Ray Kurzweil - How to Create a Mind - The Secret of Human Thought Revealed

Another limitation of the Jeopardy! game is that the answers are generally brief: It does not, for example, pose questions of the sort that ask contestants to name the five primary themes of A Tale of Two Cities . To the extent that it can find documents that do discuss the themes of this novel, a suitably modified version of Watson should be able to respond to this. Coming up with such themes on its own from just reading the book, and not essentially copying the thoughts (even without the words) of other thinkers, is another matter. Doing so would constitute a higher-level task than Watson is capable of today—it is what I call a Turing test–level task. (That being said, I will point out that most humans do not come up with their own original thoughts either but copy the ideas of their peers and opinion leaders.) At any rate, this is 2012, not 2029, so I would not expect Turing test–level intelligence yet. On yet another hand, I would point out that evaluating the answers to questions such as finding key ideas in a novel is itself not a straightforward task. If someone is asked who signed the Declaration of Independence, one can determine whether or not her response is true or false. The validity of answers to higher-level questions such as describing the themes of a creative work is far less easily established.

It is noteworthy that although Watson’s language skills are actually somewhat below that of an educated human, it was able to defeat the best two Jeopardy! players in the world. It could accomplish this because it is able to combine its language ability and knowledge understanding with the perfect recall and highly accurate memories that machines possess. That is why we have already largely assigned our personal, social, and historical memories to them.

Although I’m not prepared to move up my prediction of a computer passing the Turing test by 2029, the progress that has been achieved in systems like Watson should give anyone substantial confidence that the advent of Turing-level AI is close at hand. If one were to create a version of Watson that was optimized for the Turing test, it would probably come pretty close.

American philosopher John Searle (born in 1932) argued recently that Watson is not capable of thinking. Citing his “Chinese room” thought experiment (which I will discuss further in chapter 11), he states that Watson is only manipulating symbols and does not understand the meaning of those symbols. Actually, Searle is not describing Watson accurately, since its understanding of language is based on hierarchical statistical processes—not the manipulation of symbols. The only way that Searle’s characterization would be accurate is if we considered every step in Watson’s self-organizing processes to be “the manipulation of symbols.” But if that were the case, then the human brain would not be judged capable of thinking either.

It is amusing and ironic when observers criticize Watson for just doing statistical analysis of language as opposed to possessing the “true” understanding of language that humans have. Hierarchical statistical analysis is exactly what the human brain is doing when it is resolving multiple hypotheses based on statistical inference (and indeed at every level of the neocortical hierarchy). Both Watson and the human brain learn and respond based on a similar approach to hierarchical understanding. In many respects Watson’s knowledge is far more extensive than a human’s; no human can claim to have mastered all of Wikipedia, which is only part of Watson’s knowledge base. Conversely, a human can today master more conceptual levels than Watson, but that is certainly not a permanent gap.

One important system that demonstrates the strength of computing applied to organized knowledge is Wolfram Alpha, an answer engine (as opposed to a search engine) developed by British mathematician and scientist Dr. Wolfram (born 1959) and his colleagues at Wolfram Research. For example, if you ask Wolfram Alpha (at WolframAlpha.com), “How many primes are there under a million?” it will respond with “78,498.” It did not look up the answer, it computed it, and following the answer it provides the equations it used. If you attempted to get that answer using a conventional search engine, it would direct you to links where you could find the algorithms required. You would then have to plug those formulas into a system such as Mathematica, also developed by Dr. Wolfram, but this would obviously require a lot more work (and understanding) than simply asking Alpha.

Indeed, Alpha consists of 15 million lines of Mathematica code. What Alpha is doing is literally computing the answer from approximately 10 trillion bytes of data that have been carefully curated by the Wolfram Research staff. You can ask a wide range of factual questions, such as “What country has the highest GDP per person?” (Answer: Monaco, with $212,000 per person in U.S. dollars), or “How old is Stephen Wolfram?” (Answer: 52 years, 9 months, 2 days as of the day I am writing this). As mentioned, Alpha is used as part of Apple’s Siri; if you ask Siri a factual question, it is handed off to Alpha to handle. Alpha also handles some of the searches posed to Microsoft’s Bing search engine.

In a recent blog post, Dr. Wolfram reported that Alpha is now providing successful responses 90 percent of the time. 17 He also reports an exponential decrease in the failure rate, with a half-life of around eighteen months. It is an impressive system, and uses handcrafted methods and hand-checked data. It is a testament to why we created computers in the first place. As we discover and compile scientific and mathematical methods, computers are far better than unaided human intelligence in implementing them. Most of the known scientific methods have been encoded in Alpha, along with continually updated data on topics ranging from economics to physics. In a private conversation I had with Dr. Wolfram, he estimated that self-organizing methods such as those used in Watson typically achieve about an 80 percent accuracy when they are working well. Alpha, he pointed out, is achieving about a 90 percent accuracy. Of course, there is self-selection in both of these accuracy numbers in that users (such as myself) have learned what kinds of questions Alpha is good at, and a similar factor applies to the self-organizing methods. Eighty percent appears to be a reasonable estimate of how accurate Watson is on Jeopardy! queries, but this was sufficient to defeat the best humans.

It is my view that self-organizing methods such as I articulated in the pattern recognition theory of mind are needed to understand the elaborate and often ambiguous hierarchies we encounter in real-world phenomena, including human language. An ideal combination for a robustly intelligent system would be to combine hierarchical intelligence based on the PRTM (which I contend is how the human brain works) with precise codification of scientific knowledge and data. That essentially describes a human with a computer. We will enhance both poles of intelligence in the years ahead. With regard to our biological intelligence, although our neocortex has significant plasticity, its basic architecture is limited by its physical constraints. Putting additional neocortex into our foreheads was an important evolutionary innovation, but we cannot now easily expand the size of our frontal lobes by a factor of a thousand, or even by 10 percent. That is, we cannot do so biologically, but that is exactly what we will do technologically.