That’s called the scientific method

> Yes of course relationship questions don’t have a “correct” answer. But physics questions do. Code vulnerability questions do. Math questions do. I mean seriously? But as per Gödel's incompleteness theorem and the Halting Problem, math questions (and consequently physics and CS questions) don't always have an answer.

I read the full article (really resonated with it, fwiw), and I'm struggling to understand the issues you're describing. > Yes of course relationship questions don’t have a “correct” answer. But physics questions do. Code vulnerability questions do. Math questions do. I mean seriously? Can you say more? It seems to me the article says the same thing you are. > I don’t actually think the above paragraph makes any sense, does anyone disagree with me? “Instead of planning we observe and hypothesize”? I think the author is drawing a connection to the world of science, specifically quantum mechanics, where the best way to make progress has been to describe and test theories (as opposed to math where we have proofs). Though it's not a great analog since LLMs are not probabilistic in the same way quantum mechanics is. In any case, I appreciated the article because it talks through a shift from …

> I’d recommend Baudrillards work on hyperreality. Any specific piece of writing you can recommend? I tried reading Simulacra and Simulation (English translation) a while ago and I found it difficult to follow.

> I’m not saying future computing won’t be probabilistic Current and past computing has always been probabilistic in part, doesn't mean it will become 100% so. Almost all of the implementationof LLMs is deterministic except the part that is randomized. Its output is used in the same way. Humans combine the two approaches as well. Even reality is a combination of quantum uncertainty at a low level and very deterministic physics everywhere else. > We're moving away from deterministic mechanicism, a world of perfect information and perfect knowledge, and walking into one made of emergent unknown behaviors, where instead of planning and engineering we observe and hypothesize. The hype machine always involves pseudo-scientific babble and this is a particularly cringey example. The idea that seems to be promoted, that AI will be god like and therein we'll find all truth and knowledge is beyond…

It's already wrong at the first step. A probabilistic system is by definition not a function (it is a relation). This is such a basic mistake I don't know how anyone can take this seriously. Many existing systems are also not strictly functions (internal state can make them return different outputs for a given input). People love to abuse mathematics and employ its concepts hastily and irresponsibly.

There's another couple of principles underlying the most uses of science, which are consistency and smoothness. That is extrapolation and interpolation makes sense. Also, that if an experiment works now, it will work forever. Critically, the physical world is knowable .

You seem to be having strong emotions about this stuff, so I'm a little nervous that I'm going to get flamed in response, but my best take at a well-intentioned response: I don't think the author is arguing that all computing is going to become probabilistic. I don't get that message at all - in fact they point out many times that LLMs can't be trusted for problems with definite answers ("if you need to add 1+1 use a calculator"). Their opening paragraph was literally about not blindly trusting LLM output. > I don’t actually think the above paragraph makes any sense, does anyone disagree with me? Yes - it makes perfect sense to me. Working with LLMs requires a shift in perspective. There isn't a formal semantics you can use to understand what they are likely to do (unlike programming languages). You really do need to resort to observation and hypothesis testing, which yes, the scientific…