I would suggest to remove "I dont think you are calibrated properly about the ideas that are most commonly shared in the LW community. " and present your argument, without speaking for the whole community.
Very interesting division, thanks for your comment.
Paraphrasing what you said, in the informational domain we are very close to post scarcity already (minimal effort to distribute high level education and news globally), while in the material and human attention domain we likely still need advancements in robotics and AI to scale.
You mean the edit functionality of Gitlab?
Thanks for the gitbook tip, I will look into it.
Yes, the code is open source: https://gitlab.com/postscarcity/map
As other commented, I see multiple flaws:
Not conclusive, but still worth doing in my view due to the relative easiness. Create the spreadsheet, make it public and let's see how it goes.
I would add the actual year in which you think it will happen.
Yea, what I meant is that the slides of Full Stack Deep Learning course materials provide a decent outline of all of the significant architectures worth learning.
I would personally not go to that low level of abstraction (e.g. implementing NNs in a new language) unless you really feel your understanding is shaky. Try building an actual side project, e.g. an object classifier for cars, and problems will arise naturally.
I fear that measuring modifications it's like measuring a moving target. I suspect it will be very hard to consider all the modifications, and many AIs may blend each other under large modifications. Also it's not clear how hard some modifications will be without actually carrying out those modifications.
Why not fixing a target, and measuring the inputs needed (e.g. flops, memory, time) to achieve goals?
I'm working on this topic too, I will PM you.
Also feel free to reach out if topic is of interest.
Other useful references:
-On the Measure of Intelligence https://arxiv.org/abs/1911.01547
-S. Legg and M. Hutter, A collection of definitions of intelligence, Frontiers in Artificial Intelligence and applications, 157 (2007),
-S. Legg and M. Hutter, Universal intelligence: A definition of machine intelligence, Minds and Machines, 17 (2007), pp. 391-444. https://arxiv.org/pdf/0712.3329.pdf
-P. Wang, On Defining Artificial Intelligence, Journal of Artificial General Intelligence, 10 (2019), pp. 1-37.
-J. Hernández-Orallo, The measure of all minds: evaluating natural and artificial intelligence, Cambridge University Press, 2017.
This is the most likely scenario, with AGI getting heavily regulated, similarly to nuclear. It doesn't get much publicity because it's "boring".