How would we know the difference between a problem that was interesting to solve but was hard versus a problem that just needed Google's money to solve?
Besides, since when was cloud compute was getting cheaper? I'm pretty sure, considering how increasingly profitable it is despite the "discounts," cloud compute has gotten more expensive, in the sense of increasing margins for the seller.
I guess we'll all find out what OpenAI does with its Azure credits.
How seriously can we take that phrase? Please mathematically model the Covid-19 pandemic's next 6 months. The epidemiology community dropped the ball and you can hardly do worse. Take the "Covid Challenge!"
mtgp1000 says>"These next few decades are going going to see a phenomenal acceleration that's already starting ...assuming we don't hit some unforseen[sic] limit or block."< -
Nobody can predict next year's economy and certainly nothing beyond 5 years! You're predicting for the "next few decades"?
mtgp1000 says*>"But even with what we've discovered now, we have more than enough in the way of new tools to refine across industry and society for decades."
Spoken like a true non-English marketeer!
It would be like trying to understand gravity by throwing thousands of rocks, measuring the motions, and build an approximation on a huge matrix, without ever being able to get the « real » netwonian formula.
Am i correct ?
On the other hand, the difference between a team of people and a particular person with mens rea, that's not unique to AI either.
This isn't true.
Intelligence might be so messy that the only way to get to it is to grow it organically, which to me seems to be happening in research. People are trying a bunch of stuff based on intuitions for what may work, and post hoc adding some theoretical justification for why it's working. Human intelligence didn't emerge from some grand theory of intelligence either.
Also complete novice so be merciful if I’m off base.
Same for ML powered solutions to data management - I don't want to say enough to identify anything.
My team has been working on a rudimentary humanlike reasoning engine based loosely on what AlphaGo proved: that machines can learn heuristics identical, equal to, or better than those of humans.
And for perspective, AlphaGo was what...4 years ago? Just look at the growth of compute available cheaply in the cloud since then! These next few decades are going going to see a phenomenal acceleration that's already starting...assuming we don't hit some unforseen limit or block. But even with what we've discovered now, we have more than enough in the way of new tools to refine across industry and society for decades.