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“Deep Learning has outlived its usefulness as a buzz-phrase”

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Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#61
post #49

I wish we could come up with a catchier name, but I LOVE the idea of calling this programming , because that is precisely what we do when we compose deep neural nets. For example, here's how you compose a neural net consisting of two "dense" layers (linear transformations), using Keras's functional API, and then apply these two layers to some tensor x to obtain a tensor y: f = Dense(n) g = Dense(n) y = f(g(x)) This l…

LeCun specifically calls out imperative programming, not just typical data flow methods.

You're right. I softened the reference to functional programming.

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#63
post #35

[Text from post] OK, Deep Learning has outlived its usefulness as a buzz-phrase. Deep Learning est mort. Vive Differentiable Programming! Yeah, Differentiable Programming is little more than a rebranding of the modern collection Deep Learning techniques, the same way Deep Learning was a rebranding of the modern incarnations of neural nets with more than two layers. But the important point is that people are now build…

it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.

Mostly because the loss function space is not well understood, we need to do some kind of descent

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#64
post #35

[Text from post] OK, Deep Learning has outlived its usefulness as a buzz-phrase. Deep Learning est mort. Vive Differentiable Programming! Yeah, Differentiable Programming is little more than a rebranding of the modern collection Deep Learning techniques, the same way Deep Learning was a rebranding of the modern incarnations of neural nets with more than two layers. But the important point is that people are now build…

it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.

[deleted]

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#65
post #35

[Text from post] OK, Deep Learning has outlived its usefulness as a buzz-phrase. Deep Learning est mort. Vive Differentiable Programming! Yeah, Differentiable Programming is little more than a rebranding of the modern collection Deep Learning techniques, the same way Deep Learning was a rebranding of the modern incarnations of neural nets with more than two layers. But the important point is that people are now build…

it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.

Following a gradient is smarter than trial and error. You can make an argument that, in high-dimensional parameter spaces, it’s hard to do better (because, gradient descent is linear in the number of dimensions).

Ordinary Metropolis-Hastings, for example, is closer to trial and error.

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#66
post #35

Earlier quoted context omitted.

it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.

Assuming that AI tries to mimic the way humans learn and evolve, those methods haven't changed for hundreds of thousands of years and brute-force trial and error is just one of them. It's kind of fundamental...

It doesn’t really try to mimic the way humans learn and evolve...

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#67

"See more of Yann LeCun on Facebook" popup, no access to the page. No, I don't want to create a Facebook account to read a blog post. Perhaps links to walled-garden pages where you need an account and need to be logged in should be prohibited or at least discouraged.

reg/pay-walled sites are no the only kind that should be discouraged - sites that will track you globally unless you block them globally should be discouraged too IMHO. Facebook fits both categories.

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#68
post #29

"See more of Yann LeCun on Facebook" popup, no access to the page. No, I don't want to create a Facebook account to read a blog post. Perhaps links to walled-garden pages where you need an account and need to be logged in should be prohibited or at least discouraged.

this will get downvoted as all held, but I really think most socially apt people should have Facebook accounts these days

No. E.g. I'm fine with Telegram & Signal + WhatsApp & Skype for the elderly. Having no intention to become a public person, do I really need a public profile on a web site that's whole purpose is to spy on me everywhere, analyse my behavior and contacts, sell the data to others and show me ads?

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#69
post #35

Earlier quoted context omitted.

it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.

As much of a pity that, 70 years later, we are still using transistor based computers originally derived from three wires stuck in a piece of rock[1] by some very innovative fellows at Bell Labs[2]? [1] http://images.computerhistory.org/revonline/images/500004836... [2] http://www.computerhistory.org/revolution/digital-logic/12/2...

Our transistors have nothing to do with those transistors.

Re: “Deep Learning has outlived its usefulness as a buzz-phrase”

#70
post #65
post #35

Earlier quoted context omitted.

it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.

Following a gradient is smarter than trial and error. You can make an argument that, in high-dimensional parameter spaces, it’s hard to do better (because, gradient descent is linear in the number of dimensions). Ordinary Metropolis-Hastings, for example, is closer to trial and error.

> because, gradient descent is linear in the number of dimensions

Yes, but increasing dimensions does not mean the manifold of the problem space increases the same.

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