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@grimalkina @mavnn Huh. Well, OK! Practical data beats theoretical concerns, so if it's helpful I shall grumble more quietly. (What, me, not grumble? Hah, as if :)
I suspect this is another facet of "everything everywhere is a mess, nothing is ever perfect including the definition of 'perfect', so you do what you can with what you have at hand."
Life. Just a never ending series of compromises, but beats the alternative.
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Granted, there's a lot of stupid in the data gathering (why this moron thinks that monoscopic video is a good way to monitor what's going on in a three dimensional world is... a subject for another rant) but even still. *I* learned to drive more reliably than these systems did and I think I clocked maybe 500 miles total in practice driving. Probably less, tbh, to be well past the point that these current systems are right now.
Same's true for the LLMs. Seventy bajillion parameters from every written word they could scrape up, with millions of hours of human tagging assistance and trillions of CPU hours, and they're still not quite there. Most humans get to this point of literacy with a few hundred books (at most) and maybe a couple thousand hours of actual reading time.
So yeah. Machine learning has done some great things, but it's still really pathetically inefficient.
@wordshaper we actually just recorded a podcast about learning and one of the points I made is a classic psych point, that humans are incredible at learning from a relatively small number of examples. It is truly one of our remarkable abilities
@wordshaper culture is fascinating not just in terms of understanding the concrete realities around us but because you can see it as a vehicle for distributing an extraordinarily scalable set of structural patterns from which people extrapolate to many unimagined novel behaviors and situations (eg, cog scientist Cristine Legare gives the example, culture is how you know how to file into a room you've never seen, with people you've never met, and successfully take your seat in an auditorium)
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