This is gonna be the new "I'm more of a left-brained person" thing isn't it

  --- Phil & [Pre-]CogSci ---
  | rational  | intuitive     |
  | reason    | understanding | (Kant & Hegel's goofy wording)
  | deductive | inductive     |
  | animated  | automatic     | (from ~Aristotle, ultimately)
  | ~higher   | ~lower        | (as in "higher faculties")

  --- CS & AI ---
  | logical  | analogical |
  | symbolic | stochastic | 
  | Neats    | Scruffies  | (related academic 'camps')
  | ~deterministic | ~non-determnistic | (a common heuristic)
  | Good Ol' Fashioned AI (GOFAI) | Evil Datacenter AI (EDAI) | (a common honorific)

  --- Modern [Cog-]Neuro[-Psych] ---

  | slow thinking | fast thinking |
  | S2            | S1            |
  ...ok I assumed I knew more but maybe I don't? Would love to hear from experts!

  --- Colloquial ---
  | left brain   | right brain |
  | intelligence | wisdom      |
  | intention    | instinct    |
  | me           | AD[H]D      |

Brains aside, it S1/S2 seems like a good description?

System 1 is quick and intuitive. System 2 is slow and deliberate.

Compared to models that compose responses with many forward passes, thinking in token space, these models make a quick decision after processing their inputs.

what's wrong w "system one"? it feels like a good frame to think about use-cases that will fit jev etc.

Yeah, the github landing page is much less misleading and clearly says this. I don't know why they try to make the marketing page gloss over this detail... it speaks to the mindset of the authors.

> The latter is a strategy: there usually isn’t a correct answer, now or in the future. The model is playing a game consisting of repeated rounds, and the only way to evaluate it is to see how well it plays

This is self contradictory. You can tell a correct answer as you said, by looking at the score. In my $DAYJOB I am making hundreds of RL environments that produce a score for each intermediate state.

Who said anything about agentic, or reasoning? Those are post-hoc additions to autoregressive usage of LLMs. The base case of predicting next tokens generally (rather than with a specific objective) requires a faithful representation of the input embeddings, which is plausibly repurposed for classification.

I'm increasingly of the opinion that stuff like Jev and this are overfitting and producing illusory but confident "probabilities" that are complete bullshit. You can't see the underlying reasoning... but it's incredibly tempting for people who want to place faith in them. Rather than even trying to understand the complex system at play, it's easy to give up on trying to find reason, and just accept the second- or third-tier outputs of massively complex things that, on the first tier, are not necessarily reliable sources of truth.

Let's give an example: Suppose you ran a Jev that tried to determine when another Jev was wrong about something ...baseball games, let's say. The second Jev would come up with a perfect list of when the first Jev was wrong and when not to believe it. So now you have a second-order system that you believe more than the first-order one.

Only after 100 baseball games, the second-order Jev is only as good as the first one, it just inverted a bunch of games that could have gone either way. So hell, you make a third-order Jev that analyzes the first two...and its results are AMAZING when you look at the historical record! Only, you know, that's what's called overfitting.

Honestly, [edit: Fuck, I just wrote "honestly". I've been brain damaged by you-know-who] the invention of the "noul" is a bit of a giveaway. Imagine what junk bond traders could've done with that in the '80s. Not-not-not-falsy is how we all like our stock picks served up, right?

Reading the raw statistical output of an LLM as if it were an oracular source of truth is literally idol worship and gambling in the purest sense.

Take it from a guy who lost $10k on baseball this season having Claude rewrite my original code to reverse and re-reverse underdogs and favorites; there is no fucking "probability" of anything coming out of an LLM, even if your source of truth isn't an LLM but an evolutionary algorithm you designed yourself. Which would've performed better before the LLM started interpreting ways to bucket it and make up bullshit probabilities around it.

Anyone going down the Jev path is deeply misguided, but will see the light once they realize they have re-invented the magic 8 ball. Or that executive decision maker cube from the 80s with 6 random answers.

Here's your Jev. Note the preponderance of nouls ;)

https://www.ebay.com/itm/146783842141

If you happen to gamble or just watch baseball, you'll see that reality is not so easily reduced to one number. On the most basic level, a single probability number leaves out volatility, without which it's utterly useless for predicting anything other than a sequence it's already trained and fitted for. But as a gambling addict and occasional patron of oracles and gurus, it's very easy and tempting to mistake a clear-cut "probably" for whatever you want to hear. Listening to an LLM's heartbeat for probabilities is just a gambler's fallacy taken out to the 19th degree.

It already did, since 15 years ago.

https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow