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dasnihil t1_j7kvu8z wrote

we just need a team of a few math wizards to come up with better algorithms for training, matrix multiplications and whatever np problems are there in meta learning.. oh wait! we can just throw all our data into current AI and they will come up with the algorithms!!

this is how AGI will be achieved, there is no other way because humans don't get too many emmy noethers to come up with some new ways to do math. humans are busy with their short life and various indulgence.


SoylentRox t1_j7lb053 wrote

Pretty much. It's also that those math wizards may be smarter than current AI but they often duplicate work. And it's an iterative process - AI starts with what we know, tries some things very rapidly. A few hours later it has the results and tries some more things based on that and so on.

Those math wizards need to publish and then read what others published. Even with rapid publishing like Deepmind does to a blog - they do this because academic publications take too long - it's a few months between cycles.


dasnihil t1_j7kvz2l wrote

and we need this to cut that cost from $100bn to potato because biology runs on potato hardware, not a $100bn super computer. only if these pseudonerds realized it in the AI industry, we'd be expediting our search for more optimally converging networks.


SoylentRox t1_j7la9hx wrote

We got 100b to spare on this. More than that. Might as well use it. Once we find working AGI we can work on power efficiency.