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Comment by mtgp1000 | Hacker News Reader

Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment.

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.

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> And for perspective, AlphaGo was what...4 years ago? Just look at the growth of compute available cheaply in the cloud since then

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.

mtgp1000 says>"mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment."<

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!

since you’re working on the field : i’m still under the impression that modern ML (neural network in particular) are not producing any science, nor are they producing any level of understanding of the phenomenon it’s trying to model. And that as such, we can’t provide a reliable estimate of the limitations of the produced model, other than by feeding it tons of inputs and measure the result.

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 ?

Some of this would be considered a feature by companies. It's more defensible to have unknowable AI deciding to do illegal things than programmers hard coding illegal things. Which really boggles my mind. When my kid does something illegal I'm held liable. When an ML algorithm programmed by a team of people does, nothing we can do about that!

I'm not sure if the courts will see it this way. Anyway, in some fields some stuff (e.g. credit scoring in banka in my country) is regulated and it's the humans who have to make the final decisions - algoriths can only provide an input. In practice, the input is "approve/deny", but it's still a human who is making a "decision" based on this "input".

I don't see this as really a unique situation or one where people are exempt from liability. If someone starts a fire, they are held liable; that's analogous.

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.

>When my kid does something illegal I'm held liable

This isn't true.

What makes you think Newtonian physics is in any form more real than computation? If Wolfram is correct, physics is inherently a computation in the form of cellular automata.

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.

Your training set must be something unique to make those claims - very intrigued to know if you chose another closed system environment (I.e. any structured game with predefined scoring), or an open system with carefully selected labeling with multiple “scores.”

Also complete novice so be merciful if I’m off base.

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