Comment by byearthithatius

Shifting goalposts usually (at least in my understanding) refers to changing something without valid justification that was explicitly set in a previous step (subjective wording I realize - this is off the top of my head). In an adversarial context it would be someone attempting to gain an advantage by subtly changing a premise in order to manipulate the conclusion.

An incomplete list, in contrast, is not a full set of goalposts. It is more akin to a declared lower bound.

I also don't think it to applies to the case where the parties are made aware of a change in circumstances and update their views accordingly.

> You feel that LLMs are not intelligent. I feel that they may be intelligent.

Weirdly enough I almost agree with you. LLMs have certainly challenged my notion of what intelligence is. At this point I think it's more a discussion of what sorts of things people are referring to when they use that word and if we can figure out an objective description that distinguishes those things from everything else.

> Without a decent definition of what intelligence is, the entire argument is silly.

I completely agree. My only objection is to the notion that goalposts have been shifted since in my view they were never established in the first place.

> I dunno, that seems like a pretty good distillation of what moving the goalposts is.

Only if you don't understand what "the goalposts" means. The goalpost isn't "pass the turing test", the goalpost is "manage to do all the same kind of intellectual tasks that humans are", nobody has moved that since the start in the quest for AI.

Seems pretty reasonable to presuppose this when you filter to people who are literate. That’s darn near a definition of literate, that you can engage with the text intelligently.

My whole comment was accepting LLMs as intelligent. It’s the first sentence.

…things that look like problem solving strategies in performance, then.

>In practice most LLM systems use this distribution (along with a “temperature” multiplier) to make a weighted random choice among the tokens

In other words, LLMs are not deterministic in just about any real setting. What you said there only compounds with MoE architectures, variable test-time compute allocation, and o3-like sampling.

i've heard it actually depends on the model / hosting architecture. some are not deterministic at the numeric level because there is so much floating point math going on in distributed fashion across gpus, with unpredictable rounding/syncing across machines

Depends on what you mean specifically by the output. The actual neural network will produce deterministic outputs that could be interpreted as probability values for various tokens. But the interface you'll commonly see used in front of these models will then non-deterministiclaly choose a single next token to output based on those probabilities. Then, this single randomly chosen output is fed back into the network to produce another token, and this process repeats.

I would ultimately call the result non-deterministic. You could make it deterministic relatively easily by having a deterministic process for choosing a single token from all of the outputs of the NN (say, always pick the one with the highest weight, and if there are multiple with the same weight, pick the first one in token index order), but no one normally does this, because the results aren't that great per my understanding.

Now compare a human to an LSTM with persistent internal state that you can't reset.

You can turn the determinism knob on humans. Psychedelics are one method.