Tag Archives: predictive policing

True love with AI (artificial intelligence): The Nature of Things explores emotional and creative AI (long read)

The Canadian Broadcasting Corporation’s (CBC) science television series,The Nature of Things, which has been broadcast since November 1960, explored the world of emotional, empathic and creative artificial intelligence (AI) in a Friday, November 19, 2021 telecast titled, The Machine That Feels,

The Machine That Feels explores how artificial intelligence (AI) is catching up to us in ways once thought to be uniquely human: empathy, emotional intelligence and creativity.

As AI moves closer to replicating humans, it has the potential to reshape every aspect of our world – but most of us are unaware of what looms on the horizon.

Scientists see AI technology as an opportunity to address inequities and make a better, more connected world. But it also has the capacity to do the opposite: to stoke division and inequality and disconnect us from fellow humans. The Machine That Feels, from The Nature of Things, shows viewers what they need to know about a field that is advancing at a dizzying pace, often away from the public eye.

What does it mean when AI makes art? Can AI interpret and understand human emotions? How is it possible that AI creates sophisticated neural networks that mimic the human brain? The Machine That Feels investigates these questions, and more.

In Vienna, composer Walter Werzowa has — with the help of AI — completed Beethoven’s previously unfinished 10th symphony. By feeding data about Beethoven, his music, his style and the original scribbles on the 10th symphony into an algorithm, AI has created an entirely new piece of art.

In Atlanta, Dr. Ayanna Howard and her robotics lab at Georgia Tech are teaching robots how to interpret human emotions. Where others see problems, Howard sees opportunity: how AI can help fill gaps in education and health care systems. She believes we need a fundamental shift in how we perceive robots: let’s get humans and robots to work together to help others.

At Tufts University in Boston, a new type of biological robot has been created: the xenobot. The size of a grain of sand, xenobots are grown from frog heart and skin cells, and combined with the “mind” of a computer. Programmed with a specific task, they can move together to complete it. In the future, they could be used for environmental cleanup, digesting microplastics and targeted drug delivery (like releasing chemotherapy compounds directly into tumours).

The film includes interviews with global leaders, commentators and innovators from the AI field, including Geoff Hinton, Yoshua Bengio, Ray Kurzweil and Douglas Coupland, who highlight some of the innovative and cutting-edge AI technologies that are changing our world.

The Machine That Feels focuses on one central question: in the flourishing age of artificial intelligence, what does it mean to be human?

I’ll get back to that last bit, “… what does it mean to be human?” later.

There’s a lot to appreciate in this 44 min. programme. As you’d expect, there was a significant chunk of time devoted to research being done in the US but Poland and Japan also featured and Canadian content was substantive. A number of tricky topics were covered and transitions from one topic to the next were smooth.

In the end credits, I counted over 40 source materials from Getty Images, Google Canada, Gatebox, amongst others. It would have been interesting to find out which segments were produced by CBC.

David Suzuki’s (programme host) script was well written and his narration was enjoyable, engaging, and non-intrusive. That last quality is not always true of CBC hosts who can fall into the trap of overdramatizing the text.

Drilling down

I have followed artificial intelligence stories in a passive way (i.e., I don’t seek them out) for many years. Even so, there was a lot of material in the programme that was new to me.

For example, there was this love story (from the ‘I love her and see her as a real woman.’ Meet a man who ‘married’ an artificial intelligence hologram webpage on the CBC),

In the The Machine That Feels, a documentary from The Nature of Things, we meet Kondo Akihiko, a Tokyo resident who “married” a hologram of virtual pop singer Hatsune Miku using a certificate issued by Gatebox (the marriage isn’t recognized by the state, and Gatebox acknowledges the union goes “beyond dimensions”).

I found Akihiko to be quite moving when he described his relationship, which is not unique. It seems some 4,000 men have ‘wed’ their digital companions, you can read about that and more on the ‘I love her and see her as a real woman.’ Meet a man who ‘married’ an artificial intelligence hologram webpage.

What does it mean to be human?

Overall, this Nature of Things episode embraces certainty, which means the question of what it means to human is referenced rather than seriously discussed. An unanswerable philosophical question, the programme is ill-equipped to address it, especially since none of the commentators are philosophers or seem inclined to philosophize.

The programme presents AI as a juggernaut. Briefly mentioned is the notion that we need to make some decisions about how our juggernaut is developed and utilized. No one discusses how we go about making changes to systems that are already making critical decisions for us. (For more about AI and decision-making, see my February 28, 2017 posting and scroll down to the ‘Algorithms and big data’ subhead for Cathy O’Neil’s description of how important decisions that affect us are being made by AI systems. She is the author of the 2016 book, ‘Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy’; still a timely read.)

In fact, the programme’s tone is mostly one of breathless excitement. A few misgivings are expressed, e.g,, one woman who has an artificial ‘texting friend’ (Replika; a chatbot app) noted that it can ‘get into your head’ when she had a chat where her ‘friend’ told her that all of a woman’s worth is based on her body; she pushed back but intimated that someone more vulnerable could find that messaging difficult to deal with.

The sequence featuring Akihiko and his hologram ‘wife’ is followed by one suggesting that people might become more isolated and emotionally stunted as they interact with artificial friends. It should be noted, Akihiko’s wife is described as ‘perfect’. I gather perfection means that you are always understanding and have no needs of your own. She also seems to be about 18″ high.

Akihiko has obviously been asked about his ‘wife’ before as his answers are ready. They boil down to “there are many types of relationships” and there’s nothing wrong with that. It’s an intriguing thought which is not explored.

Also unexplored, these relationships could be said to resemble slavery. After all, you pay for these friends over which you have control. But perhaps that’s alright since AI friends don’t have consciousness. Or do they? In addition to not being able to answer the question, “what is it to be human?” we still can’t answer the question, “what is consciousness?”

AI and creativity

The Nature of Things team works fast. ‘Beethoven X – The AI Project’ had its first performance on October 9, 2021. (See my October 1, 2021 post ‘Finishing Beethoven’s unfinished 10th Symphony’ for more information from Ahmed Elgammal’s (Director of the Art & AI Lab at Rutgers University) technical perspective on the project.

Briefly, Beethoven died before completing his 10th symphony and a number of computer scientists, musicologists, AI, and musicians collaborated to finish the symphony.)

The one listener (Felix Mayer, music professor at the Technical University Munich) in the hall during a performance doesn’t consider the work to be a piece of music. He does have a point. Beethoven left some notes but this ’10th’ is at least partly mathematical guesswork. A set of probabilities where an algorithm chooses which note comes next based on probability.

There was another artist also represented in the programme. Puzzlingly, it was the still living Douglas Coupland. In my opinion, he’s better known as a visual artist than a writer (his Wikipedia entry lists him as a novelist first) but he has succeeded greatly in both fields.

What makes his inclusion in the Nature of Things ‘The Machine That Feels’ programme puzzling, is that it’s not clear how he worked with artificial intelligence in a collaborative fashion. Here’s a description of Coupland’s ‘AI’ project from a June 29, 2021 posting by Chris Henry on the Google Outreach blog (Note: Links have been removed),

… when the opportunity presented itself to explore how artificial intelligence (AI) inspires artistic expression — with the help of internationally renowned Canadian artist Douglas Coupland — the Google Research team jumped on it. This collaboration, with the support of Google Arts & Culture, culminated in a project called Slogans for the Class of 2030, which spotlights the experiences of the first generation of young people whose lives are fully intertwined with the existence of AI. 

This collaboration was brought to life by first introducing Coupland’s written work to a machine learning language model. Machine learning is a form of AI that provides computer systems the ability to automatically learn from data. In this case, Google research scientists tuned a machine learning algorithm with Coupland’s 30-year body of written work — more than a million words — so it would familiarize itself with the author’s unique style of writing. From there, curated general-public social media posts on selected topics were added to teach the algorithm how to craft short-form, topical statements. [emphases mine]

Once the algorithm was trained, the next step was to process and reassemble suggestions of text for Coupland to use as inspiration to create twenty-five Slogans for the Class of 2030. [emphasis mine]

I would comb through ‘data dumps’ where characters from one novel were speaking with those in other novels in ways that they might actually do. It felt like I was encountering a parallel universe Doug,” Coupland says. “And from these outputs, the statements you see here in this project appeared like gems. Did I write them? Yes. No. Could they have existed without me? No.” [emphases mine]

So, the algorithms crunched through Coupland’s word and social media texts to produce slogans, which Coupland then ‘combed through’ to pick out 25 slogans for the ‘Slogans For The Class of 2030’ project. (Note: In the programme, he says that he started a sentence and then the AI system completed that sentence with material gleaned from his own writings, which brings to Exquisite Corpse, a collaborative game for writers originated by the Surrealists, possibly as early as 1918.)

The ‘slogans’ project also reminds me of William S. Burroughs and the cut-up technique used in his work. From the William S. Burroughs Cut-up technique webpage on the Language is a Virus website (Thank you to Lake Rain Vajra for a very interesting website),

The cutup is a mechanical method of juxtaposition in which Burroughs literally cuts up passages of prose by himself and other writers and then pastes them back together at random. This literary version of the collage technique is also supplemented by literary use of other media. Burroughs transcribes taped cutups (several tapes spliced into each other), film cutups (montage), and mixed media experiments (results of combining tapes with television, movies, or actual events). Thus Burroughs’s use of cutups develops his juxtaposition technique to its logical conclusion as an experimental prose method, and he also makes use of all contemporary media, expanding his use of popular culture.

[Burroughs says] “All writing is in fact cut-ups. A collage of words read heard overheard. What else? Use of scissors renders the process explicit and subject to extension and variation. Clear classical prose can be composed entirely of rearranged cut-ups. Cutting and rearranging a page of written words introduces a new dimension into writing enabling the writer to turn images in cinematic variation. Images shift sense under the scissors smell images to sound sight to sound to kinesthetic. This is where Rimbaud was going with his color of vowels. And his “systematic derangement of the senses.” The place of mescaline hallucination: seeing colors tasting sounds smelling forms.

“The cut-ups can be applied to other fields than writing. Dr Neumann [emphasis mine] in his Theory of Games and Economic behavior introduces the cut-up method of random action into game and military strategy: assume that the worst has happened and act accordingly. … The cut-up method could be used to advantage in processing scientific data. [emphasis mine] How many discoveries have been made by accident? We cannot produce accidents to order. The cut-ups could add new dimension to films. Cut gambling scene in with a thousand gambling scenes all times and places. Cut back. Cut streets of the world. Cut and rearrange the word and image in films. There is no reason to accept a second-rate product when you can have the best. And the best is there for all. Poetry is for everyone . . .”

First, John von Neumann (1902 – 57) is a very important figure in the history of computing. From a February 25, 2017 John von Neumann and Modern Computer Architecture essay on the ncLab website, “… he invented the computer architecture that we use today.”

Here’s Burroughs on the history of writers and cutups (thank you to QUEDEAR for posting this clip),

You can hear Burroughs talk about the technique and how he started using it in 1959.

There is no explanation from Coupland as to how his project differs substantively from Burroughs’ cut-ups or a session of Exquisite Corpse. The use of a computer programme to crunch through data and give output doesn’t seem all that exciting. *(More about computers and chatbots at end of posting).* It’s hard to know if this was an interview situation where he wasn’t asked the question or if the editors decided against including it.

Kazuo Ishiguro?

Given that Ishiguro’s 2021 book (Klara and the Sun) is focused on an artificial friend and raises the question of ‘what does it mean to be human’, as well as the related question, ‘what is the nature of consciousness’, it would have been interesting to hear from him. He spent a fair amount of time looking into research on machine learning in preparation for his book. Maybe he was too busy?

AI and emotions

The work being done by Georgia Tech’s Dr. Ayanna Howard and her robotics lab is fascinating. They are teaching robots how to interpret human emotions. The segment which features researchers teaching and interacting with robots, Pepper and Salt, also touches on AI and bias.

Watching two African American researchers talk about the ways in which AI is unable to read emotions on ‘black’ faces as accurately as ‘white’ faces is quite compelling. It also reinforces the uneasiness you might feel after the ‘Replika’ segment where an artificial friend informs a woman that her only worth is her body.

(Interestingly, Pepper and Salt are produced by Softbank Robotics, part of Softbank, a multinational Japanese conglomerate, [see a June 28, 2021 article by Ian Carlos Campbell for The Verge] whose entire management team is male according to their About page.)

While Howard is very hopeful about the possibilities of a machine that can read emotions, she doesn’t explore (on camera) any means for pushing back against bias other than training AI by using more black faces to help them learn. Perhaps more representative management and coding teams in technology companies?

While the programme largely focused on AI as an algorithm on a computer, robots can be enabled by AI (as can be seen in the segment with Dr. Howard).

My February 14, 2019 posting features research with a completely different approach to emotions and machines,

“I’ve always felt that robots shouldn’t just be modeled after humans [emphasis mine] or be copies of humans,” he [Guy Hoffman, assistant professor at Cornell University)] said. “We have a lot of interesting relationships with other species. Robots could be thought of as one of those ‘other species,’ not trying to copy what we do but interacting with us with their own language, tapping into our own instincts.”

[from a July 16, 2018 Cornell University news release on EurekAlert]

This brings the question back to, what is consciousness?

What scientists aren’t taught

Dr. Howard notes that scientists are not taught to consider the implications of their work. Her comment reminded me of a question I was asked many years ago after a presentation, it concerned whether or not science had any morality. (I said, no.)

My reply angered an audience member (a visual artist who was working with scientists at the time) as she took it personally and started defending scientists as good people who care and have morals and values. She failed to understand that the way in which we teach science conforms to a notion that somewhere there are scientific facts which are neutral and objective. Society and its values are irrelevant in the face of the larger ‘scientific truth’ and, as a consequence, you don’t need to teach or discuss how your values or morals affect that truth or what the social implications of your work might be.

Science is practiced without much if any thought to values. By contrast, there is the medical injunction, “Do no harm,” which suggests to me that someone recognized competing values. E.g., If your important and worthwhile research is harming people, you should ‘do no harm’.

The experts, the connections, and the Canadian content

It’s been a while since I’ve seen Ray Kurzweil mentioned but he seems to be getting more attention these days. (See this November 16, 2021 posting by Jonny Thomson titled, “The Singularity: When will we all become super-humans? Are we really only a moment away from “The Singularity,” a technological epoch that will usher in a new era in human evolution?” on The Big Think for more). Note: I will have a little more about evolution later in this post.

Interestingly, Kurzweil is employed by Google these days (see his Wikipedia entry, the column to the right). So is Geoffrey Hinton, another one of the experts in the programme (see Hinton’s Wikipedia entry, the column to the right, under Institutions).

I’m not sure about Yoshu Bengio’s relationship with Google but he’s a professor at the Université de Montréal, and he’s the Scientific Director for Mila ((Quebec’s Artificial Intelligence research institute)) & IVADO (Institut de valorisation des données), Note: IVADO is not particularly relevant to what’s being discussed in this post.

As for Mila, the Canada Google blog in a November 21, 2016 posting notes a $4.5M grant to the institution,

Google invests $4.5 Million in Montreal AI Research

A new grant from Google for the Montreal Institute for Learning Algorithms (MILA) will fund seven faculty across a number of Montreal institutions and will help tackle some of the biggest challenges in machine learning and AI, including applications in the realm of systems that can understand and generate natural language. In other words, better understand a fan’s enthusiasm for Les Canadien [sic].

Google is expanding its academic support of deep learning at MILA, renewing Yoshua Bengio’s Focused Research Award and offering Focused Research Awards to MILA faculty at University of Montreal and McGill University:

Google reaffirmed their commitment to Mila in 2020 with a grant worth almost $4M (from a November 13, 2020 posting on the Mila website, Note: A link has been removed),

Google Canada announced today [November 13, 2020] that it will be renewing its funding of Mila – Quebec Artificial Intelligence Institute, with a generous pledge of nearly $4M over a three-year period. Google previously invested $4.5M US in 2016, enabling Mila to grow from 25 to 519 researchers.

In a piece written for Google’s Official Canada Blog, Yoshua Bengio, Mila Scientific Director, says that this year marked a “watershed moment for the Canadian AI community,” as the COVID-19 pandemic created unprecedented challenges that demanded rapid innovation and increased interdisciplinary collaboration between researchers in Canada and around the world.

COVID-19 has changed the world forever and many industries, from healthcare to retail, will need to adapt to thrive in our ‘new normal.’ As we look to the future and how priorities will shift, it is clear that AI is no longer an emerging technology but a useful tool that can serve to solve world problems. Google Canada recognizes not only this opportunity but the important task at hand and I’m thrilled they have reconfirmed their support of Mila with an additional $3,95 million funding grant until 22.

– Yoshua Bengio, for Google’s Official Canada Blog

Interesting, eh? Of course, Douglas Coupland is working with Google, presumably for money, and that would connect over 50% of the Canadian content (Douglas Coupland, Yoshua Bengio, and Geoffrey Hinton; Kurzweil is an American) in the programme to Google.

My hat’s off to Google’s marketing communications and public relations teams.

Anthony Morgan of Science Everywhere also provided some Canadian content. His LinkedIn profile indicates that he’s working on a PhD in molecular science, which is described this way, “My work explores the characteristics of learning environments, that support critical thinking and the relationship between critical thinking and wisdom.”

Morgan is also the founder and creative director of Science Everywhere, from his LinkedIn profile, “An events & media company supporting knowledge mobilization, community engagement, entrepreneurship and critical thinking. We build social tools for better thinking.”

There is this from his LinkedIn profile,

I develop, create and host engaging live experiences & media to foster critical thinking.

I’ve spent my 15+ years studying and working in psychology and science communication, thinking deeply about the most common individual and societal barriers to critical thinking. As an entrepreneur, I lead a team to create, develop and deploy cultural tools designed to address those barriers. As a researcher I study what we can do to reduce polarization around science.

There’s a lot more to Morgan (do look him up; he has connections to the CBC and other media outlets). The difficulty is: why was he chosen to talk about artificial intelligence and emotions and creativity when he doesn’t seem to know much about the topic? He does mention GPT-3, an AI programming language. He seems to be acting as an advocate for AI although he offers this bit of almost cautionary wisdom, “… algorithms are sets of instructions.” (You can can find out more about it in my April 27, 2021 posting. There’s also this November 26, 2021 posting [The Inherent Limitations of GPT-3] by Andrey Kurenkov, a PhD student with the Stanford [University] Vision and Learning Lab.)

Most of the cautionary commentary comes from Luke Stark, assistant professor at Western [Ontario] University’s Faculty of Information and Media Studies. He’s the one who mentions stunted emotional growth.

Before moving on, there is another set of connections through the Pan-Canadian Artificial Intelligence Strategy, a Canadian government science funding initiative announced in the 2017 federal budget. The funds allocated to the strategy are administered by the Canadian Institute for Advanced Research (CIFAR). Yoshua Bengio through Mila is associated with the strategy and CIFAR, as is Geoffrey Hinton through his position as Chief Scientific Advisor for the Vector Institute.


Getting back to “The Singularity: When will we all become super-humans? Are we really only a moment away from “The Singularity,” a technological epoch that will usher in a new era in human evolution?” Xenobots point in a disconcerting (for some of us) evolutionary direction.

I featured the work, which is being done at Tufts University in the US, in my June 21, 2021 posting, which includes an embedded video,

From a March 31, 2021 news item on ScienceDaily,

Last year, a team of biologists and computer scientists from Tufts University and the University of Vermont (UVM) created novel, tiny self-healing biological machines from frog cells called “Xenobots” that could move around, push a payload, and even exhibit collective behavior in the presence of a swarm of other Xenobots.

Get ready for Xenobots 2.0.

Also from an excerpt in the posting, the team has “created life forms that self-assemble a body from single cells, do not require muscle cells to move, and even demonstrate the capability of recordable memory.”

Memory is key to intelligence and this work introduces the notion of ‘living’ robots which leads to questioning what constitutes life. ‘The Machine That Feels’ is already grappling with far too many questions to address this development but introducing the research here might have laid the groundwork for the next episode, The New Human, telecast on November 26, 2021,

While no one can be certain what will happen, evolutionary biologists and statisticians are observing trends that could mean our future feet only have four toes (so long, pinky toe) or our faces may have new combinations of features. The new humans might be much taller than their parents or grandparents, or have darker hair and eyes.

And while evolution takes a lot of time, we might not have to wait too long for a new version of ourselves.

Technology is redesigning the way we look and function — at a much faster pace than evolution. We are merging with technology more than ever before: our bodies may now have implanted chips, smart limbs, exoskeletons and 3D-printed organs. A revolutionary gene editing technique has given us the power to take evolution into our own hands and alter our own DNA. How long will it be before we are designing our children?

As the story about the xenobots doesn’t say, we could also take the evolution of another species into our hands.

David Suzuki, where are you?

Our programme host, David Suzuki surprised me. I thought that as an environmentalist he’d point out that the huge amounts of computing power needed for artificial intelligence as mentioned in the programme, constitutes an environmental issue. I also would have expected a geneticist like Suzuki might have some concerns with regard to xenobots but perhaps that’s being saved for the next episode (The New Human) of the Nature of Things.

Artificial stupidity

Thanks to Will Knight for introducing me to the term ‘artificial stupidity’. Knight, a senior writer covers artificial intelligence for WIRED magazine. According to its Wikipedia entry,

Artificial stupidity is commonly used as a humorous opposite of the term artificial intelligence (AI), often as a derogatory reference to the inability of AI technology to adequately perform its tasks.[1] However, within the field of computer science, artificial stupidity is also used to refer to a technique of “dumbing down” computer programs in order to deliberately introduce errors in their responses.

Knight was using the term in its humorous, derogatory form.


The episode certainly got me thinking if not quite in the way producers might have hoped. ‘The Machine That Feels’ is a glossy, pretty well researched piece of infotainment.

To be blunt, I like and have no problems with infotainment but it can be seductive. I found it easier to remember the artificial friends, wife, xenobots, and symphony than the critiques and concerns.

Hopefully, ‘The Machine That Feels’ stimulates more interest in some very important topics. If you missed the telecast, you can catch the episode here.

For anyone curious about predictive policing, which was mentioned in the Ayanna Howard segment, see my November 23, 2017 posting about Vancouver’s plunge into AI and car theft.

*ETA December 6, 2021: One of the first ‘chatterbots’ was ELIZA, a computer programme developed from1964 to 1966. The most famous ELIZA script was DOCTOR, where the programme simulated a therapist. Many early users believed ELIZA understood and could respond as a human would despite Joseph Weizenbaum’s (creator of the programme) insistence otherwise.

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,


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.

How to get people to trust artificial intelligence

Vyacheslav Polonski’s (University of Oxford researcher) January 10, 2018 piece (originally published Jan. 9, 2018 on The Conversation) on phys.org isn’t a gossip article although there are parts that could be read that way. Before getting to what I consider the juicy bits (Note: Links have been removed),

Artificial intelligence [AI] can already predict the future. Police forces are using it to map when and where crime is likely to occur [Note: See my Nov. 23, 2017 posting about predictive policing in Vancouver for details about the first Canadian municipality to introduce the technology]. Doctors can use it to predict when a patient is most likely to have a heart attack or stroke. Researchers are even trying to give AI imagination so it can plan for unexpected consequences.

Many decisions in our lives require a good forecast, and AI agents are almost always better at forecasting than their human counterparts. Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions. Recent cases show that people don’t like relying on AI and prefer to trust human experts, even if these experts are wrong.

The part (juicy bits) that satisfied some of my long held curiosity was this section on Watson and its life as a medical adjunct (Note: Links have been removed),

IBM’s attempt to promote its supercomputer programme to cancer doctors (Watson for Onology) was a PR [public relations] disaster. The AI promised to deliver top-quality recommendations on the treatment of 12 cancers that accounted for 80% of the world’s cases. As of today, over 14,000 patients worldwide have received advice based on its calculations.

But when doctors first interacted with Watson they found themselves in a rather difficult situation. On the one hand, if Watson provided guidance about a treatment that coincided with their own opinions, physicians did not see much value in Watson’s recommendations. The supercomputer was simply telling them what they already know, and these recommendations did not change the actual treatment. This may have given doctors some peace of mind, providing them with more confidence in their own decisions. But IBM has yet to provide evidence that Watson actually improves cancer survival rates.

On the other hand, if Watson generated a recommendation that contradicted the experts’ opinion, doctors would typically conclude that Watson wasn’t competent. And the machine wouldn’t be able to explain why its treatment was plausible because its machine learning algorithms were simply too complex to be fully understood by humans. Consequently, this has caused even more mistrust and disbelief, leading many doctors to ignore the seemingly outlandish AI recommendations and stick to their own expertise.

As a result, IBM Watson’s premier medical partner, the MD Anderson Cancer Center, recently announced it was dropping the programme. Similarly, a Danish hospital reportedly abandoned the AI programme after discovering that its cancer doctors disagreed with Watson in over two thirds of cases.

The problem with Watson for Oncology was that doctors simply didn’t trust it. Human trust is often based on our understanding of how other people think and having experience of their reliability. …

It seems to me there might be a bit more to the doctors’ trust issues and I was surprised it didn’t seem to have occurred to Polonski. Then I did some digging (from Polonski’s webpage on the Oxford Internet Institute website),

Vyacheslav Polonski (@slavacm) is a DPhil [PhD] student at the Oxford Internet Institute. His research interests are located at the intersection of network science, media studies and social psychology. Vyacheslav’s doctoral research examines the adoption and use of social network sites, focusing on the effects of social influence, social cognition and identity construction.

Vyacheslav is a Visiting Fellow at Harvard University and a Global Shaper at the World Economic Forum. He was awarded the Master of Science degree with Distinction in the Social Science of the Internet from the University of Oxford in 2013. He also obtained the Bachelor of Science degree with First Class Honours in Management from the London School of Economics and Political Science (LSE) in 2012.

Vyacheslav was honoured at the British Council International Student of the Year 2011 awards, and was named UK’s Student of the Year 2012 and national winner of the Future Business Leader of the Year 2012 awards by TARGETjobs.

Previously, he has worked as a management consultant at Roland Berger Strategy Consultants and gained further work experience at the World Economic Forum, PwC, Mars, Bertelsmann and Amazon.com. Besides, he was involved in several start-ups as part of the 2012 cohort of Entrepreneur First and as part of the founding team of the London office of Rocket Internet. Vyacheslav was the junior editor of the bi-lingual book ‘Inspire a Nation‘ about Barack Obama’s first presidential election campaign. In 2013, he was invited to be a keynote speaker at the inaugural TEDx conference of IE University in Spain to discuss the role of a networked mindset in everyday life.

Vyacheslav is fluent in German, English and Russian, and is passionate about new technologies, social entrepreneurship, philanthropy, philosophy and modern art.

Research interests

Network science, social network analysis, online communities, agency and structure, group dynamics, social interaction, big data, critical mass, network effects, knowledge networks, information diffusion, product adoption

Positions held at the OII

  • DPhil student, October 2013 –
  • MSc Student, October 2012 – August 2013

Polonski doesn’t seem to have any experience dealing with, participating in, or studying the medical community. Getting a doctor to admit that his or her approach to a particular patient’s condition was wrong or misguided runs counter to their training and, by extension, the institution of medicine. Also, one of the biggest problems in any field is getting people to change and it’s not always about trust. In this instance, you’re asking a doctor to back someone else’s opinion after he or she has rendered theirs. This is difficult even when the other party is another human doctor let alone a form of artificial intelligence.

If you want to get a sense of just how hard it is to get someone to back down after they’ve committed to a position, read this January 10, 2018 essay by Lara Bazelon, an associate professor at the University of San Francisco School of Law. This is just one of the cases (Note: Links have been removed),

Davontae Sanford was 14 years old when he confessed to murdering four people in a drug house on Detroit’s East Side. Left alone with detectives in a late-night interrogation, Sanford says he broke down after being told he could go home if he gave them “something.” On the advice of a lawyer whose license was later suspended for misconduct, Sanders pleaded guilty in the middle of his March 2008 trial and received a sentence of 39 to 92 years in prison.

Sixteen days after Sanford was sentenced, a hit man named Vincent Smothers told the police he had carried out 12 contract killings, including the four Sanford had pleaded guilty to committing. Smothers explained that he’d worked with an accomplice, Ernest Davis, and he provided a wealth of corroborating details to back up his account. Smothers told police where they could find one of the weapons used in the murders; the gun was recovered and ballistics matched it to the crime scene. He also told the police he had used a different gun in several of the other murders, which ballistics tests confirmed. Once Smothers’ confession was corroborated, it was clear Sanford was innocent. Smothers made this point explicitly in an 2015 affidavit, emphasizing that Sanford hadn’t been involved in the crimes “in any way.”

Guess what happened? (Note: Links have been removed),

But Smothers and Davis were never charged. Neither was Leroy Payne, the man Smothers alleged had paid him to commit the murders. …

Davontae Sanford, meanwhile, remained behind bars, locked up for crimes he very clearly didn’t commit.

Police failed to turn over all the relevant information in Smothers’ confession to Sanford’s legal team, as the law required them to do. When that information was leaked in 2009, Sanford’s attorneys sought to reverse his conviction on the basis of actual innocence. Wayne County Prosecutor Kym Worthy fought back, opposing the motion all the way to the Michigan Supreme Court. In 2014, the court sided with Worthy, ruling that actual innocence was not a valid reason to withdraw a guilty plea [emphasis mine]. Sanford would remain in prison for another two years.

Doctors are just as invested in their opinions and professional judgments as lawyers  (just like  the prosecutor and the judges on the Michigan Supreme Court) are.

There is one more problem. From the doctor’s (or anyone else’s perspective), if the AI is making the decisions, why do he/she need to be there? At best it’s as if AI were turning the doctor into its servant or, at worst, replacing the doctor. Polonski alludes to the problem in one of his solutions to the ‘trust’ issue (Note: A link has been removed),

Research suggests involving people more in the AI decision-making process could also improve trust and allow the AI to learn from human experience. For example,one study showed people were given the freedom to slightly modify an algorithm felt more satisfied with its decisions, more likely to believe it was superior and more likely to use it in the future.

Having input into the AI decision-making process somewhat addresses one of the problems but the commitment to one’s own judgment even when there is overwhelming evidence to the contrary is a perennially thorny problem. The legal case mentioned here earlier is clearly one where the contrarian is wrong but it’s not always that obvious. As well, sometimes, people who hold out against the majority are right.

US Army

Getting back to building trust, it turns out the US Army Research Laboratory is also interested in transparency where AI is concerned (from a January 11, 2018 US Army news release on EurekAlert),

U.S. Army Research Laboratory [ARL] scientists developed ways to improve collaboration between humans and artificially intelligent agents in two projects recently completed for the Autonomy Research Pilot Initiative supported by the Office of Secretary of Defense. They did so by enhancing the agent transparency [emphasis mine], which refers to a robot, unmanned vehicle, or software agent’s ability to convey to humans its intent, performance, future plans, and reasoning process.

“As machine agents become more sophisticated and independent, it is critical for their human counterparts to understand their intent, behaviors, reasoning process behind those behaviors, and expected outcomes so the humans can properly calibrate their trust [emphasis mine] in the systems and make appropriate decisions,” explained ARL’s Dr. Jessie Chen, senior research psychologist.

The U.S. Defense Science Board, in a 2016 report, identified six barriers to human trust in autonomous systems, with ‘low observability, predictability, directability and auditability’ as well as ‘low mutual understanding of common goals’ being among the key issues.

In order to address these issues, Chen and her colleagues developed the Situation awareness-based Agent Transparency, or SAT, model and measured its effectiveness on human-agent team performance in a series of human factors studies supported by the ARPI. The SAT model deals with the information requirements from an agent to its human collaborator in order for the human to obtain effective situation awareness of the agent in its tasking environment. At the first SAT level, the agent provides the operator with the basic information about its current state and goals, intentions, and plans. At the second level, the agent reveals its reasoning process as well as the constraints/affordances that the agent considers when planning its actions. At the third SAT level, the agent provides the operator with information regarding its projection of future states, predicted consequences, likelihood of success/failure, and any uncertainty associated with the aforementioned projections.

In one of the ARPI projects, IMPACT, a research program on human-agent teaming for management of multiple heterogeneous unmanned vehicles, ARL’s experimental effort focused on examining the effects of levels of agent transparency, based on the SAT model, on human operators’ decision making during military scenarios. The results of a series of human factors experiments collectively suggest that transparency on the part of the agent benefits the human’s decision making and thus the overall human-agent team performance. More specifically, researchers said the human’s trust in the agent was significantly better calibrated — accepting the agent’s plan when it is correct and rejecting it when it is incorrect– when the agent had a higher level of transparency.

The other project related to agent transparency that Chen and her colleagues performed under the ARPI was Autonomous Squad Member, on which ARL collaborated with Naval Research Laboratory scientists. The ASM is a small ground robot that interacts with and communicates with an infantry squad. As part of the overall ASM program, Chen’s group developed transparency visualization concepts, which they used to investigate the effects of agent transparency levels on operator performance. Informed by the SAT model, the ASM’s user interface features an at a glance transparency module where user-tested iconographic representations of the agent’s plans, motivator, and projected outcomes are used to promote transparent interaction with the agent. A series of human factors studies on the ASM’s user interface have investigated the effects of agent transparency on the human teammate’s situation awareness, trust in the ASM, and workload. The results, consistent with the IMPACT project’s findings, demonstrated the positive effects of agent transparency on the human’s task performance without increase of perceived workload. The research participants also reported that they felt the ASM as more trustworthy, intelligent, and human-like when it conveyed greater levels of transparency.

Chen and her colleagues are currently expanding the SAT model into bidirectional transparency between the human and the agent.

“Bidirectional transparency, although conceptually straightforward–human and agent being mutually transparent about their reasoning process–can be quite challenging to implement in real time. However, transparency on the part of the human should support the agent’s planning and performance–just as agent transparency can support the human’s situation awareness and task performance, which we have demonstrated in our studies,” Chen hypothesized.

The challenge is to design the user interfaces, which can include visual, auditory, and other modalities, that can support bidirectional transparency dynamically, in real time, while not overwhelming the human with too much information and burden.

Interesting, yes? Here’s a link and a citation for the paper,

Situation Awareness-based Agent Transparency and Human-Autonomy Teaming Effectiveness by Jessie Y.C. Chen, Shan G. Lakhmani, Kimberly Stowers, Anthony R. Selkowitz, Julia L. Wright, and Michael Barnes. Theoretical Issues in Ergonomics Science May 2018. DOI 10.1080/1463922X.2017.1315750

This paper is behind a paywall.

Predictive policing in Vancouver—the first jurisdiction in Canada to employ a machine learning system for property theft reduction

Predictive policing has come to Canada, specifically, Vancouver. A July 22, 2017 article by Matt Meuse for the Canadian Broadcasting Corporation (CBC) news online describes the new policing tool,

The Vancouver Police Department is implementing a city-wide “predictive policing” system that uses machine learning to prevent break-ins by predicting where they will occur before they happen — the first of its kind in Canada.

Police chief Adam Palmer said that, after a six-month pilot project in 2016, the system is now accessible to all officers via their cruisers’ onboard computers, covering the entire city.

“Instead of officers just patrolling randomly throughout the neighbourhood, this will give them targeted areas it makes more sense to patrol in because there’s a higher likelihood of crime to occur,” Palmer said.


Things got off to a slow start as the system familiarized itself [during a 2016 pilot project] with the data, and floundered in the fall due to unexpected data corruption.

But Special Const. Ryan Prox said the system reduced property crime by as much as 27 per cent in areas where it was tested, compared to the previous four years.

The accuracy of the system was also tested by having it generate predictions for a given day, and then watching to see what happened that day without acting on the predictions.

Palmer said the system was getting accuracy rates between 70 and 80 per cent.

When a location is identified by the system, Palmer said officers can be deployed to patrol that location. …

“Quite often … that visible presence will deter people from committing crimes [altogether],” Palmer said.

Though similar systems are used in the United States, Palmer said the system is the first of its kind in Canada, and was developed specifically for the VPD.

While the current focus is on residential break-ins, Palmer said the system could also be tweaked for use with car theft — though likely not with violent crime, which is far less predictable.

Palmer dismissed the inevitable comparison to the 2002 Tom Cruise film Minority Report, in which people are arrested to prevent them from committing crimes in the future.

“We’re not targeting people, we’re targeting locations,” Palmer said. “There’s nothing dark here.”

If you want to get a sense of just how dismissive Chief Palmer was, there’s a July 21, 2017 press conference (run time: approx. 21 mins.) embedded with a media release of the same date. The media release offered these details,

The new model is being implemented after the VPD ran a six-month pilot study in 2016 that contributed to a substantial decrease in residential break-and-enters.

The pilot ran from April 1 to September 30, 2016. The number of residential break-and enters during the test period was compared to the monthly average over the same period for the previous four years (2012 to 2015). The highest drop in property crime – 27 per cent – was measured in June.

The new model provides data in two-hour intervals for locations where residential and commercial break-and-enters are anticipated. The information is for 100-metre and 500-metre zones. Police resources can be dispatched to that area on foot or in patrol cars, to provide a visible presence to deter thieves.

The VPD’s new predictive policing model is built on GEODASH – an advanced machine-learning technology that was implemented by the VPD in 2015. A public version of GEODASH was introduced in December 2015 and is publicly available on vpd.ca. It retroactively plots the location of crimes on a map to provide a general idea of crime trends to the public.

I wish Chief Palmer had been a bit more open to discussion about the implications of ‘predictive policing’. In the US where these systems have been employed in various jurisdictions, there’s some concern arising after an almost euphoric initial response as a Nov. 21, 2016 article by Logan Koepke for the slate.com notes (Note: Links have been removed),

When predictive policing systems began rolling out nationwide about five years ago, coverage was often uncritical and overly reliant on references to Minority Report’s precog system. The coverage made predictive policing—the computer systems that attempt to use data to forecast where crime will happen or who will be involved—seem almost magical.

Typically, though, articles glossed over Minority Report’s moral about how such systems can go awry. Even Slate wasn’t immune, running a piece in 2011 called “Time Cops” that said, when it came to these systems, “Civil libertarians can rest easy.”

This soothsaying language extended beyond just media outlets. According to former New York City Police Commissioner William Bratton, predictive policing is the “wave of the future.” Microsoft agrees. One vendor even markets its system as “better than a crystal ball.” More recent coverage has rightfully been more balanced, skeptical, and critical. But many still seem to miss an important point: When it comes to predictive policing, what matters most isn’t the future—it’s the past.

Some predictive policing systems incorporate information like the weather, a location’s proximity to a liquor store, or even commercial data brokerage information. But at their core, they rely either mostly or entirely on historical crime data held by the police. Typically, these are records of reported crimes—911 calls or “calls for service”—and other crimes the police detect. Software automatically looks for historical patterns in the data, and uses those patterns to make its forecasts—a process known as machine learning.

Intuitively, it makes sense that predictive policing systems would base their forecasts on historical crime data. But historical crime data has limits. Criminologists have long emphasized that crime reports—and other statistics gathered by the police—do not necessarily offer an accurate picture of crime in a community. The Department of Justice’s National Crime Victimization Survey estimates that from 2006 to 2010, 52 percent of violent crime went unreported to police, as did 60 percent of household property crime. Essentially: Historical crime data is a direct record of how law enforcement responds to particular crimes, rather than the true rate of crime. Rather than predicting actual criminal activity, then, the current systems are probably better at predicting future police enforcement.

Koepke goes on to cover other potential issues with ‘predicitive policing’ in this thoughtful piece. He also co-authored an August 2016 report, Stuck in a Pattern; Early evidence on “predictive” policing and civil rights.

There seems to be increasing attention on machine learning and bias as noted in my May 24, 2017 posting where I provide links to other FrogHeart postings on the topic and there’s this Feb. 28, 2017 posting about a new regional big data sharing project, the Cascadia Urban Analytics Cooperative where I mention Cathy O’Neil (author of the book, Weapons of Math Destruction) and her critique in a subsection titled: Algorithms and big data.

I would like to see some oversight and some discussion in Canada about this brave new world of big data.

One final comment, it is possible to get access to the Vancouver Police Department’s data through the City of Vancouver’s Open Data Catalogue (home page).