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We are already there. Machine learning is the flavor of A.I. that keeps business barriers of entry high. If we had invested in symbolic A.I., things would be di

We are already there. Machine learning is the flavor of A.I. that keeps business barriers of entry high. If we had invested in symbolic A.I., things would be different. A similar thing happens with programming language flavors. PHP lowers barriers of entry so it is discredited by the incumbents.

deleted 5y ago
[deleted]
lostdog 5y ago
The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. At my job, dropping the computational load of our ML models is heavily invested in, and every success is celebrated. Everybody wants it to be easier and cheaper to train high quality models, but some things are still intrinsically hard.
- CodeGlitch 5y ago
  > The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work?
  - adgjlsfhk1 5y ago
    they didn't. that was just alpha beta search with some custom hardware to speed it up. also at this point, both of the strongest chess ai (stockfish and lc0) are using neutral networks and are roughly 1000 elo above where deep blue was (and most of that is from software, not hardware)
    - shmageggy 5y ago
      > just alpha beta search I will cling to these goal posts every time. Search was and still is AI, unless you think Russell and Norvig should have named the field's foundational textbook something other than "Artificial Intelligence: A Modern Approach"
      - adgjlsfhk1 5y ago
        I was more arguing with symbolic than AI.
  - PeterisP 5y ago
    1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; 2. For your example of chess, for some time now ML engines are pretty much untouchable by engines based on pre-ML methods.
    - CodeGlitch 5y ago
      Yes I agree with all your points - I was however responding to the point being made that symbolic AI "wasn't useful"...which in the past it was. Perhaps in the future some new method or breakthrough will mean it becomes useful once again?
      - panabee 5y ago
        this is a great point. much like deep learning was invented decades ago but didn't become feasible until technology caught up, could the same be true for symbolic AI? i.e., is the ceiling for symbolic AI technical and transient or fundamental and permanent?
        PeterisP 5y ago
        My feeling is that even in our own thinking symbols are used mostly to communicate our (inherently non-symbolic) thoughts to others or record them; i.e. they are a solution to a bandwidth-limited transfer of information while the actual thinking process happens with concepts that have more similarity to collections of vague parameters and associations which can be compressed to symbols only imperfectly with losses. From that perspective, I don't see how symbolic AI would be competitive but there would be a role for symbolic AI in designing systems that can be comprehensible for humans, but perhaps just as a distillation/compression output from a non-symbolic system. I.e. have a strong "black box" ML system that learns to solve a task, and then have it construct a symbolic system that solves that task worse, but in an explainable way.
    - YeGoblynQueenne 5y ago
      >> 1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; Perception tasks were traditionally attempted with statistical machine learning approaches rather than symbolic AI, for example the Perceptron was a very early neural network that was used in machine vision, created by Frank Rosenblatt in 1958. A lot of that research was carried out under the rubrik of "pattern recognition" rather than machine learning. In any case, no, "we" did not try "to solve [those problms] for decades with symbolic AI". Symbolic AI has traditionally focused on reasoning, which is generally considered to be on some kind of separate level to perception. As to chess engines, they're still symbolic-statistical hybrids. E.g. the Alpha-x family combines Monte Carlo Tree Search with neural nets that learn an evaluation function etc.
  - lostdog 5y ago
    > IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work? Ok, I should be clearer. ML approaches are way way better than symbolic approaches. Given almost any problem, it is much much easier to make an ML approach work than any symbolic approach. Yes, chess was first solved symbolically, but it's since been solved by ML better and more easily, to the point that stockfish now incorporates neural nets [1]. ML has also given extremely high levels of performance on Go, Starcraft, DoTA, and on protein folding, image recognition, text processing, speech recognition, and pretty much everything else. I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. [1] https://stockfishchess.org/blog/2020/stockfish-12/ https://stockfishchess.org/blog/2020/stockfish-12/
    - CodeGlitch 5y ago
      Thanks for clearing that up, I do agree that ML-based AI has surpassed symbolic approaches in every field.
    - goodside 5y ago
      “I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art.” Lossless file compression. As far as I know none of the algorithms in widespread use are neural-based, despite the fact that compression is clearly a rich statistical modeling problem, at least on par with GPT-3-style language understanding in difficulty. There are published attempts to solve the problem with neural networks, but they simply don’t work well enough to date. Modern solutions also still use old-fashioned AI ingredients like compiled dictionaries of common natural-language words — any other domain where nat-lang dictionaries are useful has been conquered by neural solutions, e.g. spelling and grammar checkers.
      - _game_of_life 5y ago
        I'm far from an expert in this subject but doesn't this ranking of large text compression algorithms with NNCP coming first suggest that neural-nets are pretty great at compression? http://mattmahoney.net/dc/text.html http://mattmahoney.net/dc/text.html https://bellard.org/nncp/ https://bellard.org/nncp/ I don't see examples of high performing symbolic AI based compression algorithms anywhere, but again I am very ignorant, do you have examples?
- iamcurious 5y ago
  >The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. There was a point when it was the other way around, this is not static but the result of resources being poured. The data heavy, computational heavy, black box style of ML gives power to large business over small business. So it's seen as a safer bet than symbolic A.I. This in turn makes it work better, which makes it an even safer bet. Notice that startups dream of being big business so they still pick ML. Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles.
  - lostdog 5y ago
    > Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles. Classical AI has failed in robotics. There's practically no field where it does worse than in robotics. It's getting cut out of every part of every system one piece at a time, and being replaced by ML methods that really work. Even Kalman Filters aren't safe.
    - iamcurious 5y ago
      It's been a few years since I investigated, but Boston dynamics was using mostly old non ML methods. See [1] IIRC and Waymo vehicles used ML for some of its perception but also depended heavily on the lidar and a rules based approach. Sadly I can't find a link at the moment. What examples are you thinking of? [1] https://www.quora.com/How-does-Boston-Dynamics-use-AI-machine-learning https://www.quora.com/How-does-Boston-Dynamics-use-AI-machin...
      - lostdog 5y ago
        Here's Tesla's AI day presentation on how they are going to use AI in their planning system because traditional methods aren't scalable enough: https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s (1:20:15). You can see hints of this in presentations from the other self-driving companies. Boston Dynamics was definitely an exception for a long time. Certainly the perception systems that they are adding use ML, but you are right that they use expressly use classical methods for their control system.
j45 5y ago
Your point about incumbents not wanting it to be easier to create beginners in a language or technology is very understated. Excluding participation in having the time and resources available to overcome the initial inertia required to become productive is a form of opportunity and earning segregation. Despite having a background in your tech, there is little more if satisfying than people experiencing putting tech to work for them, rather than the other way around or being dependent on others.
DonHopkins 5y ago
PHP wasn't discredited by the incumbents. It was discredited by its creator. "I'm not a real programmer. I throw together things until it works then I move on. The real programmers will say Yeah it works but you're leaking memory everywhere. Perhaps we should fix that. I'll just restart Apache every 10 requests." -Rasmus Lerdorf "I was really, really bad at writing parsers. I still am really bad at writing parsers." -Rasmus Lerdorf "We have things like protected properties. We have abstract methods. We have all this stuff that your computer science teacher told you you should be using. I don't care about this crap at all." -Rasmus Lerdorf
- iamcurious 5y ago
  To most programmers that doesn't discredit PHP at all. He cares about a working product, much like 90% of programmers, who don't have the privilige to worry about theory. They just need an ecommerce, or blog or whatever, running asap. To use a pg's analogy, they are there to paint not to worry about painting chemistry. The incumbents do discredit PHP though. For instance, facebook was built on PHP, and still runs on it. They used the language of personal home pages to give every person on the planet a personal home page. Nevertheless, once they suceeded they forked PHP with a new name and isolated devs culturally.
  - DonHopkins 5y ago
    PHP is also discredited by its apologists. If you're just there to paint, but painting with mashed potatoes, you SHOULD have been more worried about your paint chemistry. "Using these toolkits is like trying to make a bookshelf out of mashed potatoes." -JWZ
  - thephyber 5y ago
    It’s not about practice versus theory. It’s about the actual costs of writing fast PHP versus the cost of writing good secure code (which is also possible in variants of PHP). Terrible code in PHP is possible, therefore likely. I say this having spent over a decade writing it and half of that time fixing OWASP bugs created in it. The incumbents hate it because their vendors use it and everyone is worse off for having their vendors use it. And Facebook did use PHP in the first few years, but they quickly started compiling it (HipHop) and later converted their code based to use a different strongly-typed language based on PHP (Hack). They stopped using PHP because it is a starter language.
fragmede 5y ago
And Facebook's success would like a word with them. Even if the company magically disappeared tomorrow, the millionaires who've already cashed out built atop some shitty PHP app that a college kid wrote don't care one whit over that discreditation.

deleted 5y ago

[deleted]

lostdog 5y ago

The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. At my job, dropping the computational load of our ML models is heavily invested in, and every success is celebrated. Everybody wants it to be easier and cheaper to train high quality models, but some things are still intrinsically hard.

CodeGlitch 5y ago
> The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work?
- adgjlsfhk1 5y ago
  they didn't. that was just alpha beta search with some custom hardware to speed it up. also at this point, both of the strongest chess ai (stockfish and lc0) are using neutral networks and are roughly 1000 elo above where deep blue was (and most of that is from software, not hardware)
  - shmageggy 5y ago
    > just alpha beta search I will cling to these goal posts every time. Search was and still is AI, unless you think Russell and Norvig should have named the field's foundational textbook something other than "Artificial Intelligence: A Modern Approach"
    - adgjlsfhk1 5y ago
      I was more arguing with symbolic than AI.
- PeterisP 5y ago
  1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; 2. For your example of chess, for some time now ML engines are pretty much untouchable by engines based on pre-ML methods.
  - CodeGlitch 5y ago
    Yes I agree with all your points - I was however responding to the point being made that symbolic AI "wasn't useful"...which in the past it was. Perhaps in the future some new method or breakthrough will mean it becomes useful once again?
    - panabee 5y ago
      this is a great point. much like deep learning was invented decades ago but didn't become feasible until technology caught up, could the same be true for symbolic AI? i.e., is the ceiling for symbolic AI technical and transient or fundamental and permanent?
      - PeterisP 5y ago
        My feeling is that even in our own thinking symbols are used mostly to communicate our (inherently non-symbolic) thoughts to others or record them; i.e. they are a solution to a bandwidth-limited transfer of information while the actual thinking process happens with concepts that have more similarity to collections of vague parameters and associations which can be compressed to symbols only imperfectly with losses. From that perspective, I don't see how symbolic AI would be competitive but there would be a role for symbolic AI in designing systems that can be comprehensible for humans, but perhaps just as a distillation/compression output from a non-symbolic system. I.e. have a strong "black box" ML system that learns to solve a task, and then have it construct a symbolic system that solves that task worse, but in an explainable way.
  - YeGoblynQueenne 5y ago
    >> 1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; Perception tasks were traditionally attempted with statistical machine learning approaches rather than symbolic AI, for example the Perceptron was a very early neural network that was used in machine vision, created by Frank Rosenblatt in 1958. A lot of that research was carried out under the rubrik of "pattern recognition" rather than machine learning. In any case, no, "we" did not try "to solve [those problms] for decades with symbolic AI". Symbolic AI has traditionally focused on reasoning, which is generally considered to be on some kind of separate level to perception. As to chess engines, they're still symbolic-statistical hybrids. E.g. the Alpha-x family combines Monte Carlo Tree Search with neural nets that learn an evaluation function etc.
- lostdog 5y ago
  > IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work? Ok, I should be clearer. ML approaches are way way better than symbolic approaches. Given almost any problem, it is much much easier to make an ML approach work than any symbolic approach. Yes, chess was first solved symbolically, but it's since been solved by ML better and more easily, to the point that stockfish now incorporates neural nets [1]. ML has also given extremely high levels of performance on Go, Starcraft, DoTA, and on protein folding, image recognition, text processing, speech recognition, and pretty much everything else. I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. [1] https://stockfishchess.org/blog/2020/stockfish-12/ https://stockfishchess.org/blog/2020/stockfish-12/
  - CodeGlitch 5y ago
    Thanks for clearing that up, I do agree that ML-based AI has surpassed symbolic approaches in every field.
  - goodside 5y ago
    “I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art.” Lossless file compression. As far as I know none of the algorithms in widespread use are neural-based, despite the fact that compression is clearly a rich statistical modeling problem, at least on par with GPT-3-style language understanding in difficulty. There are published attempts to solve the problem with neural networks, but they simply don’t work well enough to date. Modern solutions also still use old-fashioned AI ingredients like compiled dictionaries of common natural-language words — any other domain where nat-lang dictionaries are useful has been conquered by neural solutions, e.g. spelling and grammar checkers.
    - _game_of_life 5y ago
      I'm far from an expert in this subject but doesn't this ranking of large text compression algorithms with NNCP coming first suggest that neural-nets are pretty great at compression? http://mattmahoney.net/dc/text.html http://mattmahoney.net/dc/text.html https://bellard.org/nncp/ https://bellard.org/nncp/ I don't see examples of high performing symbolic AI based compression algorithms anywhere, but again I am very ignorant, do you have examples?
iamcurious 5y ago
>The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. There was a point when it was the other way around, this is not static but the result of resources being poured. The data heavy, computational heavy, black box style of ML gives power to large business over small business. So it's seen as a safer bet than symbolic A.I. This in turn makes it work better, which makes it an even safer bet. Notice that startups dream of being big business so they still pick ML. Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles.
- lostdog 5y ago
  > Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles. Classical AI has failed in robotics. There's practically no field where it does worse than in robotics. It's getting cut out of every part of every system one piece at a time, and being replaced by ML methods that really work. Even Kalman Filters aren't safe.
  - iamcurious 5y ago
    It's been a few years since I investigated, but Boston dynamics was using mostly old non ML methods. See [1] IIRC and Waymo vehicles used ML for some of its perception but also depended heavily on the lidar and a rules based approach. Sadly I can't find a link at the moment. What examples are you thinking of? [1] https://www.quora.com/How-does-Boston-Dynamics-use-AI-machine-learning https://www.quora.com/How-does-Boston-Dynamics-use-AI-machin...
    - lostdog 5y ago
      Here's Tesla's AI day presentation on how they are going to use AI in their planning system because traditional methods aren't scalable enough: https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s (1:20:15). You can see hints of this in presentations from the other self-driving companies. Boston Dynamics was definitely an exception for a long time. Certainly the perception systems that they are adding use ML, but you are right that they use expressly use classical methods for their control system.

j45 5y ago

Your point about incumbents not wanting it to be easier to create beginners in a language or technology is very understated. Excluding participation in having the time and resources available to overcome the initial inertia required to become productive is a form of opportunity and earning segregation. Despite having a background in your tech, there is little more if satisfying than people experiencing putting tech to work for them, rather than the other way around or being dependent on others.

DonHopkins 5y ago

PHP wasn't discredited by the incumbents. It was discredited by its creator. "I'm not a real programmer. I throw together things until it works then I move on. The real programmers will say Yeah it works but you're leaking memory everywhere. Perhaps we should fix that. I'll just restart Apache every 10 requests." -Rasmus Lerdorf "I was really, really bad at writing parsers. I still am really bad at writing parsers." -Rasmus Lerdorf "We have things like protected properties. We have abstract methods. We have all this stuff that your computer science teacher told you you should be using. I don't care about this crap at all." -Rasmus Lerdorf

iamcurious 5y ago
To most programmers that doesn't discredit PHP at all. He cares about a working product, much like 90% of programmers, who don't have the privilige to worry about theory. They just need an ecommerce, or blog or whatever, running asap. To use a pg's analogy, they are there to paint not to worry about painting chemistry. The incumbents do discredit PHP though. For instance, facebook was built on PHP, and still runs on it. They used the language of personal home pages to give every person on the planet a personal home page. Nevertheless, once they suceeded they forked PHP with a new name and isolated devs culturally.
- DonHopkins 5y ago
  PHP is also discredited by its apologists. If you're just there to paint, but painting with mashed potatoes, you SHOULD have been more worried about your paint chemistry. "Using these toolkits is like trying to make a bookshelf out of mashed potatoes." -JWZ
- thephyber 5y ago
  It’s not about practice versus theory. It’s about the actual costs of writing fast PHP versus the cost of writing good secure code (which is also possible in variants of PHP). Terrible code in PHP is possible, therefore likely. I say this having spent over a decade writing it and half of that time fixing OWASP bugs created in it. The incumbents hate it because their vendors use it and everyone is worse off for having their vendors use it. And Facebook did use PHP in the first few years, but they quickly started compiling it (HipHop) and later converted their code based to use a different strongly-typed language based on PHP (Hack). They stopped using PHP because it is a starter language.

fragmede 5y ago

And Facebook's success would like a word with them. Even if the company magically disappeared tomorrow, the millionaires who've already cashed out built atop some shitty PHP app that a college kid wrote don't care one whit over that discreditation.