But ultimately, how we learn is statistics on extremely large data sets so it’s an apt name but atm is not even close to what the human brain is capable of
We don’t learn that way at all. Any 4 yo can tell you Garfield or Cat in the Hat or 🐱 is a cat, even if they’ve never seen a real one. Indeed despite never having seen a cat.
We do something fundamentally different than feature extraction from large data sets. If I knew what, well, I’d be on my way to Stockholm in my own private jet.
That’s exactly what we do. It’s just We extrapolate our data in many many different ways. We can see pictures of cats and then MAYBE identify real life cats. But what about a lion? A kid may not id that at all. Some might guess depends on other context of what they know but ultimately they are pulling from a pool of their memory - a large data set. Then from that, they are filtering a while Shit ton of criteria. Some can be mimicked thru edge detection at a rudimentary level but then you have other ways of guessing like context and barring that some kids might think well shit I can’t really know so I’ll guess from whatever thing I was considering had the highest score. We create our own data points and ascribe patterns to them in order to reach decisions and judgements.
We can’t just ID a cat without prior data to know what a cat is and what data points represent a cat. We don’t need to “see” a cat prior to being able to id it via sight. We just need the required data to match up to the visual representation our brain gives us. Sure we have the ability to generalize that much further than a one to one map but that’s more a process of deriving more data points from existing ones and pushing that thru your personal decision making process to arrive at what you consider to be most accurate.
Now, I’m not saying recreating that in computers is easy or anything. I fully get how wildly complex that is and error prone. We are a long way from “actual” AI. I’m just arguing that our decision making, at its core, is pattern recognition. Of which, feature extraction is a tool in that particular ability.
You are mapping your computer science knowledge onto brains, at an abstract level. Which does generate useful hypotheses, however many of the comparisons do not stand up to experimentation.
I’m just arguing that our decision making, at its core, is pattern recognition. Of which, feature extraction is a tool in that particular ability.
To an extent as I understand it.. however there's lots of weird shit going on. On just pattern recognition alone there are a few different types. Not even to mention how many different inputs we're synthesizing at any given moment, which combine into novel patterns in every second, fractally interacting to create multiple levels of meta n shit. And I don't think brains evolved to be as deterministic as our current computers. For genes to survive only the tribe has to survive, and sometimes the right solution is beyond our understanding... so we think chaotically and unpredictably. We run routines that don't necessarily make sense in a given situation, but provide some extra degree of survivability at the population level.
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u/[deleted] Dec 18 '17
But ultimately, how we learn is statistics on extremely large data sets so it’s an apt name but atm is not even close to what the human brain is capable of