What we see here is that Artificial Intelligence (AI), in the truest sense of emerging sentience, is becoming visible outside of the cognitive science, eccentric, and science fiction communities. We also feel the powerful currents that will drive us, one day, to abiologic sentience.
The economic advantages of cheap focused intellect are irresistible. From computer gaming to stock selection to military planning to anti-missile systems to advanced self-guided robotic planes to cheap mammogram interpretation -- on every front billions of dollars push an emergent agenda.
Unless al Qaeda wins its battle to restore the 14th century, we will have fundamentally (biologic subsystems may play a role in early systems) abiologic sentience that will outstrip our current wetware. One uncertainty is whether this is 20 years away or 100 years away. The other uncertainty is whether we should be hoping for the 14th century.
First from yesterday's NYT:
November 24, 2006, New York TimesThis aticle suggests a new business model for journalism -- for better and for worse. Journalists now have a funding stream in which the buyers are computer programs, but the value of the commodity depends on it being consumed by humans as well. So the trading programs may replace the lost revenue from classified ads. There's a dark side (of course!). Imagine how much money will be made by manipulating the news stream to provide transient arbitrage opportunities. The article also suggests a rather substantial business model for natural language processing; this has implications for many domains.
A Smarter Computer to Pick Stocks By CHARLES DUHIGG
... For decades, Wall Street firms and hedge funds like D. E. Shaw have snapped up math and engineering Ph.D.s and assigned them to find hidden market patterns. When these analysts discover subtle relationships, like similarities in the price movements of Microsoft and I.B.M., investors seek profits by buying one stock and selling the other when their prices diverge, betting historical patterns will eventually push them back into synchronicity.
... New software programs, like the Apama Algorithmic Trading Platform, have made it possible for day traders to build complicated trading algorithms almost as easily as they drag an icon across a digital desktop.
“Five years ago it would have taken $500,000 and 12 people to do what today takes a few computers and co-workers,” said Louis Morgan, managing director of HG Trading, a three-person hedge fund in Wisconsin....
... “Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”
... “neural networks” and “genetic algorithms” have become common in higher-level computer science. Neural networks permit computers to create new rules and automatically change underlying assumptions by experimenting with thousands of random sequences and processes. Genetic algorithms encourage software to “evolve” by letting different rules compete, and combining the most successful outcomes.
Wall Street has rushed to mimic the techniques. Because arbitrage opportunities disappear so quickly now, neural networks have emerged that can consider thousands of scenarios at once. It is unlikely, for instance, that Microsoft will begin selling ice-cream or I.B.M. will declare bankruptcy, but a nonlinear system can consider such possibilities, and thousands of others, without overtaxing computers that must be ready to react in milliseconds.
... In the pursuit of previously undetectable patterns, hedge funds are racing to quantify things — like newspaper headlines — that were previously immune from number-crunching.
Both Dow Jones Newswires and Reuters have transformed decades of news archives into numerical data for use in designing and testing algorithmic systems. The companies are beginning to structure news so it can be absorbed by quantitative models within milliseconds of release.
Moreover, companies like Progress Software are working with news agencies to create computer programs that instantly translate news — for example, a headline regarding Microsoft’s earnings — into data. M.I.T. is examining, among other things, evaluating companies by seeing how many positive versus negative words are used in a newspaper article.
Software in development could potentially respond automatically to almost anything; changes in weather forecasts on television news, shifting analyst sentiments or what a particular movie critic said about the new blockbuster...
Secondly, for another sign of the emergence of AI into the public mind, read this March 2006 blog posting: Marginal Revolution: AI, Consciousness and Robot Outsourcing. Alex clearly grew up without science fiction, so these ideas are new to him. The point is not that Alex, a bright person in general, has anything interesting to say, the point is that he's typical of the new wave of intellectuals who are contemplating the future. AI is going mainstream, gradually.
Incidentally, I've read a representative sample of the cognitive science literature arguing that we can't make a thinking abiologic entity. Chinese rooms, etc. The anti-AI group essentially argue that we have souls, or something like a soul, and things without souls can't think. We'll see. I rather doubt it.
This construction of abiologic sentience feels like a universal constant, an emergent property of any sentient organism with constrained resources and the ability to use technology. In a universe in which the three laws of thermodynamics apply, resources are always constrained. It seems plausible that all civilizations across space and time that survive long enough create abiologic sentience. I wonder if that has something to do with the great silence ...