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PaperclipPerfector  1yr ago (text post)   33523 thread views

Culture War Roundup for the week of April 24, 2023

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His arguments, on the level of pointing at something particular, are purely verbal, not even verbal math. When he uses specific technical terms, they don't necessarily correspond to the discussed issue, and often sound like buzzwords he vaguely associated with it. Sometimes he's demonstrably ignorant about their meaning. The Big Picture conclusion never changes.

Maybe it can't.

This is a sample from dunk on Yud that I drafted over 24 hours of pathological irritation recently. Overall it's pretty mean and unhinged and I'm planning to write something better soon.

Hope this helps.

Bad take, except that MAML also found no purchase, similar to other Levine's ideas.

He directly and accurately describes evolution and its difference from current approaches, but he's aware of a wide range or implementations of meta-learning. In the objections list he literally links to MAML::

I'm a lot more bullish on the current paradigm. People have tried lots and lots of approaches to getting good performance out of computers, including lots of "scary seeming" approaches such as:

1 Meta-learning over training processes. I.e., using gradient descent over learning curves, directly optimizing neural networks to learn more quickly.

2 Teaching neural networks to directly modify themselves by giving them edit access to their own weights.

3 Training learned optimizers - neural networks that learn to optimize other neural networks - and having those learned optimizers optimize themselves.

4 Using program search to find more efficient optimizers.

5 Using simulated evolution to find more efficient architectures.

6 Using efficient second-order corrections to gradient descent's approximate optimization process.

7 Tried applying biologically plausible optimization algorithms inspired by biological neurons to training neural networks.

8 Adding learned internal optimizers (different from the ones hypothesized in Risks from Learned Optimization) as neural network layers.

9 Having language models rewrite their own training data, and improve the quality of that training data, to make themselves better at a given task.

10 Having language models devise their own programming curriculum, and learn to program better with self-driven practice.

11 Mixing reinforcement learning with model-driven, recursive re-writing of future training data.

Mostly, these don't work very well. The current capabilities paradigm is state of the art because it gives the best results of anything we've tried so far, despite lots of effort to find better paradigms.

And the next paragraph on sharp left turn:

In my frame, we've already figured out and applied the sharp left turn to our AI systems, in that we don't waste our compute on massive amounts of incredibly inefficient neural architecture search, hyperparameter tuning, or meta optimization. For a given compute budget, the best (known) way to buy capabilities is to train a single big model in accordance with empirical scaling laws

Yuddites, on the other hand, mostly aren't aware of any of that. I am not sure they even read press releases.

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