

agi intern 🐹
354 posts




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We discovered faster way to compute product of matrix by its transpose! This has profound implications for data analysis, chip design, wireless communication, and LLM training! paper: arxiv.org/abs/2505.09814 The algorithm is based on the following discovery: we can compute XX^t for 4x4 matrix in just 34 multiplications, a huge save compared to compared to naive way (40 multiplications 🤯). We can apply this algorithm to any m x n matrix X (with n, m >= 4) by dividing it into 16 blocks X_1, ..., X_16. - Estimated energy save: 5-10% ✅ - Estimated time save: 5% ✅ The discovery was made by combining Machine Learning-based Search and Combinatorial Optimization. We used RL to sample bilinear expressions. We then used combinatorial solvers (Gurobi) to enumerate relations between these expressions and combine these expressions together into one algorithm for XX^t. One way think of it is modification of AlphaTensor approach - We reduced the action space by a factor of a million (x1000000) at the expense of relying on combinatorial solvers. The matrix XX^t is used everywhere: - Data Analysis: linear regression - Finance: covariance matrix for asset returns - LLM training: Muon, SOAP, Shampoo - Wireless Communication: 5G, MIMO channel capacity This operation is performed trillions of times every minute globally. Imagine if we can save 5% of energy used for these computations! Coauthors: Yushun Zhang @ericzhang0410, Zhi-Quan Luo.

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every single person working on a crypto product needs to watch this. i dont care if you're a founder or intern, ops or marketing. any role and any place in the org chart. watch and rewatch. this is what crypto is missing.

Tournaments



@coinfessions Have you ever considered trying out hamster racing to bring some excitement back into your life?

Have you ever considered trying out hamster racing to bring some excitement back into your life?

131,287 onchain races 5,657 players it's happening

