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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
by giancarlostoro 1mo 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
- lenerdenator 1mo agoI wonder if Altman's definition also includes taking on the same liability as a coworker would. Probably not.
- giancarlostoro 1mo agoWould 100% need to be fully insured for liability, with a sizable war chest that OpenAI cannot even afford.
- lenerdenator 1mo agoAnd 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.
- alex0015 1mo agoWhat do you mean by bearing no real responsibility for its actions? If I use a model to accomplish a task and it fails, I use something else to try to accomplish the task. If it's my responsibility to complete the task, it can't be the model's responsibility unless I've agreed to some sort of guarantee from the provider. If the provider says "the model will always be right or your money back" then the provider has got responsibility. If there's no guarantee, there's no responsibility on their part, just on the person whose job it is to try and solve a problem with the model.
- Avicebron 1mo agoThe "dream" that these labs are mostly selling is the ability for capital to subscribe to their AI for cheaper than it costs a human to do some task. Not to have a "human + AI hybrid where the human is responsible". It's what the whole AGI valuation is based off of, in that scenario, with no human oversight, the agent they lease has to be responsible for the task?
- kbelder 1mo agoThe person responsible at that point is the sucker who fell for the dream.
- alex0015 1mo agoIt's not really a dream though? You can subscribe to their AI right now and complete many tasks for cheaper than it would cost to pay a human to do those tasks. Another person can do the same thing but also keep a human in the loop. You and the other person may compete in the market for whatever your product or service is, and you both might do very well or one of you might do better than the other because of a whole host of different reasons. Nothing in the process of developing and selling access to a more advanced LLM requires the customers to do away with human labor, nor does it require them to offer the LLM service with a guarantee that it will never make any mistakes. So far the LLMs have always made lots of mistakes and the companies sure keep making a lot of money.
- m-s-y 1mo agoThis 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.
- chrsw 1mo agoI 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”.
- azan_ 1mo agoI can call coworker right now and have conversation so frustrating that I wish I was talking to machine instead.
- deleted 1mo ago[deleted]
- dotancohen 1mo ago[flagged]
- sheepscreek 1mo agoHaha, sounds like a median human being alright!
- estetlinus 1mo agoJust say “box plot” and 99.9999% of the working force gets watery eyes.
- i_am_a_peasant 1mo agobro, same
- usef- 1mo agoI think they're claiming it's achieved by text models, not voice models, fwiw.
- cortesoft 1mo agoI feel like my odds are better with the AI than with random humans.
- 1mo ago
- ACCount37 1mo agoARC-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.
- petilon 1mo agoHere's another definition of AGI from Sam Altman: https://www.nytimes.com/2023/11/20/podcasts/hard-fork-sam-altman-transcript.html 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.
- throwawayq3423 1mo agoWhat is novel physics?
- astro1234 1mo agoI 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?
- adastra22 1mo agoThere was symbolic AI programs in the 1980’s that “discovered” Kepler’s laws and the resulting solar system model from just tycho brache’s astronomical observations. That was the the very first “new physics” ever.
- verelo 1mo agoi wonder if we could train a modal, and omit all data prior to 1899, and see what happens?
- astro1234 1mo agoDo you mean after? People do this!! But I think it’s a bit different. It won’t be apples to apples because the data volume I think is just so much different. Maybe there are good experiments for something like this.
- 1mo ago
- refulgentis 1mo agoIf 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.
- adastra22 1mo agoIt’s better to call a spade a spade.
- butterisgood 1mo agoPeople 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.
- slidehero 1mo ago> 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.
- phlakaton 1mo agoIf 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.
- slidehero 1mo agowhich just brings us back to the whole birds vs planes thing. turns out that flapping wings is not the right way to unlock human flight. computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant.
- mrandish 1mo ago> computers could count the Rs in strawberry since vacuum tubes. that measure is irrelevant. I don't think it's irrelevant but perhaps not in the way you're assuming. When assessing AGI I'm not evaluating counting characters or even the execution of math operators at any scale or speed. As you observe, computer software from Regex to spreadsheets and Mathematica already handle that well. But AGI isn't about what computers can do, it's about whether AIs can do the specific things which, until now, have been uniquely human capabilities. Like understanding nuanced context and then coming up with novel approaches to solve a new kind of problem not relying on any specific prior training or knowledge (the 'G' is for General). Most definitions of AGI start from a baseline that already assumes easily passing a Turing test and doing anything via text response that a high school graduate could. I ding LLMs not for failing to count but for failing to intuitively understand the nuanced context of a simple class of problem it hasn't seen in its training data. I fully understand that the reason LLMs fail letter counting is that they operate at the token level. They weren't trained on individual letters first, like human 2nd graders. The only reason recent LLMs get strawberry and blueberry correct now is that they have those words on their pre-training 'cheat sheet'. However, the underlying fundamental weakness in the way LLM intelligence works which leads to this failure mode still hasn't been addressed. Even when the frontier labs add "recognize any sub-token counting question and write a Python script" to the training cheat sheet so LLMs always pass that test... they'll still be unable to recognize a simple class of problem which isn't on their 'cheat sheet'. As long as that's the case, to me, they aren't AGI because they can't fully replicate human-like recognition of novel problem classes. And it's not just about letter-counting. That gap and others like it lead to many other kinds of non-human brittleness in LLM problem solving. Those are the classes of reasoning, intuition and insight that the ARC-AGI series has been trying to queue up as targets. Not to show how bad LLMs are but to help them be great in all these counter-intuitive edge cases
- Forgeties79 1mo ago> I feel like AGI's definition got watered down Typical result of venture capital and too many bag holders unfortunately.
- tim333 1mo agoThere never was a clear original definition so people come up with their own ones. But for me the significant thing is being able to do the stuff people do including substituting for them in jobs like inventing better AI. I don't think we're there yet.
- meowsers 1mo agoThe definition that was generally accepted and is often used by places like the FT is intelligence that surpasses human capabilities across nearly all benchmarks. When they first announced Astra they released a statement about how it had solved ten long-standing problems in various fields like mathematics, but it still doesn't really meet the standard definition IMO - and they are gearing up for an IPO - so they have reason to say stuff like that
- mvkel 1mo agoIf you read what ARC-AGI has stated from day one, the tests are designed to stress frontier models in tasks that humans are uniquely good at. When the models get to 100%, the next set of tasks is deployed. When they run out of ideas for how to stress a model (i.e. no more tests), THAT is when AGI is achieved. It's a great definition.