Watson battles it out on Jeopardy

Last Tuesday, Dr. David Ferrucci spoke at The Computer History Museum in Mountain View with The Financial Times' Richard Waters about IBM's Watson computer and the future of artificial intelligence (AI).

Watson is the most successful AI computer since Deep Blue, the chess-playing computer that played against and defeated chess grandmaster Garry Kasparov in 1997.

Watson's mission was to compete against the top champions of the popular trivia show Jeopardy. Dr. Ferrucci was in charge of the Watson project at IBM, and explained to a packed audience how the project came to be and where IBM is taking this technology in the future.

Artificial intelligence began to experience a renaissance in 1995 when research began on teaching computers how to digest and comprehend knowledge in the manner that humans do — natural language. Natural language comprehension, the goal of Watson, really strikes at the heart of AI. After the success of Deep Blue, IBM executives wanted to find the "next big thing". An executive came up with the idea of a computer that could compete playing Jeopardy. It took a couple years to gain mindshare within IBM to take on what seemed like a harebrained idea, but Dr. Ferrucci stepped up to the plate.

Watson was significantly more challenging to build than its predecessor. As Dr. Ferrucci explained, when Deep Blue was created to play chess, its goal was to follow the rules and strategies of a well-defined game. There is no background knowledge or natural language processing needed to play chess; as such it is well-suited for a machine to play.

The project ran the risk of killing Ferrucci's career if it failed, but he described it as "a challenge too irresistible not to try". Over four years, 27 IBM researchers built Watson with Ferrucci.

Watson uses ~80 kilowatts of electrical power (approximately equal to 700 incandescent light bulbs), 20 tons of air conditioned cooling capacity, 15 terabytes of RAM (the approximate size of 4 million MP3 songs), and 2,880 CPU cores. It's built inside of a self-contained box, so as to constrain it in a way that would make clear that it was not communicating with the outside world or with humans while playing. It holds information equivalent to ~1,000,000 books.

At its core, Watson relies on computing many different possible answers to a question and generating and ranking probabilities about the correctness of each possible response. Watson will only answer a question if the highest probability is over a certain threshold (for example, 50%).

In order for Watson to buzz in, it:

1. Hears the spoken question, and turns that speech into text.
2. Generates queries for more information and gets back data.
3. Compiles a series of competing hypotheses from that data.
4. Ranks each competing hypothesis based on supporting data that it finds.
5. Picks and buzzes in with the top-ranked result (assuming it surpasses the "correctness" threshold).

In order to fairly interact with the Jeopardy game as a human does, Watson was outfitted with a robotic hand to depress the buzzer.

While Watson may no longer be competing against Jeopardy contestants its underlying technology, Deep QA, has a variety of applications in the real world. In the field of health care, Deep QA can reason over language and can deliver explanations of diagnoses it finds in natural language.

While Deep QA will never replace doctors in performing a diagnosis, it is a vast improvement over existing computer systems that perform diagnoses by giving doctors a 5,000 world deductive proof rather than an readily understandable explanation. Deep QA's abilities are applicable across law, education and defense.

To learn more about Watson, check out the TED talk, Final Jeopardy! and the Future of Watson.

Stanford has three classes up for online registrations: Introduction to Artificial Intelligence (AI), Introduction to Databases and Machine Learning. Since opening registration this August, over 100,000 people have registered for these three courses combined.

All three courses begin on October 10th. As with the Introduction to AI course, there is a basic track and an advanced track. The basic track requires participants to watch lectures and complete quizzes while the advanced track also requires homework and exam participation. Both tracks provide a certificate of completion.

With everything free, there are some limitations. These classes do not count as Stanford university credit and do not provide direct access to the instructor. I might not be an expert in computer science, but I am definitely interested enough to follow at least one of these classes.

Let us know in the comments what you think of Stanford's offer and if you plan to take any of these courses!

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.