7 ms·

Are we heading to the (distant) future where to make progress in any field you have to spend big $$$ to train a model?

YetAnotherNick 5y ago
Training model is getting cheaper. GPT-3 is one of the very few countable examples where it is so expensive. In the end it all depends on the size of data you have that you could scale up the model without overfitting. And the internet text data is one of the only data size is this big.
jowday 5y ago
That’s not even distant - most of the self-supervised vision and language models at the bleeding edge of the field require huge compute budgets to train.
iamcurious 5y ago
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?
  - 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.
minimaxir 5y ago
Fortunately, costs for training superlarge models are coming down rapidly thanks to TPUs (which was the approach used to train GPT-J 6B) and DeepSpeed improvements.
- Nextgrid 5y ago
  Are there any TPUs that can be purchased off-the-shelf and then owned, like you can do with a CPU or GPU? Or are you just limited to paying rent to cloud providers and ultimately being at their mercy when it comes to pricing, ToS, etc?
  - 6gvONxR4sf7o 5y ago
    No, but you probably aren't going to buy an A100 either, so it's a moot point.
    - fragmede 5y ago
      An A100 looks to be about $12k or so. A bit out of reach for individuals, but not so bad as a business expense, but maybe you can use it to mine Bitcoin when you're not using it to train models to help pay for itself or something.
    - gjs278 5y ago
      why not? people are spending 6k on cloud costs to train models in this thread. it pays for itself after two models.
  - timschmidt 5y ago
    https://aiyprojects.withgoogle.com/edge-tpu/ https://aiyprojects.withgoogle.com/edge-tpu/
    - ericye16 5y ago
      I don't think these are good for training though, unfortunately.
slewis 5y ago
For most purposes you don’t need to train from scratch. Instead you fine-tune an existing model, on smaller amounts of data and for a fraction of the time. This is akin to teaching an adult human about a specific domain. Better to just do that than make a whole new human from scratch!

Training model is getting cheaper. GPT-3 is one of the very few countable examples where it is so expensive. In the end it all depends on the size of data you have that you could scale up the model without overfitting. And the internet text data is one of the only data size is this big.

jowday 5y ago

That’s not even distant - most of the self-supervised vision and language models at the bleeding edge of the field require huge compute budgets to train.

iamcurious 5y ago

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?
- 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.

minimaxir 5y ago

Fortunately, costs for training superlarge models are coming down rapidly thanks to TPUs (which was the approach used to train GPT-J 6B) and DeepSpeed improvements.

Nextgrid 5y ago
Are there any TPUs that can be purchased off-the-shelf and then owned, like you can do with a CPU or GPU? Or are you just limited to paying rent to cloud providers and ultimately being at their mercy when it comes to pricing, ToS, etc?
- 6gvONxR4sf7o 5y ago
  No, but you probably aren't going to buy an A100 either, so it's a moot point.
  - fragmede 5y ago
    An A100 looks to be about $12k or so. A bit out of reach for individuals, but not so bad as a business expense, but maybe you can use it to mine Bitcoin when you're not using it to train models to help pay for itself or something.
  - gjs278 5y ago
    why not? people are spending 6k on cloud costs to train models in this thread. it pays for itself after two models.
- timschmidt 5y ago
  https://aiyprojects.withgoogle.com/edge-tpu/ https://aiyprojects.withgoogle.com/edge-tpu/
  - ericye16 5y ago
    I don't think these are good for training though, unfortunately.

slewis 5y ago

For most purposes you don’t need to train from scratch. Instead you fine-tune an existing model, on smaller amounts of data and for a fraction of the time. This is akin to teaching an adult human about a specific domain. Better to just do that than make a whole new human from scratch!