Cleo Nardo

Wiki Contributions


The proposition "I am currently on Earth" is implemented both in the parameters and in the architecture, independently.

I think my definition of  is correct. It's designed to abstract away all the messy implementation details of the ML architecture and ML training process.

Now, you can easily amend the definition to include an infinite context window . In fact, if you let  then that's essentially an infinite context window. But it's unclear what optimal inference is supposed to look like when . When the context window is infinite (or very large) the internet corpus consists of a single datapoint.

Yep, but it's statistically unlikely. It is easier for order to disappear than for order to emerge.

I've spoken to some other people about Remark 1, and they also seem doubtful that token deletion is an important mechanism to think about, so I'm tempted to defer to you.

But on the inside view:

The finite context window is really important. 32K is close enough to infinity for most use-cases, but that's because users and orgs are currently underutilising the context window. The correct way to utilise the 32K context window is to fill it with any string of tokens which might help the computation.

Here's some fun things to fill the window with —

  • A summary of facts about the user.
  • A summary of news events that occurred after training ended. (This is kinda what Bing Sydney does — Bing fills the context window with the search results key words in the user's prompt.)
  • A "compiler prompt" or "library prompt". This is a long string of text which elicits high-level, general-purpose, quality-assessed simulacra which can then be mentioned later by the users. (I don't mean simulacra in a woo manner.) Think of "import numpy as np" but for prompts.
  • Literally anything other than null tokens.

I think no matter how big the window gets, someone will work out how to utilise it. The problem is that the context window has grown faster than prompt engineering, so no one has realised yet how to properly utilise the 32K window. Moreover, the orgs (Anthropic, OpenAI, Google) are too much "let's adjust the weights" (fine-tuning, RLHF, etc), rather than "let's change the prompts".

My guess is that the people voting "disagree" think that including the distillation in your general write-up is sufficient, and that you don't need to make the distillation its own post.

  1. Almost certainly ergodic in the limit. But it's highly period due to English grammar.
  2. Yep, just for convenience.
  3. Yep.
  4. Temp = 0 would give exactly absorbing states.

Maybe I should break this post down into different sections, because some of the remarks are about LLM Simulators, and some aren't.

Remarks about LLM Simulators: 7, 8, 9, 10, 12, 17

Other remarks : 1, 2, 3, 4, 5, 6, 11, 13, 14, 15, 16, 18

Yeah, I broadly agree.

My claim is that the deep metaphysical distinction is between "the computer is changing transistor voltages" and "the computer is multiplying matrices", not between "the computer is multiplying matrices" and "the computer is recognising dogs".

Once we move to a language game in which "the computer is multiplying matrices" is appropriate, then we are appealing to something like the X-Y Criterion for assessing these claims.

The sentences are more true the tighter the abstraction is —

  • The machine does X with greater probability.
  • The machine does X within a larger range of environments.
  • The machine has fewer side effects.
  • The machine is more robust to adversarial inputs.
  • Etc

But SOTA image classifiers are better at recognising dogs than humans are, so I'm quite happy to say "this machine recognises dogs". Sure, you can generate adversarial inputs, but you could probably do that to a human brain as well if you had an upload.

We could easily train an AI to be 70 percentile in recognising human emotions, but (as far as I know) no one has bothered to do this because there is ~ 0 tangible benefit so it wouldn't justify the cost.

Recognising dogs by ML classification is different to recognising dogs using cells in your brain and eyes

Yeah, and the way that you recognise dogs is different from the way that cats recognise dogs. Doesn't seem to matter much.

as though it were exactly identical

Two processes don't need to be exactly identical to do the same thing. My calculator adds numbers, and I add numbers. Yet my calculator isn't the same as my brain.

when you invoke pop sci


No it's not because one is sacred and the other is not, you've confused sacredness with varying degrees of complexity.

What notion of complexity do you mean? People are quite happy to accept that computers can perform tasks with high k-complexity or t-complexity. It is mostly "sacred" things (in the Hansonian sense) that people are unwilling to accept.

or you could continue to say AI feels things

Nowhere in this article to I address AI sentience.

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