PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro – Dr. Juan Camilo Orduz https://
buff.ly/TjG17kR
#AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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Stochastic Variational Inference Introduction NumPyro PyData Berlin
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Top Trending Model on Hugging Face Platform
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Top trending model on HF for the past few days! https://t.co/nXICZFlqbe
— clem 🤗 (@ClementDelangue) 14 janvier 2026Top trending model on HF for the past few days!
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Founder’s Eczema Battle Sparks Data-Driven Healthtech Startup
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How a founder's battle with eczema led to a data-driven healthtech startup
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @Scobleizer @AndrewYNg @drfeifei @KirkDBorne @fchollet @rowancheung @antgrasso -
Novel Algebraic Geometry Theorem Proven Using AI-Specialized Gemini
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New paper out today, proving a novel theorem in algebraic geometry with an internal math-specialized version of Gemini. This was a collaboration between @GoogleDeepMind (Professor Freddie Manners and @GSalafatinos, hosted by the Blueshift team) and Professors Jim Bryan, Balazs Elek, and Ravi Vakil. arxiv.org/abs/2601.07222
→ View original post on X — @oriolvinyalsml, 2026-01-13 23:08 UTC
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MIT Free Course: Machine Learning Techniques in Healthcare
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A free throwback MIT course breaking down how machine learning techniques can be applied to healthcare: https://t.co/TrQlckLh8o (v/@MITOCW)
— MIT CSAIL (@MIT_CSAIL) 13 janvier 2026
Here, MIT prof. & CSAIL principal investigator David Sontag discusses how AI can help sort thru medical data (Lecture 1). pic.twitter.com/MJyvJXIMQQA free throwback MIT course breaking down how machine learning techniques can be applied to healthcare: https://
bit.ly/3YyrGj9 (v/
@MITOCW
) Here, MIT prof. & CSAIL principal investigator David Sontag discusses how AI can help sort thru medical data (Lecture 1). -
Efficiency challenges in AI world models
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I see some breakthroughs coming on compute, so we'll see. That's the big question: can they get the world models to be very, very efficient?
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The Advancement and Future Potential of AI World Models
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A new real-time world model.
— Robert Scoble (@Scobleizer) 13 janvier 2026
This is a step towards instant TV or, sometime in the next 24 months, holodecks.
As I told you last year, world models are going to be the theme of 2026. https://t.co/M2MGsjVGQ0A new real-time world model. This is a step towards instant TV or, sometime in the next 24 months, holodecks. As I told you last year, world models are going to be the theme of 2026.
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Agricultural Robotics: Automating Broccoli Harvesting at Scale
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Broccoli might not look like a robotics problem, until you try to harvest it at scale. Machines that can identify ripe heads, coordinate multiple robot arms and pick thousands per hour show how far agricultural robotics has come. This isn’t about replacing farmers. It’s about
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AI Search Metrics Beyond SEO: Entity Recall Over Page Rankings
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We still measure AI search with SEO-era metrics. But LLMs don’t rank pages. They recall entities.
→ View original post on X — @coremention, 2026-01-13 15:01 UTC
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Improving Self-Evolving LLM Agents Learning Capabilities
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On building more powerful self-evolving agents. LLM agents struggle to learn from experience after deployment. Fine-tuning is expensive and causes catastrophic forgetting. RAG retrieves based on semantic similarity alone, often pulling noise instead of what actually works.