Artificial intelligence is losing hype

The OP is not about AI as a field of research. It's about whether the gobs of money invested in "AI" products and services in recent years, fueled by hype and FOMO, will earn a return, and whether we are approaching the bust of a classic boom-bust over-investment cycle.

Seemingly every non-tech company in the world has been trying to figure out an "AI strategy," driven by hype and FOMO, but most corporate executives have no clue as to what they're doing or ought to be doing. They are spending money on poorly thought-out ideas.

Meanwhile, every tech company providing "AI services" has been spending money like a drunken sailor, fueled by hype and FOMO. None of these AI services are generating enough revenue to cover the cost of development, training, or even, in many cases, inference.

Nvidia, the dominant software-plus-hardware platform (CUDA is a big deal), appears to be the only financial beneficiary of all this hype and FOMO.

According to the OP, the business of "AI" is losing hype, suggesting we're approaching a bust.

If you look at AI history there is often fairly steady progress in a given skill area for example chess programs improved in a steady way on ELO scores and you could project pretty well the future by drawing a line on a graph. Similarly large language models seem to be progressing from toddler like to high school student like (now) to PhD like - shortly. There are skills AI are still fairly bad at like the higher level reasoning you mention, and in robot form being able to pop to the shops to get some groceries say but I get the impression those are also improving in a steady way and it won't be so long.

While it is true that LLM’s lack agency and have many weaknesses, they form a critical part of what machine learning has lacked until transformers became all of the rage.

The things that LLM’s are bad at are largely solved problems using much simpler technology. There is no reason that LLM’s have to be the only component in an intelligent agent. Biological brains have Specialized structures for specialized tasks like arithmetic. The solution is probably integration of LLMs as a part of a composite system that includes database storage, a code execution environment, and multiple agents to form a goal directed posit - evaluate loop.

I’ve had pretty remarkable success with this architecture running on 12b models and I’m a nobody with no resources.

LLM’s by themselves just come up with the first thing that crosses their”mind”. It shouldn’t be surprising that the very first unfiltered guess about a solution might be suboptimal.

There is a vast amount of knowledge embedded in our cultural matrix, and a lot of that is captured in the common crawl and other datasets.llms are like a search engine for that data , based on meaning rather than semantics.

To be fair, I’ve talked to a lot of people who cannot consistently perform at the mistral-12b level.

I think we expect AGI to be much smarter than the average joe, and free of occasional stupidity.

What we’ve got is an 85IQ generalist with unreliable savant capabilities, that can also talk to a million people at the same time without getting distracted. I don’t see how that isn’t absolutely a fundamental shift in capability.

It’s just that we expect it to be spectacularly useful. Not like homeless joe, who lives down by the river. Unfortunately, nobody wants a 40 acre call center of homeless joes, but it’s hard to argue that HJ isn’t an intelligent entity.

Obviously LLMs don’t yet have a control and supervision loop that gives them goal directed behaviour, but they also don’t have a drinking problem and debilitating PTSD with a little TBI thrown in from the last war.

It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.

> What we’ve got is an 85IQ generalist with unreliable savant capabilities, that can also talk to a million people at the same time without getting distracted. I don’t see how that isn’t absolutely a fundamental shift in capability.

Very shallow assessment, first of all it's not a generalist at all, it has zero concept of what it's talking about, secondly it gets confused easily unless you order it to keep context in memory, and thirdly it can't perform if it does not regularly swallow petabytes of human text.

I get your optimism but it's uninformed.

> To be fair, I’ve talked to a lot of people who cannot consistently perform at the mistral-12b level.

I can find you an old-school bot that performs better than uneducated members of marginalized and super poor communities, what is your example even supposed to prove?

> it’s hard to argue that HJ isn’t an intelligent entity.

What's HJ? If it's not a human then it's extremely easy to argue that it's not an intelligent entity. We don't have intelligent machine entities, we have stochastic parrots and it's weird to pretend otherwise when the algorithms are well-known and it's very visible there's no self-optimization in there, there's no actual learning, there's only adjusting weights (and this is not what our actual neurons do btw), there's no motivation or self-drive to continue learning, there's barely anything that has been "taught" to combine segments of human speech and somehow that's a huge achievement. Sure.

> It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.

Nah, we are on no cusp of general AGI at all. We're not even at 1%. Don't know about you but I have a very clear idea what would AGI look like and LLMs are nowhere near. Not even in the same ballpark.

It helps that I am not in the area and I don't feel the need to pat myself on the back that I have managed to achieve the next AI plateau which the area will not soon recover from.

Bookmark this comment and tell me I am wrong in 10 years, I dare you.