If we use median AI timings, we will be 50 per cent dead before that moment. May be it will be useful different measure, like 10 per cent of TAI, before which our protective measures should be prepared?
Also, this model contradicts naive model of GPT growth in which the number of parameters has been growing 2 orders of magnitude a year last couple of years, and if this trend continues, it could reach human level of 100 trillion parameters in 2 years.
I agree that full distribution information is very valuable, although I consider medians to be important as well. The spreadsheet linked in the report provides the full distribution implied by my views for the probability that the amount of computation required to train a transformative model is affordable, although it requires some judgment to translate that into P(TAI), because there may be other bottlenecks besides computation and there may be other paths to TAI besides training a transformative model. I’d say it implies somewhere between 2031 and 2036 is the year by which there is a 10% chance of TAI.
As I said in a reply to Daniel above, the way to express the view that a brain-sized GPT model would constitute TAI is to assign a lot of weight to the Short Horizon Neural Network hypothesis, potentially along with shifting narrowing the effective horizon length. I think this is plausible, but don’t believe we should have a high probability on this because I expect on priors that we would need longer effective horizon lengths than GPT-3, and I don’t think that evidence from the GPT-3 paper or follow on papers have provided clear evidence to the contrary.
In my best guess inputs, I assign a 25% probability collectively to the Short Horizon Neural Network and Lifetime Anchor hypotheses; in my aggressive inputs I assign 50% probability to these two hypotheses collectively. In both cases, probabilities are smoothed to a significant extent because of uncertainty in model size requirements and scaling, with substantial weight on smaller-than-brain-sized models and larger-than-brain-sized models.
If we use median AI timings, we will be 50 per cent dead before that moment. May be it will be useful different measure, like 10 per cent of TAI, before which our protective measures should be prepared?
Also, this model contradicts naive model of GPT growth in which the number of parameters has been growing 2 orders of magnitude a year last couple of years, and if this trend continues, it could reach human level of 100 trillion parameters in 2 years.
Thanks!
I agree that full distribution information is very valuable, although I consider medians to be important as well. The spreadsheet linked in the report provides the full distribution implied by my views for the probability that the amount of computation required to train a transformative model is affordable, although it requires some judgment to translate that into P(TAI), because there may be other bottlenecks besides computation and there may be other paths to TAI besides training a transformative model. I’d say it implies somewhere between 2031 and 2036 is the year by which there is a 10% chance of TAI.
As I said in a reply to Daniel above, the way to express the view that a brain-sized GPT model would constitute TAI is to assign a lot of weight to the Short Horizon Neural Network hypothesis, potentially along with shifting narrowing the effective horizon length. I think this is plausible, but don’t believe we should have a high probability on this because I expect on priors that we would need longer effective horizon lengths than GPT-3, and I don’t think that evidence from the GPT-3 paper or follow on papers have provided clear evidence to the contrary.
In my best guess inputs, I assign a 25% probability collectively to the Short Horizon Neural Network and Lifetime Anchor hypotheses; in my aggressive inputs I assign 50% probability to these two hypotheses collectively. In both cases, probabilities are smoothed to a significant extent because of uncertainty in model size requirements and scaling, with substantial weight on smaller-than-brain-sized models and larger-than-brain-sized models.