The AI lab that created Kimi just released some pretty groundbreaking research

@andrhlt

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Kimiseen in 31 videos →https://github.com/moonshot-ai/moonshot-ai.github.io/blob/main/Attention-Residuals.pdf ↗

Why it worked

The video leverages the credibility of prominent figures like Elon Musk and Andrej Karpathy commenting on the research, and highlights the impressive achievement of a 17-year-old high school student being a lead author, which generates intrigue and shareability.

Summary

This video explains a new AI research paper called "Attention Residuals" from the AI lab Kimi, which allows AI models to be trained 25% more efficiently. It details how this new method improves upon previous transformer architectures by allowing layers to selectively access information from earlier layers, leading to better performance on benchmarks.

Structure

1Introduction of Kimi's new research: Attention Residuals
2Explanation of the problem with previous AI models (information gets buried)
3Explanation of how Attention Residuals solves this problem
4Demonstration of the improved performance on benchmarks
5

Original caption

A new way to train AI models 25% more efficiently #claude #kimi #airesearch #openai #cursor

More from @andrhlt

Their other posts in the index, biggest breakout first.

Highlighting a young author of the paper

Product placement

The video discusses and shows research papers and diagrams related to "Attention Residuals" and the AI lab "Kimi", which is presented as a new method for training AI models more efficiently. The product "Kimi" is the subject of the research.

Call to action

Check out the paper if you're interested in more of the technicals.

On-screen text

A new way to train AI models 25% more efficiently Kimi.ai @Kimi_Moonshot Scaling law experiments reveal a consistent 1.25x compute advantage across varying model sizes. ATTENTION RESIDUALS Kimi.ai @Kimi_Moonshot Introducing: Attention Residuals: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation, inspired by the duality of time and... Elon Musk Impressive work from Kimi Andrej Karpathy @karpathy SGD is a ResNet too (the blocks of it are fwd+bwd), the residual stream is the weights so... We're not taking the Attention is All You Need part literally enough? :D Attention Is All You Need Attention Mechanism Attention Residuals: Kimi's Solution to Decade-Old Residual Connections (a) Standard Residuals (b) Full Attention Residuals (c) Block Attention Residuals Output Attention Mul Attention Mul Embedding Output Attention Mul Attention Mul Embedding Output Attention Mul Attention Mul Embedding Block n-1 Block n-2 Attention Op (u) Same accuracy with less compute Attention Residuals

Transcript

The AI lab that created Kimi just released some pretty groundbreaking research called Attention Residuals, which allows AI models to be trained in 25% more efficiently. 25% on these scales is a lot. Elon and Andre have already commented on this on tech Twitter. You've probably heard of that paper, attention is all you need, arguably the most famous AI research paper. This novel concept of attention and transformers was the breakthrough necessary for things like ChatGPT and Cloud that we use every day. Well, Moonshot AI I very cleverly took the same concept of token token attention and basically on a super high level created a layer to layer attention. How this worked previously is imagine a 100 step assembly line which are the model layers in this analogy. Each step in the assembly line depends on steps previous of it passing information forward. The downside of this is you can have important information that exist in the early layers of the models that just gets buried as it moves through all the other later. layers. So, activation residents provides an archive of sorts. It allows each layer to selectively and directly access information from the previous layers. This novel method of attention residents provided better reasoning, coding on benchmarks across the board. Check out the paper if you're interested in more of the technicals. Apparently, a 17-year-old high school student is even one of the first authors.