Demis Hassabis now says AGI could arrive by 2029, a year earlier
than his previous estimate, and told Axios we're standing in the
"foothills of the singularity." Bold claim. But the field still can't agree on what AGI actually
means. Hassabis defines it one way, Altman another,
RESEARCH
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Demis Hassabis predicts AGI by 2029, sparking debate on its definition
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Perplexity open-sources Bumblebee (blog + GitHub)
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Blog post + GitHub repo: http://
perplexity.ai/hub/blog/perpl
exity-is-open-sourcing-bumblebee
… http://
github.com/perplexityai/b
umblebee
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Agent Token Efficiency: Context Re-reading Loop Performance
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input tokens are the real driver here. agent re-reads the entire context on every single loop
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MiniMax releases M2 series technical report for agentic tasks
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MiniMax just released a technical report for the MiniMax-M2 Series! The MiniMax-M2 series, a family of Mixture-of-Experts models designed from the ground up for agentic tasks. The flagship M2 packs 229.9B total parameters but activates only 9.8B per token—thanks to an
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MIT Study: AI Agent Token Costs Dominate Inference Economics
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MIT just killed the idea that cheaper tokens mean cheaper AI. A paper from MIT, Stanford, Google DeepMind, and Microsoft found agents use 1000x more tokens than chat. The cost isn't the code they write. It's re-reading context on every loop. Same task, same model: 30x cost
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MiniMax-M2 Agent-Native RL Training Paper Released
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MiniMax-M2 paper just dropped The key focus of M2 is on something more agent-native. It trains on runnable workspaces and artifact-grounded rewards, then uses Forge to scale RL over long coding, app, search, and office-task trajectories. What's interesting is that M2.7
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Super Study Guide for Transformers and Large Language Models
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Super Study Guide: Transformers & Large Language Models: http://
amzn.to/3SW6YYm by @afshinea & @shervinea Beautifully presented, excellent content, timely, thorough, educational #LLMs #MachineLearning #AI #GenAI #DataScience #DataScientist #GenerativeAI -
Huawei’s EDCO: Dynamic Curriculum Orchestration for Real-Time LLM Adaptation
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What if fine-tuning a large language model could adapt its training in real-time, not just follow a static plan? Researchers from Huawei introduce EDCO, a dynamic curriculum orchestration framework. Instead of pre-ordering training samples by difficulty, EDCO prioritizes data
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Successful optimization over trillions of variables: unprecedented feat
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Even if it's not AGI, successful optimization over trillions of variables is an unprecedented feat for humanity.
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Weight Decay Paradox in Machine Learning Training
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And in yet another instance of the AI comedy, weight decay was invented to control overfitting, but what it really does is improve training loss.