Traditional Neural Networks are overrated.
Transformer is irrelevant when we have a small set of unique examples.
And no, brute-forcing with discrete program search is not the way too.
It's so hilarious how easy, simple and yet elegant the actual approach is.
DeepSeek R1 vs Flash-thinking-01-21 2.0 in my high-leverage reasoning benchmark to test agency in test time models.
R1: 53%
Flash-thinking: 23%
o1: 30%
R1 is way better in multi step reasoning
The R1 is the most thoughtful of them all.
The last question for LLMs:
"Sort the numbers from highest to lowest: 9.1, 9.8, 9.11, 9.9, 9.12"
o1-preview: failed
o1-mini: failed
gpt-4o-latest: failed
sonnet-3.5: failed
llama-3.2-1B: success
It's absolutely insane, R1 thought for 10k+ tokens on aime problem I gave it and R1 solved it correctly.
If they release open source we are unfathomably back
With @Harvard, we built a ‘virtual rodent’ powered by AI to help us better understand how the brain controls movement. 🧠
With deep RL, it learned to operate a biomechanically accurate rat model - allowing us to compare real & virtual neural activity. → dpmd.ai/3RobU7e
Announcing AlphaFold 3: our state-of-the-art AI model for predicting the structure and interactions of all life’s molecules. 🧬
Here’s how we built it with @IsomorphicLabs and what it means for biology. 🧵 dpmd.ai/3URDiNo