AI isn’t taking off because it took off in the 60s. Heck, they were even working on neural nets back then. Same as in the 90s when they actually got them to be useful in a production environment.

We got a deep learning craze in the 2010s and then bolted that onto neural nets to get the current wave of “transformers/diffusion models will solve all problems”. They’re really just today’s LISP machines; expected to take over everything but unlikely to actually succeed.

Notably, deep learning assumes that better results come from a bigger dataset but we already trained our existing models on the sum total of all of humanity’s writings. In fact, current training is hampered by the fact that a substantial amount of all new content is already AI-generated.

Despite how much the current approach is hyped by the tech companies, I can’t see it delivering further substantial improvements by just throwing more data (which doesn’t exist) or processing power at the problem.

We need a systemically different approach and while it seems like there’s all the money in the world to fund the necessary research, the same seemed true in the 50s, the 60s, the 80s, the 90s, the 10s… In the end, a new AI winter will come as people realize that the current approach won’t live up to their unrealistic expectations. Ten to fifteen years later some new approach will come out of underfunded basic research.

And it’s all just a little bit of history repeating.