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I'm always bemused by the idea that AI is nothing but machine learning, and ML is nothing but predictive analytics. Equating research in AI with a "boring hype
by drongoking 7y ago
I'm always bemused by the idea that AI is nothing but machine learning, and ML is nothing but predictive analytics. Equating research in AI with a "boring hyperparameter search" shows how narrow it's become; saying you've "gotten 97% on a problem" refers to, obviously, classification accuracy of a model on a set of labeled instances. "Use AI/ML to solve X" means finding a way to translate X into a prediction task over feature vectors.
There's an old saying "If all you have is a hammer, all your problems start to look like nails." We may see an AI winter come about simply because we run out of things to pound with our hammer.
- rytill 7y agoIf all you have is a function, everything starts to look like a mapping between two sets.
- XorNot 7y ago97% is also 3 failures out of every 100 attempts. In a lot of day to day experience I suspect humans do much better then this still.
- solveit 7y agoIt depends. DL models have legitimately achieved superhuman accuracy on many tasks. Part of this is because deep learning is incredibly effective for a certain class of problems. But part of this is because humans are remarkably bad at some problems. Humans tend to be surprisingly bad at context-stripped tasks like "identify what object this blurry image is", and "what sequence of syllables is this short audio file?". But we have countermeasures to correct for our inaccuracy. Most importantly, we understand and use context to sanity-check the hell out of our imperfect senses, and nobody has any idea how we're going to get AI to do that.