Artificial intelligence pioneer says we need to start over
Machine learning may be nearing its ceiling.<p>The history of AI goes in cycles. Someone has a good idea which solves some problems, followed by &quot;strong AI Real Soon Now&quot; enthusiasm, followed by that idea hitting its ceiling.  AI has been through search, backtracking, the General Problem Solver, hill-climbing, and expert systems. Each was overhyped at the time, and each hit its ceiling.<p>The big difference this time is that the ceiling with machine learning is high enough for large-scale profitable applications. That wasn&#x27;t the case with the previous rounds. AI used to be a dinky field - about 30-50 people each at Stanford, CMU, and MIT, plus a few tiny groups elsewhere. Now it&#x27;s a huge field with big companies and big profits. That makes it self-sustaining.<p>Hinton has a point in that we&#x27;re missing something. Back-propagation is an extremely inefficient method, especially since the slower you do it, the better it seems to work. More generally, most of machine learning is &quot;turn the problem into an optimization problem and bang on it really hard with lots of compute power&quot;.  This works for a useful class of problems. But it has limits.<p>How long until the next big idea?  The last &quot;AI winter&quot;, after expert systems, was 15 years.