6 ms·

Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the thing

Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?

mtgp1000 6y ago
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.
- charliemil4 6y ago
  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.
- bsaul 6y ago
  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 ?
  - bcrosby95 6y ago
    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!
    - perl4ever 6y ago
      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.
    - burntoutfire 6y ago
      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".
    - smabie 6y ago
      >When my kid does something illegal I'm held liable This isn't true.
  - martindbp 6y ago
    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.
- an_opabinia 6y ago
  > 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.
- 908087 6y ago
  I know you might mean well, but I can't help but get the same feeling reading your post as I do every time I read a wide-eyed self-proclaimed "futurist" snake oil enthusiast going on about something along the lines of how we're all going to be sending our children to pre-school in autonomous flying vehicles within the next few years, or listening to blockchain snake oil salesmen rant about how the whole world is going to revolve around their shitcoin in the near future. I feel like I've been reading or hearing a repeat of the same ML/"AI" "my team has something huge just around the corner" pitch every week for at least a decade now, and always with the "but I can't be more specific because reasons" and "just look at how much this other thing improved over X years!" aspects tacked on. The only thing that changes each time is the person making the claim. Less time making grandiose claims on internet forums might give you more time to produce something that could at least be passed as partially living up to those claims, or at least give you the right to say "we tried" in your "amazing journey" post somewhere down the line. I hope to be proven wrong, but in my experiences the people actually achieving impressive things aren't out there loudly bragging about it before they have anything to show.
- giardini 6y ago
  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!
nextos 6y ago
I guess once the field starts mixing probabilistic, deep and logic approaches we will see a bit of progress towards AGI. This is one example of where things might be heading: https://science.sciencemag.org/content/350/6266/1332 https://science.sciencemag.org/content/350/6266/1332
- deepnotderp 6y ago
  This has been on my watchlist for a while. The problem is the inablity of these techniques to scale to larger problems. That being said, once that happens, it could be revolutionary.
  - nextos 6y ago
    DARPA threw some money into the problem, and stuff advanced from toy problems to things that are still small but often useful. See e.g. pyro.ai is already working on non-trivial datasets. Ultimately, I'm convinced it's a hard compilers / static analysis / abstract interpretation problem. That requires lots of resources and a decade or two of work.
archsurface 6y ago
"Hi AlphaGo, let's play tic tac toe". Consider me bored of it all. We're not close to anything. The emphasis is on "artificial", not "intelligence". It should be renamed Glorified Calculation. There are some neat tricks, writing your first neural net is great fun, but we're using the word intelligence with spectacular liberty. I'll keep an eye open for something inspiring, but I've seen nothing of the sort yet.
- 0xBABAD00C 6y ago
  Then you're not looking hard enough
  - foldr 6y ago
    The modesty of AI researchers is a major problem here. So many miraculous discoveries go completely unadvertised.
  - mellosouls 6y ago
    Proof of AGI progress would be a significant enough achievement (given there hasn't been any for decades) that it wouldn't require looking hard - but please share any links to it you have. Nobody else seems able to, especially the AI ("more power and money needed! we're just around the corner!") blowhards.
megaframe 6y ago
One big one is farming, think harvesting machinery. There's a number of startups trying to get that going. I think most are not at the scale to be successful the way the market themselves, but their collective learning will eventually lead to some consolidation. Why AI for that, it's vision AI to know when fruit is ripe or vegetables are ready for harvest. Then hand eye coordination to not bruise the fruit/vegetables and adjust force/sensitivity as the equipment wears down. Scaling that will put likely many workers out and transform the business of harvesting. What I think is most plausible is one large manufacturer will consolidate designs and rent the equipment to do the harvesting much like the contract labor used now, but at much lower prices than humans could do.
- gen220 6y ago
  Depending on the crop you’re talking about, we are still very very far away from this. People have been trying to design machines to pick fruit, even in toy scenarios, for decades, and we have little to show for it. I don’t think that image processing is the bottleneck here, it’s literally the mechanism as far as I understand. I’m skeptical that any mechanical equipment will ever be able to do these tasks as cheaply, quickly, and efficiently as a human in our lifetimes.
  - beckingz 6y ago
    Look at olive harvesting machines. Definitely easier than fruit, but we've made so much progress.
- perl4ever 6y ago
  If it's AI to know when fruit is ripe, etc., then bugs and birds and so on must be intelligent.
- OldHand2018 6y ago
  > What I think is most plausible is one large manufacturer will consolidate designs and rent the equipment to do the harvesting No way. Everybody wants the machines at the same time. You're going to incur the capital cost of buying/manufacturing the machines, the cost to store and maintain them for 50 weeks out of the year, and then rent them out at a profit for 2 weeks? No, the farmers will just keep hiring human labor. If you can solve this problem, you can sell a lot of machines for a lot of money for a short few years. After that, they'll be a commodity and be dominated by all the other capital-intensive manufacturers that can run a factory better than you can.

mtgp1000 6y ago

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.

charliemil4 6y ago
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.
bsaul 6y ago
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 ?
- bcrosby95 6y ago
  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!
  - perl4ever 6y ago
    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.
  - burntoutfire 6y ago
    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".
  - smabie 6y ago
    >When my kid does something illegal I'm held liable This isn't true.
- martindbp 6y ago
  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.
an_opabinia 6y ago
> 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.
908087 6y ago
I know you might mean well, but I can't help but get the same feeling reading your post as I do every time I read a wide-eyed self-proclaimed "futurist" snake oil enthusiast going on about something along the lines of how we're all going to be sending our children to pre-school in autonomous flying vehicles within the next few years, or listening to blockchain snake oil salesmen rant about how the whole world is going to revolve around their shitcoin in the near future. I feel like I've been reading or hearing a repeat of the same ML/"AI" "my team has something huge just around the corner" pitch every week for at least a decade now, and always with the "but I can't be more specific because reasons" and "just look at how much this other thing improved over X years!" aspects tacked on. The only thing that changes each time is the person making the claim. Less time making grandiose claims on internet forums might give you more time to produce something that could at least be passed as partially living up to those claims, or at least give you the right to say "we tried" in your "amazing journey" post somewhere down the line. I hope to be proven wrong, but in my experiences the people actually achieving impressive things aren't out there loudly bragging about it before they have anything to show.
giardini 6y ago
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!

nextos 6y ago

I guess once the field starts mixing probabilistic, deep and logic approaches we will see a bit of progress towards AGI. This is one example of where things might be heading: https://science.sciencemag.org/content/350/6266/1332 https://science.sciencemag.org/content/350/6266/1332

deepnotderp 6y ago
This has been on my watchlist for a while. The problem is the inablity of these techniques to scale to larger problems. That being said, once that happens, it could be revolutionary.
- nextos 6y ago
  DARPA threw some money into the problem, and stuff advanced from toy problems to things that are still small but often useful. See e.g. pyro.ai is already working on non-trivial datasets. Ultimately, I'm convinced it's a hard compilers / static analysis / abstract interpretation problem. That requires lots of resources and a decade or two of work.

archsurface 6y ago

"Hi AlphaGo, let's play tic tac toe". Consider me bored of it all. We're not close to anything. The emphasis is on "artificial", not "intelligence". It should be renamed Glorified Calculation. There are some neat tricks, writing your first neural net is great fun, but we're using the word intelligence with spectacular liberty. I'll keep an eye open for something inspiring, but I've seen nothing of the sort yet.

0xBABAD00C 6y ago
Then you're not looking hard enough
- foldr 6y ago
  The modesty of AI researchers is a major problem here. So many miraculous discoveries go completely unadvertised.
- mellosouls 6y ago
  Proof of AGI progress would be a significant enough achievement (given there hasn't been any for decades) that it wouldn't require looking hard - but please share any links to it you have. Nobody else seems able to, especially the AI ("more power and money needed! we're just around the corner!") blowhards.

megaframe 6y ago

One big one is farming, think harvesting machinery. There's a number of startups trying to get that going. I think most are not at the scale to be successful the way the market themselves, but their collective learning will eventually lead to some consolidation. Why AI for that, it's vision AI to know when fruit is ripe or vegetables are ready for harvest. Then hand eye coordination to not bruise the fruit/vegetables and adjust force/sensitivity as the equipment wears down. Scaling that will put likely many workers out and transform the business of harvesting. What I think is most plausible is one large manufacturer will consolidate designs and rent the equipment to do the harvesting much like the contract labor used now, but at much lower prices than humans could do.

gen220 6y ago
Depending on the crop you’re talking about, we are still very very far away from this. People have been trying to design machines to pick fruit, even in toy scenarios, for decades, and we have little to show for it. I don’t think that image processing is the bottleneck here, it’s literally the mechanism as far as I understand. I’m skeptical that any mechanical equipment will ever be able to do these tasks as cheaply, quickly, and efficiently as a human in our lifetimes.
- beckingz 6y ago
  Look at olive harvesting machines. Definitely easier than fruit, but we've made so much progress.
perl4ever 6y ago
If it's AI to know when fruit is ripe, etc., then bugs and birds and so on must be intelligent.
OldHand2018 6y ago
> What I think is most plausible is one large manufacturer will consolidate designs and rent the equipment to do the harvesting No way. Everybody wants the machines at the same time. You're going to incur the capital cost of buying/manufacturing the machines, the cost to store and maintain them for 50 weeks out of the year, and then rent them out at a profit for 2 weeks? No, the farmers will just keep hiring human labor. If you can solve this problem, you can sell a lot of machines for a lot of money for a short few years. After that, they'll be a commodity and be dominated by all the other capital-intensive manufacturers that can run a factory better than you can.