What Is Composite AI? #AI #AIio #AIInnovation #ML #DataScience #Futureofwork @HaroldSinnott @fogoros @iainljbrown @NandoDF @katecrawford @drhassanrashidi @YuHelenYu ow.ly/SMmq30sTyEB
→ View original post on X — @terence_mills, 2026-03-01 01:00 UTC
MACHINE LEARNING
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TorchLean: First Fully Verified Neural Network Framework in Lean
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We’re excited to release TorchLean which is the first fully verified neural network framework in Lean. The Lean community has largely focused on pure mathematics. TorchLean expands this frontier toward verified neural network software and scientific computing. With the recent release of CSlib, we see this as another step toward a fully verified ML stack. We support features: 1. Executable IEEE-754 floating-point semantics (and extensible alternative FP models) verified tensor abstractions with precise shape/indexing semantics 2. Formally verified autograd system for differentiation of NN programs Proof-checked certification / verification algorithms like CROWN (robustness, bounds, etc.) 3. PyTorch-inspired modeling API with eager-style development + export/lowering to a shared IR for execution and verification Project page: leandojo.org/torchlean.html Paper: [2602.22631] TorchLean: Formalizing Neural Networks in Lean Work done @Robertljg, Jennifer Cruden, Xiangru Zhong, @huan_zhang12 and @AnimaAnandkumar. #MachineLearning #ScientificComputing #Lean
→ View original post on X — @animaanandkumar, 2026-03-01 00:43 UTC
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New Jeff Dean Fact Just Dropped
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new jeff dean fact just dropped https://t.co/r5BB8J8m9B pic.twitter.com/kBey2bifLw
— Latent.Space (@latentspacepod) 28 février 2026new jeff dean fact just dropped
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Insights and Tutorials on LLMs, AI Agents, and Machine Learning
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 28 février 2026
Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!https://t.co/zXfH6v9gfyIf you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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AI subfields outperform their corresponding scientific fields.
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Every subfield of AI has a corresponding field of science that it runs rings around:
Machine learning: Statistics
NLP: Linguistics
Automated reasoning: Formal logic
Knowledge representation: Philosophy
Multiagent systems: Economics
Computer vision: Sensor systems
Robotics: -
XGBoost: A Powerful Machine Learning Algorithm
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https://t.co/hczBcjQPW1 pic.twitter.com/tC5yqoC7jz
— Bojan Tunguz (@tunguz) 28 février 2026XGB is (allegedly) all you need (@tunguz)
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Books on Graph Neural Networks and Practical Python for ML
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"Graph Neural Networks — Foundations, Frontiers, and Applications" at http://
amzn.to/3AAzrdv
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Do it with Python: http://
amzn.to/3Oj2QiG
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#DataScience #DataScientist #AI #MachineLearning #ML #DeepLearning -
Graph Machine Learning 2nd Edition — Updated Techniques and PyTorch Geometric
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2nd Edition — now at http://
amzn.to/45Y3LyI v/ @PacktDataML Graph Machine Learning — Latest advancements in Graph Data to build robust #MachineLearning algorithms 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Master new graph ML techniques through updated examples using PyTorch Geometric and -
Comprehensive guide to applying machine learning in finance
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Comprehensive guide to applying #MachineLearning in Finance: https://
github.com/mfrdixon/ML_Fi
nance_Codes
… by Matthew F. Dixon …includes some chapters (PDF) from his book. The book: http://
amzn.to/3BHA47J
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#AlgorithmicTrading #Fintech #ML #DataScience #Statistics #Quant #DataScientist -
XGBoost: A Reliable Machine Learning Algorithm
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Just remember: no matter how bad things get out there, you can always count on XGBoost. @trainxgb