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Comment by achompas | Hacker News Reader

> Yes, it's largely conceptually obsolete.

I'm sorry, what? How did you arrive at a point where you believe this is true? This is like calling compilers "obsolete."

Is it because you believe deep learning has "taken over" or something?

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Try to derive e.g. a face detector from bayes theorem. You immediately arrive at computationally intractable sums/integrals. Yet, we have super-human image classifiers. Therefore, bayes theorem is obsolete. Sure, you can try to retrofit bayes theorem on top of a neural net, but who cares?

> You immediately arrive at computationally intractable sums/integrals.

So we instead sample from that posterior.

Unless you think MCMC is also obsolete, in which case I’ll see myself out.

You're right, but a) you have comp efficiency issues with MCMC, and b) just empirically MCMC models don't work as well as gradient descent + NN for many tasks.

And you don't have computational efficiency issues with NNs?

We're also ignoring the benefits of a posterior distribution, which is useful for understanding the data-generating process.

Yeah of course. I can't explain to you why NNs outperform bayesian approaches, probably just NNs are capturing the correct type of prior for vision/language tasks. And yeah bayesian models are more interpretable but when you have millions of latent variables I'm not sure interpretability is a thing.

Yep, we arrived at my larger point: if you care about interpretability, NNs are horrible and Bayesian techniques are pretty damn great.

Well certainly, but interpretability is obsolete.

Now you're just trolling. :)

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