The legendary researcher behind the classic "Faith and Fate" paper has dropped a new paper on compositionality! I can't wait to dig into this. @nouhadziri is one of my absolute fave thinkers in this space so I have a feeling this will be a classic too…
MACHINE LEARNING
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Jeff Dean praises Stanford’s CS336 Language Models from Scratch course
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Very cool thread about the CS336 Language Models from Scratch course at Stanford taught by @percyliang et al. Makes me wish I was a student again!
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Google VideoPrism tool for automated video scene segmentation
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Aprovechando la salida del nuevo modelo opensource de Google VideoPrism, para análisis de vídeo, me he programado una herramienta que a partir de analizar la similitud de los embeddings obtenidos corta el vídeo en sus diferentes escenas.
— Carlos Santana (@DotCSV) 24 juin 2025
Idealmente en un futuro usaré la… pic.twitter.com/LTbgrM2RXqAprovechando la salida del nuevo modelo opensource de Google VideoPrism, para análisis de vídeo, me he programado una herramienta que a partir de analizar la similitud de los embeddings obtenidos corta el vídeo en sus diferentes escenas. Idealmente en un futuro usaré la
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Simple Ideas Behind Massive AI Research Compute Investments
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AI research is strange in that you spend a massive amount of compute on experiments to learn simple ideas that can be expressed in just a few sentences. Literally things like “training on A generalizes if you add B”, “X is a good way to design rewards”, or “the fact that method M is sample efficient means that we should create environments with this specific property”. But somehow if you find the correct five ideas and you really understand them deeply, suddenly you’re miles ahead of the rest of the field
→ View original post on X — @_jasonwei, 2025-06-24 19:15 UTC
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Sakana AI New Model Sparks Reinforcement Learning Revolution
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Sakana AI New Model Sparks a RL Revolution https://
youtu.be/2mezj14pCFI?si
=HozvSHRv-57IC6T-
… via @YouTube -
Lidar Cars and Tesla FSD Gap to Level 4 Autonomy
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Lidar equipped cars can be mass produced for $30k. And Tesla FSD is still L2, needs 100-1000x higher city miles per critical disengagement to get to L4.
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MedGemma: Google’s Vision-Language Model for Healthcare Applications
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🚀Meet MedGemma – Google’s powerful Vision-Language Model for healthcare, built on the Gemma 3 architecture.
— Satya Mallick (@LearnOpenCV) 24 juin 2025
In our article, we break down MedGemma’s core DNA, showcase real-world applications, walk you through the code, and highlight its stellar performance.
Whether you're… pic.twitter.com/Fp4hzU2dp1Meet MedGemma – Google’s powerful Vision-Language Model for healthcare, built on the Gemma 3 architecture.
In our article, we break down MedGemma’s core DNA, showcase real-world applications, walk you through the code, and highlight its stellar performance. Whether you're -
How AI Is Replacing Middle-Class Careers
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How AI Is Replacing Middle-Class Careers https://
youtu.be/yic4ImNSE3w?si
=nWfXgR-hVnh5Q__u
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Gaussian Mixture EM Assumptions: Separation Requirement Analysis
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After careful reading I saw that this analysis still requires the Gaussian mixtures to be separated for EM to succeed- something that the method of moments doesn’t require. Do you think that assumption can be removed?
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Diminishing Returns in Language Model Training Efficiency
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the returns have been diminishing for a while, that’s certainly true NeoBERT is 250M params but trained on 2T tokens. objectively a crazy thing to do