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> AI system can teach me to speak a human language as well as a native human speaker of that language could. Seems a long ways off. And when such a system app

> AI system can teach me to speak a human language as well as a native human speaker of that language could. Seems a long ways off. And when such a system appears, you'll claim that it's still not AI --- just a fancy pattern matching trick --- and say that it's not real AI until some other arbitrary benchmark is met. "AI" is just what machine learning can't quite do yet. > collection of very sophisticated pattern matching algorithms What do you think human brains are? Humans are Turing machines as well --- all physical computers are. We process inputs, match them against internal state, and generate outputs. You can't criticize AI on the grounds that it's "pattern matching": everything is pattern matching. You can't look at the output of GPT-3 and tell me that it's some kind of dumb regular expression here. You just can't. https://www.gwern.net/GPT-3 https://www.gwern.net/GPT-3

neatze 5y ago
Except, human brain changes on second by second bases, it is highly distributed and concurrent system with unimaginable redundancy, furthermore brain capability of learning/adaptability from just from one or few examples/tries does not compare to any AI models; what aspect of GPT-3 you comparing to human brain, specifically ?
photochemsyn 5y ago
Pattern recognition is certainly a major component of human cognition but is hardly the whole story. Solution of complex mathematical problems - those are not pattern recognition problems. Two quite similar equations may have radically different outputs for the same inputs, so that screws up the whole pattern recognition approach, doesn't it?
- quotemstr 5y ago
  > Two quite similar equations may have radically different outputs for the same inputs, so that screws up the whole pattern recognition approach, doesn't it? No, it doesn't, and that's because you have an inadequate understanding of what "pattern matching" is. The domain over which patterns are matched --- in both the brain and artificial neural networks --- isn't just the input, but a combination of the input and the previous state of the computation doing the matching. It's this recurrence, this recursive evaluation of previous state, that makes human minds Turing complete. "Pattern matching" is more powerful than you think when you combine it with attention and memory, and ML models have had both for years. Do you think ML models are dumb regex lists or something? ML models and the brain have state.
  - user_named 5y ago
    Pattern matching is like memorizing all the questions and their answers from previous exams before going in to take yours. When you encounter one you haven't seen before you obviously fail every time. That's why only the non-intelligent students do this.
    - machiaweliczny 5y ago
      But ML models soon will have ability to decompose solutions and recombine them. This with added causality and generation of test data might make them quite powerful. I think domains that will get actually solved first due to this are programs/maths as usually it's easier to verify solutions that to devise them. For other stuff you would have to simulate the universe. That's why for example self driving cars basically record everything and will simulate all possible scenarios especially these based on disengagement data.
- machiaweliczny 5y ago
  No it doesn't. Solving math is generative pattern matching. I once was very good at math and it's just works like puzzles in your mind. If you know all the trick that can be used to form solution you simply enumerate through them brute force (or something little more eloquent). It's just super hard to generate trick on your own and that's why if you miss single idea in math you are lost forever. Also it doesn't take single human to devise these algos. It took whole generations of humanity. We are just approaching possible lower bound of compute power of single brain. I think we might get program and proof synthesis in 1-30 years with this kind of progress and funding. AFAIK in 2048 silicon compute might get bigger than whole compute of humanity.
user_named 5y ago
Humans are not Turing machines.
- quotemstr 5y ago
  I'd like to think I'm Turing complete --- well, as much as any physical object can be. I can walk through any algorithm. That makes me Turing complete, yes?
  - Jensson 5y ago
    I think he meant the opposite. Humans can perform as a Turing machine, but it isn't clear that a Turing machine can perform as a human.

neatze 5y ago

Except, human brain changes on second by second bases, it is highly distributed and concurrent system with unimaginable redundancy, furthermore brain capability of learning/adaptability from just from one or few examples/tries does not compare to any AI models; what aspect of GPT-3 you comparing to human brain, specifically ?

photochemsyn 5y ago

Pattern recognition is certainly a major component of human cognition but is hardly the whole story. Solution of complex mathematical problems - those are not pattern recognition problems. Two quite similar equations may have radically different outputs for the same inputs, so that screws up the whole pattern recognition approach, doesn't it?

quotemstr 5y ago
> Two quite similar equations may have radically different outputs for the same inputs, so that screws up the whole pattern recognition approach, doesn't it? No, it doesn't, and that's because you have an inadequate understanding of what "pattern matching" is. The domain over which patterns are matched --- in both the brain and artificial neural networks --- isn't just the input, but a combination of the input and the previous state of the computation doing the matching. It's this recurrence, this recursive evaluation of previous state, that makes human minds Turing complete. "Pattern matching" is more powerful than you think when you combine it with attention and memory, and ML models have had both for years. Do you think ML models are dumb regex lists or something? ML models and the brain have state.
- user_named 5y ago
  Pattern matching is like memorizing all the questions and their answers from previous exams before going in to take yours. When you encounter one you haven't seen before you obviously fail every time. That's why only the non-intelligent students do this.
  - machiaweliczny 5y ago
    But ML models soon will have ability to decompose solutions and recombine them. This with added causality and generation of test data might make them quite powerful. I think domains that will get actually solved first due to this are programs/maths as usually it's easier to verify solutions that to devise them. For other stuff you would have to simulate the universe. That's why for example self driving cars basically record everything and will simulate all possible scenarios especially these based on disengagement data.
machiaweliczny 5y ago
No it doesn't. Solving math is generative pattern matching. I once was very good at math and it's just works like puzzles in your mind. If you know all the trick that can be used to form solution you simply enumerate through them brute force (or something little more eloquent). It's just super hard to generate trick on your own and that's why if you miss single idea in math you are lost forever. Also it doesn't take single human to devise these algos. It took whole generations of humanity. We are just approaching possible lower bound of compute power of single brain. I think we might get program and proof synthesis in 1-30 years with this kind of progress and funding. AFAIK in 2048 silicon compute might get bigger than whole compute of humanity.

user_named 5y ago

Humans are not Turing machines.

quotemstr 5y ago
I'd like to think I'm Turing complete --- well, as much as any physical object can be. I can walk through any algorithm. That makes me Turing complete, yes?
- Jensson 5y ago
  I think he meant the opposite. Humans can perform as a Turing machine, but it isn't clear that a Turing machine can perform as a human.