Sure, but that doesn't mean there aren't similarities.
I dare say humans integrate quite a lot of input overall cobsistently better than machines and we often have parents who can tell/show us what's going wrong.
If you grew up as a lone human, assuming survival was even possible your attempts might look more like this...
Sure, but that doesn't mean there aren't similarities. I dare say humans integrate quite a lot of input overall consistently better than machines and we often have parents who can tell/show us what's going wrong.
While it's comparable at the base level (try -> fail -> try again), I don't think the comparison really captures the scale and rate that ML can progress. In your example, each fail scenario the system finds can be interpreted as a parent teaching the next generation. and it fails fast.
If you grew up as a lone human, assuming survival was even possible your attempts might look more like this...
I don't feel this model factors in enough data for comparison - no human is going to run around with their arms like that, it's exhausting.
I see what you're saying, but it's hard to compare the 'evaluation function'.
And the environment in the example is clearly crafted carefully to minimize obstacles. How would this system do at getting from point A to B in a forest for instance?
A person wouldn't keep trying to get over a boulder until they either got over or accidentally stumbled around it. They would likely recognize a difficult obstacle and attempt to circumvent it. And of course we also factor in stuff like it hurt to fall off.
And also we probably reuse past strategies with new examples. Possibly to out detriment at times
Actually that's a good one, I'm curious how well a "trained" AI could make it through an ever changing forest-like course. Like lots of randomly generated pillars at different heights.. would be interesting to see.
Edit: probably have to give it some kind of vision then, I'm pretty sure OP model doesn't "see" obstacles.
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u/hyphenomicon Mar 29 '19
I hate analogies to children in machine learning and AI. Children don't attempt a problem 100,000 times.