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> building AI's that are much more than just LLM

Well, yes, by taking AI to mean something different than the current meaning. Of course if you build something that's not an LLM you may find a way to fix the confabulation* problem, but that isn't fixing what we have now.

The history of 'AI' the term is one of continually redefining it to mean something different, as the winds of technology and business blow. In this case, the assertion is that, under what we currently call 'AI', there's not a fix.

* I prefer the term confabulation over hallucination. See <https://universeodon.com/@siderea/109883198218504351>

Well, I agree with your general sentiment to a point, but I would say that in the lexicon of the day, "AI" and "LLM" are not synonymous. LLM's are one (current very popular and useful) approach to AI, but only a VERY casual observer would conflate the two terms as being completely equivalent. To me, it goes without saying that the world of AI is bigger than just LLM's.

But since we're talking about an article in Fortune, maybe it's fair to think of this strictly from a lay-person perspective.

Also, to the extent that it matters, I'm not necessarily saying "build something (completely) different than an LLM". I can picture a world with systems that include LLM's that interoperate with other components that do more of the "world model" and "commonsense reasoning" parts, yielding an aggregate system that does more/better than any of the parts could do in isolation.

No argument from me. I see good uses for LLMs, both now, with care, and as part of a toolkit for building towards more general intelligence.

One way to think of LLMs as available now is as sort of thinking partner, much like bouncing ideas off another person, except LLMs use the ideas of entire communities of persons. Of course some of the ideas bounced back at you from thousands or millions of people are going to get things wrong. Don't be like the lawyers who blindly used ChatGPT to write their legal briefs without even checking the citations, and you'll be OK.

From 2008 there was How to Build a Person by John Pollock. I don't recall whether he included some putative "explainer" disciplined by other components.

Heh. Seems to be a common pattern in book titles. There are also How to Build A Brain[1] by Chris Eliasmith and How To Create A Mind[2] by Ray Kurzweil as well.

[1]: https://www.amazon.com/How-Build-Brain-Architecture-Architec...

[2]: https://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/1...

When people say that something can’t be done, I think the majority of the time they mean (but don’t say) that it can’t be done within unspecified x,y,z constraints.

Those constraints, as you eluded to, are most often things like “within a certain timeframe” or “under a certain cost”.

Saying that something cannot be done without specifying those unspoken constraints - or being explicit that you do actually mean it can’t be done, even on an infinite timescale or with infinite money - is kind of a useless statement to make IMHO.

I’ve never really understood that tendency but maybe working in tech kind of conditions you to think in a more flexible way about what may be possible?

Yeah, like when clients ask me if something is possible. Happens incredibly frequently. What they actually mean to ask is how much would it cost to do. So I usually answer in those terms.

"Anything is possible. Just costs time and money."

So make me a warp drive like in star trek. I'll pay on delivery!

Like the sign says, "speed's just a question of money. How fast you wanna go?"

Without discussion of grammars, I think you're lost in the sauce on this.

> You need a system that has knowledge and a world model and common-sense reasoning, etc. When you're doing nothing but "predict the next token based on statistical patterns" then yeah, you're going to get hallucinations.

Arguably, LLMs already have this. They can do impressive reasoning already. The problem may be that the way they encode all that is untenable. Imagine trying to describe a car in terms of its individual pieces (tires, suspension, engine). Then imagine trying to do that in terms of its molecules. Uh oh. Then its atoms. Even worse.

> How much will it cost?

To describe a car in terms of atoms? Infinite cost. We can barely even get a baby fruit fly's connectome (total brain schematic) read and stored somewhere. A car is much bigger.

It may be that LLMs are structured such that they're trying to encode all that common sense reasoning in terms of atoms, and a fundamentally different grammar is necessary to go up to parts.

Whether this is the case is still being debated. If it is the case, you'll see LLMs near a ceiling of diminishing returns.

> How much will it cost?

To figure out how to do LLMs with more expressive grammars? Unknown. I think that'd take a qualitative breakthrough in AI, and I think LLMs are just the result of a quantitative breakthrough (we're still doing the old, non-clever stuff; we're just doing it faster than ever before thanks to continued growth in GPU compute power; more monkeys on typewriters than ever before!). I don't think we've had much in the way of qualitative breakthroughs since the AI winter.

Arguably, LLMs already have this. They can do impressive reasoning already. The problem may be that the way they encode all that is untenable. Imagine trying to describe a car in terms of its individual pieces (tires, suspension, engine). Then imagine trying to do that in terms of its molecules. Uh oh. Then its atoms. Even worse.

I will freely admit, I'm torn on how to think about this. On the one hand, some of the kinds of mistakes LLM's make lead me to think that they aren't exactly "doing reasoning" as I think of it. But the things they can do hint at some sort of reasoning.

Part of me wants to think of it as "they can simulate reasoning", but there's a fair objection to be raised that "simulating reasoning is just as good as doing reasoning." Part of me wants to reject that, but then I remind myself that I've always claimed to be something like a "behaviorist" when it comes to evaluating AI's. That is, I've always tried to eschew those "it's not real thinking" arguments and focus on the observed behavior, holding to a "if it displays intelligent behavior then it is intelligent" mindset.

LLM's have me sort of vacillating on all of this.

> They can do impressive reasoning already.

By no conception of reasoning I'm familiar with do LLMs reason, at all. They are clever software systems of an advanced sort of autocorrect/predictive text kind.

I'm not sold on this. The ability to often reasonably combine knowledge to derive new solutions to a problem to me implies some kind of model of a world. If it fails it might as well just be a model of a very different, wrong world. The idea that we have "persistent memory" of contextual knowledge is also questionable, relations generally don't seem to be hard-coded in the brain.

In the optimal scenario, it would just be sufficient to massively increase the context space of current LLMs.