You don’t get it.

I do get it. And that's why I'm disdainful towards all this "simulated reasoning" babble.

In the past, the brick throwing machine was always failing its target and nowadays it is almost always hitting near its target.

Emphasis mine: that "near" is a sleight of hand.

It doesn't really matter if it's hitting "near" or "far"; in both cases someone will need to stop the brick-throwing machine, get into the construction site (as if building a house manually), place the brick in the correct location (as if building a house manually), and then redo operations as usual.

In other words, "hitting near the target" = "failure to hit the target".

And it's obvious why it's wrong; the idea that an auto-builder should throw bricks is silly. It should detect where the brick should be placed, and lay it down gently.

The same thing applies to those large token* models; they won't reach anywhere close to reasoning, just like a brick-throwing machine won't reach anywhere close to an automatic house builder.

*I'm calling it "large token model" instead of "large language model" to highlight another thing: those models don't even model language fully, except in the brain of functionally illiterate tech bros who think language is just a bunch of words. Semantics and pragmatics are core parts of a language; you don't have language if utterances don't have meaning or purpose. The nearest of that LLMs do is to plop some mislabelled "semantic supplement" - because it's a great red herring (if you mislabel something, you're bound to get suckers confusing it with the real thing, and saying "I dun unrurrstand, they have semantics! Y u say they don't? I is so confusion... lol lmao").

It depends on how good you are asking the machine to throw bricks (you need to assume some will miss and correct accordingly).

If the machine relies on you to be an assumer (i.e. to make shit up, like a muppet), there's already something wrong with it.

Eventually, brick throwing machines will get so good that they will rely on gravitational forces to place the bricks perfectly and auto-build houses.

To be blunt that stinks "wishful thinking" from a distance.

As I implied in the other comment ("Can house construction be partially automated? Certainly. Perhaps even fully. But not through a brick-throwing machine."), I don't think reasoning algorithms are impossible; but it's clear LLMs are not the way to go.

You will be surprised of what it can achieve.

But not by what it can't.

You, random downvoter. I can't believe you thought I was serious. Shame on you.

They are obvious liars. Some people are just too invested to see it.

These models only have reasoning capabilities using the most obscure definitions of "reasoning". At best, all they're doing are climbing to local maxima with their so-called "reasoning" on a graph as wavy as the ocean.

I've mentioned this on other posts, but it's really sad because LLMs have been wildly incredible for certain NLP operations. They are that though, not AGI or whatever snake oil Altman wants to sell this week.

The CEOs you're talking about are the CEOs in the analogy.

I've met far too many people I wouldn't trust to give me a reasoned response.

Some people simply lack that capacity entirely, some just don't care enough to spend the effort on it, while others are trying to deceive me intentionally.