[CuriouslyC]

What we're seeing right now with LLMs is like music in the late 30s after the invention of the electric guitar. At that point people still have no idea how to use it so, so they were treating it like an amplified acoustic guitar. It took almost 40 years for people to come up with the idea of harnessing feedback and distortion to use the guitar to create otherworldly soundscapes, and another 30 beyond that before people even approached the limit of guitar's range with pedals and such.

LLMs are a game changer that are going to enable a new programming paradigm as models get faster and better at producing structured output. There are entire classes of app that couldn't exist before because there there was a non-trivial "fuzzy" language problem in the loop. Furthermore I don't think people have a conception of how good these models are going to get within 5-10 years.

[blauditore]

> Furthermore I don't think people have a conception of how good these models are going to get within 5-10 years.

Pretty sure it's quite the opposite of what you're implying: People see those LLMs who closely resemble actual intelligence on the surface, but have some shortcomings. Now they extrapolate this and think it's just a small step to perfection and/or AGI, which is completely wrong.

One problem is that converging to an ideal is obviously non-linear, so getting the first 90% right is relatively easy, and closer to 100% it gets exponentially harder. Another problem is that LLMs are not really designed in a way to contain actual intelligence in the way humans would expect them to, so any apparent reasoning is very superficial as it's just language-based and statistical.

In a similar spirit, science fiction stories playing in the near future often tend to have spectacular technology, like flying personal cars, in-eye displays, beam travel, or mind reading devices. In the 1960s it was predicted for the 80s, in the 80s it was predicted for the 2000s etc.

[PaulHoule]

This book

https://www.amazon.com/Friends-High-Places-W-Livingston/dp/0...

tells (among other things) a harrowing tale of a common mistake in technology development that blindsides people every time: the project that reaches an asymptote instead of completion that can get you to keep spending resources and spending resources because you think you have only 5% to go except the approach you've chosen means you'll never get the last 4%. It's a seductive situation that tends to turn the team away from Cassandras who have a clear view.

Happens a lot in machine learning projects where you don’t have the right features. (Right now I am chewing on the problem of “what kind of shoes is the person in this picture wearing?” and how many image classification models would not at all get that they are supposed to look at a small part of the image and how easy it would be to conclude that “this person is on a basketball court so they are wearing sneakers” or “this is a dude so they aren’t wearing heels” or “this lady has a fancy updo and fancy makeup so she must be wearing fancy shoes”. Trouble is all those biases make the model perform better up to a point but to get past that point you really need to segment out the person’s feet.)

[CuriouslyC]

You are looking at things like the failure of full self driving due to massive long tail complexity, and extrapolating that to LLMs. The difference is that full self driving isn't viable unless it's near perfect, whereas LLMs and text to image models are very useful even when imperfect. In any field there is a sigmoidal progress curve where things seem to move slowly at first when getting set up, accelerate quickly once a framework is in place, then start to run out of low hanging fruit and have to start working hard for incremental progress, until the field is basically mined out. Given the rate that we're seeing new stuff come out related to LLMs and image/video models, I think it's safe to say we're still in the low hanging fruit stage. We might not achieve better than human performance or AGI across a variety of fields right away, but we'll build a lot of very powerful tools that will accelerate our technological progress in the near term, and those goals are closer than many would like to admit.

[jillesvangurp]

AGI (human level intelligence) is not an really an end goal but a point that will be surpassed. So, by looking at it as something asymptotically approaching an ideal 100% is fundamentally wrong. That 100% mark is going to be in the rear view mirror at some point. And it's a bit of an arbitrary mark as well.

Of course it doesn't help that people are a bit hand wavy about what that mark exactly is to begin with. We're very good at moving the goal posts. So that 100% mark has the problem that it's poorly defined and in any case just a brief moment in time given exponential improvements in capabilities. In the eyes of most we're not quite there yet for whatever there is. I would agree with that.

At some point we'll be debating whether we are actually there, and then things move on from there. A lot of that debate is going to be a bit emotional and irrational of course. People are very sensitive about these things and they get a bit defensive when you portray them as clearly inferior to something else. Arguably, most people I deal with don't actually know a lot, their reasoning is primitive/irrational, and if you'd benchmark them against an LLM it wouldn't be that great. Or that fair.

The singularity is kind of the point where most of the improvements to AI are going to come from ideas and suggestions generated by AI rather than by humans. Whether that's this decade or the next is a bit hard to predict obviously.

Human brains are quite complicated but there's only a finite number of neurons in there; a bit under 100 billion. We can waffle a bit about the complexity of their connections. But at some point it becomes a simple matter of throwing more hardware at the problem. With LLMs pushing tens-hundreds of parameters already, you could legitimately ask what a few more doublings in numbers here enable.

[blauditore]

I think you're falling for the exact same fallacy that I was describing. Also note that the human level of intelligence is not arbitrary at all: Most LLMs are trained on human-generated data, and since they are statistical models, they won't suddenly come up with truly novel reasoning. They're generally just faster at generating stuff than humans, because they're computers.

[thfuran]

>But at some point it becomes a simple matter of throwing more hardware at the problem.

Insofar as it's simple to throw like six orders of magnitude more hardware at something that has already had a lot of hardware thrown at it.

[varelse]

In 5 to 10 years we will have likely moved on to the next big model architecture just like it was all about convolutional networks 5 to 10 years ago despite the pivotal paper being published in 2017.