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The changing goalposts of AGI and timelines

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Re: The changing goalposts of AGI and timelines

#211

Post by 10xDev »

ACCount37 wrote: post #187
sulam wrote: post #122

The reality is that current models are simply nowhere near AGI. Next token prediction has been pushed very far, and proven to have applicability far beyond the original domain it was designed for (reasoning models are an application I would not have predicted) but it is fundamentally not AGI. It has no real world model, no ability to learn in any but superficial ways, and without extensive scaffolding this is all ver…

Given the mechanistic interpretability findings? I'm not sure how people still say shit like "no real world model" seriously.

People are finding it hard to grasp emergent properties can appear at very large scales and dimensions.

Re: The changing goalposts of AGI and timelines

#212

Post by chrsw »

shepherdjerred wrote: post #207
djoldman wrote: post #116

Anytime I see "Artificial General Intelligence," "AGI," "ASI," etc., I mentally replace it with "something no one has defined meaningfully." Or the long version: "something about which no conclusions can be drawn because the proposed definitions lack sufficient precision and completeness." Or the short versions: "Skippetyboop," "plipnikop," and "zingybang."

They define AGI in their charter > artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work

I take "outperform" to mean "can replace".

Re: The changing goalposts of AGI and timelines

#213

Post by p-o »

eloisant wrote: post #204
p-o wrote: post #19

I think the brunt of the disruption regarding AI is already behind us for LLMs at least. It's possible we'll see improvements over the following months/years, but government will inevitably start to catchup to the level of disinformation and confusion that AI has brought to this world. Laws & regulations that needs to be created to reign in AI will undoubtedly increase the opportunity cost of training LLMs. For some,…

I think that even if the models were to plateau today, there are still a lot of room for improvement in all the tooling around them, people finding ideas of applications, and users getting used of them. So we're not done with the disruption. Some of the apps made possible by smartphones only appeared a decade after they were made technically possible. A lot of the new use cases made possible by the Internet and broad…

Yes, I think you're right that it is not the end of the road for LLM and the application of LLM might be adopted over time across a variety of industries.

I guess what I failed to convey in my original comment was that, like the Internet 20 years ago, the current advancement made by AI might stall at a foundational level, while the landscape evolves.

Essentially, I believe what you're saying is really close in spirit to what I'm saying.

Re: The changing goalposts of AGI and timelines

#214

Post by djoldman »

shepherdjerred wrote: post #207
djoldman wrote: post #116

Anytime I see "Artificial General Intelligence," "AGI," "ASI," etc., I mentally replace it with "something no one has defined meaningfully." Or the long version: "something about which no conclusions can be drawn because the proposed definitions lack sufficient precision and completeness." Or the short versions: "Skippetyboop," "plipnikop," and "zingybang."

They define AGI in their charter > artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work

That definition is as I said: "something about which no conclusions can be drawn because the proposed definitions lack sufficient precision and completeness."

"Highly autonomous systems" and "most economically valuable work" aren't precise enough to be useful.

"Highly" implies that there is a continuum, so where does directed end and autonomy begin?

"Most economically valuable work"... each word in that has wiggle room, not to mention that any reasonable interpretation of it is a shifting goalpost as the work done by humans over history has shifted a great deal.

The point is that none of this is defined in a way so that people can agree that something has AGI/ASI/etc. or not. If people can't agree then there's no point in talking about it.

EDIT: interestingly, the OpenAI definition of AGI specifically means that a subset of humans do not have AGI.

Re: The changing goalposts of AGI and timelines

#215

Post by kergonath »

enraged_camel wrote: post #149
ambicapter wrote: post #129

Why the title change? previous title: Based on its own charter, OpenAI should surrender the race

Yeah, that was weird. The current title editorializes. @dang can you revert to actual title please?

> @dang can you revert to actual title please?

This does not work. From the guidelines:

> Please don't post on HN to ask or tell us something. Send it to hn@ycombinator.com.

https://news.ycombinator.com/newsguidelines.html

Re: The changing goalposts of AGI and timelines

#216

Post by wongarsu »

Jensson wrote: post #185
hirvi74 wrote: post #96

Earlier quoted context omitted.

> AGI is so nebulous we will never be able to tell if we hit it. I completely agree. We can't even measure each other well, let alone machines.

It is very easy to tell if we still need humans in the loop. We still do so its not AGI.

For certain types of "human in the loop". If it can't write working code without a human in the loop then it's not AGI. But a human-level coder also has lots of humans in the loop: a more senior developer doing code review, several layers of management, a product owner that interfaces the project with outside reality, sales people, etc.

Now I already hear you typing "but those roles should also be handles by AI if it's AGI" and I agree that an AI that can claim to be AGI should be able to handle those roles (as separate agents if necessary). But in a real setup it probably won't be the best choice to do those roles for cultural and legal reasons. Or it might simply not be cost effective. Not to mention that under most definitions of AGI there can still be humans more capable than the AI, as long as the AI hits the 50th percentile mark or something like that. So even if it's an AGI with the ability to do these roles we will still have humans in the loop for a long long time

Re: The changing goalposts of AGI and timelines

#217

Post by ACCount37 »

sulam wrote: post #189
ACCount37 wrote: post #187

Earlier quoted context omitted.

Given the mechanistic interpretability findings? I'm not sure how people still say shit like "no real world model" seriously.

They have a _text_ model. There is some correlation between the text model and the world, but it’s loose and only because there’s a lot of text about the world. And of course robotics researchers are having to build world models, but these are far from general. If they had a real world model, I could tell them I want to play a game of chess and they would be able to remember where the pieces are from move to move.

What makes you think that text is inherently a worse reflection of the world than light is?

All world models are lossy as fuck, by the way. I could give you a list of chess moves and force you to recover the complete board state from it, and you wouldn't fare that much better than an off the shelf LLM would. An LLM trained for it would kick ass though.

Re: The changing goalposts of AGI and timelines

#218

Post by mirekrusin »

0xbadcafebee wrote: post #22

AGI isn't going to happen within the next 30 years so this is moot. The actual researchers have said so many times. It's only the business people and laypeople whooping about AGI always being imminent. You cannot get real, actual AGI (the same ability to perform tasks as a human) without a continuous cycle of learning and deep memory, which LLMs cannot do. The best LLM "memory" is a search engine and document summari…

I think you're somehow right and wrong at the same.

All those "it's like ..." are faulty – "post-it notes" are not 3k pages of text that can be recalled instantly in one go, copied in fraction of a second to branch off, quickly rewritten, put into hierarchy describing virtually infinite amount of information (outside of 3k pages of text limit), generated on the fly in minutes on any topic pulling all information available from computer etc.

Poor man's RL on test time context (skills and friends) is something that shouldn't be discarded, we're at 1M tokens and growing and pogressive disclosure (without anything fancy, just bunch of markdowns in directories) means you can already stuff-in more information than human can remember during whole lifetime into always-on agents/swarms.

Currently latest models use more compute on RL than pre-training and this upward trend continues (from orders of magnitude smaller than pre-training to larger that pre-training). In that sense some form of continous RL is already happening, it's just quantified on new model releases, not realtime.

With LoRA and friends it's also already possible to do continuous training that directly affects weights, it's just that economy of it is not that great – you get much better value/cost ratio with above instead.

For some definitions of AGI it already happened ie. "someboy's computer use based work" even though "it can't actually flip burgers, can it?" is true, just not relevant.

ps. I should also mention that I don't believe in "programmers loosing jobs", on the contrary, we will have to ramp up on computational thinking large numbers of people and those who are already verse with it will keep reaping benefits – regardless if somebody agrees or not that AGI is already here, it arrives through computational doors speaking computational language first and imho this property will be here to stay as it's an expression of rationality etc

Re: The changing goalposts of AGI and timelines

#219

Post by VorpalWay »

ozgung wrote: post #210
djoldman wrote: post #116

Anytime I see "Artificial General Intelligence," "AGI," "ASI," etc., I mentally replace it with "something no one has defined meaningfully." Or the long version: "something about which no conclusions can be drawn because the proposed definitions lack sufficient precision and completeness." Or the short versions: "Skippetyboop," "plipnikop," and "zingybang."

That’s the problem with the discussions on AI. No one defines the terms they use. If we define AGI as an AI not doing a preset task but can be used for general purpose, then we already have that. If we define it as human level intelligence at _every_ task, then some humans fail to be an AGI. If we define AGI as a magic algorithm that does every task autonomously and successfully then that thing may not exist at all,…

It is not just AGI that is poorly defined. Plain AI is moving goalposts too. When the A* search algorithm was introduced in the late 60s, that was considered AI, when SVM (support vector machines) and KNN (K nearest neighbor) were new, they were AI. And so on.

These days it is neural networks and transformer models for language in particular that people mean when they say unqualified AI.

It is very hard to have a meaningful discussion when different parties mean different things with the same words.

Re: The changing goalposts of AGI and timelines

#220

Post by nomel »

djoldman wrote: post #214
shepherdjerred wrote: post #207

Earlier quoted context omitted.

They define AGI in their charter > artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work

That definition is as I said: "something about which no conclusions can be drawn because the proposed definitions lack sufficient precision and completeness." "Highly autonomous systems" and "most economically valuable work" aren't precise enough to be useful. "Highly" implies that there is a continuum, so where does directed end and autonomy begin? "Most economically valuable work"... each word in that has wiggle ro…

It's a definition based on practical results. That's a good definition, because it doesn't require we already know the exact implementation. It doesn't require guessing, in a literal "put your money where your mouth is" way.

If it can do things as good as or better than humans, then either the AI has a type of general intelligence or the human does not.

Defining capabilities based on outcome rather than implementation should be very familiar to an engineer, of any kind, because that's how every unsolved implementation must start.

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