Watson: The New Jeopardy Champion

I consider myself a techno-optimist, but Watson’s performance in Jeopardy’s IBM Challenge has definitely exceeded my expectations. While I did predict Watson would win the competition, I didn’t think it would be so dominant. This was a contest I thought machine intelligence might win by a narrow margin, but the three-day, two game match wasn’t even close. AI has come a long way, baby.

As impressive as Watson’s final cash score was, what I think was more remarkable was it’s answer success rate. In the first match, out of sixty clues, Watson rang in first and answered 38 correctly, with five errors. This is an 88.4% success rate. If only the 30 questions in the Double Jeopardy portion are considered, this jumps to a whopping 96%. You’ll notice I’ve left the Final Jeopardy question out of these calculations. This is because this question had to be answered regardless of the machine’s low confidence level of 14%. It’s important to the competition, but actually indicates the success of the machine’s algorithms.

While the second game (Day 3) wasn’t quite as impressive as the first, Watson still won by a significant margin. Considering it was competing against the two best human Jeopardy players of all time, it’s safe to say IBM met its goal and then some.

Some of the more intriguing (some would rightly say, concerning) moments in the contest were those in which Watson arrived at unfathomably wrong answers. As the lead on the project, Watson Principal Investigator, Dr. David Ferrucci commented:

“Watson absolutely surprises me. People say: ‘Why did it get that one wrong?’ I don’t know. ‘Why did it get that one right?’ I don’t know.”

The fact is, even Watson’s developers often can’t fathom how it arrives at the answers it does. Parsing through millions of stored documents, Watson applies hundreds of algorithms to arrive at the answer with the highest confidence rating. (While this bears a passing resemblance to Minsky’s “society of mind” concept, it still remains very different from the way humans think.) The incredible complexity of the process means we can’t fully understand it. This is the nature of emergent systems – they generate outcomes that can’t be accurately predicted a lot of the time. They follow an internal logic of their own, one we can’t possibly follow.

In Watson, we’re seeing the barest hints, the merest beginnings of this. The potential domain of future intelligences is vast. It’s possible that one day there will be as many different kinds of machine intelligence as there are biological species. And in all likelihood, we won’t understand the behaviors and motivations of a single one.

Watson is a long way from being an artificial general intelligence. It isn’t HAL-3000. But it is a huge step forward. A step that should be making us ask serious questions about the future of AI. We face a future full of machine intelligences as smart or smarter than we are. Some experts even speculate recursive self-improvement will yield superintelligences vastly more intelligent than the entire human race combined. There should be no question we’ll be incapable of grasping the motivations of such a machine. And there’s no reason to expect it’s objectives will mesh with our own. Obviously, this could have results that are disastrous, potentially even existentially catastrophic.

We aren’t going to stop the advance of artificial intelligence or the eventual development of an artificial general intelligence. Therefore, steps will need to be taken that ensure these machines remain as benevolent as possible and not because they will necessarily be malevolent otherwise. An indifferent superintelligence would be just as big a threat to humanity because it could be capable of taking potentially world-altering actions without considering what they mean for us. Arguments for creating rules-based safeguards, such as Asimov’s “Three Laws of Robotics” will likely fail, simply because rules can be misinterpreted or circumvented given sufficient motivation.

Work toward “Friendly AI”, as proposed by AI researcher, Eliezer Yudkowsky, stands a much better chance of a human-positive outcome. Instilling a machine equivalent of morality not only protects us from the actions of a superintelligence, but from its self-improved progeny as well. Creating “Friendly” safeguards that motivate such a machine to do everything in its power to ensure humans do not come to harm now or in the future may be our best bet. As Yudkowsky states:

“Gandhi does not want to commit murder, and does not want to modify himself to commit murder.”

We can hope that a superintelligence comes to the same conclusion. But we can do more than just hope; we can work to ensure it happens.

The capabilities Watson has demonstrated using deep analytics and natural language processing are truly stunning. The technologies that will develop from this will no doubt help the world with many of its significant problems. Not least of these is dealing with the vast, escalating volumes of data our modern world generates. But there is the potential for significant dangers to arise from such technology too. I feel certain though we can overcome these threats and continue the long legacy of building a better world with the help of our technology.

How’s that for techno-optimism?

What is a Milestone in Artificial Intelligence?

On January 13, 2011, IBM’s Watson supercomputer competed in a practice round of Jeopardy, the long-running trivia quiz show. Playing against the program’s two most successful champions, Ken Jennings and Brad Rutter, Watson won the preliminary match. Is this all a big publicity stunt? Of course it is. But it also marks a significant milestone in the development of artificial intelligence.

For decades, AI – artificial intelligence – has been pursued by computer scientists and others with greater and lesser degrees of success. Promises of Turing tests passed and human-level intelligence being achieved have routinely fallen far short. Nonetheless, there has continued to be an inexorable march toward more and ever more capable machine intelligences. In the midst of all this, IBM’s achievement in developing Watson may mark a very important turning point.

Early attempts at strong AI or artificial general intelligence (AGI) brought to light the daunting complexity of trying to emulate human intelligence. However, during the last few decades, work on weak AI – intelligence targeted to very specific domains or tasks – has met with considerably more success. As a result, today AI permeates our lives, playing a role in everything from anti-lock braking systems to warehouse stocking to electronic trading on stock exchanges. Little by little, AI has taken on roles previously performed by people and bested them in ways once unimaginable. Computer phone attendants capable of routing hundreds of calls a minute. Robot-operated warehouses that deliver items to packers in seconds. Pattern matching algorithms that pick out the correct image from among thousands in a matter of moments. But until now, nothing could compete with a human being when it came to general knowledge about the world.

True, these human champions may yet best Watson, a product of IBM’s DeepQA research project. (The three day match will air February 14-16.) But we only need to think back to 1997 when IBM’s Deep Blue defeated world chess champion Garry Kasparov to understand that it doesn’t really matter. Kasparov had handily beaten Deep Blue only a year earlier, though the 1996 match did mark the first time a computer won a single game in such a match. Today, just as then, the continuing improvements in computer processing speed, memory, storage and algorithms all but ensure that any such triumph would be fleeting. We have turned a page on this once most human of intellectual feats and the world won’t be the same again.

So what can we look ahead to now that we’ve reached this milestone? In the short term, IBM plans to market their technology and profit by their achievement. Initially, the system price will be high, probably in the millions of dollars, but like so much computer technology, the price will plummet over the coming decade. As the technology becomes more widely used, a range of tasks and jobs previously considered safe from AI will no longer be performed by human workers. Protectionist regulations may attempt to save these jobs but these efforts will probably be short-lived. The resulting large-scale unemployment will require a rethinking of government institutions and safety nets, as well as corporate business models.

At the same time, this type of general knowledge AI (it’s far too early to call it AGI) will contribute to greater and more rapid advances in machine intelligence. Such technology could bootstrap the Semantic Web into broad usage. In all likelihood, it will be used to create personal intelligent agents, giving users the virtual equivalent of a staff of assistants. And eventually, it could facilitate the development of a true artificial general intelligence or at least contribute to the education of such an AGI.

Will such an intelligence be conscious? Will it be self-improving, leading to a positive feedback loop that brings about a powerful and hopefully benign superintelligence? Only time will tell. But perhaps one day, on a future holographic version of Jeopardy, we’ll be presented with clues to which the correct response will be, “What was the Singularity?”

The Supercomputer Race

that China is barreling ahead in its development of supercomputers should give the U.S. considerable cause for concern. China has devoted significant resources to their supercomputer program in recent years, resulting in their ranking earlier this year at the number two spot on the TOP500 list. TOP500.org ranks the world’s 500 fastest supercomputers according to their performance on a dense system of linear equations. These tests yield a score based on the computer’s speed measured in double precision floating point operations per second (flops).

To give a little perspective: China didn’t have a single supercomputer ranked in the TOP500 until the mid-1990s. By June 2004, they had their first ranking ever in the top ten. In May 2010, their Nebulae system became the second fastest in the world with a performance of 1.271 petaflops. (A petaflop is 1015 floating point operations per second.) While the Chinese still only have one tenth the number of TOP500 supercomputers the U.S. has, they’ve been quickly catching up based on this metric as well. (Note: TOP500.org ranks the world’s most powerful, commercially available, non-distributed computer systems. There are numerous military and intelligence agency supercomputers in many countries not included in this list.)

China’s Nebulae system operates from the newly built National Supercomputing Centre in Shenzhen. This is also the site of some very recent and very extensive construction which will presumably house some very serious supercomputing power in the near future. “There clearly seems to be a strategic and strong commitment to supercomputing at the very highest level in China,” stated Erich Strohmaier, head of the Future Technology Group of the Computational Research Division at Lawrence Berkeley National Laboratory.

The next major goal for supercomputers is the building of an exascale system sometime between 2018 and 2020. Such a system would be almost a thousand times faster than the Jaguar supercomputer at Oak Ridge National Laboratory, currently the world’s fastest. The U.S. Exascale Initiative is committed to developing this technology which brings with it many different challenges of scale. At the same time, Europe and China have accelerated their investment in high-performance systems, with Europeans on a faster development track than the U.S. There are concerns the U.S. could be bypassed if it doesn’t sustain the investment to stay ahead.

This isn’t just about who has the highest ranking on a coveted list – it’s not a sporting event with a big fanfare for the winner. These computers are crucial for modeling, simulation, and large-scale analysis – everything from modeling complex weather systems to simulating biological processes. As our understanding of highly complex systems grows, the only way we’re going to be able to keep moving forward is with more and ever more computing power. At the same time, exascale computing is anticipated to be a highly disruptive technology, not only because of what it will be able to do, but because of the technologies that will be created in the course of developing it. Ultimately, these technologies will end up in all kinds of new products, not unlike what happened with the Apollo space program. Falling behind at this stage of the game would put the U.S. at a big disadvantage in almost every aspect of science and product development.

Just as concerning, I believe, is what this would mean for developing an AGI or artificial general intelligence. There’s been a lot of speculation by experts in the field of AI as to when (if ever) we might develop a human-level artificial intelligence. A recent survey of AI experts indicates we could realize human-level AI or greater in the next couple of decades. More than half of the experts surveyed thought this milestone would occur by mid-century. While there are many different avenues which may ultimately lead to an AGI, it’s a good bet that most of these will require some pretty serious computing power both for research and potentially for the substrate of the AGI itself.

It’s been speculated that there are considerable risks in developing a computer with human-level or greater intelligence, but there are a number of risks in not doing so as well. Whoever builds the first AGI will very probably realize an enormous competitive advantage, both economically and politically. Additionally, the world faces a growing number of existential threats which AGIs could play a critical role in helping us to avoid.

During this time of budget deficits and spending cuts, it would be very easy to decide that Big Science programs, such as the Exascale Initiative, aren’t as crucial to the nation’s well-being as they really are. This would be a grave mistake. The question isn’t how we can afford to commit ourselves to this research, but how we can afford not to.

(NOTE: Beginning with this entry, I’ll be cross-posting my blog at the World Future Society – www.wfs.org.)

Advances in AI

A number of recent stories illustrate the rapid progress that’s being made in many different fields linked to artificial intelligence:

Europe’s four-year AMARSi project (Adaptive Modular Architecture for Rich Motor Skills) could lead to humanoid robots that quickly acquire skills from human co-workers as well as their environments.
AMARSi project could see robots learn from co-workers

Also from Europe is HUMAVIPS (Humanoids with Auditory and Visual Abilities in Populated Spaces) which seeks to improve the ways humans and robots communicate and interact.
Humanoid robots to gain advanced social skills

A research team at the University of Michigan at Ann Arbor has demonstrated how memristors can mimic the behavior of brain synapses.
Electronics ‘missing link’ brings neural computing closer

Natural responses to technological change

This music was composed by a prolific, young composer named Emi. During a brief career, Emi created thousands of works, many of them in the style of famous composers, such as Bach, Beethoven, and Chopin. These compositions have been described as moving, soulful, emotional. In many ways, Emi may prove to be one of the most important composers of the age. Emi is also a computer program.

When many listeners first learn the nature of these compositions, particularly after believing they were created by a person, their opinion of the work changes dramatically. Music they previously called soulful or inspiring becomes calculating, stilted, even mechanistic. This points to a human chauvinism about the growing intelligence of our machines which we would be wise to examine. As with so many developments in artificial intelligence, there seems to be resistance against any encroachment into what we regard as the realm of human intellect. Just as chess masters once denigrated the abilities of chess-playing computers, each new hurdle in AI will probably be met with similar resistance.

Though it may sound a little strange, I’m going to suggest that this response follows a pattern similar to Kubler-Ross’s five stages of grief: denial, anger, bargaining, depression and acceptance. Grief is a normal response to loss and it may be that many people feel they are losing something crucial when technology begins to take on functions previously exclusive to humans. Obviously, disbelief is a common enough reaction to new and developing technology, but it’s hard to maintain for long in the face of direct evidence. Emi’s creator, University of California, Santa Cruz professor, David Cope, has reported considerable anger from listeners in response to Emi’s work, as well as his later program, Emily Howell.

It’s almost as if the listener felt tricked or cheated once they discovered the music was created by an artificial intelligence rather than a natural one. As for bargaining and depression, I suspect these are not unfamiliar to people who have seen their career plans radically altered by the continually shifting technological landscape.

Then there’s acceptance. So much AI has been accepted that most of us no longer categorize it as AI at all. When was the last time you used spell check, or a voice menu or played a video game and acknowledged the leaps in artificial intelligence that made them possible? Typically, we don’t. They simply become another piece of the background of our technologically enhanced lives.

All of these are probably very natural responses. We humans have considered ourselves to stand at the pinnacle of intelligence for so very long. Nothing in the animal kingdom even comes close to the height of human achievement and for the most part, our machines are even further down the scale. Many will argue that the types of intelligences exhibited by these machines is very limited and domain-specific. That they are examples of weak AI – sets of rules and knowledge bases and Bayesian pattern recognition algorithms. There’s no way any of these could ever develop into something that would rival our marvelous minds.

But more and more evidence is indicating that our brains are themselves composed of myriad subsystems which together make up the sum total of our intellect. Marvin Minsky’sSociety of Mind” describes the concept very well. Why shouldn’t an artificial general intelligence be composed of modules, agents and subsystems too?

With each new leap in machine intelligence, we come a little closer to slipping from our pinnacle on Mount Intellect. Lightning-fast calculation, expert diagnostic systems, face and image recognition, real-time voice translation. Exponential improvements in both hardware and software are rapidly driving us into a new era. It will be an era in which we’ll likely share the stage of superior intelligence with many other players. That is, we will if we’re lucky.

Future of intelligence article

“Get Smart”, my new article about the future of intelligence is out in this month’s Mensa Bulletin

The domain of sapient entities may one day encompass not only humans, but transhumans, machine intelligences, augmented animals, distributed networks, group minds, even uploaded personalities.  If so, our definition of what is intelligent and sentient would need to change, along with the legal and social institutions under which we’d coexist.  There can be little doubt that it would be a very strange and different world – a world in which new forms of intelligence appear much more rapidly than has ever occurred in the past.

Originally titled “Evolution, Technology and the Future of Intelligence”, the Bulletin decided to go with the snappier “Get Smart”, presumably unaware of Jamais Cascio’s Atlantic Monthly article of the same name from two months before.  I’ll try to include a link to the full article in the near future.