Go, Marvin Minsky, and the Chasm That AI Hasn’t yet Crossed
I went through Stanford in the 1980s, just as it was becoming clear that logic-based AI had hit a wall.  That was the beginning of the &quot;AI Winter&quot;, which lasted about 15 years.  Then came machine learning.<p>AI used to be a tiny field.  In the heyday of McCarthy and Minsky, 
almost everything was at MIT, Stanford, and CMU, and the groups weren&#x27;t that big. There were probably less than 100 people doing anything interesting.  Also, the total compute power available to the Stanford AI lab in 1982 was about 5 MIPS.<p>Part of what makes machine learning go is sheer compute power.  Training a neural net is an incredibly inefficient process.  Many of the basic algorithms date from the 1980s or earlier, but nobody could hammer on them hard enough until recently. Back in the 1980s, John Koza&#x27;s group at Stanford was trying to build a cluster out of a big pile of desktop PCs. Stanford got a used NCube Hypercube with 64 processors (1 MIPS, 128KB each).  The NCube turned out to be useless. There was a suspicion that with a few more orders of magnitude in crunch power, something might work, but with the failure of AI, nobody was going to throw money at the problem.<p>At last, there are profitable AI applications, and thus the field is huge.  Progress is much faster now, just because there&#x27;s more effort going in. But understanding of why neural nets work is still poor. Things are getting better; the trick of using an image recognition neural net to generate canonical images of what it recognizes finally provided a tool to get some insight into what was going on.  At last there was a debug tool for neural nets. Early attempts in that direction determined that the recognizer for &quot;school bus&quot; was recognizing &quot;yellow with black stripe&quot;, and that some totally bogus images of noise would be mis-recognized.  Now there are somewhat ad-hoc techniques for dealing with that class of problems.<p>The next big issue is to develop something that has more of an overview than a neural net, but isn&#x27;t as structured as classic predicate-calculus AI.  One school tries to do this by working with natural language; Gary Markus, the author of the parent article, is from that group.  There&#x27;s a long tradition in this area, and it has ties to semantics and classical philosophy.<p>The Google self-driving car people are working on higher-level understanding out of necessity. They need to not just recognize other road users, but infer their intent and predict their behavior.  They need &quot;common sense&quot; at an animal level.  This may be more important than language.  Most of the mammals have that level of common sense, enough to deal with their environment, and they do it without much language.  It makes sense to get that problem solved before dealing with language. At last, there&#x27;s a &quot;killer app&quot; for this technology and big money is being spent solving it.