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AI to help researchers see the bigger picture in cell biology

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Biological engineering, Biology, Broad Institute, Cells, Computer science and technology, Diagnostics, Electrical engineering and computer science (EECS), Genetics, Health, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Medicine, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering

Studying gene expression in a cancer patient’s cells can help clinical biologists understand the cancer’s origin and predict the success of different treatments. But cells are complex and contain many layers, so how the biologist conducts measurements affects which data they can obtain. For instance, measuring proteins in a cell could yield different information about the […]

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What is Seedance 2.0? [Features, Architecture, and More]

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, Generative AI

A few years ago, generating an image from text felt magical. Then text-to-video turned prompts into moving scenes. Now models generate complete video sequences without cameras, actors, or editing timelines. ByteDance’s Seedance 2.0 pushes this further. Instead of short silent clips, it delivers a multimodal system that plans scenes in shots, synchronizes audio natively, and

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ET AI Conclave & Awards 2026: The AI room everyone wants to be in

ai, AI (Artificial Intelligence), Artificial Intelligence

India’s artificial intelligence ecosystem has seen numerous AI events, AI conferences, AI summits, and AI networking forums. But once in a while, an AI leadership summit rises above the noise. The ET AI Conclave & Awards is the defining AI event India has been waiting for, the AI leadership summit in India that industry insiders

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Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release of LFM2-24B-A2B, a 24-billion parameter model that redefines

Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs Read More »