A Definition of AGI

I'll simultaneously call all current ML models "stupid" and also say that SOTA LLMs can operate at junior (software) engineer level.

This is because I use "stupidity" as the number of examples some intelligence needs in order to learn from.

LLMs make up for being too stupid to live (literally: no living thing could survive if it needed so many examples) by going through each example faster than any living thing ever could — by as many orders of magnitude as there are between jogging and continental drift.

And this is indeed a huge problem with a lot of the attacks on LLM even as more limited AI - a lot of them are based on applying arbitrary standards without even trying to benchmark against people, and without people being willing to discuss where they draw the line for stating that a given subset of people do not possess general intelligence...

I think people get really uncomfortable trying to even tackle that, and realistically for a huge set of AI tasks we need AI that are more intelligent than a huge subset of humans for it to be useful. But there are also a lot of tasks where AI that is not needed, and we "just" need "more human failure modes".

I read this as a hypothetical well-educated adult. As in, given the same level of knowledge, the intelligence performs equally well.

I do agree that it’s a weird standard though. Many of our AI implementations exceed the level of knowledge of a well-educated adult (and still underperform with that advantage in many contexts).

Personally, I don’t think defining AGI is particularly useful. It is just a marketing term. Rather, it’s more useful to just speak about features/capabilities. Shorthand for a specific set of capabilities will arise naturally.

Human intelligence has had hundreds of thousands of years of evolution that removes any 'fatal' variance from our intelligence. Too dumb is obvious on how it's culled, but 'too smart' can get culled by social creatures too, really 'too different' in any way.

Current AI is in its infancy and we're just throwing data at it in the same way evolution throws random change at our DNA and sees what sticks.

I think you are talking about correlation in humans of, say, verbal and mathematical intelligence. Still, it is a correlation, not equality - there are many word-acknowledged writers who suck at math, and mathematical prodigies who are are not the best at writing.

If you go beyond human species (and well, computers are not even living organisms), it gets tricky. Adaptability (which is arguably a broader concept than intelligence) is very different for, say octopodes, corvids and slime molds.

It is certainly not a single line of proficiency or progress. Things look like lines only if we zoom a lot.

they do not, but the same argument can hold true by the fact the true human nature is not really known and thus trying to define what a human like intelligence would consist of can only be incomplete.

there are many parts of human cognition, phycology etc. especially related to consciousness that are known unknowns and/or completely unknown.

a mitigation for this isaue would be to call it generally applicable intelligence or something, rather than human like intelligence. implying ita not specialized AI but also not human like. (i dont see why it would need to be human like, because even with all the right logic and intelligence a human can still do something counter to all of that. humans do this everyday. intuitive action, or irrational action etc.

what we want is generally applicable intelligence, not human like intelligence.

I’m certainly not informed enough to have an intelligent conversation about this, but surely the emotions bit can’t be right?

My emotions are definitely a function of the chemical soup my brain is sitting in (or the opposite).

Is there more reason to believe otherwise? I'm not being contrarian, I'm genuinely curious what people think.

What non-biological systems do we know of that have consciousness, sentience or emotions?

None of that comes from outside of your biology and chemistry.

There is exactly one good reason, at least for consciousness and sentience. And the reason is that those are such a vaguely defined (or rather defined by prototypes; ala Wittgenstein [or JavaScript before classes]). And that reason is anthropism.

We only have one good example of consciousness and sentience, and that is our own. We have good reason to suspect other entities (particularly other human individuals, but also other animals) have that as well, but we cannot access it, and not even confirm its existence. As a result using these terms of non-human beings becomes confusing at best, but it will never be actually helpful.

Emotions are another thing, we can define that outside of our experience, using behavior states and its connection with patterns of stimuli. For that we can certainly observe and describe behavior of a non biological entity as emotional. But given that emotion is something which regulates behavior which has evolved over millions of years, whether such a description would be useful is a whole another matter. I would be inclined to use a more general description of behavior patterns which includes emotion but also other means of behavior regulators.

What if our definition of those concepts is biological to begin with?

How does a computer with full AGI experience the feeling of butterflies in your stomach when your first love is required?

How does a computer experience the tightening of your chest when you have a panic attack?

How does a computer experience the effects of chemicals like adrenaline or dopamine?

The A in AGI stands for “artificial” for good reason, IMO. A computer system can understand these concepts by description or recognize some of them them by computer vision, audio, or other sensors, but it seems as though it will always lack sufficient biological context to experience true consciousness.

Perhaps humans are just biological computers, but the “biological” part could be the most important part of that equation.

I was doing some naïve set theory the other day, and I found a proof of the Riemann hypothesis, by contradiction.

Assume the Riemann hypothesis is false. Then, consider the proposition "{a|a∉a}∈{a|a∉a}". By the law of the excluded middle, it suffices to consider each case separately. Assuming {a|a∉a}∈{a|a∉a}, we find {a|a∉a}∉{a|a∉a}, for a contradiction. Instead, assuming {a|a∉a}∉{a|a∉a}, we find {a|a∉a}∈{a|a∉a}, for a contradiction. Therefore, "the Riemann hypothesis is false" is false. By the law of the excluded middle, we have shown the Riemann hypothesis is true.

Naïve AGI is an apt analogy, in this regard, but I feel these systems aren't simple nor elegant enough to deserve the name naïve.

There are a bunch of these topics that everyone feels qualified to say something about. Consciousness, intelligence, education methods, nutrition, men vs women, economic systems etc.

It's a very emotional topic because people feel their self image threatened. It's a topic related to what is the meaning of being human. Yeah sure it should be a separate question, but emotionally it is connected to it in a deep level. The prospect of job replacement and social transformation is quite a threatening one.

So I'm somewhat understanding of this. It's not merely an academic topic, because these things will be adopted in the real world among real people. So you can't simply make everyone shut up who is an outsider or just heard about this stuff incidentally in the news and has superficial points to make.

> I consider chatgpt 3 to be "baby AGI". That's why it became so popular so fast

ChatGPT became popular because it was easy to use and amusing. (LLM UX until then had been crappy.)

Not sure AGI aspirations had anything to do with uptake.

> infallibility

Lying != fallibility.

Can you be more concrete? What kind of answer/conversation do you see as demonstrating passing the test, that you think is currently not possible.

I'd say yes, by at least one old definition made by someone who was at the time in a position to have a definition.

When deepmind was founded (2010) their definition was the following: AI is a system that learns to perform one thing; AGI is a system that learns to perform many things at the same time.

I would say that whatever we have today, "as a system" matches that definition. In other words, the "system" that is say gpt5/gemini3/etc has learned to "do" (while do is debateable) a lot of tasks (read/write/play chess/code/etc) "at the same time". And from a "pure" ML point, it learned those things from the "simple" core objective of next token prediction (+ enhancements later, RL, etc). That is pretty cool.

So I can see that as an argument for "yes".

But, even the person who had that definition has "moved the goalposts" of his own definition. From recent interviews, Hassabis has moved towards a definition that resembles the one from this paper linked here. So there's that. We are all moving the goalposts.

And it's not a recent thing. People did this back in the 80s. There's the famous "As soon as AI does something, it ceases to be AI" or paraphrased "AI is everything that hasn't been done yet".

No, just what has usually happened in the past with AI goalposts.

At first, just playing chess was considered to be a sign of intelligence. Of course, that was wrong, but not obvious at all in 1950.

It wasn't the best definition of AGI but I think if you asked an interested layman whether or not a system that can pass the Turing test was AGI 5 years ago, they would have said yes

I think, given some of the signs of the horizon, there is a level of MAD type bluffing going around, but some of the actions by various power centers suggest it is either close, people think its close or it is there.

Compared to everything that came before they are fairly general alright.