davebren
Joined 11 months ago
Joined 11 months ago
AGI is fundamentally impossible through data scaling like they tried to claim and achieving AGI is what all this depends on. The long tail problem will remain undefeated and the IPOs are a desperate move to get the cash needed to scale one last time.
They could have just kept improving the technology without all the psychosis and finding use cases and ways to make it more reliable but instead they bet everything on a language model becoming their slave god.
Slowly deflating would be nice, but I don't see how. In any case the economy is getting wrecked and any goodwill tech companies had with employees is gone after going completely adversarial towards them as soon as they had an opportunity to. The most profitable use case of gen-AI is still spams and scams.
And yes, you are obviously a fraud.
You haven't made a single technical point outside your bloviated claims to authority. I can safely assume you're a fraud because this is exactly how frauds speak. Actual scientists and engineers don't argue from authority they go and test the hypothesis for themselves, the fact that you balk at my suggestion to do this is amusing.
I said you can do a basic test because this is the best way to see it directly for yourself. It's very easy to do especially for an eminent machine learning visionary leader as yourself. You ask the LLM to produce two apps of similar complexity and technical challenge from a software perspective, one that is already in its training data and one that isn't and see which its more successful at. This isn't some controversial take, nor is it "unfalsifiable".
There's also hundreds of benchmarks demonstrating where the limitations are for LLMs, or you can study the progression of LLMs in mathematics and where the gains have been made and see that this also agrees with me. You can watch Chris Hay's videos demonstrating exactly how LLMs perform math layer by layer. Why is everyone using LLMs for search? Because it's an extremely efficient compression of all its training data. Did they figure out the Studio Ghibli art-style all on their own spontaneausly? No, they were trained on Studio Ghibli content. There's so many ways to come to this conclusion. But you seem to be too busy sniffing your own farts to be interested in learning anything about the field though.
Anyway like I said you can just test it out yourself and find out that I'm correct. Every skilled programmer already knows this and can predict what kind of complexity an LLM won't be able to handle. And anyone working on LLMs should know that they are completely dependent on their training data. The entire scaling hypothesis was based on this.
If you give it a prompt telling it to replicate a product that's in its training set then its optimal next token prediction output is going to be to a lossy copy of that product's source code.