Not really. Francois (co-creator of the ARC Prize) has this to say:
The v1 version of the benchmark is starting to saturate. There were already signs of this in the Kaggle competition this year: an ensemble of all submissions would score 81%
Early indications are that ARC-AGI-v2 will represent a complete reset of the state-of-the-art, and it will remain extremely difficult for o3. Meanwhile, a smart human or a small panel of average humans would still be able to score >95% ... This shows that it's still feasible to create unsaturated, interesting benchmarks that are easy for humans, yet impossible for AI, without involving specialist knowledge. We will have AGI when creating such evals becomes outright impossible.
For me, the main open question is where the scaling bottlenecks for the techniques behind o3 are going to be. If human-annotated CoT data is a major bottleneck, for instance, capabilities would start to plateau quickly like they did for LLMs (until the next architecture). If the only bottleneck is test-time search, we will see continued scaling in the future.
I was just thinking about how 3D game engines were perceived in the 90s. Every six months some new engine came out, blew people's minds, was declared photorealistic, and was forgotten a year later. The best of those engines kept improving and are still here, and kinda did change the world in their own way.
Software development seemed rapid and exciting until about Halo or Half Life 2, then it was shallow but shiny press releases for 15 years, and only became so again when OpenAI's InstructGPT was demonstrated.
While I'm really impressed with current AI, and value the best models greatly, and agree that they will change (and have already changed) the world… I can't help but think of the Next Generation front cover, February 1997 when considering how much further we may be from what we want: https://www.giantbomb.com/pc/3045-94/forums/unreal-yes-this-...
The transition seems to map well to the point where engines got sophisticated enough, that highly dedicated high-schoolers couldn't keep up. Until then, people would routinely make hobby game engines (for games they'd then never finish) that were MVPs of what the game industry had a year or three earlier. I.e. close enough to compete on visuals with top photorealistic games of a given year - but more importantly, this was a time where you could do cool nerdy shit to impress your friends and community.
Then Unreal and Unity came out, with a business model that killed the motivation to write your own engine from scratch (except for purely educational purposes), we got more games, more progress, but the excitement was gone.
Maybe it's just a spurious correlation, but it seems to track with:
> and only became so again when OpenAI's InstructGPT was demonstrated.
Which is again, if you exclude training SOTA models - which is still mostly out of reach for anyone but a few entities on the planet - the time where anyone can do something cool that doesn't have a better market alternative yet, and any dedicated high-schooler can make truly impressive and useful work, outpacing commercial and academic work based on pure motivation and focus alone (it's easier when you're not being distracted by bullshit incentives like user growth or making VCs happy or churning out publications, farming citations).
It's, once again, a time of dreams, where anyone with some technical interest and a bit of free time can make the future happen in front of their eyes.
The timescale you are describing for 3D graphics is 4 years from the 1997 cover you posted to the release of Halo which you are saying plateaued excitement because it got advanced enough.
An almost infinitesimally small amount of time in terms of history human development and you are mocking the magazine being excited for the advancement because it was... 4 years yearly?
The era was people getting wowed from Wolfenstein (1992) to "about Halo or Half Life 2" (2001 or 2004).
And I'm not saying the flattening of excitement was for any specific reason, just that this was roughly when it stopped getting exciting — it might have been because the engines were good enough for 3D art styles beyond "as realistic as we can make it", but for all I know it was the War On Terror which changed the tone of press releases and how much the news in general cared. Or perhaps it was a culture shift which came with more people getting online and less media being printed on glossy paper and sold in newsagents.
Whatever the cause, it happened around that time.
It's a very strange thing I've never understood.
We still barely know how to use computers effectively, and they have already transformed the world. For better or worse.
I've been blessed with grandchildren recently, a little boy that's 2 1/2 and just this past Saturday a granddaughter. Major events notwithstanding, the world will largely resemble today when they are teenagers, but the future is going to look very very very different. I can't even imagine what the capability and pervasiveness of it all will be like in ten years, when they are still just kids. For me as someone that's invested in their future I'm interested in all of the educational opportunities (technical, philosphical and self-awareness) but obviously am concerned about the potential for pernicious side effects.
> There is a related “Theorem” about progress in AI: once some mental function is programmed, people soon cease to consider it as an essential ingredient of “real thinking”. The ineluctable core of intelligence is always in that next thing which hasn’t yet been programmed. This “Theorem” was first proposed to me by Larry Tesler, so I call it Tesler’s Theorem: “AI is whatever hasn’t been done yet.”
All that's invalidated each time is the idea that a general solution to that task requires a general solution to all tasks, or that a general solution to that task requires our special sauce. It's the idea that something able to to that task will also be able to do XYZ.
And yet people keep coming up with a new task that people point to saying, 'this is the one! there's no way something could solve this one without also being able to do XYZ!'
I'd love more progress on tasks in the physical world, though. There are only a few paths for countries to deal with a growing ratio of old retired people to young workers:
1) Prioritize the young people at the expense of the old by e.g. cutting old age benefits (not especially likely since older voters have greater numbers and higher participation rates in elections)
2) Prioritize the old people at the expense of the young by raising the demands placed on young people (either directly as labor, e.g. nurses and aides, or indirectly through higher taxation)
3) Rapidly increase the population of young people through high fertility or immigration (the historically favored path, but eventually turns back into case 1 or 2 with an even larger numerical burden of older people)
4) Increase the health span of older people, so that they are more capable of independent self-care (a good idea, but difficult to achieve at scale, since most effective approaches require behavioral changes)
5) Decouple goods and services from labor, so that old people with diminished capabilities can get everything they need without forcing young people to labor for them
I am continually baffled that people here throw this argument out and can't imagine the second-order effects. If white collar work is automated by AGI, all the RnD to solve robotics beyond imagination will happen in a flash. The top AI labs, the people smartest enough to make this technology, all are focusing on automating AGI Researchers and from there follows everything, obviously.
We're already seeing escape velocity in world modeling (see Google Veo2 and the latest Genesis LLM-based physics modeling framework).
The hardware for humanoid robots is 95% of the way there, the gap is control logic and intelligence, which is rapidly being closed.
Combine Veo2 world model, Genesis control planning, o3-style reasoning, and you're pretty much there with blue collar work automation.
We're only a few turns (<12 months) away from an existence proof of a humanoid robot that can watch a Youtube video and then replicate the task in a novel environment. May take longer than that to productionize.
It's really hard to think and project forward on an exponential. We've been on an exponential technology curve since the discovery of fire (at least). The 2nd order has kicked up over the last few years.
Not a rational approach to look back at robotics 2000-2022 and project that pace forwards. There's more happening every month than in decades past.
Calibrating to the current hype cycle has been challenging with AI pronouncements.
Our value proposition as humans in a capitalist society is an increasingly fragile thing.
who is going to pay for residential electrical work lol and how much will you make if some guy from MIT is going to compete with you
> while the majority of the population will be unemployable and forever left behind
Productivity improvements increase employment. A superhuman AI is a productivity improvement.
It's gone from "well the output is incoherent" to "well it's just spitting out stuff it's already seen online" to "WELL...uhh IT CAN'T CREATE NEW/NOVEL KNOWLEDGE" in the space of 3-4 years.
It's incredible.
We already have AGI.
On the other hand, there is a long, long history of AI achieving X but not being what we would casually refer to as "generally intelligent," then people deciding X isn't really intelligence; only when AI achieves Y will it be intelligence. Then AI achieves Y and...