- The physics of the universe can be completely modeled as computation, and
- It's possible to pose undecidable problems about the way the universe unfolds
This is intrinsic to the idea of undecidability even for Turing machines, e.g. "we equate computation with the functioning of Turing machines, but there are real processes executable in Turing machines that are undecidable".
This was long before. Google had conversational LLMs before ChatGPT (though they weren’t as good in my recollection), and they declined to productize. There was a sense at the time that you couldn’t productize anything with truly open ended content generation because you couldn’t guarantee it wouldn’t say something problematic.
Both would be understood and are roughly interchangeable.
"Sequential" feels more appropriate to me for the task runner scenario where we wait for one task to finish before running the next.
"Series" suggests a kind of concurrency to me because of the electrical circuit context, where the outputs of one are flowing into the next, but both are running concurrently. Processes that are Unix piped into each other would be another thing that feels more like a "series" than a "sequence".
You might be using JSON mode, which doesn’t guarantee a schema will be followed, or structured outputs not in strict mode. It is possible to get the property that the response is either a valid instance of the schema or an error (eg for refusal)
I assume you know how to program in Python? I would start with just the client libraries of the model providers you want to use. LLMs are conceptually simple when treated as black boxes. String in, string out. You don't necessarily need a framework.
I’m not as familiar with the Baby Bells, so this is a surprising comparison to me. Bell Labs was famously so productive while it had the monopoly money hose, and not as much came from it after Bell was broken up.
What are the most noteworthy accomplishments of the Baby Bells?
I don’t know of any public details on how they implement Context Caching, but that is presumably exactly what they are doing. Just caching the text would be a minimal savings.
The size of the cached internal state of the network processing the book is much larger than the size of the book. The resource that is preserved with caching is the compute required to recreate that state.
I’m guessing there was an instrumental reason for this, for instance to check that the model was listening before launching into what they wanted to demo
There are many different rules of thumb on this floating around. 1g per lb of body weight is indeed something a number of well informed people recommend, but those people generally acknowledge that it’s something like an upper bound on the amount that is useful.
Attention itself was the key idea of that paper and, as you sort of acknowledge, was definitely not just throwing things at the wall. It was the culmination of a long line of work gradually progressing toward fully dynamic routing via attention, and it was motivated, if not by deep theory, at least deep intuition from linguistics. The other details of transformers are perhaps sort of arbitrary, but made sense to everyone at the time. There was no claim that those other details were optimal - just that they were one way of surrounding the attention mechanism with computing machinery that worked.
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