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I hate to be that guy, but most ML is definitely not linear algebra. The most popular and useful ML algorithms are boosted trees and random forests; they're mor

I hate to be that guy, but most ML is definitely not linear algebra. The most popular and useful ML algorithms are boosted trees and random forests; they're more or less geometric approximators (which are all nonlinear). And as ANN nerds like to say, there's plenty of nonlinearity in neural approaches. But mostly I agree with you: modeling is horse shit, worshipping models (linear and otherwise) is childish idolatry and Dijkstra was completely correct about just about everything (speaking as an APL bum).

qsort 6y ago
> I hate to be that guy, but most ML is definitely not linear algebra... Yes, I'm not disagreeing with any of that (see also my other reply), it was a misuse of terminology on my part. > But mostly I agree with you: modeling is horse shit, worshipping models... Absolutely. The way I see it is that models are like theorems: they are useful and worthy of being studied, but if you forget to validate that your hypotheses are correct, you get wrong results: garbage in, garbage out. An alarming amount of stuff these days is the equivalent of people trying to apply the pythagorean theorem to a non-rectangle triangle and then complaining the results don't match.
- sebastianconcpt 6y ago
  Good ponits you guys. I think it would be fair to say that "AI" is nothing but algebra ran by an automata. The rest is a mix of hype and nerds taking advantage to manipulate people that has this concept out of their intellectual reach. Depending on the case, this can enable evil at global scale. As always with humanity, ideology is always a problem.
  - ericd 6y ago
    This kind of argument is common in this sort of discussion, and more than a bit reductionistic. Extreme complexity often comes from very simple processes at scale. Your computer's abilities are the result of little switches flipping between two states.
    - pessimizer 6y ago
      > Your computer's abilities are the result of little switches flipping between two states. I don't think this is a reductionist view of a computer. That's exactly what it is, and with a very little imagination describes what it has the capacity to do. What computers do now wouldn't be a surprise to people in the 1940s.
      - ericd 6y ago
        It is reductionistic, because so much comes from how you arrange those switches, the current state of the switches, and the sequence in which you tell those switches to switch. And I think most in the 1940s would be very surprised at what computers can do now. It only takes little imagination because we’ve already seen it in action. Similarly, you can say that deep learning is just a series of matrix multiplications with nonlinearities applied, which is true, but it certainly wasn’t obvious to most that when you scale that way up, that that would lead to computers being able to interpret images, sound, and text. For example, my AI course professor described neural nets as something of interest mostly for historical reasons, something that was tried, but was an evolutionary dead end. And he was no slouch. The hype has gotten ahead of its current abilities, but I simultaneously think that it will be one of the most impactful inventions we’ve ever created.
        mmcdermott 6y ago
        > The hype has gotten ahead of its current abilities, but I simultaneously think that it will be one of the most impactful inventions we’ve ever created. This statement is one of the things I find most fascinating about the current state of AI. Do I think deep learning is incredibly useful? Absolutely and I look forward to seeing more awesome stuff done with it in the future. Do I think the singularity or "true" AI will be based on deep learning? Not really. I strongly suspect anything capable of reaching scifi levels of intelligence (which a lot of hype says or strongly implies is imminent) will have to be a difference of kind, not of degree. I could be wrong, of course, but it will be interesting to wait and see.
        6y ago
  - iratewizard 6y ago
    It manipulates people who can't grasp it. It sets up implementors for failure when the hype settles. To an extent, it's responsible for the peaks and valleys you inevitably see in funding.
    - sebastianconcpt 6y ago
      Yes but what's worrisome is that AI models are ideological otimizations of cognitive drones. That can be weaponized in a psy-war.
deleted 6y ago
[deleted]
Frost1x 6y ago
I wouldn't say modeling is horse shit. It's really marketing surrounding modeling that's horse shit. Modeling is actually very very useful but is too often oversold, which drives me bananas. It's especially bad in the world of data driven or data intensive models because that's trendy and sells software, hardware, and services. The problem is that various forms of modeling are complex and therefor expensive and convincing people to invest in furthering the domain results in these poor public misrepresentations and overselling. Modeling gives us ways to abstract reality and attempt to predict the future or find patterns we might otherwise miss. Sometimes it works, a lot of times it doesn't, but when it does it's great. Everytime--its expensive and people need to feel they didnt waste their money.

qsort 6y ago

> I hate to be that guy, but most ML is definitely not linear algebra... Yes, I'm not disagreeing with any of that (see also my other reply), it was a misuse of terminology on my part. > But mostly I agree with you: modeling is horse shit, worshipping models... Absolutely. The way I see it is that models are like theorems: they are useful and worthy of being studied, but if you forget to validate that your hypotheses are correct, you get wrong results: garbage in, garbage out. An alarming amount of stuff these days is the equivalent of people trying to apply the pythagorean theorem to a non-rectangle triangle and then complaining the results don't match.

sebastianconcpt 6y ago
Good ponits you guys. I think it would be fair to say that "AI" is nothing but algebra ran by an automata. The rest is a mix of hype and nerds taking advantage to manipulate people that has this concept out of their intellectual reach. Depending on the case, this can enable evil at global scale. As always with humanity, ideology is always a problem.
- ericd 6y ago
  This kind of argument is common in this sort of discussion, and more than a bit reductionistic. Extreme complexity often comes from very simple processes at scale. Your computer's abilities are the result of little switches flipping between two states.
  - pessimizer 6y ago
    > Your computer's abilities are the result of little switches flipping between two states. I don't think this is a reductionist view of a computer. That's exactly what it is, and with a very little imagination describes what it has the capacity to do. What computers do now wouldn't be a surprise to people in the 1940s.
    - ericd 6y ago
      It is reductionistic, because so much comes from how you arrange those switches, the current state of the switches, and the sequence in which you tell those switches to switch. And I think most in the 1940s would be very surprised at what computers can do now. It only takes little imagination because we’ve already seen it in action. Similarly, you can say that deep learning is just a series of matrix multiplications with nonlinearities applied, which is true, but it certainly wasn’t obvious to most that when you scale that way up, that that would lead to computers being able to interpret images, sound, and text. For example, my AI course professor described neural nets as something of interest mostly for historical reasons, something that was tried, but was an evolutionary dead end. And he was no slouch. The hype has gotten ahead of its current abilities, but I simultaneously think that it will be one of the most impactful inventions we’ve ever created.
      - mmcdermott 6y ago
        > The hype has gotten ahead of its current abilities, but I simultaneously think that it will be one of the most impactful inventions we’ve ever created. This statement is one of the things I find most fascinating about the current state of AI. Do I think deep learning is incredibly useful? Absolutely and I look forward to seeing more awesome stuff done with it in the future. Do I think the singularity or "true" AI will be based on deep learning? Not really. I strongly suspect anything capable of reaching scifi levels of intelligence (which a lot of hype says or strongly implies is imminent) will have to be a difference of kind, not of degree. I could be wrong, of course, but it will be interesting to wait and see.
        6y ago
- iratewizard 6y ago
  It manipulates people who can't grasp it. It sets up implementors for failure when the hype settles. To an extent, it's responsible for the peaks and valleys you inevitably see in funding.
  - sebastianconcpt 6y ago
    Yes but what's worrisome is that AI models are ideological otimizations of cognitive drones. That can be weaponized in a psy-war.

deleted 6y ago

[deleted]

Frost1x 6y ago

I wouldn't say modeling is horse shit. It's really marketing surrounding modeling that's horse shit. Modeling is actually very very useful but is too often oversold, which drives me bananas. It's especially bad in the world of data driven or data intensive models because that's trendy and sells software, hardware, and services. The problem is that various forms of modeling are complex and therefor expensive and convincing people to invest in furthering the domain results in these poor public misrepresentations and overselling. Modeling gives us ways to abstract reality and attempt to predict the future or find patterns we might otherwise miss. Sometimes it works, a lot of times it doesn't, but when it does it's great. Everytime--its expensive and people need to feel they didnt waste their money.