Comment by giancarlostoro
10 hours ago
I feel like AGI's definition got watered down, and these tests do not cover the original definition, what is your definition and thoughts on aligning with what all of us understood from the original claim?
I feel like this test is just helping someone like Sam Altman pretend like he implemented AGI as originally pitched for an IPO when in fact, he has not. Shameful.
> AGI is essentially the equivalent of a median human that could be hired as a remote co-worker... capable of performing any task that one would be satisfied with a remote colleague doing via a computer.
- Sam Altman on AGI
I can call a coworker right now and have a real time conversation with them without feeling like I’m talking to a frustrating machine. Most of them, anyway. But I guess that’s “moving the goalposts”.
I can call coworker right now and have conversation so frustrating that I wish I was talking to machine instead.
Just say “box plot” and 99.9999% of the working force gets watery eyes.
Haha, sounds like a median human being alright!
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Reminds me of Turing test. https://en.wikipedia.org/wiki/Turing_test
I feel like my odds are better with the AI than with random humans.
Random humans don't have a significant cross section of human knowledge available in real-time, although many like to pretend they do, especially in internet comments :P Being able to compete with the capabilities of a median human would be an absolutely world changing achievement.
> I can call a coworker right now and have a real time conversation with them without feeling like I’m talking to a frustrating machine.
These machines were designed with the express purpose of being condescendingly sycophant to the point they hallucinate just to state "you are absolutely right".
No wonder some people even find these chatbots to be wife material.
In gemini I have the following personal context:
- Do not stroke my ego
- I never want to be complimented
And it disagrees with me a lot, granted this is in the webchat which I don't use for programming but for general usage seems to work, it isn't sycophantic
I think they're claiming it's achieved by text models, not voice models, fwiw.
I dunno, have you tried the voice chat in paid ChatGPT?
Have you? It’s a dumb model that gets a lot wrong. Codex voice is IMO only good for when I cannot dictate into the good models. The delay introduced by letting it work with a good model kills it for me.
Here's another definition of AGI from Sam Altman:
https://www.nytimes.com/2023/11/20/podcasts/hard-fork-sam-al...
Sam Altman: Let’s say we make an A.I. that is really good, but it can’t go discover novel physics. Would you call that AGI?
Kevin Roose (New York Times): I probably would, yeah. Would you?
Sam Altman: Well, again, I don’t like the term, but I wouldn’t call that done with the mission.
So, "really good" is the boundary now, whatever it means.
What's your definition of competence boundary for human coworker?
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If the new AGI benchmark is "be Einstein/Feynman" then we've hit AGI.
What if it can be Einstein, but can't draw a Pelican, write a solid college-level essay, or fold clothes?
The ability to do a ton of book learning in training, and pull in tons of related context at once, is superhuman in some ways, but lags a lot in others.
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Call me when Astra gets the Nobel prize... We'll have AGI when prizes have two categories, one for assisted humans, and one for pure-AI.
Wouldn’t that mean producing novel work like relativity and QED?
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What is novel physics?
I think they mean improve our understanding of physics with new theoretical results or paradigms. Like if it’s 1899, would Astra develop General and Special relativity on its own?
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I assume solving one of the major open problems of physics?
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Creating new physics is the new AGI goal post
If it makes you feel any better the curmudgeon who drives the ARC-AGI tests feels the same way, that's why we're on 3 and I'm sure we'll see 4. Also, we can all avoid calling it "moving the goalposts" so no one feels talked down to.
It’s better to call a spade a spade.
> I feel like AGI's definition got watered down
Typical result of venture capital and too many bag holders unfortunately.
People need to stop redefining and trying to capture the term AGI. None of this is AGI. Not even close. Can it do things an AGI could do? Yeah some of it, but the difference really matters. These things still regularly fail, and gaslight about answers to questions like "how many r's in strawberry" or "s's in espresso".
An AGI wouldn't struggle with that.
The last version to fail on those questions was GPT 4.5.
Meanwhile most humans fail to correctly answer how many f's are in the sentence, "Finished files are the result of years of scientific study combined with the experience of many years.".
It comes and goes... My point is we're not near AGI.
Nooo. I just asked Claude (Sonnet 5 Medium) how many I's are in assassin, and it said 2. Granted, it got several words correct before this. But no, they still aren't great at this letter-counting thing.
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> "how many r's in strawberry" or "s's in espresso".
And what percentage of your red retina receptors are firing?
The "number of letters" critique was broken before it was introduced the first time. The models were specifically designed with preprocessing to not be able to perceive their input as strings of letters. Blind people are not dumb. (True as a pun and in context.)
Sub-access sensory questions, or do-you-know-a-fact questions (which is what spelling becomes when you can't see the letters, and are not specifically trained to match all token encoded words to their letters) are not intelligence questions.
A lot of movies gave the impression that making phone calls and balancing checkbooks would be the easiest tasks for consumer AI to solve, while math and science might require exceedingly advanced AI. Turns out to be the opposite: computers are great at math and bad at conversation.
That's because movies were based on the "general" nature of AI, assuming we would create intelligence that would learn and grow.
Pretty much the opposite of what we can't up with, if you're willing to call what we have intelligence.
I'm going to go out on a limb and guess that there are plenty of savants who can't tell you how many r's are in strawberry.
These are like saying someone isn’t human because they have a speech impediment or an auditory processing disorder.
AGI doesn’t mean infallible, it just means it can have a reasonable crack at things it hasn’t seen or done before.
I don't understand why you're being downvoted... that's literally the definition of AGI.
> gaslight about answers to questions like "how many r's in strawberry" or "s's in espresso".
this has been debunked too many times to bother rebutting. they struggle with those things because of the way they are.
it's completely irrelevant.
If it allows you to distinguish readily between human intelligence and computer intelligence, it seems quite relevant to the question of whether computers have achieved something akin to human intelligence.
It may not be useful for anything else, but at least it can say that.
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> they struggle with those things because of the way they are. it's completely irrelevant.
I mean, they seem like fair game if you’re ever participating in a Turing Test.
I wonder if Altman's definition also includes taking on the same liability as a coworker would.
Probably not.
Would 100% need to be fully insured for liability, with a sizable war chest that OpenAI cannot even afford.
This implies that human employees don’t have insurance. But they do. My company’s cyber insurance for example covers breaches due to employee mistakes. Most companies also have umbrella liability policies. It’s just that for now, AI “employees” need a LOT more coverage.
And that's the rub, isn't it?
If it can replace a worker but does too much work to be checked routinely by a human, and bears no real responsibility for its actions, well... it's really just a way to jack up the value of the settlement the company using it gets to pay out when it does something that causes a lawsuit.
If OpenAI had just simply stuck to making "good enough" models that were open sourced (like they promised they would be when starting out) and could be used to augment a human doing a task - a human that could be given actual consequences for messing up - they wouldn't have burned all of this money trying to reach this nebulous definition of AGI. Hell, "good enough" is what many open-source models are, and that's what terrifies Altman.
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ARC-AGI was never "if this benchmark is saturated, we're at AGI". It was always about crafting adversarial tests that humans are good at, but current AIs are bad at. Point out the gap, get AI teams to attack them.
In practical terms? They usually get solved with a bigger badder LLM. "New ideas are needed?" Nah - ten times the params, ten times the test time compute.
ARC-AGI-3 was more of a failure in that regard than -1 or -2, because even on day 0, an off the shelf LLM with a harness could get 50%+. And messing with evals by forbidding "LLM with a harness" from scoring? Yeah no, that was just bad.