measurablefunc

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I don't comment on internet forums to be complimented on my response style. Address the substance of my arguments or just save yourself the keystrokes.Convincing (counter)arguments make as few assumptions as possible & clarify them as they unfold w/o making unnecessary claims that are not validated w/ the evidence presented. Furthermore, pathologizing the speaker/writer does not contribute to anything either so my recommendation is to leave those out as well.
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Slovenia becomes first EU country to introduce fuel rationing

https://www.bbc.com/news/articles/c77m4zx6zvmo

Slovenia becomes first EU state to introduce fuel rationing

Until further notice, motorists in Slovenia will be restricted to a maximum purchase of 50 litres of fuel per day.

It's literally a loop that wraps APIs from AI providers. Go ahead & explain how an open source AI wrapper fixes security holes inherent in existing AI.
All of them. It's not like AI companies have managed to fix the security issues since last time they promised they had fixed all the hallucinations & accidental database deletions.
People manage to respond to them just fine.
Still not addressing any of my points & making it personal is not going to make you any less confused.
It's neither rude nor wrong to ask for evidence to support claims being made in what appears to be corporate advertising. The claim is their LLM is better than a person, I asked for evidence. None was presented. It's not complicated.
That's great but I didn't ask & that's still not addressing my point.
I read it, you're mistaken.
Fewer instructions doesn't mean it's faster. It can be faster but it's not guaranteed in general. Obvious counterexample is single threaded vs multi-threaded code. Single threaded code will have fewer instructions but won't necessarily be faster.
Because it's a well know failure mode of neural networks & scalar valued optimization problems in general: https://www.nature.com/articles/s42256-020-00257-z
Shortcut learning in deep neural networks - Nature Machine Intelligence

Deep learning has resulted in impressive achievements, but under what circumstances does it fail, and why? The authors propose that its failures are a consequence of shortcut learning, a common characteristic across biological and artificial systems in which strategies that appear to have solved a problem fail unexpectedly under different circumstances.

Nature