In my opinion 'Machines have no common sense' because they are systems and networks of structure. What is needed are systems and networks of process to apply to the structure. Relevance may be more applicable than causality.
“The biggest problem in AI? Machines have no common sense.” First in a terrific series of videos @bigthink launching http://rebooting.ai https://bigthink.com/videos/machines-have-no-common-sense- …
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I'm working on it, but it's f'n hard, like a 1,000 piece quantum jigsaw puzzle where none of the pieces fit until all of the pieces fit (and you're guessing at what the final image is).
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So... like a really big Sudoku?https://www.youtube.com/watch?v=YX40hbAHx3s …
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I just finished your book and thoroughly enjoyed it! I have a much better understand some of the frustration around deep learning in recent years
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I believe common sense is totally a collection of ones and zeros: 1. I believe it; 0. I don’t believe it
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(Unfortunately bias is 1’s and 0’s too)
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... some consciousness might also be missing ...
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AI is nothing more than a field of advanced pattern matching input to output via application of various levels of mathematics. Unfortunately, the hype is driving too many to believe the illusions of intelligence are real, not limited simulations created by error prone humans.
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Would you agree that it is equivalent to say they don't reason => causality can't be result from correlation models.?
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