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People are anything but slow and fragile, at least in 1 sense of the word, in comparison with computers of today. Modern computer models which are typically vie

People are anything but slow and fragile, at least in 1 sense of the word, in comparison with computers of today. Modern computer models which are typically viewed as the edge of computational intelligence are very susceptible to getting tricked into incomplete models by their input data. Human beings process unfathomable number of stimuli everyday and get placed in unknown situations on a daily basis and very few break down, where as almost all current computer models break down very quickly when viewing previously unseen patterns.

seiji 11y ago
Modern computer models which are typically viewed as the edge of computational intelligence are very susceptible to getting tricked into incomplete models by their input data. What?! A human person lives for years figuring out how the world works before they can even talk. Our trained-in-one-month AI systems aren't yet approaching anything training for years over real-world, mobile, 3D inputs, can approximate. Your objections are being filed under "not enough future vision."
- crazypyro 11y ago
  You are missing the point of my objection. For starters, I said modern. Most of the big recent breakthroughs in AI have come from neural nets. There is a inherent problem with deep learning with neural nets. Neural nets are exceptional at finding patterns within a huge dataset, but they are REALLY, REALLY bad at predicting new patterns that they haven't seen. Even something as simple as the pattern a^2b is impossible for a neural network to generalize past whatever length of the pattern the neural network has seen without modifications (check out stack-rnn by facebook). Even if an AI is trained for years, I don't see this limitation being overcome by pure magnitude of data with our current techniques. You say I don't have enough "future vision", but all you are doing is basically guessing that AI researchers will make some huge breakthrough that significantly changes the direction of the entire field. You are playing the lottery with your guesses, I'm simply extrapolating current trends.
  - seiji 11y ago
    I'm simply extrapolating current trends. Extrapolating current trends doesn't work in the face of exponential mumbo jumbo. Who would have expected training a character-by-character network could produce reasonable output? http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Sure, there's no "understanding" or "consciousness" behind the output, but purely statistically, it generates something readable by humans. Also, let's not forget scale. A human brain has between 100 and 200 trillion synapses connecting ~100 billion neurons to make consciousness work. Our toy neural nets so far aren't anywhere near those levels.
    - crazypyro 11y ago
      I think you are basically arguing the same point I am (current trends wouldn't approach human intelligence without exponential growth), except you are taking the optimist road and I'm taking the pessimistic road. I guess we will just have to wait and see!

seiji 11y ago

Modern computer models which are typically viewed as the edge of computational intelligence are very susceptible to getting tricked into incomplete models by their input data. What?! A human person lives for years figuring out how the world works before they can even talk. Our trained-in-one-month AI systems aren't yet approaching anything training for years over real-world, mobile, 3D inputs, can approximate. Your objections are being filed under "not enough future vision."

crazypyro 11y ago
You are missing the point of my objection. For starters, I said modern. Most of the big recent breakthroughs in AI have come from neural nets. There is a inherent problem with deep learning with neural nets. Neural nets are exceptional at finding patterns within a huge dataset, but they are REALLY, REALLY bad at predicting new patterns that they haven't seen. Even something as simple as the pattern a^2b is impossible for a neural network to generalize past whatever length of the pattern the neural network has seen without modifications (check out stack-rnn by facebook). Even if an AI is trained for years, I don't see this limitation being overcome by pure magnitude of data with our current techniques. You say I don't have enough "future vision", but all you are doing is basically guessing that AI researchers will make some huge breakthrough that significantly changes the direction of the entire field. You are playing the lottery with your guesses, I'm simply extrapolating current trends.
- seiji 11y ago
  I'm simply extrapolating current trends. Extrapolating current trends doesn't work in the face of exponential mumbo jumbo. Who would have expected training a character-by-character network could produce reasonable output? http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Sure, there's no "understanding" or "consciousness" behind the output, but purely statistically, it generates something readable by humans. Also, let's not forget scale. A human brain has between 100 and 200 trillion synapses connecting ~100 billion neurons to make consciousness work. Our toy neural nets so far aren't anywhere near those levels.
  - crazypyro 11y ago
    I think you are basically arguing the same point I am (current trends wouldn't approach human intelligence without exponential growth), except you are taking the optimist road and I'm taking the pessimistic road. I guess we will just have to wait and see!