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."
There are lots of meaningful definitions, the people saying we haven't reached AGI just don't use them. For most of the last half-century people would have agreed that machines that can pass the Turing test and win Math Olympiad gold are AGI.
We’ll know AGI when we see it, and this ain’t it. This complaining about changing goalposts is so transparently sour grapes from people over-invested in hyping the current LLM paradigm.
Says who? I had already found this study, published almost a year ago, saying that they do: https://arxiv.org/abs/2503.23674
There doesn't seem to be a super-rigorous definition of the Turing Test, but I don't think it's reasonable to require it to fool an expert whose life depends on the correct choice. It already seems to be decently able to fool a person of average intelligence who has a basic knowledge of LLMs.
I agree that we don't really have AGI yet, but I'd hope we can come up with a better definition of what it is than "we'll know it when we see it". I think it is a legitimate point that we've moved the goalposts some.
Now, you could argue that this right here is the aforementioned moving of the goalposts. After all, we're deciding that the casual Turing test wasn't interesting precisely after having seen that LLMs could pass it.
However, in my view, the Turing test _always_ implied the "rigorous" Turing test, and it's only now that we're actually flirting with passing it that it had to be clarified what counts as a true Turing test. As I see it, the Turing test can still be salvaged as a criteria for genera intelligence, but only if you allow it to be a no-holds-barred, life-depends-on-it test to exhaustion. This would involve allowing arbitrarily long questioning periods, for instance. I think this is more in the spirit of the original formulation, because the whole idea is to pit a machine against all of human intelligence, proving it has a similar arsenal of adaptability at its disposal. If it only has to passingly fool a human for brief periods, well... I'm afraid that just doesn't prove much. All sorts of stuff briefly fools humans. What requires intelligence is to consistently anticipate and adapt to all lines of questioning in a sustained manner until the human runs out of ideas for how to differentiate.
Artificial General Intelligence will exist when the grifters who profit from it claim it exists. The meaning of it will shift to benefit certain entrepreneurs. It will never actually be a useful term in science nor philosophy.
Searles thought experiment is stupid and debunked nothing. What neuron, cell, atom of your brain understands English ? That's right. You can't answer that anymore than you can answer the subject of Searles proposition, ergo the brain is a Chinese room. If you conclude that you understand English, then the Chinese room understands Chinese.
> Searle’s response to the Systems Reply is simple: in principle, he could internalize the entire system, memorizing all the instructions and the database, and doing all the calculations in his head. He could then leave the room and wander outdoors, perhaps even conversing in Chinese. But he still would have no way to attach “any meaning to the formal symbols”. The man would now be the entire system, yet he still would not understand Chinese. For example, he would not know the meaning of the Chinese word for hamburger. He still cannot get semantics from syntax.
Really, here the only issue is Searle's inability to grasp the concept that the process is what does the understanding, not the person (or machine, or neurons) that performs it.
Does it? Where?
Thinking cool and all but not that extraordinary. Even plants does it.
I would argue that Schrödinger’s cat has done more damage to the general understanding of quantum physics then it has done good. In contrary though, I don‘t think the same about the Turing test. I think it has resulted in a net positive for the theory of mind as long as people take Searle’s rebuttal into account. Without it (as is sadly common in popular philosophy) the Turing test is simply just wrong, and offers no good insight for neither philosophy nor science.
Turing's imitation game is about making it difficult for a human to tell whether they are communicating with a computer or not. If a computer can trick the human, then... what? The computer is "thinking" ?
I think most people would say that's an insufficient act to prove thinking. Even though no one has a rigorous definition of thinking either.
All this stuff goes around in circles and like most philosophy makes little progress.
Turing’s point in his 1950 paper was actually to provide a substitute to the question of whether machines could think. If a machine can win the imitation game, he argued, is a better question to ask rather then “can a machine think”. Searle showed that this is in fact this criteria was not a good one. But by 1980 philosophy of mind had advanced significantly, partially thanks to Turing’s contributions, particularly via cognitive science, but in the 1980s we also had neuropsychology, which kind of revolutionized this subfield of philosophy.
I think philosophy is actually rather important when formulating questions like these, and even more so when evaluating the quality of the answers. That said, I am not the biggest fan of the state of mainstream philosophy in the 1940s. I kind of have a beef with logical positivism, and honestly believe that even Turing’s mediocre philosophy was on a much better track then what the biggest thinkers of the time were doing with their operational definition.
I see no reason to disqualify p-zombies from being AGI.
If you read his paper, Turing was trying to make a specific point. The Turing test itself is just one example of how that broader point might manifest.
If a thinking machine can not be distinguished from a thinking human then it is thinking. That was his idea. In broader terms, any material distinction should be testable. If it is not, then it does not exist. What do you call 'fake gold' that looks, smells etc and reacts as 'real gold' in every testable way ? That's right - Real gold. And if you claimed otherwise, you would just look like a mad man, but swap gold for thinking, intelligence etc and it seems a lot of mad men start to appear.
You don't need to 'prove' anything, and it's not important or relevant that anyone try to do so. You can't prove to me that you think, so why on earth should the machine do so ? And why would you think it matters ? Does the fact you can't prove to me that you think change the fact that it would be wise to model you as someone that does ?
The test is to showcase that the question of whether machines can think is meaningless. The point of Turing's thesis is that passing his test just proves the machine has the capability to pass such a test, which is actually meaningful.
Are you involved in politics somehow?
The same problem exists defining human intelligence, it's a problem with "intelligence" in general, artificial or not.
I was thinking something similar - this isn't AI, and none of "those people" care if it is or isn't. They don't care philosophically, or even pragmatically.
They're selling a product. That product is the IDEA of replacement of the majority of human labor with what's basically slave labor but with substantially disregardable ethical quandaries.
It's honestly a genius product. I'm not surprised it's selling so well. I'm vaguely surprised so many people who don't stand to benefit in any way shape or form, or who will even potentially starve if it works out, are so keen on it. But there are always bootlickers.
The most unfortunate part is that when the party ends, it's none of "those people" who will suffer even in the slightest. I'm not even optimistic their egos will suffer, as Musk seems to show they are utterly immune even as their companies collapse under them.
I've been getting more and more disappointed by software engineers (in aggregate) as the years go by. They don't even have to be bootlickers to do what you describe, I think a lot of it is pride in their "intelligence," which they express by believing and regurgitating the propaganda they've consumed. They prove their smarts by (among other things) having opinions that align with a zeitgeist of some group of powerful elites. They're too-easily manipulated.
And it's not just AI, it's also things like libertarianism. You've got workers identifying as capitalist tycoons, because they read a book and have some shares in a 401k.
Sometimes I am dismayed by the lack of political and social consciousness in this group. Decade or two of digital boom coupled with handsome paychecks was enough to convince them that their position is different than it really is.
"it is AGI when we can no longer come up with tasks easy for humans to solve but hard for computers"
for abstract tasks (like ARC-AGI), who knows?
> artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work
"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.
* Which is a much larger class of jobs than just engineering. And also excludes field engineers and other types of engineers that need a physical body for interacting with customers, etc.**
** Though even then, you could in theory divvy up the engineering part and the customer interaction part of the job, where the human that's doing the interaction part is primarily a proxy to the engineering agent that's in his earbud.
> there's no reason we'd need to have humans working jobs that only involve typing stuff into a computer and going to meetings all day
But I also think that's a bad line to draw. Many of those jobs include a lot more than just typing into a computer. By your criteria we'd also be replacing most scientists, as so many are not doing physical experiments and using the computer to read the work of peers and develop new models. But also does get definition intended to exclude jobs where the computer just isn't the most convenient interface? We should be including more in that case since we can then make the connection for that interface.
I think we need a much more refined definition. I don't like the broad strokes "is computer". Nor do I like skills based definitions. They're much easier to measure but easily hackable. I think we should try to define more by our actual understanding of what intelligence is. While we don't have a precise definition we have some pretty good answers already. I know people act like the lack of an exact definition is the same as having no definition but that's a crazy framing. If we had that requirement we wouldn't have any definitions as we know nothing with infinite precision. Even physics is just an approximation, but it's about the convergence to the truth [1]
[side note] the conventional way to do references or notes here is with brackets like I did. So you don't have to escape your asterisks. *Also* if it lead a paragraph with two spaces you get verbatim text
[0] farmer, construction worker, plumber, machinist, welder, teacher, doctors, etc
[1] https://hermiene.net/essays-trans/relativity_of_wrong.html
Intelligence is one thing, being able to figure out how get a task done (say). But understanding that no, I don't want you to exploit a backdoor or blackmail my teammate or launch a warhead even though that might expedite the task. Or why some task is more important than another. Or that solving the P=NP problem is more fulfilling than computing the trillionth digit of pi. That's perhaps a different thing entirely, completely disjoint with intelligence.
And by that definition, maybe we are in the neighborhood of AGI already. The things can already accomplish many challenging tasks more reliably than most humans. But the lack of wisdom, emotion, human alignment, or whatever we want to call it, lead it to accomplish the wrong tasks, or accomplish them in the wrong way, or overlook obvious implicit requirements, may cause people to view it as unintelligent, even if intelligence is not the issue.
And that may be an unsolvable problem because AI simply isn't a living being, much less human. It doesn't have goals or ambitions or want a better future for its children. But it doesn't mean we can never achieve AGI.
Oh, and to your first question, yes it's a huge number of jobs, maybe half of jobs in developed nations. And why not? If you can get AI to do the work of the scientist for a tenth of the price, just give it a general role description and budget and let it rip, with the expectation that it'll identify the most promising experiments, process the results, decide what could use further investigation, look for market trends, grow the operation accordingly, that's all you need from a human scientist too. Plausibly the same for executives and other roles. Of course maybe sometimes the role needs a human face for press conferences or whatever, and I don't know how AI would be able to take that, but especially for jobs that are entirely internal-facing, it seems like there's no particular need for a human. Except that maybe, given the above, yes, you still need a human at the helm.
> we'd still need desk jobs to maintain the guardrails.
It would be absolutely bonkers to just hand over absolute control of any system to a machine before substantial verification. These vetting processes are in place for a reason. They can be annoying because they slow things down, but they're there because they speed things up in the long run. Because their existence tends to make things less sloppy, so they are less needed. But their existence also catches mistakes that were they made slow down processes far more than all the QA annoyances and slowdowns could ever cause combined.
> And why not? If you can get AI to do the work of the scientist for a tenth of the price
AI could go the same way. It's a creation engine like nothing that's ever been seen before, but it can also become a destruction engine in ways that we could never understand or hope to counter, and left unchecked, the odds of that soar to near certainty. So the first job is to place dummy guardrails around it. That's where we are now. But soon that becomes too restrictive. What can we loosen? How do we know? How can we recover if we're wrong? We're not quite there yet, but we're not not there either.
Of course eventually somebody is going to trigger it and it's going to go ballistic. Our only hope is that it happens at exactly the right time where AGI can cause enough damage for people to notice, but not enough to be irrecoverable. Maybe we should rename this whole AGI thing to Project Icarus.
Simply put, an economy and society of managers. That's terrifying.
The reason AGI couldn't do these is the lack of a suitable interface to the physical world. It would take a trivial amount of effort for these to be designed and built by the AGI. Humans could be cut from the loop after an initial production run made up of just the subset of these physical interface devices needed to build more advanced ones.
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.
> 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.
By your definition every machine has a type of general intelligence. Not just a bog standard calculator, but also my broom. It doesn't matter if you slap "smart" on the side, I'm not going to call my washing machine "intelligent". Especially considering it's over a decade old.
I don't think these definitions make anything any clearer. If anything, they make them less. They equate humans to mindless automata. They create AGI by sly definition and let the proposer declare success arbitrarily.
> If it can do things as good as or better than humans, in general, then either the AI has a type of general intelligence ...
> By your definition every machine has a type of general intelligence. Not just a bog standard calculator, but also my broom.
> General: 1. affecting or concerning all or most people, places, or things; widespread.
To me, a general intelligence is one that is not just specific: it's affecting or concerning all or most areas of intelligence.
A calculator is a very specific, very inflexible, type of intelligence, so it's not, by definition, general. And, I'm not talking about the indirect applications of a calculator or a specific intelligence.
If you want to argue that we don't need the concept of AGI, because something like specific experts could be enough to drastically change the economy, then sure! That would be true. But I think that's a slightly different, complimentary, conversation. Even then, say we have all these experts, then a system to intelligently dispatch problems to them...maybe that's a specific implementation of AGI that would work. I think how less dependent on human intelligence the economy becomes, and how more dependent on non-human decision makers it becomes, is a reasonable measure. This seems controversial, which I can't really understand. I'm in hardware engineering, so maybe I have a different perspective, but goals based on outcome are the only ones that actually matter, especially if nobody has done it before.
> To me, a general intelligence is one that is not just specific
> A calculator is a very specific, very inflexible, type of intelligence, so it's not, by definition, general
> I'm in hardware engineering, so maybe I have a different perspective, but goals based on outcome are the only ones that actually matter, especially if nobody has done it before.
This was an attempt to prevent this exact chain of response.
I calculator can only be used indirectly to solve a practical problem. A more general intelligence is required to know a calculator is needed, and how to break down the problem in a way that a calculator can be used effectively.
For example, you can't solve any real world problem with a calculator, beyond holding some papers down, or maybe keeping a door open. But, an engineer (or other general intelligence) with a calculator can solve real world problems with it. Tools vs tool users. The user is the general bit, not the specific tool that's useless on its own!
I think we've reached the limits of communication. Cheers!
> If you want to argue that we don't need the concept of AGI, because something like specific experts could be enough to drastically change the economy, then sure! That would be true. But I think that's a slightly different, complimentary, conversation. Even then, say we have all these experts, then a system to intelligently dispatch problems to them...maybe that's a specific implementation of AGI that would work.
was to prevent this comment chain.
I think we'll have to give up at this point, with whatever smells you may be attempting to communicate with me. Cheers!
Are you asking for the current understanding of what specific parts of human intelligence are economically valuable?
But beyond that, part of the nature of that change over time is that things tend to be valuable because they're scarce.
So the definition from upthread becomes roughly "highly autonomous systems that outperform humans at [useful things where the ability to do those things is scarce]", or alternatively "highly autonomous systems that outperform humans at [useful things that can't be automated]".
Which only makes sense if the reflexive (it's dependent on the thing being observed) part that I'm substituting in brackets is pinned to a specific as-of date. Because if it's floating / references the current date that that definition is being evaluated for, the definition is nonsensical.
But to extend your point, I think we really need to be explicit about the assumptions being made. Everyone loves to say intelligence is easy to define but if it were then we'd have a definition. But if "you" figure it out and it's so simple then "we" are all too dumb and it needs better explaining for our poor simple minds. Or there's a lot of details that make it hard to pin down and that's why there's not a definition of it yet. Kinda like how there's no formal definition of life
Google search can't achieve anything practical, because it has no agency. It has no agency partly because it doesn't have the required intelligence to do anything on its own, other than display results for something else, that does have agency, to use.
The applicable definitions, from the dictionary:
Knowledge: facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Intelligence: the ability to acquire and apply knowledge and skills.
Agency: the ability to make decisions and act independently.
> I think you're conflating "knowledge" with "intelligence"
> And, "agency" seems to be a missing concept,
>> I think we really need to be explicit about the assumptions being made
> Do you know how the human brain works?
https://en.wikipedia.org/wiki/Large_language_model
> Do you know how the human brain works? That science is still in its infancy but that's not stopped us.
Stopped us from doing what exactly?
We didn't need an understanding of how it worked, or even a word for intelligence, let alone a definition of it, to get good practical results.
Nobody is talking about current LLM. And, we don't know if gradient decent will be involved, since the systems don't exist yet. Maybe it all be some runtime gradient decent. If there's an optimization problem involved in any part of the process, and numbers are involved, there's a near guarantee it will include gradient decent.
The definition reminds me of the common quip about robotics, "it's robotics when it doesn't work, once it works it's a machine".
If the definition has shifted once again to mean "a computer program that does a task pretty well for us", then what's the new term we're using to define human-level artificial intelligence?
Is doing a ton of heavy lifting. What is considered economically valuable work is going to change from decade to decade, if not from year to year. What’s considered economically valuable also is going to be way different depending across individuals and nations within the exact same time frames too.
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.
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.
A sibling comment questions the relevance of this by asking what would change if that were true of some low-level component of the human thinking engine, which is a good point, but also: what "actual bit" does this? Both commercial backends and even desktop inference software usually does prefix caching in memory, so arguably that doesn't model the core piece of software running low-level inference, except when either the past context is changed (e.g., when compacting to manage context, or if you are using one software instance and swapping logical histories because you are running multiple agents concurrently — but not in parallel — on one software engine.) And it obviously doesn't match the system at any higher level.
> This is not "highly autonomous" Artifical Intelligence.
Even if that was an accurate model of a component of the system, that a component considered alone is not highly autonomous artificial intelligence is not an argument that the aggregate system is not a highly anonymous artificial intelligence.
I don't know enough about neuroscience to really answer your question in depth.
My opinion, uninformed as it is, is basically around the intuitive reasoning that something cannot be "highly autonomous" if it has to be kicked every second ;) Autonomous is defined as not needing to be controlled externally. And coupling that part with something as simple as cron job doesn't solve that in any meaningful way or make it "autonomous".
A batch file coupled with a cron job that triggers it once a day is not an "autonomous system" to my mind. It's a scheduled system, and there's a significant difference between those things.
I guess that's fine, autonomy has lots of definitions (some in overlapping domains) and I guess one more doesn't hurt, but I'm pretty sure the intended use in the discussion here is the standard mechanical one where it is a behavioral trait defined by the capacity of a system to decide on action without the involvement of another system or operator, and therefore it is something that could be achieved by a system composed of a processing and action component called repeatedly by a looping component.
what does "economically" means here? would it cover teaching? child care? healthcare? etc.
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, even inside our brains.
When the AGI term was first coined they probably meant something like HAL 9000. We have that now (and HAL gaining self-awareness or refusing commands are just for dramatic effect and not necessary). Goalposts are not stable in this game.
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.
So I'm very curious if any AI we have today would pass the Turing test under all circumstances, for example if: the examiner was allowed to continue as long as they wanted (even days/weeks), the examiner could be anybody (not just random selections of humans), observations other than the text itself were fair game (say, typing/response speed, exhaustion, time of day, the examiner themselves taking a break and asking to continue later), both subjects were allowed and expected to search on the internet, etc.
Are you actually curious about this? Does any model at all come even remotely close to this?
(By the way, if something like a regression model or decision tree can solve your problem, you should prefer those. Much cheaper to train and to run inference with those than with neural networks. Much cheaper than deep neural networks especially.)
This is what I learnt at university some decades ago, and it matches what wikipedia says today: https://en.wikipedia.org/wiki/Decision_tree_learning
In which case you could argue that neither DTs nor NNs are ML. Only the training itself is ML/AI. An interesting perspective, but this will probably just confuse the discussion even further.
All humans fail to be AGI, by definition.
Yes.