Sofus Mortensen @sofusmort

Zug, Switzerland Joined October 2011

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Sofus Mortensen @sofusmort

4 weeks ago

@ChShersh Thanks to AI there’s still zero reason to learn Python.

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Sofus Mortensen @sofusmort

a month ago

@HowToAI_ Deterministic breakthrough was 1999 with SSSP in linear time. dl.acm.org/doi/10.1145/31…

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Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

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Sofus Mortensen @sofusmort

2 months ago

5 months ago

@RpsAgainstTrump Mac’n cheese guy.

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Sofus Mortensen @sofusmort

5 months ago

@davepl1968 @iluminatibot Hey @grok. Where are you more likely to die from atherosclerosis - considering quality and availability of healthcare - Canada or USA?

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Rasmus Jarlov @RasmusJarlov

5 months ago

Every insult, threat, tariff and lie that we receive strengthens our resolve. The answer from Denmark and Greenland is final: We will never hand over Greenland. We pray that our true allies will stand with us because we are going to need it.

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Sofus Mortensen @sofusmort

5 months ago

@ChShersh @JayD0ubleu Me too. You’re a better engineer understanding what your tools produce and how they do it. Applies when using a C++ compiler and even more so when relying on AI to do your work.