We assembled a list of major technical insights in the history of progress in AI and metadata on the discoverer(s) of each insight.
Based on this dataset, we developed an interactive model that calculates the time it would take to reach the cumulation of all AI research, based on a guess at what percentage of AI discoveries have been made.
Feasibility of Training an AGI using Deep Reinforcement Learning: A Very Rough Estimate
Several months ago, we were presented with a scenario for how artificial general intelligence (AGI) may be achieved in the near future. We found the approach surprising, so we attempted to produce a rough model to investigate its feasibility. The document presents the model and its conclusions.
The usual cliches about the folly of trying to predict the future go without saying and this shouldn’t be treated as a rigorous estimate, but hopefully it can give a loose, rough sense of some of the relevant quantities involved. The notebook and the data used for it can be found in the Median Group numbers GitHub repo if the reader is interested in using different quantities or changing the structure of the model.
(note: second has a hard-to-estimate “real life vs alphago” difficulty parameter that the result is somewhat dependent on, although this parameter can be adjusted in the model)
From 2018, AI timelines section of mediangroup.org/research.
(note: second has a hard-to-estimate “real life vs alphago” difficulty parameter that the result is somewhat dependent on, although this parameter can be adjusted in the model)
I recommend articles (not by me) Why I am not an AI doomer, Diminishing Returns in Machine Learning.
So your timelines are the same as in 2018?
Thanks for the article recommendations.