Does it teach us anything fundamentally new? No, it still has horrible sample complexity and does not generalize to anything outside of Atari unless you completely re-tune it. And I don't mean re-train. I mean changing the architecture and assumptions. That's different from projects such as e.g. AlphaZero or MuZero.
IMO this would've been more appropriate to be published as an open-source system so that it can be applied and tuned to other problems as opposed to a research paper. As research, nobody outside of DeepMind can ever reproduce this.
You are completely changing the topic with this:
> While Never Give Up (NGU) is not fundamentally new, it is an important step in computers learning. You need to be able to generalize solutions to problems where you don't have contextual information
We're not even talking about NGU, we're talking about the paper linked in this post. This specific paper proposes little new in that regard. It just engineers a system to do this specifically for Atari games by taking a previous paper and changing some parameters. Neat, but it's not some kind of breakthrough.
I thought it was interesting that they let the agent learn the exploration/exploitation tradeoff, also combining memory and intrinsic motivation.
Another contribution of this paper would be that they showed all these tricks can build on each other, thus, are complementary.
Humans brains are also a bag of tricks fine-tuned to the goal and requirements of making more humans.
The fundamental question is, is a representational (contextual) bootstrap required in the long run for a contained computational system to perform at human level across a large number of domains? This isn't a solved problem.
So yes, AI would be better if it could "figure things out by itself" however humans don't "figure things out by themselves" they come pre-wired with a lot out of the box and a lot of help cleaning the data (parents, teachers, literal labels etc...)
While Never Give Up (NGU) is not fundamentally new, it is an important step in computers learning. You need to be able to generalize solutions to problems where you don't have contextual information. Imagine if you were a caveman and asked to operate an iPhone. You're not stupid if you don't know how, but if I tell you, "never give up", and put you in a room for 5 years I'd expect some results from a sentient being. This is an important process too.
We would be much closer to good AI if it can figure things out by itself instead of being constantly fed "clean" data.