Matrix Calculus (for Machine Learning and Beyond) Paige Bright, Alan Edelman, Steven G. Johnson: https://
arxiv.org/abs/2501.14787 #ArtificialIntelligence #DeepLearning #MachineLearning
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Matrix Calculus for Machine Learning and Beyond Paper
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Matrix Calculus for Machine Learning and Beyond
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Matrix Calculus (for Machine Learning and Beyond) Paige Bright, Alan Edelman, Steven G. Johnson: https://
arxiv.org/abs/2501.14787 #ArtificialIntelligence #DeepLearning #MachineLearning -
Building AI Systems Faster: From 40 People to 2-3 Engineers
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we did godinabox in 2 days alphactr and autocodepro in a couple of weeks with just 2-3 people. This needed 40 and a lot of time.
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CodeLLM Advances: Image Upload, Grok 3 Integration, Autocomplete Improvements
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New CodeLLM Release – Upload images in Agents and generate code
– Grok 3 and Grok-3 mini in CodeLLM agents
– One more step change in Autocomplete Coming soon – Rich prompting
– Large code bases
– Better human-in-the-loop management -
AI Studio integrates native documentation with new stack
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We are moving the docs natively into AI Studio and building a new docs stack to do it!
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Google’s Marketing Weakness Versus Strong Technology Leadership
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Google has never been good at marketing its products, but boy is it good at technology. Very bullish on Gemini, the Gemma line of open models, and the JAX/Keras/TPU stack
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Google Releases RecML: JAX-Optimized Recommender Systems Library
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Google quietly released a powerful recommender systems library optimized for JAX and TPUs, based on Keras. It's called RecML. It has native support for SparseCore (latest hardware for handling large distributed embeddings)
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SWiRL: Synthetic Data and RL for Language Model Reasoning
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Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use This paper introduces Step-Wise Reinforcement Learning (SWiRL), a method for improving multi-step reasoning and tool use in language models through synthetic data generation and offline RL. Problem: Standard RL
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Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
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Hogwild! Inference: Parallel LLM Generation via Concurrent Attention This paper introduces Hogwild! Inference, a parallel LLM inference framework where multiple model instances collaborate by sharing a synchronized attention cache and dynamically adapting their strategies in
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Vaana: Illustration of How It Works on GitHub
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The illustration below demonstrates how Vaana works! Their GitHub repository: https://
github.com/vanna-ai/vanna