of ELIZA, the theoretical mathematician Doron Zeilberger now names his computer as a co-author of his papers. He calls it Shalosh B. Ekhad, a play on the Hebrew name of his IBM 3B1.) 258 The AI systems designer Roger Grosse has named two paths to this sort of wired sensibility: “Predictive Learning” and “Representational Learning”.25° That first approach is what Maes’s movie machine pusued. The computer is simply checking what it encounters against a database. It teaches itself to predict based on what has been seen before. This sort of knowledge begins with massive amounts of data and then hunts for patterns, tests their reliability, and improves by mapping quirks and similarities. Google engineers have a device that can gaze into a human eye and spot signs of impending optical failure. Is the machine smarter than your ophthalmologist? Hard to know, but let’s just say this: It has seen, studied and compared millions of eyes to find patterns that nearly perfectly predict a diagnosis. It can review in seconds more cases than your doctor will see in a lifetime - let alone recall and compare at sub-millimeter accuracy. Fast, thorough predictive algorithms make what might once have been regarded as AI disappear. The machine isn’t all that wise; it just knows a lot. On the other path, the one of “representational learning” the machine uses a self- sketched image of the world, a “representation.” Computers using predictive methods to recognize 10,000 numbers pulled from a database of scrawled hand writing now identify 90 percent of the images. Self-trained machines, however, line up each scanned pixel against a representation of the very idea of writing. They screen millions of pictures with nary a mistake. Faces, disease markers, obscure sounds - all these become scrutable not because the machines have been told what to look for, but because they’ve sort of figured it out themselves. The Al is actually starting to think, much as you or | might, first by building up a pic