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Comment by xvector | Hacker News Reader

It's a bit strange to call it a dead end when this was trained on just a single NVIDIA GB200 NVL72. LLMs couldn't do much back then, either.

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But the thing that makes this special, is explicitly cutting things out that make LLM's capable of that. As gpm's sibling comment points out, if you would add that back in, you would just end up in the converging to the same place (with the same downsides).

The only way around that would be to somehow get all world knowledge and tool calls into a audio space, which seems like quite the stretch.

> be to somehow get all world knowledge and tool calls into a audio space, which seems like quite the stretch.

It seems quite easy? They have the natural language bit nailed, they just need to convert the text with it. And it wouldn't be hard to extend architecture this into outputting tool calls.

This thing has the "the natural language nailed" to the same degree that a BERT has. That's a far cry from the intelligence level required to output text of any sensible length, and even farther from the structural adherence required for tool calls.

Highly-structured outputs (like what you'd need for tool calls) seems like it'd be easier to put out than arbitrary text, no? At the very least, even something like BERT could make the decision of “Would any of my available tools be useful here?” and delegate to some deeper-thinking model to structure that tool call.

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