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must invent and optimize them. Hardware that can support such a task doesn’t appear out of nowhere; we must build it. Much is learned along the way to solving those problems. (2/2)
For sure, such an endeavor can be valuable in terms of PR, inspiration, and as an engineering exercise with positive 2nd-order effects. My point from the perspective of learning something about intelligence, specifically. The science of AI.
Do game agent projects need to advance the boundaries of GAI theory? It’s an exercise in applying known theory/processes to a complicated problem space to achieve super-human performance. To say there’s “nothing to learn” from that (process or outcome) is a bit hyperbolic
One could easily turn such a game AI project into an experiment that you could learn from, if one were to care less about PR impact. See my prior comments about measuring generalization power rather than skill.
That statement applies to most AI research nowadays IMO. Anything involving deep learning is highly unlikely to advance AGI research, and that’s OK. Core AGI research is akin to walking through a maze blind-folded because we’re trying to mimic something we don’t understand.
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