This won't happen in our lifetime. Not only because this is more complex than rambling vaguely correlated human speech while hallucinating half the time.

[–] [email protected] 3 points 1 year ago (6 children)

Off the shelf models do this, yes.

Sophisticated local trained models on expensive private hardware are already dunking on publicly available versions. The problem of hallucination is generally resolved in those contexts

[–] sacredfire 1 points 1 year ago (1 children)

Why does a pre-trained model need expensive private hardware after it was trained, other than to handle API requests faster? Is Open AI training chat-GPT on inferior hardware compared to these sophisticated private versions you mentioned?

[–] [email protected] 3 points 1 year ago

The fine tuning, while much more efficient than starting fresh, can still be a large amount of work.

Then consider that your target corpus of data may also be large.

Then consider to do your reasoning tasks across that corpus also takes strong hardware to get production ready response times.

No, openai isn't using inferior hardware, but their model goals, token chunking strategies and overall corpus are generalist in nature.

There are then processing strategies teams are using to go beyond the "memory" limitations gpt 4 has, that provide massive benefits to coherency, essentially anti hallucination and better overall reasoning

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