Lorenzo Rex

Knowledge Seeker https://lorenzopieri.com/

Wiki Contributions


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

Interesting paradox. 

As other commented, I see multiple flaws:

  1. We believe to seem to know that there is a reality that exists. I doubt we can conceive reality, but only a vague understanding of it. Moreover we have no experience of "not existing", so it's hard to argue that we have a strong grasp on deeply understanding that there is a reality that exists.
  2. Biggest issue is here imho  (this is a very common misunderstanding): math is just a tool which we use to describe our universe, it is not (unless you take some approach like the mathematical universe) our universe. The fact that it works well is selection bias. We use math that works well to describe our universe, we discard the rest (see e.g. negative solution to the equation of motion in newtonian mechanics). Math by itself is infinite, we just use a small subset to describe our universe.  Also we take insipiration from our universe to build math. 

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". 

Load More