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David Smooke

Smooke
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https://hackernoon.com

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data, software, news, currency, cryptocurrency, software development, llms, internet usage, software market shares, startup investment data, startup location data, hackernoon

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posted an update 15 days ago
🚀 Building or experimenting with AI? Find free compute & software credits from major & emerging programs→ https://hackernoon.ai/software-compute-grants-credits-search 💰 $50M+ in active programs for AI developers, projects, and startups.
published an article 18 days ago
Why Does MCP Matter? What is Google's Strategic Approach to MCP?
posted an update about 1 month ago
"How AI Reasoning Mirrors Borges' Library of Babel" https://hackernoon.com/how-ai-reasoning-mirrors-borges-library-of-babel Many people have the intuition that an LLM doesn't really understand what it's saying. It doesn't really reason, it has no intent to convey anything, and it lacks an innate distinction between truth and falsehood. The intuition is sound, but not so easy to substantiate. If you have a bit of technical savvy, you can point out that an LLM is just a fancy autocomplete, or a "token predictor". This consolidates the intuition a little - predicting tokens (i.e. word fragments, short words, symbols…) does seem like a far cry from deliberation - but it remains an intuition, which can be reasonably questioned: could you finish this sentence if you didn't build up an understanding of what the paragraph is getting at? Now, those with more charity towards our talking machines will argue that LLMs are still poorly understood "black boxes", and accuse the skeptics of reductionism: isn't Nature full of "mundane" mechanisms that give rise to emergent phenomena, with qualities that differ from their basic causes? Maybe the complex mathematical machinery behind token prediction somehow produces genuine understanding? Eh, probably not. The key thing to understand about the token predictor perspective is that it isn't actually reductionist - at least not in the way of calling the brain "just a clump of neurons". It's a decent characterization of the high-level, functional definition of the model. Yes, the model itself is a huge ensemble of small parts, but they all work together to satisfy exactly one demand: given N previous tokens, predict the next one. The ability to complete this task is the emergent property you'd expect to be downstream from numerical data flowing through the model's layers. But couldn't something extra still emerge out of repeated token prediction? ... https://hackernoon.com/how-ai-reasoning-mirrors-borges-library-of-babel
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