A former MIT researcher just mapped the path from a worm to a digital human brain. The plan scales from a 302-neuron worm to 86 billion neurons. Three technologies are making this tractable. 1. High-resolution imaging now maps neurons at scale
2. Functional scans capture
MACHINE LEARNING
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MIT Researcher Maps Path From Worm to Digital Human Brain
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Beginner’s Guide to KNN Applied to MNIST Handwritten Digits
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Beginner’s Guide to KNN and MNIST Handwritten Digit using KNN. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #Linux #Programming #Coding #100DaysofCode https://
geni.us/Beginner-KNN -
Creating Machine Learning Models Directly in Power BI
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Creating Machine Learning in #PowerBI. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #PowerBI https://
geni.us/ML-PowerBI -
A2A Protocol: A Common Language for AI Agent Frameworks
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16- Le protocole A2A : faire parler des agents de frameworks différents LangGraph, LangChain, CrewAI chacun parle son propre dialecte. A2A est la langue commune qui les connecte tous. 3 concepts à retenir : ① Agent Skill = la description de ce que l'agent sait faire ②
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Building a Birthday Planner AI Agent with Python
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8- Ton premier agent : le Birthday Planner Tu passes juste 4 paramètres à la classe LLMAgent : LLMAgent( name="birthday_planner", model=Claude("claude-sonnet-4-5"), description="Planifie des fêtes d'anniversaire", instruction="Gère la liste d'invités et propose des
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AI Cell Modeling Could Accelerate Medical Cures
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If #AI Can Model Cells, Science Can Deliver Cures
by Priscilla Chan @time Learn more: https://
bit.ly/4ta5RDA #ArtificialIntelligence #ML #MachineLearning -
Open Source Models Still Trail Frontier Models in Capabilities
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Los modelos OSS ya tienen muy buenas capacidades para resolver muchas tareas, peeeeero aún están atrás de los modelos frontera.
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Domain-Specific Neurosymbolic AI Offers More Hope Than LLMs for Science
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Agreed with the first part, but I think the nuanced part is what you mean by “this technology”. AI may help all of this, but pure, domain-general LLMs per se probably won’t. Domain-specific neurosymbolic hybrids offer more hope for science than chatbots do. See my October NYT
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Diverse Simulated Personas Boost ReviewerToo Performance
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Oh, and in case that's interesting: one data point supporting the value of diversity in point of view from our work on ReviewerToo is that we get the best results when we pool more simulated reviewing personas in our system
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Open vs Closed AI Models: Beyond Benchmark Gaps
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This is a good explanation of why the gap between open and closed models is larger than it appears in benchmarks. I would add in that current open models are also more fragile than closed: they handle out-of-distribution problems far less well & have lower emergent capabilities.