead author and PhD student at the Institute for Biomedical Ethics at the University of Basel, said: “The mind is considered to be the last refuge of personal freedom and self-determination, but advances in neural engineering, brain imaging and neurotechnology put the freedom of the mind at risk. Our proposed laws would give people the right to refuse coercive and invasive neurotechnology, protect the privacy of data collected by neurotechnology, and protect the physical and psychological aspects of the mind from damage by the misuse of neurotechnology.”

Advances in neurotechnology, such as sophisticated brain imaging and the development of brain-computer interfaces, have led to these technologies moving away from a clinical setting and into the consumer domain. While these advances may be beneficial for individuals and society, there is a risk that the technology could be misused and create unprecedented threats to personal freedom.

Professor Roberto Andorno, co-author of the research, explained: “Brain imaging technology has already reached a point where there is discussion over its legitimacy in criminal court, for example as a tool for assessing criminal responsibility or even the risk of reoffending. Consumer companies are using brain imaging for ‘neuromarketing’, to understand consumer behaviour and elicit desired responses from customers. There are also tools such as ‘brain decoders’ which can turn brain imaging data into images, text or sound. All of these could pose a threat to personal freedom which we sought to address with the development of four new human rights laws.”

The authors explain that as neurotechnology improves and becomes commonplace, there is a risk that the technology could be hacked, allowing a third-party to ‘eavesdrop’ on someone’s mind. In the future, a brain-computer interface used to control consumer technology could put the user at risk of physical and psychological damage caused by a third-party attack on the technology. There are also ethical and legal concerns over the protection of data generated by these devices that need to be considered.

International human rights laws make no specific mention to neuroscience, although advances in biomedicine have become intertwined with laws, such as those concerning human genetic data. Similar to the historical trajectory of the genetic revolution, the authors state that the on-going neurorevolution will force a reconceptualization of human rights laws and even the creation of new ones.

Marcello Ienca added: “Science-fiction can teach us a lot about the potential threat of technology. Neurotechnology featured in famous stories has in some cases already become a reality, while others are inching ever closer, or exist as military and commercial prototypes. We need to be prepared to deal with the impact these technologies will have on our personal freedom.”

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

Towards new human rights in the age of neuroscience and neurotechnology by Marcello Ienca and Roberto Andorno. Life Sciences, Society and Policy201713:5 DOI: 10.1186/s40504-017-0050-1 Published: 26 April 2017

©  The Author(s). 2017

This paper is open access.

Algorithms in decision-making: a government inquiry in the UK

Yesterday’s (Feb. 28, 2017) posting about the newly launched Cascadia Urban Analytics Cooperative grew too big to include interesting tidbits such as this one from Sense about Science, (from a Feb. 28, 2017 announcement received via email),

The House of Commons science and technology select committee announced
today that it will launch an inquiry into the use of algorithms in
decision-making […].

Our campaigns and policy officer Dr Stephanie Mathisen brought this
important and under-scrutinised issue to the committee as part of their
#MyScienceInquiry initiative; so fantastic news that they are taking up
the call.

A Feb. 28, 2017 UK House of Commons Science and Technology Select Committee press release gives more details about the inquiry,

The Science and Technology Committee is launching a new inquiry into the use of algorithms in public and business decision making.

In an increasingly digital world, algorithms are being used to make decisions in a growing range of contexts. From decisions about offering mortgages and credit cards to sifting job applications and sentencing criminals, the impact of algorithms is far reaching.

How an algorithm is formulated, its scope for error or correction, the impact it may have on an individual—and their ability to understand or challenge that decision—are increasingly relevant questions.

This topic was pitched to the Committee by Dr Stephanie Mathisen (Sense about Science) through the Committee’s ‘My Science Inquiry’ open call for inquiry suggestions, and has been chosen as the first subject for the Committee’s attention following that process. It follows the Committee’s recent work on Robotics and AI, and its call for a standing Commission on Artificial Intelligence.

Submit written evidence

The Committee would welcome written submissions by Friday 21 April 2017 on the following points:

  • The extent of current and future use of algorithms in decision-making in Government and public bodies, businesses and others, and the corresponding risks and opportunities;
  • Whether ‘good practice’ in algorithmic decision-making can be identified and spread, including in terms of:
    —  The scope for algorithmic decision-making to eliminate, introduce or amplify biases or discrimination, and how any such bias can be detected and overcome;
    — Whether and how algorithmic decision-making can be conducted in a ‘transparent’ or ‘accountable’ way, and the scope for decisions made by an algorithm to be fully understood and challenged;
    — DThe implications of increased transparency in terms of copyright and commercial sensitivity, and protection of an individual’s data;
  • Methods for providing regulatory oversight of algorithmic decision-making, such as the rights described in the EU General Data Protection Regulation 2016.

The Committee would welcome views on the issues above, and submissions that illustrate how the issues vary by context through case studies of the use of algorithmic decision-making.

You can submit written evidence through the algorithms in decision-making inquiry page.

I looked at the submission form and while it assumes the submitter is from the UK, there doesn’t seem to be any impediment to citizens of other countries from making a submission. Since there is some personal information included as part of the submission, there is a note about data protection on the Guidance on giving evidence to a Select Committee of the House of Commons webpage.

Big data in the Cascadia region: a University of British Columbia (Canada) and University of Washington (US state) collaboration

Before moving onto the news and for anyone unfamiliar with the concept of the Cascadia region, it is an informally proposed political region or a bioregion, depending on your perspective. Adding to the lack of clarity, the region generally includes the province of British Columbia in Canada and the two US states, Washington and Oregon but Alaska (another US state) and the Yukon (a Canadian territory) may also be included, as well as, parts of California, Wyoming, Idaho, and Montana. (You can read more about the Cascadia bioregion here and the proposed political region here.)  While it sounds as if more of the US is part of the ‘Cascadia region’, British Columbia and the Yukon cover considerably more territory than all of the mentioned states combined, if you’re taking a landmass perspective.

Cascadia Urban Analytics Cooperative

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.

“Thanks to this generous gift from Microsoft, our two universities are poised to help transform the Cascadia region into a technological hub comparable to Silicon Valley and Boston,” said Professor Santa J. Ono, President of the University of British Columbia. “This new partnership transcends borders and strives to unleash our collective brain power, to bring about economic growth that enriches the lives of Canadians and Americans as well as urban communities throughout the world.”

“We have an unprecedented opportunity to use data to help our communities make decisions, and as a result improve people’s lives and well-being. That commitment to the public good is at the core of the mission of our two universities, and we’re grateful to Microsoft for making a community-minded contribution that will spark a range of collaborations,” said UW President Ana Mari Cauce.

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.

Microsoft President Brad Smith, who helped convene the conference, said, “We believe that joint research based on data science can help unlock new solutions for some of the most pressing issues in both Vancouver and Seattle. But our goal is bigger than this one-time gift. We hope this investment will serve as a catalyst for broader and more sustainable efforts between these two institutions.”

As part of the Emerging Cascadia conference, British Columbia Premier Christy Clark and Washington Governor Jay Inslee signed a formal agreement that committed the two governments to work closely together to “enhance meaningful and results-driven innovation and collaboration.”  The agreement outlined steps the two governments will take to collaborate in several key areas including research and education.

“Increasingly, tech is not just another standalone sector of the economy, but fully integrated into everything from transportation to social work,” said Premier Clark. “That’s why we’ve invested in B.C.’s thriving tech sector, but committed to working with our neighbours in Washington – and we’re already seeing the results.”

“This data-driven collaboration among some of our smartest and most creative thought-leaders will help us tackle a host of urgent issues,” Gov. Inslee said. “I’m encouraged to see our partnership with British Columbia spurring such interesting cross-border dialogue and excited to see what our students and researchers come up with.”

The Cascadia Urban Analytics Cooperative will revolve around four main programs:

  • The Cascadia Data Science for Social Good (DSSG) Summer Program, which builds on the success of the DSSG program at the UW eScience Institute. The cooperative will coordinate a joint summer program for students across UW and UBC campuses where they work with faculty to create and incubate data-intensive research projects that have concrete benefits for urban communities. One past DSSG project analyzed data from Seattle’s regional transportation system – ORCA – to improve its effectiveness, particularly for low-income transit riders. Another project sought to improve food safety by text mining product reviews to identify unsafe products.
  • Cascadia Data Science for Social Good Scholar Symposium, which will foster innovation and collaboration by bringing together scholars from UBC and the UW involved in projects utilizing technology to advance the social good. The first symposium will be hosted at UW in 2017.
  • Sustained Research Partnerships designed to establish the Pacific Northwest as a center of expertise and activity in urban analytics. The cooperative will support sustained research partnerships between UW and UBC researchers, providing technical expertise, stakeholder engagement and seed funding.
  • Responsible Data Management Systems and Services to ensure data integrity, security and usability. The cooperative will develop new software, systems and services to facilitate data management and analysis, as well as ensure projects adhere to best practices in fairness, accountability and transparency.

At UW, the Cascadia Urban Analytics Collaborative will be overseen by Urbanalytics (urbanalytics.uw.edu), a new research unit in the Information School focused on responsible urban data science. The Collaborative builds on previous investments in data-intensive science through the UW eScience Institute (escience.washington.edu) and investments in urban scholarship through Urban@UW (urban.uw.edu), and also aligns with the UW’s Population Health Initiative (uw.edu/populationhealth) that is addressing the most persistent and emerging challenges in human health, environmental resiliency and social and economic equity. The gift counts toward the UW’s Be Boundless – For Washington, For the World campaign (uw.edu/boundless).

The Collaborative also aligns with the UBC Sustainability Initiative (sustain.ubc.ca) that fosters partnerships beyond traditional boundaries of disciplines, sectors and geographies to address critical issues of our time, as well as the UBC Data Science Institute (dsi.ubc.ca), which aims to advance data science research to address complex problems across domains, including health, science and arts.

Brad Smith, President and Chief Legal Officer of Microsoft, wrote about the joint centre in a Feb. 23, 2017 posting on the Microsoft on the Issues blog (Note:,

The cities of Vancouver and Seattle share many strengths: a long history of innovation, world-class universities and a region rich in cultural and ethnic diversity. While both cities have achieved great success on their own, leaders from both sides of the border realize that tighter partnership and collaboration, through the creation of a Cascadia Innovation Corridor, will expand economic opportunity and prosperity well beyond what each community can achieve separately.

Microsoft supports this vision and today is making a $1 million investment in the Cascadia Urban Analytics Cooperative (CUAC), which is a new joint effort by the University of British Columbia (UBC) and the University of Washington (UW).  It will use data to help local cities and communities address challenges from traffic to homelessness and will be the region’s single largest university-based, industry-funded joint research project. While we recognize the crucial role that universities play in building great companies in the Pacific Northwest, whether it be in computing, life sciences, aerospace or interactive entertainment, we also know research, particularly data science, holds the key to solving some of Vancouver and Seattle’s most pressing issues. This grant will advance this work.

An Oct. 21, 2016 article by Hana Golightly for the Ubyssey newspaper provides a little more detail about the province/state agreement mentioned in the joint UBC/UW news release,

An agreement between BC Premier Christy Clark and Washington Governor Jay Inslee means UBC will be collaborating with the University of Washington (UW) more in the future.

At last month’s [Sept. 2016] Cascadia Conference, Clark and Inslee signed a Memorandum of Understanding with the goal of fostering the growth of the technology sector in both regions. Officially referred to as the Cascadia Innovation Corridor, this partnership aims to reduce boundaries across the region — economic and otherwise.

While the memorandum provides broad goals and is not legally binding, it sets a precedent of collaboration between businesses, governments and universities, encouraging projects that span both jurisdictions. Aiming to capitalize on the cultural commonalities of regional centres Seattle and Vancouver, the agreement prioritizes development in life sciences, clean technology, data analytics and high tech.

Metropolitan centres like Seattle and Vancouver have experienced a surge in growth that sees planners envisioning them as the next Silicon Valleys. Premier Clark and Governor Inslee want to strengthen the ability of their jurisdictions to compete in innovation on a global scale. Accordingly, the memorandum encourages the exploration of “opportunities to advance research programs in key areas of innovation and future technologies among the region’s major universities and institutes.”

A few more questions about the Cooperative

I had a few more questions about the Feb. 23, 2017 announcement, for which (from UBC) Gail C. Murphy, PhD, FRSC, Associate Vice President Research pro tem, Professor, Computer Science of UBC and (from UW) Bill Howe, Associate Professor, Information School, Adjunct Associate Professor, Computer Science & Engineering, Associate Director and Senior Data Science Fellow,, UW eScience Institute Program Director and Faculty Chair, UW Data Science Masters Degree have kindly provided answers (Gail Murphy’s replies are prefaced with [GM] and one indent and Bill Howe’s replies are prefaced with [BH] and two indents),

  • Do you have any projects currently underway? e.g. I see a summer programme is planned. Will there be one in summer 2017? What focus will it have?

[GM] UW and UBC will each be running the Data Science for Social Good program in the summer of 2017. UBC’s announcement of the program is available at: http://dsi.ubc.ca/data-science-social-good-dssg-fellowships

  • Is the $1M from Microsoft going to be given in cash or as ‘in kind goods’ or some combination?

[GM] The $1-million donation is in cash. Microsoft organized the Emerging Cascadia Innovation Corridor Conference in September 2017. It was at the conference that the idea for the partnership was hatched. Through this initiative, UBC and UW will continue to engage with Microsoft to further shared goals in promoting evidence-based innovation to improve life for people in the Cascadia region and beyond.

  • How will the money or goods be disbursed? e.g. Will each institution get 1/2 or is there some sort of joint account?

[GM] The institutions are sharing the funds but will be separately administering the funds they receive.

  • Is data going to be crossing borders? e.g. You mentioned some health care projects. In that case, will data from BC residents be accessed and subject to US rules and regulations? Will BC residents know that there data is being accessed by a 3rd party? What level of consent is required?

[GM] As you point out, there are many issues involved with transferring data across the border. Any projects involving private data will adhere to local laws and ethical frameworks set out by the institutions.

  • Privacy rules vary greatly between the US and Canada. How is that being addressed in this proposed new research?

[No Reply]

  • Will new software and other products be created and who will own them?

[GM] It is too soon for us to comment on whether new software or other products will be created. Any creation of software or other products within the institutions will be governed by institutional policy.

  • Will the research be made freely available?

[GM] UBC researchers must be able to publish the results of research as set out by UBC policy.

[BH] Research output at UW will be made available according to UW policy, but I’ll point out that Microsoft has long been a fantastic partner in advancing our efforts in open and reproducible science, open source software, and open access publishing. 

 UW’s discussion on open access policies is available online.

 

  • What percentage of public funds will be used to enable this project? Will the province of BC and the state of Washington be splitting the costs evenly?

[GM] It is too soon for us to report on specific percentages. At UBC, we will be looking to partner with appropriate funding agencies to support more research with this donation. Applications to funding agencies will involve review of any proposals as per the rules of the funding agency.

  • Will there be any social science and/or ethics component to this collaboration? The press conference referenced data science only.

[GM] We expect, but cannot yet confirm, that some of the projects will involve collaborations with faculty from a broad range of research areas at UBC.

[BH] We are indeed placing a strong emphasis on the intersection between data science, the social sciences, and data ethics.  As examples of activities in this space around UW:

* The Information School at UW (my home school) is actively recruiting a new faculty candidate in data ethics this year

* The Education Working Group at the eScience Institute has created a new campus-wide Data & Society seminar course.

* The Center for Statistics in the Social Sciences (CSSS), which represents the marriage of data science and the social sciences, has been a long-term partner in our activities.

More specifically for this collaboration, we are collecting requirements for new software that emphasizes responsible data science: properly managing sensitive data, combating algorithmic bias, protecting privacy, and more.

Microsoft has been a key partner in this work through their Civic Technology group, for which the Seattle arm is led by Graham Thompson.

  • What impact do you see the new US federal government’s current concerns over borders and immigrants hav[ing] on this project? e.g. Are people whose origins are in Iran, Syria, Yemen, etc. and who are residents of Canada going to be able to participate?

[GM] Students and others eligible to participate in research projects in Canada will be welcomed into the UBC projects. Our hope is that faculty and students working on the Cascadia Urban Analytics Cooperative will be able to exchange ideas freely and move freely back and forth across the border.

  • How will seed funding for Sustained Research Partnerships’ be disbursed? Will there be a joint committee making these decisions?

[GM] We are in the process of elaborating this part of the program. At UBC, we are already experiencing, enjoying and benefitting from increased interaction with the University of Washington and look forward to elaborating more aspects of the program together as the year unfolds.

I had to make a few formatting changes when transferring the answers from emails to this posting: my numbered questions (1-11) became bulleted points and ‘have’ in what was question 10 was changed to ‘having’. The content for the answers has been untouched.

I’m surprised no one answered the privacy question but perhaps they thought the other answers sufficed. Despite an answer to my question *about the disbursement of funds*, I don’t understand how the universities are sharing the funds but that may just mean I’m having a bad day. (Or perhaps the folks at UBC are being overly careful after the scandals rocking the Vancouver campus over the last 18 months to two years (see Sophie Sutcliffe’s Dec. 3, 2015 opinion piece for the Ubyssey for details about the scandals).

Bill Howe’s response about open access (where you can read the journal articles for free) and open source (where you have free access to the software code) was interesting to me as I once worked for a company where the developers complained loud and long about Microsoft’s failure to embrace open source code. Howe’s response is particularly interesting given that Microsoft’s president is also the Chief Legal Officer whose portfolio of responsibilities (I imagine) includes patents.

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 University of Washington and the University of British Columbia announced the establishment of a joint data-science research unit, called the Cascadia Urban Analytics Cooperative, funded by a $1 million grant from Microsoft.

The collaboration will support study of shared urban issues, from health to transit to homelessness, drawing on faculty and student input from both universities.

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].

There’s nothing wrong with a business offering to contribute to the social good but it does well to remember that a business’s primary agenda is not the social good.  So in this case, it seems that public affairs and lobbying is really governmental affairs and that Microsoft has anticipated, for some time, greater difficulties with getting workers from all sorts of countries across the US border to work in Washington state making an outpost in British Columbia and closer relations between the constituencies quite advantageous. I wonder what else is on their agenda.

Getting back to UBC and UW, thank you to both Gail Murphy (in particular) and Bill Howe for taking the time to answer my questions. I very much appreciate it as answering 10 questions is a lot of work.

There were one area of interest (cities) that I did not broach with the either academic but will mention here.

Cities and their increasing political heft

Clearly Microsoft is focused on urban issues and that would seem to be the ‘flavour du jour’. There’s a May 31, 2016 piece on the TED website by Robert Muggah and Benjamin Fowler titled: ‘Why cities rule the world‘ (there are video talks embedded in the piece),

Cities are the the 21st century’s dominant form of civilization — and they’re where humanity’s struggle for survival will take place. Robert Muggah and Benjamin Barber spell out the possibilities.

Half the planet’s population lives in cities. They are the world’s engines, generating four-fifths of the global GDP. There are over 2,100 cities with populations of 250,000 people or more, including a growing number of mega-cities and sprawling, networked-city areas — conurbations, they’re called — with at least 10 million residents. As the economist Ed Glaeser puts it, “we are an urban species.”

But what makes cities so incredibly important is not just population or economics stats. Cities are humanity’s most realistic hope for future democracy to thrive, from the grassroots to the global. This makes them a stark contrast to so many of today’s nations, increasingly paralyzed by polarization, corruption and scandal.

In a less hyperbolic vein, Parag Khanna’s April 20,2016 piece for Quartz describes why he (and others) believe that megacities are where the future lies (Note: A link has been removed),

Cities are mankind’s most enduring and stable mode of social organization, outlasting all empires and nations over which they have presided. Today cities have become the world’s dominant demographic and economic clusters.

As the sociologist Christopher Chase-Dunn has pointed out, it is not population or territorial size that drives world-city status, but economic weight, proximity to zones of growth, political stability, and attractiveness for foreign capital. In other words, connectivity matters more than size. Cities thus deserve more nuanced treatment on our maps than simply as homogeneous black dots.

Within many emerging markets such as Brazil, Turkey, Russia, and Indonesia, the leading commercial hub or financial center accounts for at least one-third or more of national GDP. In the UK, London accounts for almost half Britain’s GDP. And in America, the Boston-New York-Washington corridor and greater Los Angeles together combine for about one-third of America’s GDP.

By 2025, there will be at least 40 such megacities. The population of the greater Mexico City region is larger than that of Australia, as is that of Chongqing, a collection of connected urban enclaves in China spanning an area the size of Austria. Cities that were once hundreds of kilometers apart have now effectively fused into massive urban archipelagos, the largest of which is Japan’s Taiheiyo Belt that encompasses two-thirds of Japan’s population in the Tokyo-Nagoya-Osaka megalopolis.

Great and connected cities, Saskia Sassen argues, belong as much to global networks as to the country of their political geography. Today the world’s top 20 richest cities have forged a super-circuit driven by capital, talent, and services: they are home to more than 75% of the largest companies, which in turn invest in expanding across those cities and adding more to expand the intercity network. Indeed, global cities have forged a league of their own, in many ways as denationalized as Formula One racing teams, drawing talent from around the world and amassing capital to spend on themselves while they compete on the same circuit.

The rise of emerging market megacities as magnets for regional wealth and talent has been the most significant contributor to shifting the world’s focal point of economic activity. McKinsey Global Institute research suggests that from now until 2025, one-third of world growth will come from the key Western capitals and emerging market megacities, one-third from the heavily populous middle-weight cities of emerging markets, and one-third from small cities and rural areas in developing countries.

Khanna’s megacities all exist within one country. If Vancouver and Seattle (and perhaps Portland?) were to become a become a megacity it would be one of the only or few to cross national borders.

Khanna has been mentioned here before in a Jan. 27, 2016 posting about cities and technology and a public engagement exercise with the National Research of Council of Canada (scroll down to the subsection titled: Cities rising in important as political entities).

Muggah/Fowler’s and Khanna’s 2016 pieces are well worth reading if you have the time.

For what it’s worth, I’m inclined to agree that cities will be and are increasing in political  importance along with this area of development:

Algorithms and big data

Concerns are being raised about how big data is being utilized so I was happy to see specific initiatives to address ethics issues in Howe’s response. For anyone not familiar with the concerns, here’s an excerpt from Cathy O’Neil’s Oct. 18, 2016 article for Wired magazine,

The age of Big Data has generated new tools and ideas on an enormous scale, with applications spreading from marketing to Wall Street, human resources, college admissions, and insurance. At the same time, Big Data has opened opportunities for a whole new class of professional gamers and manipulators, who take advantage of people using the power of statistics.

I should know. I was one of them.

Information is power, and in the age of corporate surveillance, profiles on every active American consumer means that the system is slanted in favor of those with the data. This data helps build tailor-made profiles that can be used for or against someone in a given situation. Insurance companies, which historically sold car insurance based on driving records, have more recently started using such data-driven profiling methods. A Florida insurance company has been found to charge people with low credit scores and good driving records more than people with high credit scores and a drunk driving conviction. It’s become standard practice for insurance companies to charge people not what they represent as a risk, but what they can get away with. The victims, of course, are those least likely to be able to afford the extra cost, but who need a car to get to work.

Big data profiling techniques are exploding in the world of politics. It’s estimated that over $1 billion will be spent on digital political ads in this election cycle, almost 50 times as much as was spent in 2008; this field is a growing part of the budget for presidential as well as down-ticket races. Political campaigns build scoring systems on potential voters—your likelihood of voting for a given party, your stance on a given issue, and the extent to which you are persuadable on that issue. It’s the ultimate example of asymmetric information, and the politicians can use what they know to manipulate your vote or your donation.

I highly recommend reading O’Neil’s article and, if you have the time, her book ‘Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy’.

Finally

I look forward to hearing more about the Cascadia Urban Analytics Cooperative and the Cascadia Innovation Corridor as they develop. This has the potential to be very exciting although I do have some concerns such as MIcrosoft and its agendas, both stated and unstated. After all, the Sept. 2016 meeting was convened by Microsoft and its public affairs/lobbying group and the topic was innovation, which is code for business and as hinted earlier, business is not synonymous with social good. Having said that I’m not about to demonize business either. I just think a healthy dose of skepticism is called for. Good things can happen but we need to ensure they do.

Thankfully, my concerns regarding algorithms and big data seem to be shared in some quarters, unfortunately none of these quarters appear to be located at the University of British Columbia. I hope that’s over caution with regard to communication rather than a failure to recognize any pitfalls.

ETA Mar. 1, 2017: Interestingly, the UK House of Commons Select Committee on Science and Technology announced an inquiry into the use of algorithms in public and business decision-making on Feb. 28, 2017. As this posting as much too big already, I’ve posted about the UK inquire separately in a Mar. 1, 2017 posting.

*’2016′ added for clarity on March 24, 2017.

*’disbursement of funds’ added for clarity on Sept. 21, 2017.

Handling massive digital datasets the quantum way

A Jan. 25, 2016 news item on phys.org describes a new approach to analyzing and managing huge datasets,

From gene mapping to space exploration, humanity continues to generate ever-larger sets of data—far more information than people can actually process, manage, or understand.

Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.

Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at [Massachusetts Institute of Technology] MIT, the University of Waterloo, and the University of Southern California [USC}.

A Jan. 25, 2016 MIT news release (*also on EurekAlert*), which originated the news item, describes the theory in more detail,

… Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.

In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd [ explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”

It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”

Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.

Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.

“That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.

“Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.

There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.

The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.

While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”

Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”

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

Quantum algorithms for topological and geometric analysis of data by Seth Lloyd, Silvano Garnerone, & Paolo Zanardi. Nature Communications 7, Article number: 10138 doi:10.1038/ncomms10138 Published 25 January 2016

This paper is open access.

ETA Jan. 25, 2016 1245 hours PST,

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

*’also on EurekAlert’ text and link added Jan. 26, 2016.

Simon Fraser University (Vancouver, Canada) and its president’s (Andrew Petter) dream colloquium: big data

They have a ‘big data’ start to 2016 planned for the President’s (Andrew Petter at Simon Fraser University [SFU] in Vancouver, Canada) Dream Colloquium according to a Jan. 5, 2016 news release,

Big data explained: SFU launches spring 2016 President’s Dream Colloquium

Speaker series tackles history, use and implications of collecting data

 

Canadians experience and interact with big data on a daily basis. Some interactions are as simple as buying coffee or as complex as filling out the Canadian government’s mandatory long-form census. But while big data may be one of the most important technological and social shifts in the past five years, many experts are still grappling with what to do with the massive amounts of information being gathered every day.

 

To help understand the implications of collecting, analyzing and using big data, Simon Fraser University is launching the President’s Dream Colloquium on Engaging Big Data on Tuesday, January 5.

 

“Big data affects all sectors of society from governments to businesses to institutions to everyday people,” says Peter Chow-White, SFU Associate Professor of Communication. “This colloquium brings together people from industry and scholars in computing and social sciences in a dialogue around one of the most important innovations of our time next to the Internet.”

 

This spring marks the first President’s Dream Colloquium where all faculty and guest lectures will be available to the public. The speaker series will give a historical overview of big data, specific case studies in how big data is used today and discuss what the implications are for this information’s usage in business, health and government in the future.

 

The series includes notable guest speakers such as managing director of Microsoft Research, Surajit Chaudhuri, and Tableau co-founder Pat Hanrahan.  

 

“Pat Hanrahan is a leader in a number of sectors and Tableau is a leader in accessing big data through visual analytics,” says Chow-White. “Rather than big data being available to only a small amount of professionals, Tableau makes it easier for everyday people to access and understand it in a visual way.”

 

The speaker series is free to attend with registration. Lectures will be webcast live and available on the President’s Dream Colloquium website.

 

FAST FACTS:

  • By 2020, over 1/3 of all data will live in or pass through the cloud.
  • Data production will be 44 times greater in 2020 than it was in 2009.
  • More than 70 percent of the digital universe is generated by individuals. But enterprises have responsibility for the storage, protection and management of 80 percent of that.

(Statistics provided by CSC)

 

WHO’S SPEAKING AT THE COLLOQUIUM:

 

The course features lectures from notable guest speakers including:

  • Sasha Issenberg, Author and Journalist
    Tuesday, January 12, 2016
  • Surajit ChaudhuriScientist and Managing Director of XCG (Microsoft Research)
    Tuesday, January 19, 2016
  • Pat Hanrahan, Professor at the Stanford Computer Graphics Laboratory, Cofounder and Chief Scientist of Tableau, Founding member of Pixar
    Wednesday, February 3, 2016
  • Sheelagh Carpendale, Professor of Computing Science University of Calgary, Canada Research Chair in Information Visualization
    Tuesday, February 23, 2016, 3:30pm
  • Colin HillCEO of GNS Healthcare
    Tuesday, March 8, 2016
  • Chad Skelton, Award-winning Data Journalist and Consultant
    Tuesday, March 22, 2016

Not to worry, even though the first talk with Sasha Issenberg and Mark Pickup (strangely, he’s [Pickup is an SFU professor of political science] not mentioned in the news release or on the event page) has taken place, a webcast is being posted to the event page here.

I watched the first event live (via a livestream webcast which I accessed by clicking on the link found on the Event’s Speaker’s page) and found it quite interesting although I’m not sure about asking Issenberg to speak extemporaneously. He rambled and offered more detail about things that don’t matter much to a Canadian audience. I couldn’t tell if part of the problem might lie with the fact that his ‘big data’ book (The Victory Lab: The Secret Science of Winning Campaigns) was published a while back and he’s since published one on medical tourism and is about to publish one on same sex marriages and the LGBTQ communities in the US. As someone else who moves from topic to topic, I know it’s an effort to ‘go back in time’ and to remember the details and to recapture the enthusiasm that made the piece interesting.  Also, he has yet to get the latest scoop on big data and politics in the US as embarking on the 2016 campaign trail won’t take place until sometime later in January.

So, thanks to Issenberg for managing to dredge up as much as he did. Happily, he did recognize that there are differences between Canada and the US and the type of election data that is gathered and other data that can accessed. He provided a capsule version of the data situation in the US where they can identify individuals and predict how they might vote, while Pickup focused on the Canadian scene. As one expects from Canadian political parties and Canadian agencies in general, no one really wants to share how much information they can actually access (yes, that’s true of the Liberals and the NDP [New Democrats] too). By contrast, political parties and strategists in the US quite openly shared information with Issenberg about where and how they get data.

Pickup made some interesting points about data and how more data does not lead to better predictions. There was one study done on psychologists which Pickup replicated with undergraduate political science students. The psychologists and the political science students in the two separate studies were given data and asked to predict behaviour. They were then given more data about the same individuals and asked again to predict behaviour. In all. there were four sessions where the subjects were given successively more data and asked to predict behaviour based on that data. You may have already guessed but prediction accuracy decreased each time more information was added. Conversely, the people making the predictions became more confident as their predictive accuracy declined. A little disconcerting, non?

Pickup made another point noting that it may be easier to use big data to predict voting behaviour in a two-party system such as they have in the US but a multi-party system such as we have in Canada offers more challenges.

So, it was a good beginning and I look forward to more in the coming weeks (President’s Dream Colloquium on Engaging Big Data). Remember if you can’t listen to the live session, just click through to the event’s speaker’s page where they have hopefully posted the webcast.

The next dream colloquium takes place Tuesday, Jan. 19, 2016,

Big Data since 1854

Dr. Surajit Chaudhuri, Scientist and Managing Director of XCG (Microsoft Research)
Standford University, PhD
Tuesday, January 19, 2016, 3:30–5 pm
IRMACS Theatre, ASB 10900, Burnaby campus [or by webcast[

Enjoy!

Nanotechnology takes the big data dive

Duke University’s (North Carolina, US) Center for Environmental Implications of Nano Technology (CEINT) is back in the news. An August 18, 2015 news item on Nanotechnology Now  highlights two new projects intended to launch the field of nanoinformatics,

In two new studies, researchers from across the country spearheaded by Duke University faculty have begun to design the framework on which to build the emerging field of nanoinformatics.

An August 18, 2015 Duke University news release on EurekAlert, which originated the news item, describes the notion of nanoinformatics and how Duke is playing a key role in establishing this field,

Nanoinformatics is, as the name implies, the combination of nanoscale research and informatics. It attempts to determine which information is relevant to the field and then develop effective ways to collect, validate, store, share, analyze, model and apply that information — with the ultimate goal of helping scientists gain new insights into human health, the environment and more.

In the first paper, published on August 10, 2015, in the Beilstein Journal of Nanotechnology, researchers begin the conversation of how to standardize the way nanotechnology data are curated.

Because the field is young and yet extremely diverse, data are collected and reported in different ways in different studies, making it difficult to compare apples to apples. Silver nanoparticles in a Florida swamp could behave entirely differently if studied in the Amazon River. And even if two studies are both looking at their effects in humans, slight variations like body temperature, blood pH levels or nanoparticles only a few nanometers larger can give different results. For future studies to combine multiple datasets to explore more complex questions, researchers must agree on what they need to know when curating nanomaterial data.

“We chose curation as the focus of this first paper because there are so many disparate efforts that are all over the road in terms of their missions, and the only thing they all have in common is that somehow they have to enter data into their resources,” said Christine Hendren, a research scientist at Duke and executive director of the Center for the Environmental Implications of NanoTechnology (CEINT). “So we chose that as the kernel of this effort to be as broad as possible in defining a baseline for the nanoinformatics community.”

The paper is the first in a series of six that will explore what people mean — their vocabulary, definitions, assumptions, research environments, etc. — when they talk about gathering data on nanomaterials in digital form. And to get everyone on the same page, the researchers are seeking input from all stakeholders, including those conducting basic research, studying environmental implications, harnessing nanomaterial properties for applications, developing products and writing government regulations.

The daunting task is being undertaken by the Nanomaterial Data Curation Initiative (NDCI), a project of the National Cancer Informatics Nanotechnology Working Group (NCIP NanoWG) lead by a diverse team of nanomaterial data stakeholders. If successful, not only will these disparate interests be able to combine their data, the project will highlight what data are missing and help drive the research priorities of the field.

In the second paper, published on July 16, 2015, in Science of The Total Environment, Hendren and her colleagues at CEINT propose a new, standardized way of studying the properties of nanomaterials.

“If we’re going to move the field forward, we have to be able to agree on what measurements are going to be useful, which systems they should be measured in and what data gets reported, so that we can make comparisons,” said Hendren.

The proposed strategy uses functional assays — relatively simple tests carried out in standardized, well-described environments — to measure nanomaterial behavior in actual systems.

For some time, the nanomaterial research community has been trying to use measured nanomaterial properties to predict outcomes. For example, what size and composition of a nanoparticle is most likely to cause cancer? The problem, argues Mark Wiesner, director of CEINT, is that this question is far too complex to answer.

“Environmental researchers use a parameter called biological oxygen demand to predict how much oxygen a body of water needs to support its ecosystem,” explains Wiesner. “What we’re basically trying to do with nanomaterials is the equivalent of trying to predict the oxygen level in a lake by taking an inventory of every living organism, mathematically map all of their living mechanisms and interactions, add up all of the oxygen each would take, and use that number as an estimate. But that’s obviously ridiculous and impossible. So instead, you take a jar of water, shake it up, see how much oxygen is taken and extrapolate that. Our functional assay paper is saying do that for nanomaterials.”

The paper makes suggestions as to what nanomaterials’ “jar of water” should be. It identifies what parameters should be noted when studying a specific environmental system, like digestive fluids or wastewater, so that they can be compared down the road.

It also suggests two meaningful processes for nanoparticles that should be measured by functional assays: attachment efficiency (does it stick to surfaces or not) and dissolution rate (does it release ions).

In describing how a nanoinformatics approach informs the implementation of a functional assay testing strategy, Hendren said “We’re trying to anticipate what we want to ask the data down the road. If we’re banking all of this comparable data while doing our near-term research projects, we should eventually be able to support more mechanistic investigations to make predictions about how untested nanomaterials will behave in a given scenario.”

Here are links to and citations for the papers,

The Nanomaterial Data Curation Initiative: A collaborative approach to assessing, evaluating, and advancing the state of the field by Christine Ogilvie Hendren, Christina M. Powers, Mark D. Hoover, and Stacey L. Harper.  Beilstein J. Nanotechnol. 2015, 6, 1752–1762. doi:10.3762/bjnano.6.179 Published 18 Aug 2015

A functional assay-based strategy for nanomaterial risk forecasting by Christine Ogilvie Hendren, Gregory V. Lowry, Jason M. Unrine, and Mark R. Wiesner. Science of The Total Environment Available online 16 July 2015 In Press, Corrected Proof  DOI: 10.1016/j.scitotenv.2015.06.100.

The first paper listed in open access while the second paper is behind a paywall.

I’m (mostly) giving the final comments to Dexter Johnson who in an August 20, 2015 posting on his Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website) had this to say (Note: Links have been removed),

It can take days for a supercomputer to unravel all the data contained in a single human genome. So it wasn’t long after mapping the first human genome that researchers coined the umbrella term “bioinformatics” in which a variety of methods and computer technologies are used for organizing and analyzing all that data.

Now teams of researchers led by scientists at Duke University believe that the field of nanotechnology has reached a critical mass of data and that a new field needs to be established, dubbed “nanoinformatics.

While being able to better organize and analyze data to study the impact of nanomaterials on the environment should benefit the field, what seems to remain a more pressing concern is having the tools for measuring nanomaterials outside of a vacuum and in water and air environments.”

I gather Christine Hendren has succeeded Mark Weisner as CEINT’s executive director.

Big data, data visualization, and spatial relationships with computers

I’m going to tie together today’s previous postings (Sporty data science Digitizing and visualizing the humanities, and Picture worth more than a thousand numbers? Yes and no with a future-oriented Feb. 2010 TED talk by John Underkoffler (embedded below). I have mentioned this talk previously in my June 14, 2012 posting titled, Interacting with stories and/or with data. From his TED speaker’s webpage,

Remember the data interface from Minority Report? Well, it’s real, John Underkoffler invented it — as a point-and-touch interface called g-speak — and it’s about to change the way we interact with data.

When Tom Cruise put on his data glove and started whooshing through video clips of future crimes, how many of us felt the stirrings of geek lust? This iconic scene in Minority Report marked a change in popular thinking about interfaces — showing how sexy it could be to use natural gestures, without keyboard, mouse or command line.

John Underkoffler led the team that came up with this interface, called the g-speak Spatial Operating Environment. His company, Oblong Industries, was founded to move g-speak into the real world. Oblong is building apps for aerospace, bioinformatics, video editing and more. But the big vision is ubiquity: g-speak on every laptop, every desktop, every microwave oven, TV, dashboard. “It has to be like this,” he says. “We all of us every day feel that. We build starting there. We want to change it all.”

Before founding Oblong, Underkoffler spent 15 years at MIT’s Media Laboratory, working in holography, animation and visualization techniques, and building the I/O Bulb and Luminous Room Systems.

He’s talking about human-computer interfaces but I found the part where he manipulates massive amounts of data (from approx. 8 mins. – 9.5 mins.) particularly instructive. This video is longer (approx. 15.5 mins. as opposed to 5 mins. or less) than the videos I usually embed.

I think the real game changer for science  (how it’s conducted, how it’s taught, and how it’s communicated) and other disciplines is data visualization.

ETA Aug. 3, 2012 1:20 pm PDT: For those who might want to see this video in its ‘native’ habitat, go here http://www.ted.com/talks/john_underkoffler_drive_3d_data_with_a_gesture.html.