World models, intuitive physics, planning, problem solving, discrete search for solutions, continuous optimization of control parameters...
Dogs manage to do all this with 2 billion neurons.
Why debate human-level AI when we can't approach dog-level intelligence yet?
1/N
We, humans, give way too much importance to language and symbols as the substrate of intelligence.
But primates, dogs, cats, crows, parrots, octopus, and many other animals don't have humans-like languages, yet exhibit intelligent behavior beyond that of our best AI systems.
2/N
What they do have is an ability to learn powerful "world models" that allow them to predict the consequences of their actions and to search for and plan actions to achieve a goal.

The ability to learning such world models is what's missing from AI systems today.
3/N, N=3

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More from @ylecun

19 Oct
"Learning in High Dimension Always Amounts to Extrapolation"
by Randall Balestriero, Jerome Pesenti, and Yann LeCun.
arxiv.org/abs/2110.09485

Thread 1/N
1. given a function on d-dimensional vertors known solely by its value on N training samples, interpolation is defined as estimating the value of the function on a new sample inside the convex hull of the N vectors.
2/N
2. under mild assumptions, a new sample has a very low probability of being inside the convex hull of training samples, *unless* N grows exponentially with d.
3/N
Read 6 tweets
6 Jul
There were two patents on ConvNets: one for ConvNets with strided convolution, and one for ConvNets with separate pooling layers.
They were filed in 1989 and 1990 and allowed in 1990 and 1991.
1/N
We started working with a development group that built OCR systems from it. Shortly thereafter, AT&T acquired NCR, which was building check imagers/sorters for banks. Images were sent to humans for transcription of the amount. Obviously, they wanted to automate that.
2/N
A complete check reading system was eventually built that was reliable enough to be deployed.
Commercial deployment in banks started in 1995.
The system could read about half the checks (machine printed or handwritten) and sent the other half to human operators.
3/N
Read 9 tweets
10 Jun
Very nice work from Google on deep RL- based optimization for chip layout.
Simulated annealing and its heirs are finally dethroned after 40 years.
This uses graph NN and deConvNets, among other things.
I did not imagined back in the 90s that (de)ConvNets could be used for this.
This is the kind of problems where gradient-free optimization must be applied, because the objectives are not differentiable with respect to the relevant variables. [Continued...]
In this application, RL is used as a particular type of gradient-free optimization to produce a *sequence* of moves.
It uses deep models learn good heuristics as to what action to take in every situation.

This is exactly the type of setting in which RL shines.
Read 4 tweets
12 May
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.
By Adrien Bardes, Jean Ponce, and yours truly.
arxiv.org/abs/2105.04906
Insanely simple and effective method for self-supervised training of joint-embedding architectures (e.g. Siamese nets).
1/N
TL;DR: Joint-embedding archis (JEA) are composed of 2 trainable models Gx(x) and Gy(y), trained with pairs of "compatible" inputs (x,y).
For ex: x and y are distorted versions of the same image, successive sequences of video frames.
The main difficulty is to prevent collapse
2/N
VICReg is a loss for JAE with 3 terms:
1. Variance: Hinge loss to maintain the std-dev of each component of Gx(x) & Gy(y) above a margin
2. Invariance: ||Gx(x)-Gy(y)||^2
3. Covariance: sum of the squares of the off-diag terms of the covariance matrices of Gx(x) and Gy(y).
3/N
Read 12 tweets
8 May
Barlow Twins: a new super-simple self-supervised method to train joint-embedding architectures (aka Siamese nets) non contrastively.
arxiv.org/abs/2103.03230
1/N
Basic idea: maximize the normalized correlation between a variable in the left branch and the same var in the right branch, while making the normalized cross-correlation between one var in the left branch and all other vars in the right branch as close to zero as possible.
2/N Image
In short: the loss tries to make the normalized cross-correlation between the embedding vectors coming out of the left branch and the right branch as close to the identity matrix as possible.
3/N
Read 14 tweets
12 Mar
@mcCronjaeger @BloombergME The list is much too long for a Twitter thread.
I'll leave that for FB's comm people to do.
@mcCronjaeger @BloombergME More importantly, the whole premise of the article is wrong.
The SAIL / Responsible AI group's role *never* was to deal with hate speech and misinformation.
That's in the hands of other groups with *hundreds* of people in them.
In fact, "integrity" involves over 30,000 people...
@mcCronjaeger @BloombergME So the central theme of the article, that RespAI wasn't given the necessary resources to do its job is patently false.

Second, AI is heavily used for content moderation: filtering hate speech, polarizing content, violence, bullying, etc...
Read 10 tweets

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