7/ So what does help? Tool calling. Context engineering. Retrieval-augmented generation (RAG). External fact-checking. If you care about truth, offloading to real tools and live sources is your best shot.
MACHINE LEARNING
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LLMs: No truth engine, no knowledge base, only probabilities
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4/ That’s the trick. LLMs don’t have a truth engine. They don’t even have a “knowledge base.” They only have probabilities: “What kind of thing usually comes next when people ask something like this?”
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LLMs: Predicting language, not understanding truth or facts.
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1/ Everyone says: "LLMs don’t understand language – they predict it." Correct. But that also means:
They don’t understand truth, accuracy, or even verifying a fact. They only learn how people talk about facts. -
LLMs: Predicting Language, Not Understanding Truth or Facts
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1/ Everyone says: "LLMs don’t understand language – they predict it." Correct. But that also means:
They don’t understand truth, accuracy, or even verifying a fact. They only learn how people talk about facts. -
LLMs improvise, not hallucinate: the uncomfortable truth
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LLMs don’t “hallucinate.” They improvise. Calling it a bug is just a polite lie. Here’s the uncomfortable truth about how AI really “thinks” And why your prompt can’t fix it :
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UCLA wearable brain-computer interface enables paralyzed users control robotic arms
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UCLA bridges mind and machine A wearable brain-computer interface lets paralyzed users control robotic arms using AI-powered EEG signals—no surgery required. https://
shorturl.at/aWSGB -
AI World Levels Up Across Every Frontier This Week
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The AI world didn’t slow down this week — it leveled up across every frontier. From living rooms to labs, here’s what happened:
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Named Tensors in Deep Learning: Shape Propagation Challenges
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see this link below, it was a failure and we stopped development. @zou3519 can give any more insights (or @srush_nlp )
naming dimensions is cool until you have to preserve the names across shape propagation (the output of an operation who's input is a named tensor).
so named -
Understanding Deep Learning: How It Works Explained
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For example, if you want to understand how deep learning works, you could read *our* book. There's a few paragraphs at the start about some of the history (because it's pretty interesting, and helps understand "why") but 99% is "how it works".
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AI Curves: Correlation Instead of Causation, a Solution
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eli5 version: right now, ai “stores” what it learns as a giant curve. when you ask it something new, it just guesses a spot between points on the curve. that’s why it often sounds right but isn’t – it’s correlation, not causation. the fix? instead of only curves, we also give ai