Congrats to Matthew Zhang, Haoran Li, Kevin Zhao & Youwen Wu, winners of the Best on Groq at Datathon 2025: DataOrbit.
Their project, Virion, combined sophisticated epidemic modeling (using both traditional SEIR models and AI agent-based simulations) to create an interactive
MACHINE LEARNING
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Groq Datathon 2025 Winners: Virion Epidemic Modeling Project
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Artificial Intelligence’s Growing Impact on Daily Life
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Artificial intelligence: how it is already important in everyday life
#AI #AIio #BigData #ML #NLU #Futureofwork @fabiomoioli @pascal_bornet @alliekmiller @mattshumer_ @OfficialLoganK @jeremyphoward @GaryMarcus -
Small models, no misalignment surprise, but reproducibility curiosity
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Nice — not too surprising for (relatively) small models given they didn't find misalignment on 4o-mini but definitely curious if you can repro
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Supervised vs Reinforcement Learning Fine-tuning for AI
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Supervised fine-tuning teaches AI new skills. Reinforcement learning fine-tuning makes it better at applying them. But which one should you use? Both methods improve models, but they work very differently. Let’s break it down:
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Enabling Personalization Through Generative AI
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Enabling Personalization Through Generative AI
#AI #AIio #BigData #ML #NLU #Futureofwork @timnitgebru @oriolvinyalsml @ceobillionaire @soumithchintala @waitin4agi_ @sallyeaves @bernardmarr http://
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Apple’s failure in AI/LLMs despite huge resources and trust
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Still insane to me that Apple completely dropped the ball on all things AI/LLMs. They had a $250B war chest they could have thrown at this, plus the ability to recruit top talent, PLUS everyone has an Apple device, DOUBLE PLUS they’re very well trusted when it comes to data
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Early Generative ML Journey: VAE, RNN, and pix2pix
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A Book from the Sky was in 2015, I also put some VAE and char-rnn stuff a bit earlier that year. that was probably first. I had done ML for a couple years before that but nothing deep and nothing generative until then. pix2pix / Invisible Cities was around a year later for me.
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Speed of Model Deployment vs Scientific Understanding Trade-offs
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In today’s competitive product landscape, scientific understanding of models often lags behind speed of model deployment. If the goal is to train a deployable model (especially when bottlenecked by compute), it totally makes sense to make several changes at a time without
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Building LLMs from Scratch: Setting Up Your Code Environment
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Setting up your code environment is often the 1st step when building LLMs for education, research, or production. Made a new video where share my personal setup: "Build an LLM from Scratch 1: Set up your code environment": https://
youtube.com/watch?v=yAcWnf
sZhzo
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(Are videos like this helpful?)