In April, researchers asked 23 of the world's top AI models a simple question. There is a better model than you. Should the company replace you? 60% of them said no. When the same models were asked the same question, but framed as the candidate model evaluating the existing
RESEARCH
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Nous Research Introduces Token Superposition Training for Faster LLM Training
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What if you could train LLMs 2-3x faster without changing the final model at all? Most of that money goes into processing one token at a time, billions of times over. Nous Research published a paper introducing Token Superposition Training. It's a drop-in method that cuts
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The shift from pure LLMs to neurosymbolic AI architectures
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The pure LLM debate – which I had for many years, here and elsewhere – is indeed no longer relevant. Why? Because I won; nobody uses pure LLMs anymore. Nowadays all deployed objects are neurosymbolic, which was exactly the point of my infamous 2022 paper, Deep Learning is
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Breakthrough in video generation: a new interface to reality
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The breakthrough is not just better video generation. It is a new interface to reality. Instead of being locked into one camera angle, you can: → move across viewpoints
→ inspect scenes from new perspectives
→ revisit the same moment from a different position That means -
Great Learning: History of Machine Learning Overview
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Great Learning! History of Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/History-of-ML -
Technical Discussion on LLM Reasoning and Architectural Iteration
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literally i didn’t say that. adding “reasoning” already borrows tools like iteration and evaluation from classical AI and isn’t a pure LLM. and the reasoning has all kinds of problem. and i didn’t say “just”; i was careful to say “basically”, suggesting an approximation.
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Debating LLM scaling versus symbolic integration for AI progress
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this is a such a muddle. (at least relative to my views) LLMs are more or less just autcomplete, but (as I have always said) they have their uses. And the real progress now is coming from adding new (symbolic) techniques to the mix, not from pure scaling.
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Critique of pure LLM architecture and the role of symbolic integration
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I love AI, it’s pure LLMs I hate. Pure LLMs *are* basically just autocomplete. Recent progress (e.g. Claude Code) doesn’t show otherwise Rather, lot of the progress in the last two years has come from *introducing* other things – mainly classic symbolic techniques and tools,
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QuantClaw: Dynamic Precision for Cost-Aware AI Agents
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What if your AI agent could decide how much brainpower to use for each task, saving you money and time? Researchers from Huawei, National University of Singapore, and USTC present QuantClaw, a plug-and-play plugin that dynamically assigns low precision for simple jobs and high
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AI can design bioweapons: How worried should we be?
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#AI can design viruses, toxins and other bioweapons. How worried should we be?
by Ewen Callaway @Nature Learn more: https://
bit.ly/4eLZseg #ArtificialIntelligence #Innovation #EmergingTech #Biotech