Yann LeCun raises $1B to build AI that understands the physical world

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Mog has no exceptions. There is no throw, no invisible stack unwinding, no try/finally cleanup semantics. When a function can fail, it says so in its return type, and the caller decides what to do. Errors are values — you create them, return them, match on them, and propagate them with the same tools you use for everything else.

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The converse is also worth asking — whether simulating artificial environments (for instance a 3d representation of a Youtube video) might have unintended negative consequences. Fei-Fei Li’s startup World Labs, which aims to make the leading “world model” — an alternative to language models based on tokenizing physical space rather than words — recently raised a substantial amount of money. As consumer-facing robots become more plausible, the business case for such a model is obvious. But what physical spaces are “world” models actually being trained on? The contemporary physical environment, sound-proofed, plastic-coated, and artificially-colored, is radically different from the environment that Homo sapiens evolved to excel in.

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