this post was submitted on 25 Jun 2024
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Hurling ordure at the TREACLES, especially those closely related to LessWrong.

AI-Industrial-Complex grift is fine as long as it sufficiently relates to the AI doom from the TREACLES. (Though TechTakes may be more suitable.)

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Folks in the field of AI like to make predictions for AGI. I have thoughts, and I’ve always wanted to write them down. Let’s do that.

Since this isn’t something I’ve touched on in the past, I’ll start by doing my best to define what I mean by “general intelligence”: a generally intelligent entity is one that achieves a special synthesis of three things:

A way of interacting with and observing a complex environment. Typically this means embodiment: the ability to perceive and interact with the natural world. A robust world model covering the environment. This is the mechanism which allows an entity to perform quick inference with a reasonable accuracy. World models in humans are generally referred to as “intuition”, “fast thinking” or “system 1 thinking”. A mechanism for performing deep introspection on arbitrary topics. This is thought of in many different ways – it is “reasoning”, “slow thinking” or “system 2 thinking”. If you have these three things, you can build a generally intelligent agent. Here’s how:

First, you seed your agent with one or more objectives. Have the agent use system 2 thinking in conjunction with its world model to start ideating ways to optimize for its objectives. It picks the best idea and builds a plan. It uses this plan to take an action on the world. It observes the result of this action and compares that result with the expectation it had based on its world model. It might update its world model here with the new knowledge gained. It uses system 2 thinking to make alterations to the plan (or idea). Rinse and repeat.

My definition for general intelligence is an agent that can coherently execute the above cycle repeatedly over long periods of time, thereby being able to attempt to optimize any objective.

The capacity to actually achieve arbitrary objectives is not a requirement. Some objectives are simply too hard. Adaptability and coherence are the key: can the agent use what it knows to synthesize a plan, and is it able to continuously act towards a single objective over long time periods.

So with that out of the way – where do I think we are on the path to building a general intelligence?

World Models We’re already building world models with autoregressive transformers, particularly of the “omnimodel” variety. How robust they are is up for debate. There’s good news, though: in my experience, scale improves robustness and humanity is currently pouring capital into scaling autoregressive models. So we can expect robustness to improve.

With that said, I suspect the world models we have right now are sufficient to build a generally intelligent agent.

Side note: I also suspect that robustness can be further improved via the interaction of system 2 thinking and observing the real world. This is a paradigm we haven’t really seen in AI yet, but happens all the time in living things. It’s a very important mechanism for improving robustness.

When LLM skeptics like Yann say we haven’t yet achieved the intelligence of a cat – this is the point that they are missing. Yes, LLMs still lack some basic knowledge that every cat has, but they could learn that knowledge – given the ability to self-improve in this way. And such self-improvement is doable with transformers and the right ingredients.

Reasoning There is not a well known way to achieve system 2 thinking, but I am quite confident that it is possible within the transformer paradigm with the technology and compute we have available to us right now. I estimate that we are 2-3 years away from building a mechanism for system 2 thinking which is sufficiently good for the cycle I described above.

Embodiment Embodiment is something we’re still figuring out with AI but which is something I am once again quite optimistic about near-term advancements. There is a convergence currently happening between the field of robotics and LLMs that is hard to ignore.

Robots are becoming extremely capable – able to respond to very abstract commands like “move forward”, “get up”, “kick ball”, “reach for object”, etc. For example, see what Figure is up to or the recently released Unitree H1.

On the opposite end of the spectrum, large Omnimodels give us a way to map arbitrary sensory inputs into commands which can be sent to these sophisticated robotics systems.

I’ve been spending a lot of time lately walking around outside talking to GPT-4o while letting it observe the world through my smartphone camera. I like asking it questions to test its knowledge of the physical world. It’s far from perfect, but it is surprisingly capable. We’re close to being able to deploy systems which can commit coherent strings of actions on the environment and observe (and understand) the results. I suspect we’re going to see some really impressive progress in the next 1-2 years here.

This is the field of AI I am personally most excited in, and I plan to spend most of my time working on this over the coming years.

TL;DR In summary – we’ve basically solved building world models, have 2-3 years on system 2 thinking, and 1-2 years on embodiment. The latter two can be done concurrently. Once all of the ingredients have been built, we need to integrate them together and build the cycling algorithm I described above. I’d give that another 1-2 years.

So my current estimate is 3-5 years for AGI. I’m leaning towards 3 for something that looks an awful lot like a generally intelligent, embodied agent (which I would personally call an AGI). Then a few more years to refine it to the point that we can convince the Gary Marcus’ of the world.

Really excited to see how this ages. 🙂

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[–] BigMuffin69@awful.systems 21 points 5 months ago* (last edited 5 months ago) (2 children)

Reasoning There is not a well known way to achieve system 2 thinking, but I am quite confident that it is possible within the transformer paradigm with the technology and compute we have available to us right now. I estimate that we are 2-3 years away from building a mechanism for system 2 thinking which is sufficiently good for the cycle I described above.

Wow, what are the odds! The exact same transformer paradigm that OAI co-opted from Google is also the key to solving 'system 2' reasoning, meta cognition, recursive self improvement, and the symbol grounding problem! All they need is a couple trillion more dollars of VC Invesment, a couple of goat sacrifices here and there, and AGI will just fall out. They definitely aren't tossing cash into a bottomless money pit chasing a dead-end architecture!

... right?

[–] 200fifty@awful.systems 15 points 5 months ago* (last edited 5 months ago)

Well, if this guy's quite confident, then I'm sure it'll all pan out in the end. How hard could symbolic reasoning be, really? Incidentally, I've been in a coma since 1970

[–] diz@awful.systems 7 points 5 months ago

Frigging exactly. Its a dumb ass dead end that is fundamentally incapable of doing vast majority of things ascribed to it.

They keep imagining that it would actually learn some underlying logic from a lot of text. All it can do is store a bunch of applications of said logic, as in a giant table. Deducing underlying rules instead of simply memorizing particular instances of rules, that's a form of compression, there wasn't much compression going on and now that the models are so over-parametrized, even less.

[–] dgerard@awful.systems 16 points 5 months ago

I’ve been spending a lot of time lately walking around outside talking to GPT-4o

and it loves me very much, I just need to send it money for its ticket over via western union

[–] stingpie@lemmy.world 12 points 5 months ago* (last edited 5 months ago) (2 children)

Why do the leaders in AI know so little about it? Transformers are completely incapable of maintaining any internal state, yet techbros somehow think it will magically have one. Sometimes, machine learning can be more of an art than a science, but they seem to think it's alchemy. They think they're making pentagrams out of noncyclic graphs, but are really just summoning a mirror into their own stupidity.

It's really unfortunate, since they drown out all the news about novel and interesting methods of machine learning. KANs, DNCs, MAMBA, they all have a lot of promise, but can't get any recognition because transformers are the laziest and most dominant methods.

Honestly, I think we need another winter. All this hype is drowning out any decent research, and so all we are getting are bogus tests and experiments that are irreproducible because they're so expensive. It's crazy how unscientific these 'research' organizations are. And OpenAI is being paid by Microsoft to basically jerk-off sam Altman. It's plain shameful.

[–] BlueMonday1984@awful.systems 11 points 5 months ago (1 children)

Honestly, I think we need another winter. All this hype is drowning out any decent research, and so all we are getting are bogus tests and experiments that are irreproducible because they’re so expensive. It’s crazy how unscientific these ‘research’ organizations are. And OpenAI is being paid by Microsoft to basically jerk-off sam Altman. It’s plain shameful.

If an AI winter does happen, I expect it'll be particularly lengthy/severe. Unlike previous AI hype cycles, this particular cycle has come with some serious negative externalities (large-scale copyright infringement, climate change/water consumption, the flood of AI slop, disinformation, etc).

Said externalities have turned the public strongly against AI, to the point where refusing to use it has become a viable marketing strategy.

You want my suspicion, any further AI research will probably be viewed with immediate distrust, at least for a while.

[–] dgerard@awful.systems 11 points 5 months ago

The key difference is that in previous AI springs, the customer was the DoD, and winter set in when they declined to set more money on fire for an approach that wasn't working.

[–] scruiser@awful.systems 9 points 5 months ago* (last edited 5 months ago)

I am probably giving most of them too much credit, but I think some of them took the Bitter Lesson and learned the wrong things from it. LLMs performed better than originally expected just off context, and (apparently) scaled better with bigger model and more training than expected, so now they think they just need to crank up the size and tweak things slightly (i.e. "prompt engineering" and RLHF) and don't appreciate the limits built into the entire approach.

The annoying thing about another winter is that it would probably result in funding being cut for other research. And laymen don't appreciate all the academic funding that goes into research for decades before an approach becomes interesting and viable enough to scale up and commercialize (and then overhyped and oversold before some more modest practical usages become common, and relabeled as something other than AI).

Edit: or more cynically, the leaders and hype-men know that algorithmic advances aren't an automatic dump money in, get out disruptive product process, so they don't bother putting as much monetary investment or hype into algorithmic advances. Like compare the attention paid towards Yann LeCunn talking about algorithmic developments vs. Sam Altman promising grad student level LLMs (as measured by a spurious benchmark) in two years.

[–] sailor_sega_saturn@awful.systems 9 points 5 months ago* (last edited 5 months ago) (1 children)

Robots are becoming extremely capable – able to respond to very abstract commands like “move forward”, “get up”, “kick ball”, “reach for object”, etc.

Computers now have the cutting edge ability to-- *checks notes*-- parse Zork text adventure style user input.

If you've done any reading on the history of AI you may recognize these sorts of commands as "blocks world", which goes back to the '60s. Sure there's been a ton of advancements since then, but just the basic act of a computer understanding "kick ball" is not exactly groundbreaking.

[–] gnomicutterance@awful.systems 7 points 5 months ago

if we're lucky, silicon valley will be eaten by a grue.

[–] lurklurk@lemmy.world 4 points 5 months ago

It uses system 2 thinking to make alterations to the plan (or idea). Rinse and repeat.

They probably meant to write system 1 thinking here.