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> The fact that you don't consider ants as general intelligences means you agree that we aren't making progress towards general intelligences, since ants intell

> The fact that you don't consider ants as general intelligences means you agree that we aren't making progress towards general intelligences, since ants intelligence is way closer to general intelligence than any computer program we have. Please don't argue like this. It's your job to explain what you think is true, and my job to explain what I think is true. Up to this point in this conversation I explicitly haven't taken a stance about how generally intelligent our best AI are, because it's pointless to argue that until the epistemics are valid. In fact I didn't intend to argue past that point at all; I'm objecting to bad arguments, not the position per se. > The way we do AI can never achieve that This is not meaningfully true. We have unsupervised image recognition, for example, and there's plenty of work on all sort other unsupervised learning of all sorts of things. The idea that insects ‘don't have a reward function’ is not really valid; a reward function is just the function you're optimizing on.

username90 6y ago
I still don't see your point. The way we do AI today is that we have a static dataset, train a fuzzy function and then let it do work in production. The production model doesn't identify when it makes mistakes and update itself like every animal does. Ants does this. They don't do it perfectly, but they do it. So the way I see it until AI starts meaning only models that updates themselves as they evaluate input it is a pointless term. Currently basically every single instance of AI you see people talk about are just dumb immutable functions. Edit: And to clarify, I believe it is impossible to reach human level intelligence without having a model that mutates itself as it tries to solve the problem. Humans are smart since we learn while doing, not that we first take a long course before we start doing.
- Veedrac 6y ago
  There is plenty of research on exactly this sort of learning-while-doing, termed ‘online learning’. It's easiest to train on IID data, and also common to train in specific RL environments, because those paper over some issues with catastrophic forgetting and overfitting, and it's also preferable to do all the compute-intensive updates during training for efficiency. However, lots of people agree that online learning is fundamental to cracking AGI, and it's neither neglected nor unsuccessful.
  - username90 6y ago
    > There is plenty of research on exactly this sort of learning-while-doing, termed ‘online learning’. For some definition of plenty. I'd argue there isn't since almost all resources poured into AI are spent getting better at optimizing immutable functions. I don't see progress in getting better at optimizing immutable functions as progress towards general intelligence, hence I don't see almost anything people do in AI today as progress towards general intelligence. Do you understand now? I don't say nobody works on the things I think is important, just that it is mostly neglected compared to the enormous amount of resources spent on dumb models.
    - Veedrac 6y ago
      Progress on online learning builds on progress on offline learning, because it's the same optimization procedure. I don't really agree with your stance on it, but I was not objecting to the claim that people should focus more on online learning. I was objecting to the claims “super computers can't even compete with the intelligence of an ant”, which is false, since the impressive behaviours an ant displays are not learned, and the learned aspects aren't competitive with what computers can do, and that “[the] way we do AI can never achieve that, since it always relies on human intelligence to decide all of those things”, which is also false, because online learning research exists and works, if imperfectly.
      - username90 6y ago
        > Progress on online learning builds on progress on offline learning, because it's the same optimization procedure. I disagree with this assertion, treating online learning like offline learning means you aren't making any progress. The main difference is that online learning lets you interact with the problem and seek more information, offline learning doesn't. Offline learning doesn't help you at all solving that problem.
        Veedrac 6y ago
        Exploration is a major part of RL already, see AlphaGo for example.

username90 6y ago

I still don't see your point. The way we do AI today is that we have a static dataset, train a fuzzy function and then let it do work in production. The production model doesn't identify when it makes mistakes and update itself like every animal does. Ants does this. They don't do it perfectly, but they do it. So the way I see it until AI starts meaning only models that updates themselves as they evaluate input it is a pointless term. Currently basically every single instance of AI you see people talk about are just dumb immutable functions. Edit: And to clarify, I believe it is impossible to reach human level intelligence without having a model that mutates itself as it tries to solve the problem. Humans are smart since we learn while doing, not that we first take a long course before we start doing.

Veedrac 6y ago
There is plenty of research on exactly this sort of learning-while-doing, termed ‘online learning’. It's easiest to train on IID data, and also common to train in specific RL environments, because those paper over some issues with catastrophic forgetting and overfitting, and it's also preferable to do all the compute-intensive updates during training for efficiency. However, lots of people agree that online learning is fundamental to cracking AGI, and it's neither neglected nor unsuccessful.
- username90 6y ago
  > There is plenty of research on exactly this sort of learning-while-doing, termed ‘online learning’. For some definition of plenty. I'd argue there isn't since almost all resources poured into AI are spent getting better at optimizing immutable functions. I don't see progress in getting better at optimizing immutable functions as progress towards general intelligence, hence I don't see almost anything people do in AI today as progress towards general intelligence. Do you understand now? I don't say nobody works on the things I think is important, just that it is mostly neglected compared to the enormous amount of resources spent on dumb models.
  - Veedrac 6y ago
    Progress on online learning builds on progress on offline learning, because it's the same optimization procedure. I don't really agree with your stance on it, but I was not objecting to the claim that people should focus more on online learning. I was objecting to the claims “super computers can't even compete with the intelligence of an ant”, which is false, since the impressive behaviours an ant displays are not learned, and the learned aspects aren't competitive with what computers can do, and that “[the] way we do AI can never achieve that, since it always relies on human intelligence to decide all of those things”, which is also false, because online learning research exists and works, if imperfectly.
    - username90 6y ago
      > Progress on online learning builds on progress on offline learning, because it's the same optimization procedure. I disagree with this assertion, treating online learning like offline learning means you aren't making any progress. The main difference is that online learning lets you interact with the problem and seek more information, offline learning doesn't. Offline learning doesn't help you at all solving that problem.
      - Veedrac 6y ago
        Exploration is a major part of RL already, see AlphaGo for example.