What should we learn from past AI forecasts?
The difference this time is that 1) AI is profitable, and 2) everything is on a much larger scale.<p>As I&#x27;ve mentioned before, I went through Stanford CS in 1985, just as it was becoming clear that Expert Systems were not going to yield Strong AI Real Soon Now.  Back then, AI research was about 20 people at Stanford, and similar sized departments at MIT and CMU, with a few other small academic groups and some people at SRI. There were a few small AI startups including Teknowledge and Denning Robotics; few if any survived.  Everybody was operating on a very small scale, and nobody was shipping a useful product. Most of this was funded by the US DoD.<p>Now, machine learning, etc., is huge.  There are hundreds of companies and hundreds of thousands of people in the field.  Billions are being spent. Companies are shipping products and making money.  This makes the field self-sustaining and keeps it moving forward.  With all those people thinking, there&#x27;s forward progress.<p>Also, we now have enough compute power to get something done. Kurtzweil claims a human brain needs about 20 petaFLOPS.  That&#x27;s about one aisle in a data center today.