Join @DigEconLab
's seminar with MIT's @asheshrambachan as he presents a framework for extracting finite automata from generative sequence models, creating interpretable summaries of next-token probabilities. For researchers exploring ML and economics: https://
digitaleconomy.stanford.edu/event/ashesh-r
ambachan-del-seminar-series/
…
MACHINE LEARNING
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Extracting Finite Automata from Generative Sequence Models
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Science needs models balancing predictive power with simplicity
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Science needs a way to process models that are only "mostly correct" in terms of their predictions, but are very compressive (high ratio between predictive power and model complexity). They are likely onto something.
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Physics History as Program Synthesis: Kepler and Newton’s Model Search
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We should view the history of physics as a long-running program synthesis task. Kepler and Newton were searching the space of possible symbolic models to find the simplest one that would best satisfy available observations.
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Bfloat16 Precision Gaps in Large Scatter Plots Beyond Origin
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Making a scatter plot of 400_000 data points, some of the plots had odd gaps in coverage. It took me a little while to realize that it was only when the data was farther from the origin — it was the raw bfloat16 precision. Everything looks great from -1 to 1, but as you go past 2 and 4, the coverage gaps get larger. My intuition didn't have it being quite so "discretely countable" at those modest numeric values. Float32 for comparison.
→ View original post on X — @id_aa_carmack, 2026-04-09 23:01 UTC
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AI Revolutionizes Medical Diagnosis and Relieves Doctor Burnout
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AI is spotting polyps in colonoscopies in real time, cutting missed cancers by up to 50%. Multi-agent systems are predicting risks earlier than ever — from pediatric developmental issues to heart failure. We're not replacing doctors. We're finally giving them time to be doctors again. The burnout relief + better outcomes combo? Game-changing. [Translated from EN to English]
→ View original post on X — @scobleizer, 2026-04-09 22:27 UTC
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How AI Agents Manage Goals and Objectives
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How #AIAgents Manage Goals and Objectives by @e_opore #AI #LLM #ArtificialIntelligence #MachineLearning #ML
→ View original post on X — @ronald_vanloon, 2026-04-09 22:16 UTC
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Frontier models improve in coding and creativity in sync
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And all the evidence is is that is that models are getting better all this other stuff at the same time as they are improving in coding. More recent models are more creative, for example. Still plenty of jaggedness, but the frontier moves more in synch than we might expect
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AI is jagged but also surprisingly general across domains
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AI is jagged, but I think sometimes it is easy to overly focus on that. The generalness is a surprise too! LLMs may be optimized for verifiable fields like coding, but AI is also not bad at corporate strategy & medical advice & writing a sestina & expressing empathy & ideation.
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5 Pillars for AI in Revenue Growth Management
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5 pillars for #AI use in revenue growth management by @antgrasso #Finance #MachineLearning #ArtificialIntelligence #ML #MI #Tech #Technology
→ View original post on X — @ronald_vanloon, 2026-04-09 21:20 UTC
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Tesla Autonomous Driving Technology Operating in Los Angeles
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Tesla driving itself around LA pic.twitter.com/NyM36R6J7a
— Elon Musk (@elonmusk) 9 avril 2026Tesla driving itself around LA