Fair enough, I suppose I take RSI more seriously than most people here so I wonder if there will be much of a fire alarm.
It’s terrifying to consider how good language models are at writing code considering there is still a lot of low-hanging fruit unplucked. Under my model, 2023 is going to be crazy year—an acquaintance of mine knows some people at OpenAI and he claims they are indeed doing all the obvious things.
I predict by this date 2023 your median will be at least 5 years sooner.
I predict by this date 2023 your median will be at least 5 years sooner.
That’s possible. I’m already trying to “price in” what I expect from GPT-4 into my timeline, which I expect to be very impressive.
It’s perhaps worth re-emphasizing that my median timeline is so far in the future primarily because I’m factoring in delays, and because I set a very high bar. >30% GWP growth has never happened in human history. I think we’ve seen up to 14% growth in some very fast growing nations a few times, but that’s been localized, and never at the technological frontier.
By these standards, the internet and tech revolution of the 1990s barely mattered. I could definitely see something as large as the rise of the internet happening in the next 10 years. But to meet my high bar, we’ll likely need to see something radically changing the way we live our lives (or something that makes us go extinct).
I think it’s worth forecasting AI risk timelines instead of GDP timelines, because the former is what we really care about while the latter raises a bunch of economics concerns that don’t necessarily change the odds of x-risk. Daniel Kokotajlo made this point well a few years ago.
On a separate note, you might be interested in Erik Byrnjolfsson’s work on the economic impact of AI and other technologies. For example this paper argues that general purpose technologies have an implementation lag, where many people can see the transformative potential of the technology decades before the economic impact is realized. This would explain the Solow Paradox, named after economist Robert Solow’s 1987 remark that “you can see the computer age everywhere but in the productivity statistics.” Solow was right that the long-heralded technology had not had significant economic impact at that point in time, but the coming decade would change that narrative with >4% real GDP growth in the United States driven primarily by IT. I’ve been taking notes on these and other economics papers relevant to AI timelines forecasting, send me your email if you’d like to check it out.
Overall I was similarly bearish on short timelines, and have updated this year towards a much higher probability on 5-15 year timelines, while maintaining a long tail especially on the metric of GDP growth.
I think it’s worth forecasting AI risk timelines instead of GDP timelines, because the former is what we really care about while the latter raises a bunch of economics concerns that don’t necessarily change the odds of x-risk.
I agree that’s probably the more important variable to forecast. On the other hand, if your model of AI is more continuous, you might expect a slow-rolling catastrophe, like a slow takeover of humanity’s institutions, making it harder to determine the exact “date” that we lost control. Predicting GDP growth is the easy way out of this problem, though I admit it’s not ideal.
On a separate note, you might be interested in Erik Byrnjolfsson’s work on the economic impact of AI and other technologies. For example this paper argues that general purpose technologies have an implementation lag, where many people can see the transformative potential of the technology decades before the economic impact is realized.
In fact, I cited this strand of research in my original post on long timelines. It was one of the main reasons why I had long timelines, and can help explain why it seems I still have somewhat long timelines (a median of 2047) despite having made, in my opinion, a strong update.
Fair enough, I suppose I take RSI more seriously than most people here so I wonder if there will be much of a fire alarm.
It’s terrifying to consider how good language models are at writing code considering there is still a lot of low-hanging fruit unplucked. Under my model, 2023 is going to be crazy year—an acquaintance of mine knows some people at OpenAI and he claims they are indeed doing all the obvious things.
I predict by this date 2023 your median will be at least 5 years sooner.
That’s possible. I’m already trying to “price in” what I expect from GPT-4 into my timeline, which I expect to be very impressive.
It’s perhaps worth re-emphasizing that my median timeline is so far in the future primarily because I’m factoring in delays, and because I set a very high bar. >30% GWP growth has never happened in human history. I think we’ve seen up to 14% growth in some very fast growing nations a few times, but that’s been localized, and never at the technological frontier.
By these standards, the internet and tech revolution of the 1990s barely mattered. I could definitely see something as large as the rise of the internet happening in the next 10 years. But to meet my high bar, we’ll likely need to see something radically changing the way we live our lives (or something that makes us go extinct).
I think it’s worth forecasting AI risk timelines instead of GDP timelines, because the former is what we really care about while the latter raises a bunch of economics concerns that don’t necessarily change the odds of x-risk. Daniel Kokotajlo made this point well a few years ago.
On a separate note, you might be interested in Erik Byrnjolfsson’s work on the economic impact of AI and other technologies. For example this paper argues that general purpose technologies have an implementation lag, where many people can see the transformative potential of the technology decades before the economic impact is realized. This would explain the Solow Paradox, named after economist Robert Solow’s 1987 remark that “you can see the computer age everywhere but in the productivity statistics.” Solow was right that the long-heralded technology had not had significant economic impact at that point in time, but the coming decade would change that narrative with >4% real GDP growth in the United States driven primarily by IT. I’ve been taking notes on these and other economics papers relevant to AI timelines forecasting, send me your email if you’d like to check it out.
Overall I was similarly bearish on short timelines, and have updated this year towards a much higher probability on 5-15 year timelines, while maintaining a long tail especially on the metric of GDP growth.
I agree that’s probably the more important variable to forecast. On the other hand, if your model of AI is more continuous, you might expect a slow-rolling catastrophe, like a slow takeover of humanity’s institutions, making it harder to determine the exact “date” that we lost control. Predicting GDP growth is the easy way out of this problem, though I admit it’s not ideal.
In fact, I cited this strand of research in my original post on long timelines. It was one of the main reasons why I had long timelines, and can help explain why it seems I still have somewhat long timelines (a median of 2047) despite having made, in my opinion, a strong update.
It’s 2023 and uhh...yeah.
Yeah, unfortunately I think it will still get crazier.