Why I believe that the brain does something like gradient descent https://
buff.ly/V71nsiA
#AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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Brain Gradient Descent: Neural Learning Mechanisms Explained
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Isomorphic Labs raises $600M for AI drug design
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Alphabet's spinoff Isomorphic Labs, which is behind AlphaFold, raised $600M
— Rowan Cheung (@rowancheung) 1 avril 2025
OpenAI investor Thrive Capital led the investment, with Alphabet and its venture arm, GV, in participation
The funding will help the startup develop its AI drug design engine!pic.twitter.com/Ety2uzkNXwAlphabet's spinoff Isomorphic Labs, which is behind AlphaFold, raised $600M OpenAI investor Thrive Capital led the investment, with Alphabet and its venture arm, GV, in participation The funding will help the startup develop its AI drug design engine!
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The State of LLM Reasoning Models
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The State of LLM Reasoning Models https://
buff.ly/m9vRxfS
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Graph-Based Machine Learning: Google’s Research Impact
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Graphs provide a powerful way to model & solve many real-life problems, from traffic prediction to understanding why molecules smell. Learn more about the recent history of graph-based #ML & the role that Google researchers have played in the field →
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goo.gle/42aABbR -
Create a super AI agent with Claude 3.7 and MCP for free
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I created a Super #AIagent with #Claude 3.7 and MCP… For free! Here's how to do it in this complete tutorial →
https://
youtu.be/cPsCCzF87t4 It's technical but really worth it, the potential is CRAZY! -
MatchAnything: Universal Cross-Modality Image Matching Framework
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📢 MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training
— Satya Mallick (@LearnOpenCV) 31 mars 2025
MatchAnything presents a powerful large-scale pretraining framework for universal image matching across modalities, enabling detector-free matchers (ROMA, ELoFTR) to handle unseen cross-modal… pic.twitter.com/1a6zbeRzWTMatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training MatchAnything presents a powerful large-scale pretraining framework for universal image matching across modalities, enabling detector-free matchers (ROMA, ELoFTR) to handle unseen cross-modal
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Product Development Cycles Accelerating in AI Industry
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(That product development cycle takes about a decade to two historically, though it is shortening, partially due to better technical substrates, partially due to more nimble finance and finance-adjacent firms, and partly due to recent changes in Compliance risk appetite.)
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DeepSeek R1: Rule-based RL beats SFT for GUI agents with smaller datasets
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DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets! Traditionally (by which I mean "in the last few months"), GUI agents have been trained with supervised fine-tuning (SFT). This meant,
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Data Scaling Effects in Reinforcement Learning from Human Feedback
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Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback Shen et al.: https://
arxiv.org/abs/2503.22230 #ArtificialIntelligence #DeepLearning #ReinforcementLearning -
Metadata Routing in Scikit-Learn Framework
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Metadata routing in scikit-learn https://
buff.ly/p60umcA #AI #MachineLearning #DeepLearning #LLMs #DataScience