George

[nrc @nick_r_cameron]

I wrote an intro blog post to async IO. Covers completeness vs readiness, edge- vs level-triggering, short desriptions of poll/select, epoll, iocp, and io_uring: https://t.co/bAIR6MSEhC

[hardmaru @hardmaru]

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

We explore RL agents that still work even when their observations get shuffled around a lot!

A fun paper w/ @yujin_tang

web https://t.co/rBsJeSNswA
pdf https://t.co/Ti5qZqtCIK

[Bob Nystrom @munificentbob]

Today is the day! Crafting Interpreters is available for purchase in print, Kindle, ebook, and PDF! Behold: https://t.co/BXuTOXtDin

It took a lot longer than I expected, but it's all done now. So long that I wrote a blog post about it: https://t.co/oAygYH44A9

🎉📖🎉📖🎉📖🎉📖🎉

[Jon Gjengset @jonhoo]

Big announcement time 📢 For a while now, I've been working on a book that covers the next steps of @rustlang after "the book" — everything from API design to error handling to concurrency to async to FFI. And the early access was just released over at https://t.co/A6Ox1QNxeV! 🎉

[Ken Shirriff @kenshirriff]

Here are the two dies at the same scale. The M1 is over twice as large physically as the ARM1. It has 16 billion transistors vs 25,000 for the ARM1. If you built the ARM1 using the same technology, it would be a pixel-sized speck.

[Antonin Raffin @araffin2]

Types of Reinforcement Learning Paper

Original image: @xkcd

[Zhuowei Zhang @zhuowei]

Introducing `nft_ptr`:

C++ `std::unique_ptr` that represents each object as an NFT on the Ethereum blockchain

https://t.co/K5iFKDOFb4

[cassie @porglezomp]

he went to the king with a chessboard and said "put one transistor on the first square, and then every 18 months put twice as many transistors on the next square"

[Joshua Achiam @jachiam0]

Hot take: deep RL research has stagnated because conferences have created bad incentives, rewarding researchers for vacuous claims of novelty, tenuous-at-best theoretical connections, or SOTA, while punishing boring analysis of the empirical tricks that actually make things work.

[Distill @distillpub]

Multimodal Neurons in Artificial Neural Networks -- A new Distill article by @gabeeegoooh, @nickcammarata, @csvoss, @shancarter, @mpetrov, @ludwigschubert, @AlecRad, and @ch402.

https://t.co/IsxrQ5UCTC

[Catherine @whitequark]

recently, some Rust programmers started doing something I think is quite harmful: `unsafe` zealotry. it is harmful for three reasons:
1) #![forbid(unsafe)] doesn't make your code memory-safe;
2) memory-safe code can have equally harmful bugs;
3) Rust is useful beyond safety.
🧵

[Google DeepMind @GoogleDeepMind]

Thrilled to be releasing Alchemy, a new benchmark task for meta-RL, abstract structure learning, & latent-state inference. In the paper, researchers show that Alchemy presents a formidable challenge even for state-of-the art RL agents. Game on! https://t.co/eZuPkBKuak

[OpenAI @OpenAI]

We’ve developed two neural networks which have learned by associating text and images. CLIP maps images into categories described in text, and DALL-E creates new images, like this, from text.

A step toward systems with deeper understanding of the world. https://t.co/rppy6u1zcn

[Bob Nystrom @munificentbob]

Just finished editing the last chapter of "Crafting Interpreters". I have a few issues to resolve and a copy editor is doing a pass over everything. Soon I get to concentrate on typesetting. I can't wait to hold this thing in my hands.

[Danijar Hafner @danijarh]

Excited to share Evaluating Agents without Rewards!

We compare intrinsic objectives with task reward and similarity to human players. Turns out they all correlate more w/ human than w/ reward. Two of them even correlate more w/ human than reward does.

https://t.co/hctzH7cWFz
👇

[Distill @distillpub]

Naturally Occurring Equivariance in Neural Networks -- A new Distill article by @ch402, @nickcammarata, @csvoss, @ludwigschubert, and @gabeeegoooh. This is the fourth article in the circuits thread.

https://t.co/eeBuNI2D8g

[Natasha Jaques @natashajaques]

How can we move deep RL beyond games, without having to hand-build a simulator that covers real-world complexity? We adversarially generate a curriculum of challenging yet feasible environments by maximizing regret between a pair of agents, with PAIRED... https://t.co/i11pkeLNku

[Noam Brown @polynoamial]

Excited to announce our NeurIPS paper on ReBeL, an algorithm similar to AlphaZero that plays *imperfect-information* games like poker and Liar's Dice! Joint work with @anton_bakhtin, @adamlerer, Qucheng Gong.

YouTube Video: https://t.co/3zc8qCZVgk
Paper: https://t.co/byZTE2bcpl

[Distill @distillpub]

Understanding RL Vision -- A new Distill article by @JacobHHilton, @nickcammarata, @shancarter, @gabeeegoooh, and @ch402.

https://t.co/Ymcf2avEGg