Gary Marcus - Hyping Artificial Intelligence, Yet Again

Over at The New Yorker, psychologist and cognitive scientist Gary Marcus (author of Kluge: The Haphazard Evolution of the Human Mind [2008] and The Birth of the Mind: How a Tiny Number of Genes Creates The Complexities of Human Thought [2004]) does a nice job of stripping away the hype from artificial intelligence promotion. I am grateful for Marcus.

Hyping Artificial Intelligence, Yet Again

Posted by Gary Marcus
January 1, 2014

According to the Times, true artificial intelligence is just around the corner. A year ago, the paper ran a front-page story about the wonders of new technologies, including deep learning, a neurally-inspired A.I. technique for statistical analysis. Then, among others, came an article about how I.B.M.’s Watson had been repurposed into a chef, followed by an upbeat post about quantum computation. On Sunday, the paper ran a front-page story about “biologically inspired processors,” “brainlike computers” that learn from experience.

This past Sunday’s story, by John Markoff, announced that “computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.” The deep-learning story, from a year ago, also by Markoff, told us of “advances in an artificial intelligence technology that can recognize patterns offer the possibility of machines that perform human activities like seeing, listening and thinking.” For fans of “Battlestar Galactica,” it sounds like exciting stuff.

But, examined carefully, the articles seem more enthusiastic than substantive. As I wrote before, the story about Watson was off the mark factually. The deep-learning piece had problems, too. Sunday’s story is confused at best; there is nothing new in teaching computers to learn from their mistakes. Instead, the article seems to be about building computer chips that use “brainlike” algorithms, but the algorithms themselves aren’t new, either. As the author notes in passing, “the new computing approach” is “already in use by some large technology companies.” Mostly, the article seems to be about neuromorphic processors—computer processors that are organized to be somewhat brainlike—though, as the piece points out, they have been around since the nineteen-eighties. In fact, the core idea of Sunday’s article—nets based “on large groups of neuron-like elements … that learn from experience”—goes back over fifty years, to the well-known Perceptron, built by Frank Rosenblatt in 1957. (If you check the archives, the Times billed it as a revolution, with the headline “NEW NAVY DEVICE LEARNS BY DOING.” The New Yorker similarly gushed about the advancement.) The only new thing mentioned is a computer chip, as yet unproven but scheduled to be released this year, along with the claim that it can “potentially [make] the term ‘computer crash’ obsolete.” Steven Pinker wrote me an e-mail after reading the Times story, saying “We’re back in 1985!”—the last time there was huge hype in the mainstream media about neural networks.

What’s the harm? As Yann LeCun, the N.Y.U. researcher who was just appointed to run Facebook’s new A.I. lab, put it a few months ago in a Google+ post, a kind of open letter to the media, “AI [has] ‘died’ about four times in five decades because of hype: people made wild claims (often to impress potential investors or funding agencies) and could not deliver. Backlash ensued. It happened twice with neural nets already: once in the late 60’s and again in the mid-90’s.”

A.I. is, to be sure, in much better shape now than it was then. Google, Apple, I.B.M., Facebook, and Microsoft have all made large commercial investments. There have been real innovations, like driverless cars, that may soon become commercially available. Neuromorphic engineering and deep learning are genuinely exciting, but whether they will really produce human-level A.I. is unclear—especially, as I have written before, when it comes to challenging problems like understanding natural language.

The brainlike I.B.M. system that the Times mentioned on Sunday has never, to my knowledge, been applied to language, or any other complex form of learning. Deep learning has been applied to language understanding, but the results are feeble so far. Among publicly available systems, the best is probably a Stanford project, called Deeply Moving, that applies deep learning to the task of understanding movie reviews. The cool part is that you can try it for yourself, cutting and pasting text from a movie review and immediately seeing the program’s analysis; you even teach it to improve. The less cool thing is that the deep-learning system doesn’t really understand anything.

It can’t, say, paraphrase a review or mention something the reviewer liked, things you’d expect of an intelligent sixth-grader. About the only thing the system can do is so-called sentiment analysis, reducing a review to a thumbs-up or thumbs-down judgment. And even there it falls short; after typing in “better than ‘Cats!’ ” (which the system correctly interpreted as positive), the first thing I tested was a Rotten Tomatoes excerpt of a review of the last movie I saw, “American Hustle”: “A sloppy, miscast, hammed up, overlong, overloud story that still sends you out of the theater on a cloud of rapture.” The deep-learning system couldn’t tell me that the review was ironic, or that the reviewer thought the whole was more than the sum of the parts. It told me only, inaccurately, that the review was very negative. When I sent the demo to my collaborator, Ernest Davis, his luck was no better than mine. Ernie tried “This is not a book to be ignored” and “No one interested in the subject can afford to ignore this book.” The first came out as negative, the second neutral. If Deeply Moving is the best A.I. has to offer, true A.I.—of the sort that can read a newspaper as well as a human can—is a long way away.

Overhyped stories about new technologies create short-term enthusiasm, but they also often lead to long-term disappointment. As LeCun put it in his Google+ post, “Whenever a startup claims ‘90% accuracy’ on some random task, do not consider this newsworthy. If the company also makes claims like ‘we are developing machine learning software based on the computational principles of the human brain’ be even more suspicious.”

As I noted in a recent essay, some of the biggest challenges in A.I. have to do with common-sense reasoning. Trendy new techniques like deep learning and neuromorphic engineering give A.I. programmers purchase on a particular kind of problem that involves categorizing familiar stimuli, but say little about how to cope with things we haven’t seen before. As machines get better at categorizing things they can recognize, some tasks, like speech recognition, improve markedly, but others, like comprehending what a speaker actually means, advance more slowly. Neuromorphic engineering will probably lead to interesting advances, but perhaps not right away. As a more balanced article on the same topic in Technology Review recently reported, some neuroscientists, including Henry Markram, the director of a European project to simulate the human brain, are quite skeptical of the currently implemented neuromorphic systems on the grounds that their representations of the brain are too simplistic and abstract.

As a cognitive scientist, I agree with Markram. Old-school behaviorist psychologists, and now many A.I. programmers, seem focused on finding a single powerful mechanism—deep learning, neuromorphic engineering, quantum computation, or whatever—to induce everything from statistical data. This is much like what the psychologist B. F. Skinner imagined in the early nineteen-fifties, when he concluded all human thought could be explained by mechanisms of association; the whole field of cognitive psychology grew out of the ashes of that oversimplified assumption.

At times like these, I find it useful to remember a basic truth: the human brain is the most complicated organ in the known universe, and we still have almost no idea how it works. Who said that copying its awesome power was going to be easy?

Gary Marcus is a professor of psychology at N.Y.U. and a visiting cognitive scientist at the new Allen Institute for Artificial Intelligence. This essay was written in memory of his late friend Michael Dorfman—friend of science, enemy of hype.

Photograph: Chris Ratcliffe/Bloomberg/Getty

Omnivore - Humans and Robots Coexisting & Simulated Human Brains

Via Bookforum's Omnivore blog: Some believe the age of robot domination may have already arrived (Dominic Basulto), and Kevin Kelley believes we should just step aside and let the robots run things.

Do you remember Data on Star Trek: The Next Generation? In a couple of episodes, his rights as a sentient being were central to the plot - has the time come that we need to have those discussions?

Finally, in a separate article from io9, George Dvorsky reports on the $1.6 billion Human Brain Project - the researchers will be using "a progressively scaled-up multi-layered simulation" running on a supercomputer to theoretically (my word and emphasis) "re-create the human brain." Good luck with that.


How will humans and robots coexist?

JAN 30 2013

1:00PM

• Surya Deva City (Hong Kong): Can Robots Have Human Rights Obligations? A Futuristic Exploration. 
• Josh Blackman (South Texas): Robot, Esq. 
• John Niman on rise of the military robot. 
• Has the robot uprising already happened? Dominic Basulto wonders. 
• Martin Ford on why robots are worse for the economy than you think. 
• Ben Richmond on what the Czech play that coined the term “robot” tells us about today's robonomics. 
• The FDA approves first robot for hospital use. 
• How will humans and robots coexist? Robots will be everywhere in 2030, but not necessarily as humanoid domestic help. 
• Dick Pelletier on sentient machines, the next step in human evolution. 
• From Wired, better than human: Kevin Kelly on why we should let the robots take over; and Helene Mialet on Stephen Hawking, Vader and being more machine than human. 
• Behavioural acceptance of existing superhumans: V.R. Manoj on the path to equal acceptance of the differently abled.

This story is related, of course, and it comes from io9.

New $1.6 billion supercomputer project will attempt to simulate the human brain

George Dvorsky
JAN 30, 2013

In what is the largest and most significant effort to re-create the human brain to date, an international group of researchers has secured $1.6 billion to fund the incredibly ambitious Human Brain Project. For the next ten years, scientists from various disciplines will seek to understand and map the network of over a hundred billion neuronal connections that illicit emotions, volitional thought, and even consciousness itself. And to do so, the researchers will be using a progressively scaled-up multilayered simulation running on a supercomputer.

And indeed, the project organizers are not thinking small. The entire team will consist of over 200 individual researchers in 80 different institutions across the globe. They're even comparing it the Large Hadron Colllider in terms of scope and ambition, describing the Human Brain Project as "Cern for the brain." The project, which will be based in Lausanne, Switzerland, is an initiative of the European Commission.

According to scientists working on the project, HBP will build new platforms for "neuromorphic computing" and "neurorobotics," allowing researchers to develop new computing systems and robots based on the architecture and circuitry of the brain. The researchers will attempt to reconstruct the human brain piece-by-piece, and gradually bring these cognitive components into an overarching supercomputer.

"The support of the HBP is a critical step taken by the EC to make possible major advances in our understanding of how the brain works," said Swedish Nobel Laureate Torsten Wiesel in a recent statement. "HBP will be a driving force to develop new and still more powerful computers to handle the massive accumulation of new information about the brain, while the neuroscientists are ready to use these new tools in their laboratories." He added that the research may also give rise to fundamentally new computer architectures modeled after the brain.

"This cooperation should lead to new concepts and a deeper understanding of the brain, the most complex and intricate creation on earth," he said.

The researchers are also hoping that the insights gained will help in the treatment of neurological disorders like Parkinson's and Alzheimer's. Moreover, due to the nature of the research, no animals will be required for experimentation.

• Sources: Human Brain Project, Globe & Mail, Yahoo! News.
• Image: Shutterstock/agsandrew.