AI’s Impact Accelerates
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @demishassabis @Ronald_vanLoon @TamaraMcCleary @geoffreyhinton @goodfellow_ian @jeffdean @erikbryn
MACHINE LEARNING
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AI’s Impact Accelerates: Innovation and Future Prospects
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Four Key Takeaways for Finance Teams Implementing AI
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4 takeaways for finance teams as they implement AI
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @Scobleizer @AndrewYNg @drfeifei @KirkDBorne @fchollet @rowancheung @antgrasso -
MolHIT: Hierarchical Discrete Diffusion for Molecular Graph Generation
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MolHIT Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
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HyTRec: Hybrid Temporal Attention for Sequential Recommendation
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HyTRec A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation
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Naive Bayes classification tutorial with Python code
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Naive Bayes Classification, explained with Python code: https://
github.com/taspinar/siml/
blob/master/notebooks/Naive_Bayes.ipynb
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Learn more in this book: http://
amzn.to/312hAHF
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#DataScience #MachineLearning #AI #ML #Algorithms #Statistics #DataScientist #Mathematics -
Modular Agent Architecture with Conversational and Investigatory Routing
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In a modular agent setup, a router first classifies the request as conversational or investigatory. If investigatory, a retrieval module fetches relevant docs [eval: grounding], and a synthesis module integrates the evidence into a response [eval: faithfulness, coverage].
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AI Agents Need Sequential Modularity for Better Evaluation
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2/4 Modern AI systems plan, retrieve across sources, reason, take actions, and update state. But most evals capture only the end result of that entire process. Agents need sequential modularity, like human language production, to enable evaluation per module and support
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Materials Science and Generative AI Revolutionize Scientific Discovery
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This was a very nice interview by Latent Space in sunny San Diego at Neurips. https://t.co/lHUta2ezG9
— Max Welling (@wellingmax) 26 février 2026This was a very nice interview by Latent Space in sunny San Diego at Neurips. Latent.Space (@latentspacepod) 🔬 New Science pod with @cusp_ai! We are entering a new era where materials science and discovery is transitioning from slow, manual experimentation, to a high-speed search problem powered by generative AI and "physics processing units." @wellingmax argues that the foundation of all modern technology—from GPUs to climate solutions—is a materials problem, and that unifying the mathematics of stochastic thermodynamics with generative AI will unlock a new paradigm of automated scientific discovery. — https://nitter.net/latentspacepod/status/2026716448626978955#m
→ View original post on X — @wellingmax, 2026-02-26 10:18 UTC
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Teaching Learning Algorithm Optimizes Solar PV Maximum Power Point
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New publication from @IngeniumRG on optimizing solar PV systems. Our work uses a Teaching Learning-Based algorithm with neural networks to predict maximum power points more accurately, boosting renewable energy efficiency. sciencedirect.com/science/ch… @faustospain
→ View original post on X — @faustospain, 2026-02-26 10:06 UTC
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Burning model into chip achieves 51k tokens per second
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now add this to silicon that burns the model into the chip. And we will go from 17.000 token/s to 51.000 tokens/s inference throughput will go on to expand so much faster than we ever could have predicted. This will make for the most absurd applications.