The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI

There’s a theory that human intelligence stems from a single algorithm.

The idea arises from experiments suggesting that the portion of your brain dedicated to processing sound from your ears could also handle sight for your eyes. This is possible only while your brain is in the earliest stages of development, but it implies that the brain is — at its core — a general-purpose machine that can be tuned to specific tasks.

About seven years ago, Stanford computer science professor Andrew Ng stumbled across this theory, and it changed the course of his career, reigniting a passion for artificial intelligence, or AI. “For the first time in my life,” Ng says, “it made me feel like it might be possible to make some progress on a small part of the AI dream within our lifetime.”

In the early days of artificial intelligence, Ng says, the prevailing opinion was that human intelligence derived from thousands of simple agents working in concert, what MIT’s Marvin Minsky called “The Society of Mind.” To achieve AI, engineers believed, they would have to build and combine thousands of individual computing modules. One agent, or algorithm, would mimic language. Another would handle speech. And so on. It seemed an insurmountable feat.

When he was a kid, Andrew Ng dreamed of building machines that could think like people, but when he got to college and came face-to-face with the AI research of the day, he gave up. Later, as a professor, he would actively discourage his students from pursuing the same dream. But then he ran into the “one algorithm” hypothesis, popularized by Jeff Hawkins, an AI entrepreneur who’d dabbled in neuroscience research. And the dream returned.

It was a shift that would change much more than Ng’s career. Ng now leads a new field of computer science research known as Deep Learning, which seeks to build machines that can process data in much the same way the brain does, and this movement has extended well beyond academia, into big-name corporations like Google and Apple. In tandem with other researchers at Google, Ng is building one of the most ambitious artificial-intelligence systems to date, the so-called Google Brain.

This movement seeks to meld computer science with neuroscience — something that never quite happened in the world of artificial intelligence. “I’ve seen a surprisingly large gulf between the engineers and the scientists,” Ng says. Engineers wanted to build AI systems that just worked, he says, but scientists were still struggling to understand the intricacies of the brain. For a long time, neuroscience just didn’t have the information needed to help improve the intelligent machines engineers wanted to build.

What’s more, scientists often felt they “owned” the brain, so there was little collaboration with researchers in other fields, says Bruno Olshausen, a computational neuroscientist and the director of the Redwood Center for Theoretical Neuroscience at the University of California, Berkeley.

The end result is that engineers started building AI systems that didn’t necessarily mimic the way the brain operated. They focused on building pseudo-smart systems that turned out to be more like a Roomba vacuum cleaner than Rosie the robot maid from the Jetsons.

But, now, thanks to Ng and others, this is starting to change. “There is a sense from many places that whoever figures out how the brain computes will come up with the next generation of computers,” says Dr. Thomas Insel, the director of the National Institute of Mental Health.

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Predicting the future of artificial intelligence has always been a fool’s game

From the Dartmouth Conferences to Turing’s test, prophecies about AI have rarely hit the mark. But there are ways to tell the good from the bad when it comes to futurology.

In 1956, a bunch of the top brains in their field thought they could crack the challenge of artificial intelligence over a single hot New England summer. Almost 60 years later, the world is still waiting.

The “spectacularly wrong prediction” of the Dartmouth Summer Research Project on Artificial Intelligence made Stuart Armstrong, research fellow at the Future of Humanity Institute at University of Oxford, start to think about why our predictions about AI are so inaccurate.

The Dartmouth Conference had predicted that over two summer months ten of the brightest people of their generation would solve some of the key problems faced by AI developers, such as getting machines to use language, form abstract concepts and even improve themselves.

If they had been right, we would have had AI back in 1957; today, the conference is mostly credited merely with having coined the term ” artificial intelligence”.

Their failure is “depressing” and “rather worrying”, says Armstrong. “If you saw the prediction the rational thing would have been to believe it too. They had some of the smartest people of their time, a solid research programme, and sketches as to how to approach it and even ideas as to where the problems were.”

Now, to help answer the question why “AI predictions are very hard to get right”, Armstrong has recently analysed the Future of Humanity Institute’s library of 250 AI predictions. The library stretches back to 1950, when Alan Turing, the father of computer science, predicted that a computer would be able to pass the “Turing test” by 2000. (In the Turing test, a machine has to demonstrate behaviour indistinguishable from that of a human being.)

Later experts have suggested 2013, 2020 and 2029 as dates when a machine would pass the Turing test, which gives us a clue as to why Armstrong feels that such timeline predictions — all 95 of them in the library — are particularly worthless. “There is nothing to connect a timeline prediction with previous knowledge as AIs have never appeared in the world before — no one has ever built one — and our only model is the human brain, which took hundreds of millions of years to evolve.”

His research also suggests that predictions by philosophers are more accurate than those of sociologists or even computer scientists. “We know very little about the final form an AI would take, so if they [the experts] are grounded in a specific approach they are likely to go wrong, while those on a meta level are very likely to be right”.

Scientists to simulate human brain inside a supercomputer

Scientists at its forerunner, the Switzerland-based Blue Brain Project, have been working since 2005 to feed a computer with vast quantities of data and algorithms produced from studying tiny slivers of rodent gray matter.

Last month they announced a significant advancement when they were able to use their simulator to accurately predict the location of synapses in the neocortex, effectively mapping out the complex electrical brain circuitry through which thoughts travel.

Henry Markram, the South African-born neuroscientist who heads the project, said the breakthrough would have taken “decades, if not centuries” to chart using a real neocortex. He said it was proof their concept, dubbed “brain in a box” by Nature magazine, would work.

Now the team are joining forces with other scientists to create the Human Brain Project. As its name suggests, they aim to scale up their model to recreate an entire human brain.

It is a step that will need both a huge increase in funding and access to computers so advanced that they have yet to be built.

If their current bid for €1 billion ($1.3 billion) of European Commission funding over the next 10 years is successful, Markram predicts that his computer neuroscientists are a decade away from producing a synthetic mind that could, in theory, talk and interact in the same way humans do.

Futurist Ray Kurzweil believes that the cloud will help expand the capacity of the human brain beyond its current limitations.

Futurist and author Ray Kurzweil predicts the cloud will eventually do more than store our emails or feed us streaming movies on demand: it’s going to help expand our brain capacity beyond its current limits.

In a question-and-answer session following a speech to the DEMO technology conference in Santa Clara, California last week, Kurzweil described the human brain as impressive but limited in its capacity to hold information. “By the time we’re even 20, we’ve filled it up,” he said, adding that the only way to add information after that point is to “repurpose our neocortex to learn something new.” (Computerworld has posted up the full video of the talk.)

The solution to overcoming the brain’s limitations, he added, involves “basically expanding our brains into the cloud.”

Kurzweil is one of the more prominent advocates of the technological Singularity, or the idea that computers will become super-intelligent and self-replicating, essentially reducing human progress to a sideshow. He is an optimist in this scenario, arguing in talks and books that the Singularity will effectively make humanity immortal by allowing us to transfer our consciousness into non-organic systems.

An artificially intelligent virtual gamer created by computer scientists at The University of Texas at Austin has won the BotPrize by convincing a panel of judges that it was more human-like than half the humans it competed against.

How artificial intelligence is changing our lives

The ability to create machine intelligence that mimics human thinking would be a tremendous scientific accomplishment, enabling humans to understand their own thought processes better. But even experts in the field won’t promise when, or even if, this will happen.

"We’re a long way from [humanlike AI], and we’re not really on a track toward that because we don’t understand enough about what makes people intelligent and how people solve problems," says Robert Lindsay, professor emeritus of psychology and computer science at the University of Michigan in Ann Arbor and author of “Understanding: Natural and Artificial Intelligence.”

"The brain is such a great mystery," adds Patrick Winston, professor of artificial intelligence and computer science at the Massachusetts Institute of Technology (MIT) in Cambridge. “There’s some engineering in there that we just don’t understand.”

On the topic of computers, artificial intelligence and robots, Northern Illinois University Professor David Gunkel says science fiction is fast becoming “science fact.”

Fictional depictions of artificial intelligence have run the gamut from the loyal Robot in “Lost in Space” to the killer computer HAL in “2001: A Space Odyssey” and the endearing C-3PO and R2-D2 of “Star Wars” fame.

While those robotic personifications are still the stuff of fiction, the issues they raised have never been more relevant than today, says Gunkel, an NIU Presidential Teaching Professor in the Department of Communication.

In his new book, “The Machine Question: Critical Perspectives on AI, Robots, and Ethics” (The MIT Press), Gunkel ratchets up the debate over whether and to what extent intelligent and autonomous machines of our own making can be considered to have legitimate moral responsibilities and any legitimate claim to moral treatment.

Robots are everywhere. But for them to be useful, they have to be programmed by people. Computer scientists are now looking for ways to teach robots how to teach themselves.