However, it's pretty good at regurgitating textbook answers (classic Google...). This makes it an asset for circuit design. Try one of my real queries with Gemini Pro:
"Create a differential current splitter that splits a bias current Ib into a positive current Ipos = Ib/2 + ky*Vy and a negative current Ineg = Ib/2 - ky*Vy where Vy = Vy+ - Vy- is a differential voltage input Y."
Please forgive my naivety, but are world models (once they are in a consumer-ready form) expected to outperform any currently existing LLM on these sorts of tasks (i.e. of the physical world)?
A really frustrating partial presentation, given an apparent lack of testing with a spread of efforts for each model.
Given that there's no reason to believe that Fable's xhigh is comparable to GPT-sol's xhigh, or Opus xhigh, for that matter, it would be far more useful to see the effort level where these tasks no longer achieved their goals.
Honestly, I just hate the term "physical AI". They're robots. It's unfortunate that we had to adopt a term with the words AI in it, just to get investor's attention.
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[ 0.23 ms ] story [ 22.8 ms ] threadHowever, it's pretty good at regurgitating textbook answers (classic Google...). This makes it an asset for circuit design. Try one of my real queries with Gemini Pro:
"Create a differential current splitter that splits a bias current Ib into a positive current Ipos = Ib/2 + ky*Vy and a negative current Ineg = Ib/2 - ky*Vy where Vy = Vy+ - Vy- is a differential voltage input Y."
Given that there's no reason to believe that Fable's xhigh is comparable to GPT-sol's xhigh, or Opus xhigh, for that matter, it would be far more useful to see the effort level where these tasks no longer achieved their goals.
How do the two lower models compare to e.g. gpt 5.5 or 5.4 or various opus models?