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> 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? N
by 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 agoPattern 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 agoBut 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.