Escaping the Local Minimum: Where AI Has Been and Where It Needs to Go
The big difference this time is that AI makes money.  This matters. The first two AI booms never made it to profitability, or produced much usable technology.  This time, there are applications. As a result, far more people are involved.  This AI boom is at least three orders of magnitude bigger than the first two.<p>I&#x27;ve had similar criticisms to the parent author for years, but I thought of it as a hubris problem. In each AI boom, there was a good idea, which promoter types then blew up into Strong AI Real Soon Now.  The arrogance level of the first two AI booms was way out of line with the results achieved. This time, it&#x27;s more about making money, and much of the stuff actually works.  Machine learning may hit a wall too, but it&#x27;s useful.<p>The field isn&#x27;t going to get trapped in a local minimum with neural nets because the field is too big now.  When AI was 20 people each at Stanford, MIT, and CMU, that could happen.  With 50,000 people taking machine learning courses, there are enough people for some to focus on optimizing existing technologies without taking away from new ideas.<p>We&#x27;re going to get automatic driving pretty soon. That&#x27;s working now, with cars on the road from about a half dozen groups.  Not much question about that.<p>The author rehashes symbolic systems and natural language understanding as areas of recommended work. This may or may not be correct. Time will tell.  He omits, though, the &quot;common sense&quot; problem. There&#x27;s been work on common sense, but mostly as a symbolic or linguistic problem.  Yet the systems that really need common sense are the ones that operate in the real, physical world.  What happens next?  What could go wrong? What if this is tried? That&#x27;s what Google&#x27;s self driving car project is trying to deal with. Unfortunately, Google doesn&#x27;t say much about how they do this.  That project, though, is really working on common sense.<p>Incidentally, Danny Hillis did not found Symbolics. He founded Thinking Machines, which built the Connection Machine, a big SIMD (single instruction, multiple datastream) computer with 1024 dumb processors each executing the same instruction on different data.