Category Archives: robots

‘Robomussels’ for climate change

These ‘robomussels’ are not voting but they are being used to monitor mussel bed habitats according to an Oct. 17, 2016 news item on ScienceDaily,

Tiny robots have been helping researchers study how climate change affects biodiversity. Developed by Northeastern University scientist Brian Helmuth, the “robomussels” have the shape, size, and color of actual mussels, with miniature built-in sensors that track temperatures inside the mussel beds.

Caption: This is a robomussel, seen among living mussels and other sea creatures. Credit: Allison Matzelle

Caption: This is a robomussel, seen among living mussels and other sea creatures. Credit: Allison Matzelle

An Oct. 12, 2016 Northeastern University news release (also on EurekAlert), which originated the news item, describes a project some 20 years in the making,

For the past 18 years, every 10 to 15 minutes, Helmuth and a global research team of 48 scientists have used robomussels to track internal body temperature, which is determined by the temperature of the surrounding air or water, and the amount of solar radiation the devices absorb. They place the robots inside mussel beds in oceans around the globe and record temperatures. The researchers have built a database of nearly two decades worth of data enabling scientists to pinpoint areas of unusual warming, intervene to help curb damage to vital marine ecosystems, and develop strategies that could prevent extinction of certain species.

Housed at Northeastern’s Marine Science Center in Nahant, Massachusetts, this largest-ever database is not only a remarkable way to track the effects of climate change, the findings can also reveal emerging hotspots so policymakers and scientists can step in and relieve stressors such as erosion and water acidification before it’s too late.

“They look exactly like mussels but they have little green blinking lights in them,” says Helmuth. “You basically pluck out a mussel and then glue the device to the rock right inside the mussel bed. They enable us to link our field observations with the physiological impact of global climate change on these ecologically and economically important animals.”

For ecological forecasters such as Helmuth, mussels act as a barometer of climate change. That’s because they rely on external sources of heat such as air temperature and sun exposure for their body heat and thrive, or not, depending on those conditions. Using fieldwork along with mathematical and computational models, Helmuth forecasts the patterns of growth, reproduction, and survival of mussels in intertidal zones.

Over the years, he and his colleagues have found surprises: “Our expectations of where to look for the effects of climate change in nature are more complex than anticipated,” says Helmuth. For example, in an earlier paper in the journal Science, his team found that hotspots existed not only at the southern end of the species’ distribution, in this case, southern California; they also existed at sites up north, in Oregon and Washington state.

“These datasets tell us when and where to look for the effects of climate change,” he says. “Without them we could miss early warning signs of trouble.”

The robomussels’ near-continuous measurements serve as an early warning system. “If we start to see sites where the animals are regularly getting to temperatures that are right below what kills them, we know that any slight increase is likely to send them over the edge, and we can act,” says Helmuth.

It’s not only the mussels that may be pulled back from the brink. The advance notice could inform everything from maintaining the biodiversity of coastal systems to determining the best–and worst–places to locate mussel farms.

“Losing mussel beds is essentially like clearing a forest,” says Helmuth. “If they go, everything that’s living in them will go. They are a major food supply for many species, including lobsters and crabs. They also function as filters along near-shore waters, clearing huge amounts of particulates. So losing them can affect everything from the growth of species we care about because we want to eat them to water clarity to biodiversity of all the tiny animals that live on the insides of the beds.”

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

Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors by Brian Helmuth, Francis Choi, Gerardo Zardi.  Scientific Data 3, Article number: 160087 (2016) doi:10.1038/sdata.2016.87 Published online: 11 October 2016

This paper is open access.

A computer that intuitively predicts a molecule’s chemical properties

First, we have emotional artificial intelligence from MIT (Massachusetts Institute of Technology) with their Kismet [emotive AI] project and now we have intuitive computers according to an Oct. 14, 2016 news item on Nanowerk,

Scientists from Moscow Institute of Physics and Technology (MIPT)’s Research Center for Molecular Mechanisms of Aging and Age-Related Diseases together with Inria research center, Grenoble, France have developed a software package called Knodle to determine an atom’s hybridization, bond orders and functional groups’ annotation in molecules. The program streamlines one of the stages of developing new drugs.

An Oct. 14, 2016 Moscow Institute of Physics and Technology press release (also on EurekAlert), which originated the news item, expands on the theme,

Imagine that you were to develop a new drug. Designing a drug with predetermined properties is called drug-design. Once a drug has entered the human body, it needs to take effect on the cause of a disease. On a molecular level this is a malfunction of some proteins and their encoding genes. In drug-design these are called targets. If a drug is antiviral, it must somehow prevent the incorporation of viral DNA into human DNA. In this case the target is viral protein. The structure of the incorporating protein is known, and we also even know which area is the most important – the active site. If we insert a molecular “plug” then the viral protein will not be able to incorporate itself into the human genome and the virus will die. It boils down to this: you find the “plug” – you have your drug.

But how can we find the molecules required? Researchers use an enormous database of substances for this. There are special programs capable of finding a needle in a haystack; they use quantum chemistry approximations to predict the place and force of attraction between a molecular “plug” and a protein. However, databases only store the shape of a substance; information about atom and bond states is also needed for an accurate prediction. Determining these states is what Knodle does. With the help of the new technology, the search area can be reduced from hundreds of thousands to just a hundred. These one hundred can then be tested to find drugs such as Reltagravir – which has actively been used for HIV prevention since 2011.

From science lessons at school everyone is used to seeing organic substances as letters with sticks (substance structure), knowing that in actual fact there are no sticks. Every stick is a bond between electrons which obeys the laws of quantum chemistry. In the case of one simple molecule, like the one in the diagram [diagram follows], the experienced chemist intuitively knows the hybridizations of every atom (the number of neighboring atoms which it is connected to) and after a few hours looking at reference books, he or she can reestablish all the bonds. They can do this because they have seen hundreds and hundreds of similar substances and know that if oxygen is “sticking out like this”, it almost certainly has a double bond. In their research, Maria Kadukova, a MIPT student, and Sergei Grudinin, a researcher from Inria research center located in Grenoble, France, decided to pass on this intuition to a computer by using machine learning.

Compare “A solid hollow object with a handle, opening at the top and an elongation at the side, at the end of which there is another opening” and “A vessel for the preparation of tea”. Both of them describe a teapot rather well, but the latter is simpler and more believable. The same is true for machine learning, the best algorithm for learning is the simplest. This is why the researchers chose to use a nonlinear support vector machines (SVM), a method which has proven itself in recognizing handwritten text and images. On the input it was given the positions of neighboring atoms and on the output collected hybridization.

Good learning needs a lot of examples and the scientists did this using 7605 substances with known structures and atom states. “This is the key advantage of the program we have developed, learning from a larger database gives better predictions. Knodle is now one step ahead of similar programs: it has a margin of error of 3.9%, while for the closest competitor this figure is 4.7%”, explains Maria Kadukova. And that is not the only benefit. The software package can easily be modified for a specific problem. For example, Knodle does not currently work with substances containing metals, because those kind of substances are rather rare. But if it turns out that a drug for Alzheimer’s is much more effective if it has metal, the only thing needed to adapt the program is a database with metallic substances. We are now left to wonder what new drug will be found to treat a previously incurable disease.

Scientists from MIPT's Research Center for Molecular Mechanisms of Aging and Age-Related Diseases together with Inria research center, Grenoble, France have developed a software package called Knodle to determine an atom's hybridization, bond orders and functional groups' annotation in molecules. The program streamlines one of the stages of developing new drugs. Credit: MIPT Press Office

Scientists from MIPT’s Research Center for Molecular Mechanisms of Aging and Age-Related Diseases together with Inria research center, Grenoble, France have developed a software package called Knodle to determine an atom’s hybridization, bond orders and functional groups’ annotation in molecules. The program streamlines one of the stages of developing new drugs. Credit: MIPT Press Office

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

Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates by Maria Kadukova and Sergei Grudinin. J. Chem. Inf. Model., 2016, 56 (8), pp 1410–1419 DOI: 10.1021/acs.jcim.5b00512 Publication Date (Web): July 13, 2016

Copyright © 2016 American Chemical Society

This paper is behind a paywall.

Westworld: a US television programme investigating AI (artificial intelligence) and consciousness

The US television network, Home Box Office (HBO) is getting ready to première Westworld, a new series based on a movie first released in 1973. Here’s more about the movie from its Wikipedia entry (Note: Links have been removed),

Westworld is a 1973 science fiction Western thriller film written and directed by novelist Michael Crichton and produced by Paul Lazarus III about amusement park robots that malfunction and begin killing visitors. It stars Yul Brynner as an android in a futuristic Western-themed amusement park, and Richard Benjamin and James Brolin as guests of the park.

Westworld was the first theatrical feature directed by Michael Crichton.[3] It was also the first feature film to use digital image processing, to pixellate photography to simulate an android point of view.[4] The film was nominated for Hugo, Nebula and Saturn awards, and was followed by a sequel film, Futureworld, and a short-lived television series, Beyond Westworld. In August 2013, HBO announced plans for a television series based on the original film.

The latest version is due to start broadcasting in the US on Sunday, Oct. 2, 2016 and as part of the publicity effort the producers are profiled by Sean Captain for Fast Company in a Sept. 30, 2016 article,

As Game of Thrones marches into its final seasons, HBO is debuting this Sunday what it hopes—and is betting millions of dollars on—will be its new blockbuster series: Westworld, a thorough reimagining of Michael Crichton’s 1973 cult classic film about a Western theme park populated by lifelike robot hosts. A philosophical prelude to Jurassic Park, Crichton’s Westworld is a cautionary tale about technology gone very wrong: the classic tale of robots that rise up and kill the humans. HBO’s new series, starring Evan Rachel Wood, Anthony Hopkins, and Ed Harris, is subtler and also darker: The humans are the scary ones.

“We subverted the entire premise of Westworld in that our sympathies are meant to be with the robots, the hosts,” says series co-creator Lisa Joy. She’s sitting on a couch in her Burbank office next to her partner in life and on the show—writer, director, producer, and husband Jonathan Nolan—who goes by Jonah. …

Their Westworld, which runs in the revered Sunday-night 9 p.m. time slot, combines present-day production values and futuristic technological visions—thoroughly revamping Crichton’s story with hybrid mechanical-biological robots [emphasis mine] fumbling along the blurry line between simulated and actual consciousness.

Captain never does explain the “hybrid mechanical-biological robots.” For example, do they have human skin or other organs grown for use in a robot? In other words, how are they hybrid?

That nitpick aside, the article provides some interesting nuggets of information and insight into the themes and ideas 2016 Westworld’s creators are exploring (Note: A link has been removed),

… Based on the four episodes I previewed (which get progressively more interesting), Westworld does a good job with the trope—which focused especially on the awakening of Dolores, an old soul of a robot played by Evan Rachel Wood. Dolores is also the catchall Spanish word for suffering, pain, grief, and other displeasures. “There are no coincidences in Westworld,” says Joy, noting that the name is also a play on Dolly, the first cloned mammal.

The show operates on a deeper, though hard-to-define level, that runs beneath the shoot-em and screw-em frontier adventure and robotic enlightenment narratives. It’s an allegory of how even today’s artificial intelligence is already taking over, by cataloging and monetizing our lives and identities. “Google and Facebook, their business is reading your mind in order to advertise shit to you,” says Jonah Nolan. …

“Exist free of rules, laws or judgment. No impulse is taboo,” reads a spoof home page for the resort that HBO launched a few weeks ago. That’s lived to the fullest by the park’s utterly sadistic loyal guest, played by Ed Harris and known only as the Man in Black.

The article also features some quotes from scientists on the topic of artificial intelligence (Note: Links have been removed),

“In some sense, being human, but less than human, it’s a good thing,” says Jon Gratch, professor of computer science and psychology at the University of Southern California [USC]. Gratch directs research at the university’s Institute for Creative Technologies on “virtual humans,” AI-driven onscreen avatars used in military-funded training programs. One of the projects, SimSensei, features an avatar of a sympathetic female therapist, Ellie. It uses AI and sensors to interpret facial expressions, posture, tension in the voice, and word choices by users in order to direct a conversation with them.

“One of the things that we’ve found is that people don’t feel like they’re being judged by this character,” says Gratch. In work with a National Guard unit, Ellie elicited more honest responses about their psychological stresses than a web form did, he says. Other data show that people are more honest when they know the avatar is controlled by an AI versus being told that it was controlled remotely by a human mental health clinician.

“If you build it like a human, and it can interact like a human. That solves a lot of the human-computer or human-robot interaction issues,” says professor Paul Rosenbloom, also with USC’s Institute for Creative Technologies. He works on artificial general intelligence, or AGI—the effort to create a human-like or human level of intellect.

Rosenbloom is building an AGI platform called Sigma that models human cognition, including emotions. These could make a more effective robotic tutor, for instance, “There are times you want the person to know you are unhappy with them, times you want them to know that you think they’re doing great,” he says, where “you” is the AI programmer. “And there’s an emotional component as well as the content.”

Achieving full AGI could take a long time, says Rosenbloom, perhaps a century. Bernie Meyerson, IBM’s chief innovation officer, is also circumspect in predicting if or when Watson could evolve into something like HAL or Her. “Boy, we are so far from that reality, or even that possibility, that it becomes ludicrous trying to get hung up there, when we’re trying to get something to reasonably deal with fact-based data,” he says.

Gratch, Rosenbloom, and Meyerson are talking about screen-based entities and concepts of consciousness and emotions. Then, there’s a scientist who’s talking about the difficulties with robots,

… Ken Goldberg, an artist and professor of engineering at UC [University of California] Berkeley, calls the notion of cyborg robots in Westworld “a pretty common trope in science fiction.” (Joy will take up the theme again, as the screenwriter for a new Battlestar Galactica movie.) Goldberg’s lab is struggling just to build and program a robotic hand that can reliably pick things up. But a sympathetic, somewhat believable Dolores in a virtual setting is not so farfetched.

Captain delves further into a thorny issue,

“Can simulations, at some point, become the real thing?” asks Patrick Lin, director of the Ethics + Emerging Sciences Group at California Polytechnic State University. “If we perfectly simulate a rainstorm on a computer, it’s still not a rainstorm. We won’t get wet. But is the mind or consciousness different? The jury is still out.”

While artificial consciousness is still in the dreamy phase, today’s level of AI is serious business. “What was sort of a highfalutin philosophical question a few years ago has become an urgent industrial need,” says Jonah Nolan. It’s not clear yet how the Delos management intends, beyond entrance fees, to monetize Westworld, although you get a hint when Ford tells Theresa Cullen “We know everything about our guests, don’t we? As we know everything about our employees.”

AI has a clear moneymaking model in this world, according to Nolan. “Facebook is monetizing your social graph, and Google is advertising to you.” Both companies (and others) are investing in AI to better understand users and find ways to make money off this knowledge. …

As my colleague David Bruggeman has often noted on his Pasco Phronesis blog, there’s a lot of science on television.

For anyone who’s interested in artificial intelligence and the effects it may have on urban life, see my Sept. 27, 2016 posting featuring the ‘One Hundred Year Study on Artificial Intelligence (AI100)’, hosted by Stanford University.

Points to anyone who recognized Jonah (Jonathan) Nolan as the producer for the US television series, Person of Interest, a programme based on the concept of a supercomputer with intelligence and personality and the ability to continuously monitor the population 24/7.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The eight sections discuss:

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

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

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

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

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

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

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

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

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

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

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

The AI100 website’s About page offered a surprise,

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

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

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

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

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

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

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

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

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

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

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

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

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

    Study Panels

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

    2015 Study Panel Members

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

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

Study Panels

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

2015 Study Panel Members

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

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

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

Interactive chat with Amy Krouse Rosenthal’s memoir

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

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

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

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

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

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

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

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

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

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

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

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

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

Robots built from living tissue

Biohybrid robots, as they are known, are built from living tissue but not in a Frankenstein kind of way as Victoria Webster PhD candidate at Case Western Reserve University (US) explains in her Aug. 9, 2016 essay on The Conversation (also on phys.org as an Aug. 10, 2016 news item; Note: Links have been removed),

Researchers are increasingly looking for solutions to make robots softer or more compliant – less like rigid machines, more like animals. With traditional actuators – such as motors – this can mean using air muscles or adding springs in parallel with motors. …

But there’s a growing area of research that’s taking a different approach. By combining robotics with tissue engineering, we’re starting to build robots powered by living muscle tissue or cells. These devices can be stimulated electrically or with light to make the cells contract to bend their skeletons, causing the robot to swim or crawl. The resulting biobots can move around and are soft like animals. They’re safer around people and typically less harmful to the environment they work in than a traditional robot might be. And since, like animals, they need nutrients to power their muscles, not batteries, biohybrid robots tend to be lighter too.

Webster explains how these biobots are built,

Researchers fabricate biobots by growing living cells, usually from heart or skeletal muscle of rats or chickens, on scaffolds that are nontoxic to the cells. If the substrate is a polymer, the device created is a biohybrid robot – a hybrid between natural and human-made materials.

If you just place cells on a molded skeleton without any guidance, they wind up in random orientations. That means when researchers apply electricity to make them move, the cells’ contraction forces will be applied in all directions, making the device inefficient at best.

So to better harness the cells’ power, researchers turn to micropatterning. We stamp or print microscale lines on the skeleton made of substances that the cells prefer to attach to. These lines guide the cells so that as they grow, they align along the printed pattern. With the cells all lined up, researchers can direct how their contraction force is applied to the substrate. So rather than just a mess of firing cells, they can all work in unison to move a leg or fin of the device.

Researchers sometimes mimic animals when creating their biobots (Note: Links have been removed),

Others have taken their cues from nature, creating biologically inspired biohybrids. For example, a group led by researchers at California Institute of Technology developed a biohybrid robot inspired by jellyfish. This device, which they call a medusoid, has arms arranged in a circle. Each arm is micropatterned with protein lines so that cells grow in patterns similar to the muscles in a living jellyfish. When the cells contract, the arms bend inwards, propelling the biohybrid robot forward in nutrient-rich liquid.

More recently, researchers have demonstrated how to steer their biohybrid creations. A group at Harvard used genetically modified heart cells to make a biologically inspired manta ray-shaped robot swim. The heart cells were altered to contract in response to specific frequencies of light – one side of the ray had cells that would respond to one frequency, the other side’s cells responded to another.

Amazing, eh? And, this is quite a recent video; it was published on YouTube on July 7, 2016.

Webster goes on to describe work designed to make these robots hardier and more durable so they can leave the laboratory,

… Here at Case Western Reserve University, we’ve recently begun to investigate … by turning to the hardy marine sea slug Aplysia californica. Since A. californica lives in the intertidal region, it can experience big changes in temperature and environmental salinity over the course of a day. When the tide goes out, the sea slugs can get trapped in tide pools. As the sun beats down, water can evaporate and the temperature will rise. Conversely in the event of rain, the saltiness of the surrounding water can decrease. When the tide eventually comes in, the sea slugs are freed from the tidal pools. Sea slugs have evolved very hardy cells to endure this changeable habitat.

We’ve been able to use Aplysia tissue to actuate a biohybrid robot, suggesting that we can manufacture tougher biobots using these resilient tissues. The devices are large enough to carry a small payload – approximately 1.5 inches long and one inch wide.

Webster has written a fascinating piece and, if you have time, I encourage you to read it in its entirety.

Curbing police violence with machine learning

A rather fascinating Aug. 1, 2016 article by Hal Hodson about machine learning and curbing police violence has appeared in the New Scientist journal (Note: Links have been removed),

None of their colleagues may have noticed, but a computer has. By churning through the police’s own staff records, it has caught signs that an officer is at high risk of initiating an “adverse event” – racial profiling or, worse, an unwarranted shooting.

The Charlotte-Mecklenburg Police Department in North Carolina is piloting the system in an attempt to tackle the police violence that has become a heated issue in the US in the past three years. A team at the University of Chicago is helping them feed their data into a machine learning system that learns to spot risk factors for unprofessional conduct. The department can then step in before risk transforms into actual harm.

The idea is to prevent incidents in which officers who are stressed behave aggressively, for example, such as one in Texas where an officer pulled his gun on children at a pool party after responding to two suicide calls earlier that shift. Ideally, early warning systems would be able to identify individuals who had recently been deployed on tough assignments, and divert them from other sensitive calls.

According to Hodson, there are already systems, both human and algorithmic, in place but the goal is to make them better,

The system being tested in Charlotte is designed to include all of the records a department holds on an individual – from details of previous misconduct and gun use to their deployment history, such as how many suicide or domestic violence calls they have responded to. It retrospectively caught 48 out of 83 adverse incidents between 2005 and now – 12 per cent more than Charlotte-Mecklenberg’s existing early intervention system.

More importantly, the false positive rate – the fraction of officers flagged as being under stress who do not go on to act aggressively – was 32 per cent lower than the existing system’s. “Right now the systems that claim to do this end up flagging the majority of officers,” says Rayid Ghani, who leads the Chicago team. “You can’t really intervene then.”

There is some cautious optimism about this new algorithm (Note: Links have been removed),

Frank Pasquale, who studies the social impact of algorithms at the University of Maryland, is cautiously optimistic. “In many walks of life I think this algorithmic ranking of workers has gone too far – it troubles me,” he says. “But in the context of the police, I think it could work.”

Pasquale says that while such a system for tackling police misconduct is new, it’s likely that older systems created the problem in the first place. “The people behind this are going to say it’s all new,” he says. “But it could be seen as an effort to correct an earlier algorithmic failure. A lot of people say that the reason you have so much contact between minorities and police is because the CompStat system was rewarding officers who got the most arrests.”

CompStat, short for Computer Statistics, is a police management and accountability system that was used to implement the “broken windows” theory of policing, which proposes that coming down hard on minor infractions like public drinking and vandalism helps to create an atmosphere of law and order, bringing serious crime down in its wake. Many police researchers have suggested that the approach has led to the current dangerous tension between police and minority communities.

Ghani has not forgotten the human dimension,

One thing Ghani is certain of is that the interventions will need to be decided on and delivered by humans. “I would not want any of those to be automated,” he says. “As long as there is a human in the middle starting a conversation with them, we’re reducing the chance for things to go wrong.”

h/t Terkko Navigator

I have written about police and violence here in the context of the Dallas Police Department and its use of a robot in a violent confrontation with a sniper, July 25, 2016 posting titled: Robots, Dallas (US), ethics, and killing.

Robots judge a beauty contest

I have a lot of respect for good PR gimmicks and a beauty contest judged by robots (or more accurately, artificial intelligence) is a provocative idea wrapped up in a good public relations (PR) gimmick. A July 12, 2016 In Silico Medicine press release on EurekAlert reveals more,

Beauty.AI 2.0, a platform,” a platform, where human beauty is evaluated by a jury of robots and algorithm developers compete on novel applications of machine intelligence to perception is supported by Ernst and Young.

“We were very impressed by E&Y’s recent advertising campaign with a robot hand holding a beautiful butterfly and a slogan “How human is your algorithm?” and immediately invited them to participate. This slogan captures the very essence of our contest, which is constantly exploring new ideas in machine perception of humans”, said Anastasia Georgievskaya, Managing Scientist at Youth Laboratories, the organizer of Beauty.AI.

Beauty.AI contest is supported by the many innovative companies from the US, Europe, and Asia with some of the top cosmetics companies participating in collaborative research projects. Imagene Labs, one of the leaders in linking facial and biological information from Singapore operating across Asia, is a gold sponsor and research partner of the contest.

There are many approaches to evaluating human beauty. Features like symmetry, pigmentation, pimples, wrinkles may play a role and similarity to actors, models and celebrities may be used in the calculation of the overall score. However, other innovative approaches have been proposed. A robot developed by Insilico Medicine compares the chronological age with the age predicted by a deep neural network. Another team is training an artificially-intelligent system to identify features that contribute to the popularity of the people on dating sites.

“We look forward to collaborating with the Youth Laboratories team to create new AI algorithms. These will eventually allow consumers to objectively evaluate how well their wellness interventions – such as diet, exercise, skincare and supplements – are working. Based on the results they can then fine tune their approach to further improve their well-being and age better”, said Jia-Yi Har, Vice President of Imagene Labs.

The contest is open to anyone with a modern smartphone running either Android or iOS operating system, and Beauty.AI 2.0 app can be downloaded for free from either Google or Apple markets. Programmers and companies can participate by submitting their algorithm to the organizers through the Beauty.AI website.

“The beauty of Beauty.AI pageants is that algorithms are much more impartial than humans, and we are trying to prevent any racial bias and run the contest in multiple age categories. Most of the popular beauty contests discriminate by age, gender, marital status, body weight and race. Algorithms are much less partial”, said Alex Shevtsov, CEO of Youth Laboratories.

Very interesting take on beauty and bias. I wonder if they’re building change into their algorithms. After all, standards for beauty don’t remain static, they change over time.

Unfortunately, that question isn’t asked in Wency Leung’s July 4, 2016 article on the robot beauty contest for the Globe and Mail but she does provides more details about the contest and insight into the world of international cosmetics companies and their use of technology,

Teaching computers about aesthetics involves designing sophisticated algorithms to recognize and measure features like wrinkles, face proportions, blemishes and skin colour. And the beauty industry is rapidly embracing these high-tech tools to respond to consumers’ demand for products that suit their individual tastes and attributes.

Companies like Sephora and Avon, for instance, are using face simulation technology to provide apps that allow customers to virtually try on and shop for lipsticks and eye shadows using their mobile devices. Skincare producers are using similar technologies to track and predict the effects of serums and creams on various skin types. And brands like L’Oréal’s Lancôme are using facial analysis to read consumers’ skin tones to create personalized foundations.

“The more we’re able to use these tools like augmented reality [and] artificial intelligence to provide new consumer experiences, the more we can move to customizing and personalizing products for every consumer around the world, no matter what their skin tone is, no matter where they live, no matter who they are,” says Guive Balooch, global vice-president of L’Oréal’s technology incubator.

Balooch was tasked with starting up the company’s tech research hub four years ago, with a mandate to predict and invent solutions to how consumers would choose and use products in the future. Among its innovations, his team has come up with the Makeup Genius app, a virtual mirror that allows customers to try on products on a mobile screen, and a device called My UV Patch, a sticker sensor that users wear on their skin, which informs them through an app how much UV exposure they get.

These tools may seem easy enough to use, but their simplicity belies the work that goes on behind the scenes. To create the Makeup Genius app, for example, Balooch says the developers sought expertise from the animation industry to enable users to see themselves move onscreen in real time. The developers also brought in hundreds of consumers with different skin tones to test real products in the lab, and they tested the app on some 100,000 images in more than 40 lighting conditions, to ensure the colours of makeup products appeared the same in real life as they did onscreen, Balooch says.

The article is well worth reading in its entirety.

For the seriously curious, you can find Beauty AI here, In Silico Medicine here, and Imagene Labs here. I cannot find a website for Youth Laboratories featuring Anastasia Georgievskaya.

I last wrote about In Silico Medicine in a May 31, 2016 post about deep learning, wrinkles, and aging.

Robots, Dallas (US), ethics, and killing

I’ve waited a while before posting this piece in the hope that the situation would calm. Sadly, it took longer than hoped as there was an additional shooting incident of police officers in Baton Rouge on July 17, 2016. There’s more about that shooting in a July 18, 2016 news posting by Steve Visser for CNN.)

Finally: Robots, Dallas, ethics, and killing: In the wake of the Thursday, July 7, 2016 shooting in Dallas (Texas, US) and subsequent use of a robot armed with a bomb to kill  the suspect, a discussion about ethics has been raised.

This discussion comes at a difficult period. In the same week as the targeted shooting of white police officers in Dallas, two African-American males were shot and killed in two apparently unprovoked shootings by police. The victims were Alton Sterling in Baton Rouge, Louisiana on Tuesday, July 5, 2016 and, Philando Castile in Minnesota on Wednesday, July 6, 2016. (There’s more detail about the shootings prior to Dallas in a July 7, 2016 news item on CNN.) The suspect in Dallas, Micah Xavier Johnson, a 25-year-old African-American male had served in the US Army Reserve and been deployed in Afghanistan (there’s more in a July 9, 2016 news item by Emily Shapiro, Julia Jacobo, and Stephanie Wash for abcnews.go.com). All of this has taken place within the context of a movement started in 2013 in the US, Black Lives Matter.

Getting back to robots, most of the material I’ve seen about ‘killing or killer’ robots has so far involved industrial accidents (very few to date) and ethical issues for self-driven cars (see a May 31, 2016 posting by Noah J. Goodall on the IEEE [Institute of Electrical and Electronics Engineers] Spectrum website).

The incident in Dallas is apparently the first time a US police organization has used a robot as a bomb, although it has been an occasional practice by US Armed Forces in combat situations. Rob Lever in a July 8, 2016 Agence France-Presse piece on phys.org focuses on the technology aspect,

The “bomb robot” killing of a suspected Dallas shooter may be the first lethal use of an automated device by American police, and underscores growing role of technology in law enforcement.

Regardless of the methods in Dallas, the use of robots is expected to grow, to handle potentially dangerous missions in law enforcement and the military.


Researchers at Florida International University meanwhile have been working on a TeleBot that would allow disabled police officers to control a humanoid robot.

The robot, described in some reports as similar to the “RoboCop” in films from 1987 and 2014, was designed “to look intimidating and authoritative enough for citizens to obey the commands,” but with a “friendly appearance” that makes it “approachable to citizens of all ages,” according to a research paper.

Robot developers downplay the potential for the use of automated lethal force by the devices, but some analysts say debate on this is needed, both for policing and the military.

A July 9, 2016 Associated Press piece by Michael Liedtke and Bree Fowler on phys.org focuses more closely on ethical issues raised by the Dallas incident,

When Dallas police used a bomb-carrying robot to kill a sniper, they also kicked off an ethical debate about technology’s use as a crime-fighting weapon.

The strategy opens a new chapter in the escalating use of remote and semi-autonomous devices to fight crime and protect lives. It also raises new questions over when it’s appropriate to dispatch a robot to kill dangerous suspects instead of continuing to negotiate their surrender.

“If lethally equipped robots can be used in this situation, when else can they be used?” says Elizabeth Joh, a University of California at Davis law professor who has followed U.S. law enforcement’s use of technology. “Extreme emergencies shouldn’t define the scope of more ordinary situations where police may want to use robots that are capable of harm.”

In approaching the question about the ethics, Mike Masnick’s July 8, 2016 posting on Techdirt provides a surprisingly sympathetic reading for the Dallas Police Department’s actions, as well as, asking some provocative questions about how robots might be better employed by police organizations (Note: Links have been removed),

The Dallas Police have a long history of engaging in community policing designed to de-escalate situations, rather than encourage antagonism between police and the community, have been handling all of this with astounding restraint, frankly. Many other police departments would be lashing out, and yet the Dallas Police Dept, while obviously grieving for a horrible situation, appear to be handling this tragic situation professionally. And it appears that they did everything they could in a reasonable manner. They first tried to negotiate with Johnson, but after that failed and they feared more lives would be lost, they went with the robot + bomb option. And, obviously, considering he had already shot many police officers, I don’t think anyone would question the police justification if they had shot Johnson.

But, still, at the very least, the whole situation raises a lot of questions about the legality of police using a bomb offensively to blow someone up. And, it raises some serious questions about how other police departments might use this kind of technology in the future. The situation here appears to be one where people reasonably concluded that this was the most effective way to stop further bloodshed. And this is a police department with a strong track record of reasonable behavior. But what about other police departments where they don’t have that kind of history? What are the protocols for sending in a robot or drone to kill someone? Are there any rules at all?

Furthermore, it actually makes you wonder, why isn’t there a focus on using robots to de-escalate these situations? What if, instead of buying military surplus bomb robots, there were robots being designed to disarm a shooter, or detain him in a manner that would make it easier for the police to capture him alive? Why should the focus of remote robotic devices be to kill him? This isn’t faulting the Dallas Police Department for its actions last night. But, rather, if we’re going to enter the age of robocop, shouldn’t we be looking for ways to use such robotic devices in a manner that would help capture suspects alive, rather than dead?

Gordon Corera’s July 12, 2016 article on the BBC’s (British Broadcasting Corporation) news website provides an overview of the use of automation and of ‘killing/killer robots’,

Remote killing is not new in warfare. Technology has always been driven by military application, including allowing killing to be carried out at distance – prior examples might be the introduction of the longbow by the English at Crecy in 1346, then later the Nazi V1 and V2 rockets.

More recently, unmanned aerial vehicles (UAVs) or drones such as the Predator and the Reaper have been used by the US outside of traditional military battlefields.

Since 2009, the official US estimate is that about 2,500 “combatants” have been killed in 473 strikes, along with perhaps more than 100 non-combatants. Critics dispute those figures as being too low.

Back in 2008, I visited the Creech Air Force Base in the Nevada desert, where drones are flown from.

During our visit, the British pilots from the RAF deployed their weapons for the first time.

One of the pilots visibly bristled when I asked him if it ever felt like playing a video game – a question that many ask.

The military uses encrypted channels to control its ordnance disposal robots, but – as any hacker will tell you – there is almost always a flaw somewhere that a determined opponent can find and exploit.

We have already seen cars being taken control of remotely while people are driving them, and the nightmare of the future might be someone taking control of a robot and sending a weapon in the wrong direction.

The military is at the cutting edge of developing robotics, but domestic policing is also a different context in which greater separation from the community being policed risks compounding problems.

The balance between risks and benefits of robots, remote control and automation remain unclear.

But Dallas suggests that the future may be creeping up on us faster than we can debate it.

The excerpts here do not do justice to the articles, if you’re interested in this topic and have the time, I encourage you to read all the articles cited here in their entirety.

*(ETA: July 25, 2016 at 1405 hours PDT: There is a July 25, 2016 essay by Carrie Sheffield for Salon.com which may provide some insight into the Black Lives matter movement and some of the generational issues within the US African-American community as revealed by the movement.)*