Robotic domination in I, Robot

Ray Kurzweil's book The Singularity is Near is becoming something of a cult sensation. The 672-page paperback version of the book is ranked 1,494th on Amazon (on par with The Great Gatsby). Recently, Kurzweil announced a Google-backed Singularity University ($25,000 for a 9 week summer program; $12,000 for a 3 day "Executive Program"), lending a touch of academic rigor to an idea that has lived mostly in science fiction. For the time and budget conscious, a rash of Singularity-themed documentaries is now on the horizon.

The Singularity, as I understand it, is the point in time when computers will be smart enough to build even smarter computers, effectively removing humans from the design-build loop of Artificial Intelligence (AI). Kurzweil predicts 2050. That means I'll be 68 when the robots take over!

Predicting the future is no walk in the park, but when it comes to Artificial Intelligence, everyone's packing a lunch. So while I won't try to argue that Kurzweil is wrong (I think he is), it's good to place his predictions in the cultural history of wildly inaccurate AI speculation.

Consider these predictions, both made by outstanding computer scientists actively involved in AI research:

• 1965, Herbert Simon: "machines will be capable, within twenty years, of doing any work a man can do."
• 1970, Marvin Minsky: "In from three to eight years we will have a machine with the general intelligence of an average human being."

As it turned out, these claims were not even remotely true. In fact, the whole history of AI has been one of boom and bust cycles, the product of misplaced exuberant optimism.

Take, for example, the case of machine translation. During the Cold War, the problem of automatically translating intercepted Russian messages received considerable military funding. A 1954 Georgetown-IBM demonstration (translations of 49 chemistry-themed sentences with a 250-word vocabulary) captured public interest and spawned considerable investment, especially as the researchers claimed that the general translation problem would be solved in 3-5 years. When progress turned out to be much slower, funding was cut, and research all but stopped between 1965 and 1993.

Translation research has seen a significant resurgence, especially since I've been in graduate school (for computer science), mostly due to statistical methods. Rather than frame the translation of Russian into English as a series of rules (translate word R3 into word E3; switch the order of words E2 and E4; etc.) written by expert bilingual humans, research consists of building models trained from many examples of translated sentences (word R3 translates to word E3 with probability 0.6; word E3 appears after E2 with probability 0.2; etc.) so that the translation of a Russian sentence is the sequence of English words with the largest total probability, according to the model. The statistical approach is less ambitious-today's models are too simple to capture all of language's nuances-but far more successful.

Kurzweil's Singularity prediction is based on exponential growth. The idea is that because computers have been doubling in speed every two years or so (that's a factor of 1,000 in just 20 years; 1,000,000 in 40 years) huge paradigm shifts are actually quite close. But aside from the issue that computer chips have plateaued due to limits imposed by silicon's insulation ability and the speed of light (new computers have multiple CPUs), progress in automatic translation does not follow the law of exponential progress. Rather, there have been a few periods of dramatic improvement, followed by long periods of very gradual development. This is the trend for the majority of important AI problems.

So, while speculating about the future is both interesting and important, I'd be wary of anyone trying to sell you $12,000 of it.

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Scientists gather samples on the ocean floor.
Credit: Roger Linington.

What's different about the work being done at the UC Santa Cruz Chemical Screening Center is that it a) looks to a largely unexplored medical resource: the ocean, and b) uses robots, rather than "forlorn-looking grad students" (to quote Center director Scott Lokey) to run the tests.

Here's a video I shot of one of those robots in action, with Lokey narrating.

One thing that didn't make it into the piece is that these researchers — including Lokey and Roger Linington — aren't just studying every disease they can think of. They focus on the diseases that commercial drug companies tend to neglect because there's so little profit in treating them – things like African sleeping sickness and cholera. So far, they're seeing progress on both, as well as breast cancer.

Listen to the Medicine from the Ocean Floor radio report online and check out images from this story in an online slideshow.

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Bio-inspired design borrows its creative inspiration from models and systems in nature, that is, plant and animal parts that have been slowly tweaked for over 3.8 billion years. But that doesn't mean that nature's designs are perfect. In fact, that's what makes the process of engineering things based on natural models so difficult. You have to figure out how to pull the aces from the evolutionary discard pile. As professor Bob Full at U.C. Berkeley explained in our first phone conversation, that's also why scientists now use the term "bio-inspiration" rather than the more commonly known term "biomimicry." Biologists and engineers are not looking to simply mimic nature, because there are all kinds of dead ends and redundancies in natural systems that would be pointless to recreate in an optimized, man-made piece of technology. One of the examples he gave me is a kind of grasshopper that if you were to copy it, you would copy neurons that go to nothing, they don't connect to any muscles, and that's because during evolution the adults lost their ability to fly. The neurons going to the muscles are still there, but the muscles aren't there anymore. No need to copy that, right?

So what a biomimeticist does is look to nature to find plants & animals with remarkable performance abilities, and studies their adaptations for inspiration to design something new. For example, if you want to make a tiny robot that can fly, then look at the best fliers. If you want to design a blade that moves quickly through fluids, or an Olympic swimsuit that minimizes drag, then look to the most efficient swimmers. Now that's what I call "intelligent design!"

Watch the Bio-Inspiration: Nature as Muse television story report online.

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Though it was McCarthy who persuaded his nine other colleagues at the conference to adopt the term "artificial intelligence" to describe the nascent field, the seeds of artificial intelligence were planted earlier. Alan Turing, who was instrumental in breaking the German's Enigma code during WWII, published a paper in 1950 that laid out what came to be known as the "Turing Test:" if a machine could carry out a conversation with a human in such a sophisticated manner as to trick the human into thinking that he or she was conversing with another human, then the machine would have displayed true "intelligence."

But nearly 60 years later, the world still awaits a machine capable of exhibiting "general A.I.", instead of the "narrow A.I." demonstrated by IBM's chess-playing Deep Blue or Stanford University's Stanley, an autonomous robotic vehicle, or other impressive albeit limited applications of A.I. For example, Deep Blue may be able to beat Gary Kasparov at chess but can it beat a 10 year-old at a game of checkers? The lack of a general A.I. is made even more stark when juxtaposed with Moore's Law, a maxim that goes back to 1965 when Intel founder Gordon Moore postulated that the number of transistors on a computer chip would double roughly every 18 months.

There's even a term – "Singularity" – that is being used to describe the moment when technological progress will leapfrog and herald the creation of computers that not only achieve human-like intelligence, but also give rise to a progeny of computers who will be smarter then their digital forbears. Though he didn't coin the term (sci-fi writer Vernor Vinge did), the most famous exponent of this belief is inventor Ray Kurzweil. He places the Singularity as occurring sometime before 2050 and believes that with the advent of this unheralded technological progress, mankind may solve some of our society's most pressing ills, such as global warming, and even conquer death, by uploading one's consciousness into a virtual medium.

Though this seems a far stretch from engineering a domestic robot like Stanford's Artificial Intelligence Robot, top A.I. researchers like Stanford's Andrew Ng and Daphne Koller do believe that computing systems will some day be as smart or smarter than humans. When I spoke with Dharmendra Modha about his work into cognitive computing at IBM, he talked effusively about creating an "i-Brain," a digital accessory that people could carry around, making decisions and processing information like its human cousin. But if you're like me, and lament those moments when you've misplaced your keys or other instances of poor neural performance, you can't help but think that such a device can't arrive soon enough. On second thought, I'll wait until v2.0 hits the shelves.

Watch the Artificial Intelligence: Thinking Big television story report online.

And don't miss our Web Extra: A Dose of A.I. In this QUEST web exclusive, Stanford University computer science professor and artificial intelligence (A.I.) researcher Daphne Koller provides an elegant explanation of how A.I. can be employed in the examining room to diagnose a patient's illness more accurately than a human clinician. Find out more and learn how medical diagnosis is just the tip of the iceberg when it comes to tasks that rely on making sense of a sea of data to arrive at an informed conclusion.

Additional Links

• Stanford Computer Science homepage
• Producer's Notes for Artificial Intelligence: Thinking Big

Forward camera view from Opportunity as the rover attempts to
climb up a slope toward the wall of Victoria Crater.
Photo by NASA/MER/Opportunity.

Four years after their planned three-month tour of duty began, NASA’s Mars Exploration Rovers (MER) Spirit and Opportunity roll doggedly on like a pair of aged, dusty desert prospectors looking for gold. In this case the "gold" is evidence for past water on Mars, and signs of that seem to abound.

What sparked this blog for me was the announcement of the plan to send Opportunity out of the depths of Victoria Crater, the half-mile impact crater that the rover has been exploring for almost a year now. Last September, when it was decided to send Opportunity into Victoria to get a close-up view of the sedimentary rock layers exposed in the crater walls, there was a lot of talk about this expedition possibly being the rover's last–it almost sounded like the robot was being sent into its own grave, its final resting place on Mars. After all, the rover had already operated ten times longer than what it was designed for!

What did Opportunity's year-long sojourn yield? By examining the multitude of exposed sedimentary layers, it is believed that those layers were probably originally laid down by wind (not a surprise on Mars, which even today is a world of wind-blown dust: dust devils, sand dunes, planet-wide dust storms). But there are also clues written in the rocks that the layers of sediment have been modified by the action of water.

One particular thing Opportunity has discovered are rock features dubbed "fins." These fins are raised edges around rock boundaries that are rich in the mineral hematite–a mineral that often forms in the presence of water. Opportunity found hematite on Mars early in its exploration, which supports the speculation that at least that rover’s region on Mars (Meridiani Planum) may have harbored at least shallow and intermittent bodies of water in the past.

The "fins" may have been formed when water dissolved away areas of sediment and then "filled in the holes" with deposited minerals–forming a kind of "fossil" of what was once an empty space.

When I lived in Northern Arizona, I remember driving across the plains east of Flagstaff and finding long, wide ridges of what looked like sandstone, snaking across the dusty desert like enormous gopher trails. I learned that these were the fossil remnants of what were stream beds: the streams formed deposits of sand and mud in their bed, which over time hardened into sandstone and mudstone. Later, the softer surrounding soils and sands eroded away, leaving the hardened stream beds as raised ridges of rock–dry evidence in a dry desert of past liquid water action. Though this is not the same process that formed the fins on Mars, it is analogous.

But now Opportunity's mission in Victoria Crater is done, and NASA is making plans to have the robot crawl back up the slope and exit the crater at the same place it entered last September. It will continue its mission by examining "cobbles"–small, loose stones on the surrounding planes, some of which were probably ejected by meteorite impacts in Mars' distant past.

Spirit, on the other side of the planet in Gusev Crater, is also still alive, and is making ready to do a bit more roving after a Martian winter of relative inactivity. With one of its six wheels no longer functioning, Spirit will limp along and continue prospecting–next stop: some white, silica-rich material that may have formed in hot water.

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The race is sponsored by the Department of Defense's research division, known as DARPA. Their goal is to convert one-third of their ground vehicles to unmanned vehicles. That's where the contest comes in– to develop the technology needed for such an application. Early uses could be surveillance on the ground or convoy missions, but they haven't ruled anything out. What are your thoughts on the wartime purpose of this contest?

The Stanford team, like many others, see this technology being used far and wide in the future. The laser sensors that the robots use are much more accurate than human eyes. So, robotic cars could follow each other very closely, which could have major impacts on traffic and the need for new roads. Autonomous vehicles could help elderly and disabled drivers, too. It sounds like science fiction, but scientists are on their way. Would you use a robotic car?

UPDATE: The Stanford Team's car, Junior, took second place in the race this past weekend. I've heard it was a very close race with six team completing the whole course. Check out the full race results or read a San Francisco Chronicle article on the finals.

You may listen to the "Robot Car Race" Radio report online, as well as find more resources. Also, don't miss our behind-the-scenes photos for this story on flickr.com.

Lauren Sommer reports for QUEST and Radio News at KQED-FM.

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