7:10: Oh, Watson’s got the Daily Double! He’s wagering $1,000…and he’s got it right. Ken gets a question correct about the 50s.
7:20: Watson doesn’t appear to be getting any of these decades questions correct…Man is really coming back here in the second half. Watson really does like the Beatles though. He’s tied for the lead with…Brad? What’s happening Ken? Brad wants to be an actor for heaven’s sake.
7:25: What have we learned tonight Trebek? “Watson is very bright, very fast, but he has some weird little moments…” Tomorrow is double Jeopardy and Wednesday is the final.
Spoiler space scores after the jump
Spoiler space According to a Crunchgear
scores as of the end of 2/14’s Jeopardy! round:
Brad Rutter $5000
Poor Ken Jennings $2000
Watson is a stunning example of the growing ability of computers to successfully invade this supposedly unique attribute of human intelligence. If you watch Watson’s performance, it appears to be at least as good as the best “Jeopardy!” players at understanding the nature of the question (or I should say the answer, since “Jeopardy!” presents the answer and asks for the question, which I always thought was a little tedious). Watson is able to then combine this ability to understand the level of language in a “Jeopardy!” query with a computer’s innate ability to accurately master a vast corpus of knowledge.
I’ve always felt that once a computer masters a human’s level of pattern recognition and language understanding, it would inherently be far superior to a human because of this combination.
We don’t know yet whether Watson will win this particular tournament, but it won the preliminary round and the point has been made, regardless of the outcome. There were chess machines before Deep Blue that just missed defeating the world chess champion, but they kept getting better and passing the threshold of defeating the best human was inevitable. The same is true now with :Jeopardy!.
Watson runs on 90 servers, although it does not go out to the Internet. When will this capability be available on your PC or smartphone ? It was only five years between Deep Blue in 1997, which was a specialized supercomputer, and Deep Fritz in 2002, which ran on eight personal computers, and did about as well.
I [Ray Kurzweil] do expect the type of natural language processing we see in Watson to show up in search engines and other knowledge retrieval systems over the next five years.
What will be the significance of a computer passing the Turing test? If it is really a properly designed test it would mean that this AI is truly operating at human levels. And I for one would then regard it as human. I’m expecting this to happen within two decades, but I also expect that when it does, observers will continue to find things wrong with it.
By the time the controversy dies down and it becomes unambiguous that nonbiological intelligence is equal to biological human intelligence, the AIs will already be thousands of times smarter than us. But keep in mind that this is not an alien invasion from Mars. We’re creating these technologies to extend our reach. The fact that farmers in China can access all of human knowledge with devices they carry in their pockets is a testament to the fact that we are doing this already.
Ultimately, we will vastly extend and expand our own intelligence by merging with these tools of our own creation.
IBM’s basic approach has a long history, with a lineage in the field of information retrieval that is in many ways shared with search engines. The essential idea is to start with textual documents, and then to build a system to statistically match questions that are asked to answers that are represented in the documents.
Wolfram|Alpha is a completely different kind of thing — something much more radical, based on a quite different paradigm. The key point is that Wolfram|Alpha is not dealing with documents, or anything derived from them. Instead, it is dealing directly with raw, precise, computable knowledge. And what’s inside it is not statistical representations of text, but actual representations of knowledge.
And in a sense Wolfram|Alpha fully understands every answer it gives. It’s not somehow serving up pieces of statistical matches to documents it was fed. It’s actually computing its answers, based on knowledge that it has. And most of the answers it computes are completely new: they’ve never been computed or written down before.
In IBM’s approach, the main part of the work goes into tuning the statistical matching procedures that are used — together in the case of “Jeopardy” with adding a collection of special rules to handle particular situations that come up.
So what’s the broader significance of the “Jeopardy!” project? It’s yet another example of how something that seems like artificial intelligence can be achieved with a system that’s in a sense “just doing computation” (and as such, it can be viewed as yet another piece of evidence for the general Principle of Computational Equivalence that’s emerged from my work in science).
But at a more practical level, it’s related to an activity that has been central to IBM’s business throughout its history: handling internal data of corporations and other organizations.
There are typically two general kinds of corporate data: structured (often numerical, and, in the future, increasingly acquired automatically) and unstructured (often textual or image-based). The IBM ”Jeopardy!” approach has to do with answering questions from unstructured textual data — with such potential applications as mining medical documents or patents, or doing e-discovery in litigation.
Let’s take a moment to lift our noses from the grindstone and reassess the future. Apropos is IBM’s upcoming artificial intelligence spectacular, pitting “Watson” (a 90-server, 80 teraflops, 15 terabyte supercomputer, doing natural language disambiguation and question answering from a huge mixed-format internal database — no Internet), against the two top previous human winners of the game show Jeopardy!
Fully intelligent machines will, of course, change the world. Most exciting to me is that they will open the universe to exploration and discovery on a scale unimagined now.
Our sole focus—creating and delivering Industrial Mobile Robotics (IMR) technology—is reflected in our name: “See” (vision-guided) “grid” (utilizing a 3D grid for navigational purposes). Seegrid’s core IMR technology overcomes the traditional hurdles of robotic adoption.
Seegrid’s robotics technology focuses on the very hard end, where even the most basic functions, like our visual route memorizer, are barely possible.
Using the capabilities of DeepQA to automatically generate hypotheses and gather evidence to support or refute those hypotheses, and then evaluate all of this evidence through an open, pluggable architecture of analytics, and then combine and weigh all those results to evaluate those hypotheses and make recommendations — that’s where we’re going with this technology. I think what you’re starting to get at is, in the broader context of artificial intelligence, are we making claims about DeepQA at that level? At this point, I’m not sure we’re ready to make any claims in a broader context.
If the Turing test was can you make a machine mimic a Jeopardy player then we are there.
If you were to rephrase the Turing test slightly, and couch it in terms of, if you had two players playing Jeopardy!, and you couldn’t tell which one was the computer and which one was the human, and that was the Turing test, then I think Watson would pass that very easily. But, of course, the Turing test is defined more broadly and open-ended, where you really have an open-ended dialogue, and Watson is not up to that task yet.
Watson solves a different problem than a traditional Web search engine does. I think ultimately, both of these technologies will have a role in supporting humans in everyday information seeking, gathering and analysis tasks. Watson, or DeepQA, actually uses several search engines inside it to do some of its preliminary searching and candidate answer generation.
The other applications that we’re thinking about would include things like help desk, tech support, and business intelligence applications … basically any place where you need to go beyond just document search, but you have deeper questions or scenarios that require gathering evidence and evaluating all of that evidence to come up with meaningful answers.
Q: Does Watson use speech recognition?
Brown: No, what it does use is speech synthesis, text-to-speech, to verbalize its responses and, when playing Jeopardy!, make selections. When we started this project, the core research problem was the question answering technology. When we started looking at applying it to Jeopardy!, I think very early on we decided that we did not want to introduce a potential layer of error by relying on speech recognition to understand the question.
But as contests go, this one is pretty good. IBM is the only research contestant, so it isn’t pitting researchers against each other. And the IBM team has been pretty open about its methods. Though not every detail was revealed, and the team has asked certain collaborators to sign non-disclosure agreements, I assume that more details will be forthcoming in the scientific literature after the contest.
Artificial Intelligence has suffered from the perception that it is tackling an impossible problem, tilting at windmills—that human intelligence can’t be understood in detail. That attitude sometimes discourages young people from entering the AI field.