Semantic search and government support of AI
The last few weeks have seen the introduction of several search engines that boast “semantic search” capabilities. That is, they do not just look for keywords using statistical formulas, but seek to find meaning in the search phrases and in at least some content on web. The most publicized of these include Wolfram-Alpha, Google Squared, and Bing, (Microsoft’s new search engine, whose semantic search abilities are limited to specific domains, like shopping and travel).
Today these sites are extremely limited in their ability to parse user queries. For example, one of Wolfram-Alpha’s sample queries is “gdp spain/italy”, showing that it can combine and compare different types of data. But small changes stump Wolfram-Alpha. For example, it can produce a graph of “gdp per capita spain,” but not a graph of “gdp per capita growth spain” or “growth in gdp per capita spain.” Maybe there is some way to do this, but it’s not a clever interpreter.
Meanwhile, Google Squared is fun, but not terribly useful. Type, “Mets players,” and receive an interesting list: some historic greats — David Cone, Nolan Ryan, Darryl Strawberry, and Howard Johnson — as well as some strange choices, and an amusing mistake, a link to a concert by the other Kenny Rogers (a type of mistake that can occur only with semantic search). The most entertaining feature is that you can suggest some items in a series, and it will try to find more. For example, it didn’t know of any “steroid users,” but once I suggested Barry Bonds, Roger Clemens, Rafael Palmeiro, Manny Ramirez, and Alex Rodriguez, it helpfully added Sammy Sosa and Derek Jeter (who knew!) to the grid.
Overall, these sites both showcase some achievements of artificial intelligence, while also highlighting how far we are from truly revolutionary products. The existence of such products might seem to support conclusions that there is little need for active government promotion or subsidy of artificial intelligence research, but I draw the opposite conclusion. Artificial intelligence is a technology that today produces modestly useful products, but if it were possible to create a more powerful general reasoning system that could be a technology with tremendous social value. Many lesser artificial intelligence goals, such as robust computer vision, also would be extremely valuable.
But because these goals are unlikely within the patent term of any new technologies (assuming that software even remains patentable!), private incentives for such technology development are suboptimal. Thus, if further technology development is cost/benefit justified, then there is a case for governmental support of basic research in artificial intelligence. My own intuition is that there is sufficient warrant for optimism about artificial intelligence in the next 20 to 40 years that existing levels of government spending are too low. Admittedly, projections of future technology development are speculative, so it’s hard to be sure. One thing that one can be more sure about: the government doesn’t do a good job thinking systematically about what social returns we can expect from investments in different types of basic research, from bio to energy to nanotech. Funding priorities are mostly a result of politics, which corresponds only loosely to actual need.