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Thank you for saying this. I think once things enter the culture war, normal and sensible people will get mugged by stupid, unexamined ideas so long as they’re

Thank you for saying this. I think once things enter the culture war, normal and sensible people will get mugged by stupid, unexamined ideas so long as they’re on the right side of the culture war. So many conversations boil down to AI=BAD or CRYPTO=BAD, which is a shame not because the opposition is wrong - that’s your opinion who am I to disagree - but because once people have their conclusion they just stop thinking and once you stop thinking you’ve got no way of noticing if your thoughts on a matter are totally stupid.

pierat 3y ago
One thing I will say, is that AI = means you don't understand how to quantify the problem AI is a great hack to quickly and unknowingly solve classification problems and other similar things. But you trade massive datasets for explainability. And that's a pretty bitter pill to swallow.
- AbrahamParangi 3y ago
  That it works without you understanding it is a failure of your understanding not of the approach.
  - pierat 3y ago
    Oh please. Dumb word games isn't going to make the "is this a bird" AI approach explainable. https://xkcd.com/1425/ https://xkcd.com/1425/ Even the "simple" AI's with only a few TBs of data for training still have low/no understanding what's going on to identify the features and how they relate. Sure, we can tell this neuron identifies this squiggle, but what does it mean? No fucking clue. It also shows us collectively that most of these "hard" problems we have no clue how to understand them. And although AI allows us to "do it", it's a bad hack for actually understanding, optimizing, and doing. > That it works without you understanding it is a failure of your understanding not of the approach. Cool. Then point me towards the tools to explain how weights in a moderate-ly sized NN are created with training data, and associated with each other and how they find the features in question. Oh yeah. Those tools don't exist.
    - AbrahamParangi 3y ago
      You seem to find the explanation unsatisfying and are getting mad at the tools. The program that defines how you recognize a bird is defined in the connectivity of the neurons in your eye and your brain. The circuit is the program. It's not at all clear that there is or even should be a deeper explanation than "a function with sufficiently many parameters can approximate any other function with only a small change to those parameters, and thus allows tractable solving" - whether by gradient descent or some hokey approximation our neurons are probably doing. We have even less of an idea how our own system of "understanding" works than we do of neural networks "understanding". If you're going to be mad at tools that don't provide satisfying explanations for how they work, then look no further than your own brain. Are you going to give up on thinking, just because when you look at it closely you realize you have no idea how on earth you're even doing it?
    - blueboo 3y ago
      There’s no helping the incurious, but there are indeed reams of explorations unto weights and their explanations. One of my personal favourites is Zeiler and Fergus https://arxiv.org/abs/1311.2901 https://arxiv.org/abs/1311.2901 Tom Zahavy’s “Greying the black box” on deep RL comes to mind too https://arxiv.org/abs/1602.02658 https://arxiv.org/abs/1602.02658 OpenAI has their Microscope tool for investigating individual neurons https://openai.com/research/microscope https://openai.com/research/microscope; more recently OpenAI shared research and tooling leveraging a powerful language model to explain a smaller model’s neurons. https://openai.com/research/language-models-can-explain-neurons-in-language-models https://openai.com/research/language-models-can-explain-neur... For a true toy box exploration playground.tensorflow.org is worth a play These may or may not satisfy your curiosities — your perfect tool may not exist…yet. I would argue that the sampling of extant work above are indicative that it and more are possible.
- sorenjan 3y ago
  Sounds like you're mixing up AI and deep learning. A* is a type of AI, as is scheduling, sudoku solvers, etc.
  - dymk 3y ago
    No, GP is correct, "AI" as a buzzword means very little at this point.
    - sorenjan 3y ago
      That's because AI as a buzzword means deep learning now, but there's plenty of other AI that doesn't use training data and requires good knowledge of the problem you want to solve. If you look at one of the most widely used text books machine learning is one of seven chapters, and not all of that is deep or supervised learning. https://aima.cs.berkeley.edu/ https://aima.cs.berkeley.edu/
    - thfuran 3y ago
      But as a long-standing (as far as computing is concerned at any rate) academic field of study, it has meaning, even if the precise boundaries aren't perfectly well defined.
radarsat1 3y ago
My point is a bit different though. It's not about culture wars or being anti or pro AI. It's about how silly it is to call one optimization function AI and another "not AI" and then make a whole curfuffel about bucking trends, when what you've done is actually something very similar. (Maybe very interesting in its own right and a good solution though, with its own desirable properties.. again, I'm not commenting on the paper itself, just this one off-hand comment in the blog.) In my view calling a solution "neural" and "AI" because you are optimizing a stack of relus, and then turning around and solving the same (or almost the same) problem by optimizing a bunch of Gaussian model parameters on the same data, and then saying that is "not AI" and "at least it's not neural!" is just a very specious and unnecessary way to delineate things imho. They're just two solutions to the same problem, both data driven, both generating "models".. they have differences, just not differences that I think warranted such a comment in the blog, and moreover I thought it weird to attribute some kind of normative evaluation based on how you categorize the solution.

pierat 3y ago

One thing I will say, is that AI = means you don't understand how to quantify the problem AI is a great hack to quickly and unknowingly solve classification problems and other similar things. But you trade massive datasets for explainability. And that's a pretty bitter pill to swallow.

AbrahamParangi 3y ago
That it works without you understanding it is a failure of your understanding not of the approach.
- pierat 3y ago
  Oh please. Dumb word games isn't going to make the "is this a bird" AI approach explainable. https://xkcd.com/1425/ https://xkcd.com/1425/ Even the "simple" AI's with only a few TBs of data for training still have low/no understanding what's going on to identify the features and how they relate. Sure, we can tell this neuron identifies this squiggle, but what does it mean? No fucking clue. It also shows us collectively that most of these "hard" problems we have no clue how to understand them. And although AI allows us to "do it", it's a bad hack for actually understanding, optimizing, and doing. > That it works without you understanding it is a failure of your understanding not of the approach. Cool. Then point me towards the tools to explain how weights in a moderate-ly sized NN are created with training data, and associated with each other and how they find the features in question. Oh yeah. Those tools don't exist.
  - AbrahamParangi 3y ago
    You seem to find the explanation unsatisfying and are getting mad at the tools. The program that defines how you recognize a bird is defined in the connectivity of the neurons in your eye and your brain. The circuit is the program. It's not at all clear that there is or even should be a deeper explanation than "a function with sufficiently many parameters can approximate any other function with only a small change to those parameters, and thus allows tractable solving" - whether by gradient descent or some hokey approximation our neurons are probably doing. We have even less of an idea how our own system of "understanding" works than we do of neural networks "understanding". If you're going to be mad at tools that don't provide satisfying explanations for how they work, then look no further than your own brain. Are you going to give up on thinking, just because when you look at it closely you realize you have no idea how on earth you're even doing it?
  - blueboo 3y ago
    There’s no helping the incurious, but there are indeed reams of explorations unto weights and their explanations. One of my personal favourites is Zeiler and Fergus https://arxiv.org/abs/1311.2901 https://arxiv.org/abs/1311.2901 Tom Zahavy’s “Greying the black box” on deep RL comes to mind too https://arxiv.org/abs/1602.02658 https://arxiv.org/abs/1602.02658 OpenAI has their Microscope tool for investigating individual neurons https://openai.com/research/microscope https://openai.com/research/microscope; more recently OpenAI shared research and tooling leveraging a powerful language model to explain a smaller model’s neurons. https://openai.com/research/language-models-can-explain-neurons-in-language-models https://openai.com/research/language-models-can-explain-neur... For a true toy box exploration playground.tensorflow.org is worth a play These may or may not satisfy your curiosities — your perfect tool may not exist…yet. I would argue that the sampling of extant work above are indicative that it and more are possible.
sorenjan 3y ago
Sounds like you're mixing up AI and deep learning. A* is a type of AI, as is scheduling, sudoku solvers, etc.
- dymk 3y ago
  No, GP is correct, "AI" as a buzzword means very little at this point.
  - sorenjan 3y ago
    That's because AI as a buzzword means deep learning now, but there's plenty of other AI that doesn't use training data and requires good knowledge of the problem you want to solve. If you look at one of the most widely used text books machine learning is one of seven chapters, and not all of that is deep or supervised learning. https://aima.cs.berkeley.edu/ https://aima.cs.berkeley.edu/
  - thfuran 3y ago
    But as a long-standing (as far as computing is concerned at any rate) academic field of study, it has meaning, even if the precise boundaries aren't perfectly well defined.

radarsat1 3y ago

My point is a bit different though. It's not about culture wars or being anti or pro AI. It's about how silly it is to call one optimization function AI and another "not AI" and then make a whole curfuffel about bucking trends, when what you've done is actually something very similar. (Maybe very interesting in its own right and a good solution though, with its own desirable properties.. again, I'm not commenting on the paper itself, just this one off-hand comment in the blog.) In my view calling a solution "neural" and "AI" because you are optimizing a stack of relus, and then turning around and solving the same (or almost the same) problem by optimizing a bunch of Gaussian model parameters on the same data, and then saying that is "not AI" and "at least it's not neural!" is just a very specious and unnecessary way to delineate things imho. They're just two solutions to the same problem, both data driven, both generating "models".. they have differences, just not differences that I think warranted such a comment in the blog, and moreover I thought it weird to attribute some kind of normative evaluation based on how you categorize the solution.