is there any research into making a "generic" AI that can solve any problem without the researcher first having to know what that problem is? i.e., human style learning.
I think the main problem is you end up needing a language to describe the problem, and that ends up limiting the problems that can be solved, or you have to explain so carefully what the problem is it feels like cheating.
We are still quite far from anything we could call "human-style learning" though, but definitely getting there (just look at all the recent publications with reinforcement learning and elaborate ways to use memory in neural nets).
1. dynamically notice world-features ("instrumental goal features") that seem to correlate with terminal reward signals;
2. build+train entirely new contextual sub-models in response, that "notice" features relevant to activating the instrumental-goal feature;
3. shape goal-planning in terms of exploiting sub-model features to activate instrumental goals, rather than attempting to achieve terminal preferences directly. (And maybe also in terms of discovering sense-data that is "surprising" to the N most-useful sub-models.)
In other words, the AI should be able to interact with reward-stimuli at least as well as Pavlov's dog.
Right now, ML research does include the concept of "general game-playing" agents—but AFAIK, these agents are only expected to ever play one game per instance of the agent, with the generality being in how the same algorithm can become good at different games when "born into" different environments.
Humans (most animals, really) can become good at far more than a single game, because biological minds seem to build contextual models, that communicate with—but don't interfere with—the functioning of the terminal-preference-trained model.
So: is anyone trying to build an AI that can 1. learn that treats are tasty, and then 2. learn to play an unlimited number of games for treats, at least as well as a not-especially-smart dog?
When people give credit to the human designers for AlphaGo's wins, that it is really a win for humanity, I disagree. The wins are alphago's even if the design is of human ingenuity.
When You say that the outputs of human ingenuity should be credited to Evolution, I similarly disagree. You might as well credit evolution for AlphaGo's win. While it is true that Evolution invented the first AGI (and in some though not all ways, a superior intelligence to it), it still makes sense to separate the products of human learning from whatever structural priors DNA passed along. I'll also point out that compared to most animals, humans actually have weaker priors and spend a lot of their early days learning to learn.
I think it's fascinating that we have developed from un-self-reflective animals, to abstract thinkers on the verge of creating wholly new abstract-thinking entities from scratch, in only two and half thousand steps. Especially given that the majority of the technical knowledge necessary was developed only in the last 500 years, or 25 generations.