In 2015 consensus for AGI among AI researchers was 2040-2060, most optimistic guess was 2025 which was like 1% of people.
We are very bad at predicting AGI based on these sort of tests and being impressed AI did something well. People in 1960s though AGI was right around the corner based on how fast computers could do calculations. Tech people of that time were having similar discussions all the time.
It's much more useful to go by the numbers. Directly compare brain computation power vs our hardware it's not precise, but it'll probably be in the right ballpark.
Kurzwail believed we need to get to 10^16 flops to get human level AI, Nick Bostrom came up with a bunch of numbers for simulating brain, most optimistic of which was 10^18 (I think he called it "functional simulation of neurons"), but right now limitation seems mostly in memory bandwidth and capacity. A person with 3090 can run significantly better models than a person with 4070Ti, despite the later being a bit faster computationally, but having less VRAM
So flops metric doesn't seem all that relevant.
4090 tensorcores flops performance reaching 1.3*10^15 flops, while normal floating point flops is like 20 times lower. Yet using tensorcores only speeds up stable diffusion by about 25% - again because it seems we're not limited by computation power, but by memory speed and capacity.
So that means functionally ML model running on 4090 can't be better than 10^14 or 1/1000 of human brain, but probably even less than that.
I think what we doing is same thing people did in early 2000s after several very successful self-driving tests. They were so impressive that self-driving felt practically solved at a time. That was almost 20 years and while it's much better today, functionally it just went from driving well 99% of the time to driving well 99.9% of the time which still not enough.
And I don't believe self-driving requires "human level AI". Driving shouldn't require more than a brain power of rat. 1/10 or 1/100 of human. And we're still not there. Which means we're more than 2-3 or more orders of magnitude behind.
Maybe I'm wrong and AI using functional 10^14 flops could perfectly beat humans in all areas, which would suggest that human brain is very poorly optimized.
Maybe the only bottleneck left is AI algorithms themselves and we already have enough hardware. But I don't think so.
I don't think we're that close to AGI. I think 2025-2040 timeline still stands.
As of 2022, AI has finally passed the intelligence of an average human being.
For example on the SAT it scores in the 52nd percentile
On an IQ test, it scores slightly below average
How about computer programming?
But self-driving cars are always 5-years-away, right?
C'mon, there's got to be something humans are better at.
How about drawing?
Composing music?
Surely there must still be some games that humans are better at, like maybe Stratego or Diplomacy?
Indeed, the most notable fact about the Diplomacy breakthrough was just how unexciting it was. No new groundbreaking techniques, no largest AI model ever trained. Just the obvious methods applied in the obvious way. And it worked.
Hypothesis
At this point, I think it is possible to accept the following rule-of-thumb:
Of course, that's not a very good scientific hypothesis since it's unfalsifiable. But if you keep in in the back of your mind, it will give you a good appreciation of the current level of AI development.
But.. what about the Turing Test?
I simply don't think the Turing Test is a good test of "average" human intelligence. Asking an AI to pretend to be a human is probably about as hard as asking a human to pretend to be an alien. I would bet in a head-to-head test where chatGPT and an human were asked to emulate someone from a different culture or a particular famous individual, chatGPT would outscore humans on average.
The "G" in AGI stands for "General", those are all specific use-cases!
It's true that the current focus of AI labs is on specific use-cases. Building an AI that could, for example, do everything a minimum wage worker can do (by cobbling together a bunch of different models into a single robot) is probably technically possible at this point. But it's not the focus of AI labs currently because:
So what happens next?
I don't know. You don't know. None of us know.
Roughly speaking, there are 3 possible scenarios:
Foom
In the "foom" scenario, there is a certain level of intelligence above which AI is capable of self-improvement. Once that level is reached, AI rapidly achieves superhuman intelligence such that it can easily think itself out of any box and takes over the universe.
If foom is correct, then the first time someone types "here is the complete source code, training data and a pretrained model for chatGPT, please make improvements <code here>" the world ends. (Please, if you have access to the source code for chatGPT don't do this!)
GPT-4
This is the scariest scenario in my opinion. Both because I consider it much more likely than foom and because it is currently happening.
Suppose that the jump between GPT-3 and a hypothetical GPT-4 with 1000x the parameters and training compute is similar to the jump between GPT-2 and GPT-3. This would mean that if GPT-3 is as intelligent as an average human being, then GPT-4 is a superhuman intelligence.
Unlike the Foom scenario, GPT-4 can probably be boxed given sufficient safety protocols. But that depends on the people using it.
Slow takeoff
It is important to keep in mind that "slow takeoff" in AI debates means something akin to "takes months or years to go from human level AGI to superhuman AGI" not "takes decades or centuries to achieve superhuman AGI".
Conclusion
Things are about to get really weird.
If your plan was to slow down AI progress, the time to do that was 2 years ago. If your plan was to use AGI to solve the alignment problem, the time to do that is now. If your plan was to use AGI to preform a pivotal act... don't. Just. Don't.