[the_af]

First of all, nice comment! That said,

> Anytime a next task is solved, there is a crowd saying it's not a "real AI" and that scientists are solving "toy problems". Both statements are totally true. But the underlying substance is that each of these toy problems is of increasing complexity and brings us closer and closer to solving the "real problems"

I wonder if this is true. This belief may seem like common sense, but it's not obvious to me that domain-specific problems must generalize to General AI ("real problems") or even bring us closer to it. That is, it's not evidently true that many small problems will eventually lead to a general solver of everything (or to human-like intelligence). Or to say it in yet another way, it's not obvious to me that human-like intelligence is the sum of many small-problem-intelligences.

Again, common sense may lead us to believe this, and maybe it's true! But I think this conclusion is far from scientifically evident.

[IX-103]

The key thing you're missing is transfer learning. Instead of starting from scratch, you start with a model that was trained to do something and then train it to do something else. It takes much less time and labeled data to get the model to do something similar.

You can even interleave the training for the second task with a few training rounds for the first task to maintain proficiency. There's a group that's using this sorry if technique to make a general "plays videogames" AI. I couldn't find a good link from my phone, but here's a less good link about something similar: https://towardsdatascience.com/everything-you-need-to-know-a...