Knowledge Seeker https://lorenzopieri.com/
Lorenzo Rex(Lorenzo Pieri)
Karma: 147
This is also known as Simplicity Assumption: “If we randomly select the simulation of a civilization in the space of all possible simulations of that civilization that have ever been run, the likelihood of picking a given simulation is inversely correlated to the computational complexity of the simulation.”
In a nutshell, the amount of computation needed to perform simulations matters (if resources are somewhat finite in base reality, which is fair to imagine), and over the long term simple simulations will dominate the space of sims.
See here for more info.
Regarding (D), it has been elaborated more in this paper (The Simplicity Assumption and Some Implications of the Simulation Argument for our Civilization).
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.
A pragmatic metric for Artificial General Intelligence
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:
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.
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.
Basic Post Scarcity Q&A
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”.
Nice link, thanks for sharing.
The 1 million prize problem should be “clearly define the AI alignement problem”. I’m not even joking, actually understanding the problem and enstablising that there is a problem in the first place may give us hints to the solution.
In research there are a lot of publications, but few stand the test of time. I would suggest to you to look at the architectures which brought significant changes and ideas, those are still very relevant as they:
- often form the building block of current solutions
- they help you build intuition on how architectures can be improved
- it is often assumed in the field that you know about them
- they are often still useful, especially when having low resources
You should not need to look at more than 1-2 architectures per year in each field (computer vision, NLP, RL). Only then I would focus on SOTA.
You may want to check https://fullstackdeeplearning.com/spring2021/ it should have enough historic material to get you covered and expand from there, while also going quickly to modern topics.
Thanks for the link, I will check it out.
ARC is a nice attempt. I also participated in the original challenge on Kaggle. The issue is that the test can be gamed (as anyone on Kaggle did) brute forcing over solution strategies.
An open-ended or interactive version of ARC may solve this issue.
Using the Universal Distribution in the context of the simulation argument makes a lot of sense if we think that the base reality has no intelligent simulators, as it fits with our expectations that a randomly generated simulator is very likely to be coincise. But for human (or any agent-simulators) generated simulations, a more natural prior is how easy is the simulation to be run (Simplicity Assumption), since agent-simulators face concrete tradeoffs in using computational resources, while they have no pressing tradeoffs on the length of the program.
See here for more info on the latter assumption.