Readit NewsEngineering in the broader sense often deals with managing the outputs of variable systems to get known good outcomes to acceptable tolerances.
Edit: added second paragraph
But really!?
I'll keep calling it in nice round powers of two, thank you very much.
(A fact that he does mention)
I'm not sure why he thinks current LLM technologies (with better training) won't be able to do more and more of this as time passes.
what does this even mean?
Some understandable short sentence or paragraph early on needs to answer the main question the title raises.
Then again, just throwing rocks might be pretty effective.
> This experiment was inspired by @swyx’s tweet about Ted Chiang’s short story “Understand” (1991). The story imagines a superintelligent AI’s inner experience—its reasoning, self-awareness, and evolution. After reading it and following the Hacker News discussion, ...
Umm... I <3 love <3 Understand by Ted Chiang, But the story is about super intelligent *humans*.
Like Tatja Grimm's World or the movie Limitless.
PS: Referenced tweet for the interested: https://x.com/swyx/status/2006976415451996358
Ted C
He's mostly in very-confident-but-not-even-wrong kind of territory here.
One comment on his note:
> As an example, let’s say an LLM is correct 95% of the time (0.95) in predicting the “right” tokens to drive tools that power an “agent” to accomplish what you’ve asked of it. Each step the agent has to take therefore has a probability of being 95% correct. For a task that takes 2 steps, that’s a probability of 0.95^2 = 0.9025 (90.25%) that the agent will get the task right. For a task that takes 30 steps, we get 0.95^30 = 0.2146 (21.46%). Even if the LLMs were right 99% of the time, a 30-step task would only have a probability of about 74% of having been done correctly.
The main point that for sequential steps of tasks errors can accumulate and that this needs to be handled is valid and pertinent, but the model used to "calculate" this is quite wrong - steps don't fail probabilistically independently.
Given that actions can depend on outcomes of previous step actions and given that we only care about final outcomes and not intermediate failing steps, errors can be corrected. Thus even steps that "fail" can still lead to success.
(This is not a Bernoulli process.)
I think he's referencing some nice material and he's starting in a good direction with defining agency as goal directed behaviour, but otherwise his confidence far outstrips the firmness of his conceptual foundations or clarity of his deductions.
I'm specifically addressing your use of the concept of determinism.
An LLM is a set of matrix multiplies and function applications. The only potentially non-deterministic step is selecting the next token from the final output and that can be done deterministically.
By your strict use of the definition they absolutely can be deterministic.
But that is not actually interesting for the point at hand. The real point has to do with reproducibility, understand ability and tolerances.
3blue1brown has a really nice set of videos on showing how the LLM machinery fits together.