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Well said, AI in practice is just stats rebranded. Neural networks are shiny and new, but they are just an implementation for solutions from stats that have be
by codesushi42 7y ago
Well said, AI in practice is just stats rebranded.
Neural networks are shiny and new, but they are just an implementation for solutions from stats that have been around for decades.
Regression? MSE loss. Now with a neural network trained on MSE loss.
Classification? Logistic regression with cross entropy loss.
Anomaly detection? Feature extraction? Plenty of people still use PCA, which is nothing new. Autoencoders may get you more mileage, but conceptually work very similarly to PCA for these use cases.
Image data? Use methods from signal processing, also decades old. Convolutions are nothing new, you're just now implementing them with neural networks, and adding a loss function based on what you're trying to predict.
Time series data? You could be better off just sticking to ARIMA. Depends on your use case, but using RNNs may not even work here.
Reinforcement learning is more exciting, and is solving new problems that weren't even being approached before. Same goes for GANs, and unsupervised learning in general stays exciting and fresh.
But most of the applications of AI are ho hum. Just use decades old methods, now implemented with neural networks. At least, sometimes. What has really changed is the amount of data available now, and the ability to process it. Not necessarily the approaches to analyzing it.
- sjg007 7y agoNeural networks add computational depth. So I would disagree with the statement that AI is just "stats rebranded". That's about as useful an analogy as saying that statistics in practice is just applied linear algebra.
- codesushi42 7y agoDefine computational depth. Non linearity? Parallelizable? Computational depth sounds like hyperbole. You're still approaching stats problems with the same methodologies. Your just using NNs as your optimizer.
- sjg007 7y agoIf you are interested, I would suggest reading up on the https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore...
- 6gvONxR4sf7o 7y agoThere are theorems like that for polynomials and fourier series and all sorts of other function classes too. They are just as practically relevant (or irrelevant).
- sjg007 7y agoSure... I mean it is matrices all the way down but the claim that AI (e.g. deep learning) is just applied statistics is disingenuous.
- 6gvONxR4sf7o 7y agoStats is getting rebranded as AI, but that's not the extent of it. You approximately solve a traveling salesman problem and it's AI too now. The label is growing to encompass all kinds of algorithmic decision making. You don't even need data, which is the sine qua non of stats.