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The changing goalposts of AGI and timelines

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Re: The changing goalposts of AGI and timelines

#341

Post by xyzal »

chrysoprace wrote: post #250
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."

I've largely avoided using the term "AI" to refer to the current LLM and generative technology because it's loaded with too much ambiguity and glosses over the problems with those technologies in the context of conversations around it.

"applied statistics"

Re: The changing goalposts of AGI and timelines

#342

Post by xyzal »

onlyrealcuzzo wrote: post #232
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…

How many months has it been since we were told there would be zero software engineers left in the world in 12 months?

>12

Re: The changing goalposts of AGI and timelines

#343

Post by bob1029 »

sulam wrote: post #331
stavros wrote: post #248

Earlier quoted context omitted.

Everybody says "but they just predict tokens" as if that's not just "I hope you won't think too much about this" sleight of hand. Why does predicting the next token mean that they aren't AGI? Please clarify the exact logical steps there, because I make a similar argument that human brains are merely electrical signals propagating, and not real intelligence, but I never really seem to convince people.

Because there are some really fundamental things they cannot do with next token prediction. For instance, their memory is akin to someone who reads the phone book and memorizes the entire thing, but can't tell you what a phone number is for. Moreover, they can mimic semantic knowledge, because they have been trained on that knowledge, but take them out of their training distribution and they get into a "creative stor…

Next token prediction is about predicting the future by minimizing the number of bits required to encode the past. It is fundamentally causal and has a discrete time domain. You can't predict token N+2 without having first predicted token N+1. The human brain has the same operational principles.

Re: The changing goalposts of AGI and timelines

#344

Post by pu_pe »

sulam wrote: post #331
stavros wrote: post #248

Earlier quoted context omitted.

Everybody says "but they just predict tokens" as if that's not just "I hope you won't think too much about this" sleight of hand. Why does predicting the next token mean that they aren't AGI? Please clarify the exact logical steps there, because I make a similar argument that human brains are merely electrical signals propagating, and not real intelligence, but I never really seem to convince people.

Because there are some really fundamental things they cannot do with next token prediction. For instance, their memory is akin to someone who reads the phone book and memorizes the entire thing, but can't tell you what a phone number is for. Moreover, they can mimic semantic knowledge, because they have been trained on that knowledge, but take them out of their training distribution and they get into a "creative stor…

You didn't actually give an example of what the issue with next token prediction is. You just mentioned current constraints (ie generalization and learning are difficult, needs mountains of data to train, can't play chess very well) that are not fundamental problems. You can trivially train a transformer to play chess above the level any human can play at, and they would still be doing "next token prediction". I wouldn't be surprised if every single thing you list as a challenge is solved in a few years, either through improvement at a basic level (ie better architectures) or harnessing.

We don't know how human brains produce intelligence. At a fundamental level, they might also be doing next token prediction or something similarly "dumb". Just because we know the basic mechanism of how LLMs work doesn't mean we can explain how they work and what they do, in a similar way that we might know everything we need to know about neurons and we still cannot fully grasp sentience.

Re: The changing goalposts of AGI and timelines

#345

Post by casey2 »

devonkelley wrote: post #264

[flagged]

When did search replace all research jobs? How can an AI replace a masseuse? How far away away are we getting from an LLM that scratches your back for less than it costs you to do it?

People have unrealistic expectations because they literally think they are summoning god instead of accelerating a few concurrent tasks. If you want to break causality you need to pay the entropy demon it's due.

Re: The changing goalposts of AGI and timelines

#346

Post by tim333 »

neurocline wrote: post #251
slavik81 wrote: post #139

Earlier quoted context omitted.

Do you mean Dory, the fish from Finding Nemo?

I have to imagine the poster was referring to Dora the Explorer, a popular and charming cartoon from the start of this century.

I think it must be Dory who has short term memory loss https://youtu.be/B6178Ac90S4?t=22

Re: The changing goalposts of AGI and timelines

#347

Post by [deleted] »

sulam wrote: post #237
famouswaffles wrote: post #208

Earlier quoted context omitted.

People just overstate their understanding and knowledge, the usual human stuff. The same user has a comment in this thread that contains: 'If you actually know what models are doing under the hood to product output that...' Any one that tells you they know 'what models are dong under the hood' simply has no idea what they're talking about, and it's amazing how common this is.

Fair, I should define what I mean by under the hood. By “under the hood” I mean that models are still just being fed a stream of text (or other tokens in the case of video and audio models), being asked to predict the next token, and then doing that again. There is no technique that anyone has discovered that is different than that, at least not that is in production. If you think there is, and people are just keepin…

[deleted]

Re: The changing goalposts of AGI and timelines

#348

Post by marcus_holmes »

dragonwriter wrote: post #322
marcus_holmes wrote: post #320

Earlier quoted context omitted.

y'see, I would not define a system as "highly autonomous" if it only responds to requests. And I get that there are workarounds; effectively a cron job every second prompting "do the next thing". But in my personal definition of "highly autonomous" it would not need prompting at all. It would be thinking all the time, independently of requests.

The model is not the system. The model is a component of the system. The "cron job" (or other means by which a continuous action loop is implemented) and the necessary prompting for it to gather input (including subsequent user input or other external data) and to pursue a set of objectives which evolves based on input are all also parts of the system.

yeah, I get that.

But the actual bit that's doing the thinking is restarting from scratch every time. It loads the context, does the next thing, maybe updates the context, shuts down. One second later the same thing. This is not "highly autonomous" Artifical Intelligence. Just IMHO. Other opinions are also valid.

Re: The changing goalposts of AGI and timelines

#349

Post by erichocean »

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."

> "something about which no conclusions can be drawn because the proposed definitions lack sufficient precision and completeness."

The same problem exists defining human intelligence, it's a problem with "intelligence" in general, artificial or not.

Re: The changing goalposts of AGI and timelines

#350

Post by ghoblin »

casey2 wrote: post #345
devonkelley wrote: post #264

[flagged]

When did search replace all research jobs? How can an AI replace a masseuse? How far away away are we getting from an LLM that scratches your back for less than it costs you to do it? People have unrealistic expectations because they literally think they are summoning god instead of accelerating a few concurrent tasks. If you want to break causality you need to pay the entropy demon it's due.

Everyone cites some niche "human-only" jobs to argue AI won't replace labor. But most of the economy runs on things like document processing, logistics, retail, and factories. High-volume, repeatable, rule-driven tasks, and in those areas, we're already on the brink of full automation. Autonomous retail stores, delivery fleets, and smart factories are either here or imminent. It's not about AI scratching backs, it's about replacing jobs that move trillions of dollars. Sure, top-tier researchers, system engineers, and other highly skilled knowledge workers will still be in demand, but for mass labor disruption, AI doesn't need to beat them, it only needs to outperform the average human
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