Gott famously estimated the future time duration of the Berlin wall's existence:
“Gott first thought of his "Copernicus method" of lifetime estimation in 1969 when stopping at the Berlin Wall and wondering how long it would stand. Gott postulated that the Copernican principle is applicable in cases where nothing is known; unless there was something special about his visit (which he didn't think there was) this gave a 75% chance that he was seeing the wall after the first quarter of its life. Based on its age in 1969 (8 years), Gott left the wall with 75% confidence that it wouldn't be there in 1993 (1961 + (8/0.25)). In fact, the wall was brought down in 1989, and 1993 was the year in which Gott applied his "Copernicus method" to the lifetime of the human race”. “https://en.wikipedia.org/wiki/J._Richard_Gott
The most interesting unknown in the future is the time of creation of Strong AI. Our priors are insufficient to predict it because it is such a unique task. So it is reasonable to apply Gott’s method.
AI research began in 1950, and so is now 65 years old. If we are currently in a random moment during AI research then it could be estimated that there is a 50% probability of AI being created in the next 65 years, i.e. by 2080. Not very optimistic. Further, we can say that the probability of its creation within the next 1300 years is 95 per cent. So we get a rather vague prediction that AI will almost certainly be created within the next 1000 years, and few people would disagree with that.
But if we include the exponential growth of AI research in this reasoning (the same way as we do in Doomsday argument where we use birth rank instead of time, and thus update the density of population) we get a much earlier predicted date.
We can get data on AI research growth from Luke’s post:
“According to MAS, the number of publications in AI grew by 100+% every 5 years between 1965 and 1995, but between 1995 and 2010 it has been growing by about 50% every 5 years. One sees a similar trend in machine learning and pattern recognition.”
From this we could conclude that doubling time in AI research is five to ten years (update by adding the recent boom in neural networks which is again five years)
This means that during the next five years more AI research will be conducted than in all the previous years combined.
If we apply the Copernican principle to this distribution, then there is a 50% probability that AI will be created within the next five years (i.e. by 2020) and a 95% probability that AI will be created within next 15-20 years, thus it will be almost certainly created before 2035.
This conclusion itself depends of several assumptions:
• AI is possible
• The exponential growth of AI research will continue
• The Copernican principle has been applied correctly.
Interestingly this coincides with other methods of AI timing predictions:
• Conclusions of the most prominent futurologists (Vinge – 2030, Kurzweil – 2029)
• Survey of the field of experts
• Prediction of Singularity based on extrapolation of history acceleration (Forrester – 2026, Panov-Skuns – 2015-2020)
• Brain emulation roadmap
• Computer power brain equivalence predictions
• Plans of major companies
It is clear that this implementation of the Copernican principle may have many flaws:
1. The one possible counterargument here is something akin to a Murphy law, specifically one which claims that any particular complex project requires much more time and money before it can be completed. It is not clear how it could be applied to many competing projects. But the field of AI is known to be more difficult than it seems to be for researchers.
2. Also the moment at which I am observing AI research is not really random, as it was in the Doomsday argument created by Gott in 1993, and I probably will not be able to apply it to a time before it become known.
3. The number of researchers is not the same as the number of observers in the original DA. If I were a researcher myself, it would be simpler, but I do not do any actual work on AI.
Perhaps this method of future prediction should be tested on simpler tasks. Gott successfully tested his method by predicting the running time of Broadway shows. But now we need something more meaningful, but testable in a one year timeframe. Any ideas?