Tag Archives: artificial intelligence

Essays on Frankenstein

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

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

Introductions

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

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

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

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

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

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

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

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

Is it actually about bad science, though?

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

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

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

Does that make it into a warning against playing God?

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

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

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

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

Key Players

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

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

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

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

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

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

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

Lingo

….

Debates

Popular Culture

Further Reading

….

‘Franken’ and CRISPR

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

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

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

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

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

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

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

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

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

Playing God

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

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

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

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

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

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

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

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

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

Frankenstein and anti-vaxxers

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

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

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

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

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

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

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

The end of this piece

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

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

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

Spintronics-based artificial intelligence

Courtesy: Tohoku University

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

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

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

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

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

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

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

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

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

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

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

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

© 2017 The Japan Society of Applied Physics

This is an open access paper.

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

Artificial intelligence and industrial applications

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The eight sections discuss:

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

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

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

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

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

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

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

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

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

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

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

The AI100 website’s About page offered a surprise,

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

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

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

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

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

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

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

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

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

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

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

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

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

    Study Panels

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

    2015 Study Panel Members

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

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

Study Panels

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

2015 Study Panel Members

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

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

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

Interactive chat with Amy Krouse Rosenthal’s memoir

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Byte-size learning

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

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

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

Video games to the rescue

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Artificial synapse rivals biological synapse in energy consumption

How can we make computers be like biological brains which do so much work and use so little power? It’s a question scientists from many countries are trying to answer and it seems South Korean scientists are proposing an answer. From a June 20, 2016 news item on Nanowerk,

News) Creation of an artificial intelligence system that fully emulates the functions of a human brain has long been a dream of scientists. A brain has many superior functions as compared with super computers, even though it has light weight, small volume, and consumes extremely low energy. This is required to construct an artificial neural network, in which a huge amount (1014)) of synapses is needed.

Most recently, great efforts have been made to realize synaptic functions in single electronic devices, such as using resistive random access memory (RRAM), phase change memory (PCM), conductive bridges, and synaptic transistors. Artificial synapses based on highly aligned nanostructures are still desired for the construction of a highly-integrated artificial neural network.

Prof. Tae-Woo Lee, research professor Wentao Xu, and Dr. Sung-Yong Min with the Dept. of Materials Science and Engineering at POSTECH [Pohang University of Science & Technology, South Korea] have succeeded in fabricating an organic nanofiber (ONF) electronic device that emulates not only the important working principles and energy consumption of biological synapses but also the morphology. …

A June 20, 2016 Pohang University of Science & Technology (POSTECH) news release on EurekAlert, which originated the news item, describes the work in more detail,

The morphology of ONFs is very similar to that of nerve fibers, which form crisscrossing grids to enable the high memory density of a human brain. Especially, based on the e-Nanowire printing technique, highly-aligned ONFs can be massively produced with precise control over alignment and dimension. This morphology potentially enables the future construction of high-density memory of a neuromorphic system.

Important working principles of a biological synapse have been emulated, such as paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), spike-timing dependent plasticity (STDP), and spike-rate dependent plasticity (SRDP). Most amazingly, energy consumption of the device can be reduced to a femtojoule level per synaptic event, which is a value magnitudes lower than previous reports. It rivals that of a biological synapse. In addition, the organic artificial synapse devices not only provide a new research direction in neuromorphic electronics but even open a new era of organic electronics.

This technology will lead to the leap of brain-inspired electronics in both memory density and energy consumption aspects. The artificial synapse developed by Prof. Lee’s research team will provide important potential applications to neuromorphic computing systems and artificial intelligence systems for autonomous cars (or self-driving cars), analysis of big data, cognitive systems, robot control, medical diagnosis, stock trading analysis, remote sensing, and other smart human-interactive systems and machines in the future.

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

Organic core-sheath nanowire artificial synapses with femtojoule energy consumption by Wentao Xu, Sung-Yong Min, Hyunsang Hwang, and Tae-Woo Lee. Science Advances  17 Jun 2016: Vol. 2, no. 6, e1501326 DOI: 10.1126/sciadv.1501326

This paper is open access.

A human user manual—for robots

Researchers from the Georgia Institute of Technology (Georgia Tech), funded by the US Office of Naval Research (ONR), have developed a program that teaches robots to read stories and more in an effort to educate them about humans. From a June 16, 2016 ONR news release by Warren Duffie Jr. (also on EurekAlert),

With support from the Office of Naval Research (ONR), researchers at the Georgia Institute of Technology have created an artificial intelligence software program named Quixote to teach robots to read stories, learn acceptable behavior and understand successful ways to conduct themselves in diverse social situations.

“For years, researchers have debated how to teach robots to act in ways that are appropriate, non-intrusive and trustworthy,” said Marc Steinberg, an ONR program manager who oversees the research. “One important question is how to explain complex concepts such as policies, values or ethics to robots. Humans are really good at using narrative stories to make sense of the world and communicate to other people. This could one day be an effective way to interact with robots.”

The rapid pace of artificial intelligence has stirred fears by some that robots could act unethically or harm humans. Dr. Mark Riedl, an associate professor and director of Georgia Tech’s Entertainment Intelligence Lab, hopes to ease concerns by having Quixote serve as a “human user manual” by teaching robots values through simple stories. After all, stories inform, educate and entertain–reflecting shared cultural knowledge, social mores and protocols.

For example, if a robot is tasked with picking up a pharmacy prescription for a human as quickly as possible, it could: a) take the medicine and leave, b) interact politely with pharmacists, c) or wait in line. Without value alignment and positive reinforcement, the robot might logically deduce robbery is the fastest, cheapest way to accomplish its task. However, with value alignment from Quixote, it would be rewarded for waiting patiently in line and paying for the prescription.

For their research, Riedl and his team crowdsourced stories from the Internet. Each tale needed to highlight daily social interactions–going to a pharmacy or restaurant, for example–as well as socially appropriate behaviors (e.g., paying for meals or medicine) within each setting.

The team plugged the data into Quixote to create a virtual agent–in this case, a video game character placed into various game-like scenarios mirroring the stories. As the virtual agent completed a game, it earned points and positive reinforcement for emulating the actions of protagonists in the stories.

Riedl’s team ran the agent through 500,000 simulations, and it displayed proper social interactions more than 90 percent of the time.

“These games are still fairly simple,” said Riedl, “more like ‘Pac-Man’ instead of ‘Halo.’ However, Quixote enables these artificial intelligence agents to immerse themselves in a story, learn the proper sequence of events and be encoded with acceptable behavior patterns. This type of artificial intelligence can be adapted to robots, offering a variety of applications.”

Within the next six months, Riedl’s team hopes to upgrade Quixote’s games from “old-school” to more modern and complex styles like those found in Minecraft–in which players use blocks to build elaborate structures and societies.

Riedl believes Quixote could one day make it easier for humans to train robots to perform diverse tasks. Steinberg notes that robotic and artificial intelligence systems may one day be a much larger part of military life. This could involve mine detection and deactivation, equipment transport and humanitarian and rescue operations.

“Within a decade, there will be more robots in society, rubbing elbows with us,” said Riedl. “Social conventions grease the wheels of society, and robots will need to understand the nuances of how humans do things. That’s where Quixote can serve as a valuable tool. We’re already seeing it with virtual agents like Siri and Cortana, which are programmed not to say hurtful or insulting things to users.”

This story brought to mind two other projects: RoboEarth (an internet for robots only) mentioned in my Jan. 14, 2014 which was an update on the project featuring its use in hospitals and RoboBrain, a robot learning project (sourcing the internet, YouTube, and more for information to teach robots) was mentioned in my Sept. 2, 2014 posting.