Hi Clement, I do not have much to add to the previous critiques, I also think that what needs to be simulated is just a consistent enough simulation, so the concept of CI doesn't seem to rule it out.
You may be interested in a related approach ruling out the sim argument based on computational requirements, as simple simulations should be more likely than complex one, but we are pretty complex. See "The Simplicity Assumption and Some Implications of the Simulation Argument for our Civilization" (https://philarchive.org/rec/PIETSA-6)
Cheers!
A somewhat similar statistical reasoning can be done to argue that the abundance of optional complexity (things could have been similar but simpler) is evidence against the simulation hyphotesis.
See https://philpapers.org/rec/PIETSA-6 (The Simplicity Assumption and Some Implications of the Simulation Argument for our Civilization)
This is based on the general principle of computational resources being finite for any arbitrary civilisations (assuming infinities are not physical) and therefore minimised when possible by the simulators. In particular one...
Let’s start with one of those insights that are as obvious as they are easy to forget: if you want to master something, you should study the highest achievements of your field.
Even if we assume this, it does not follow that we should try to recreate the subjective conditions that led to (perceived) "success". The environment is always changing (tech, knowledge base, tools), so many learnings will not apply. Moreover, biographies tend to create a narrative after the fact, emphasizing the message the writer want to convey.
I prefer the strategy to master the basics from previous works and then figure out yourself how to innovate and improve the state of the art.
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.&nbs...
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 fo...
Regarding (D), it has been elaborated more in this paper (The Simplicity Assumption and Some Implications of the Simulation Argument for our Civilization).
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.
Interesting paradox.
As other commented, I see multiple flaws:
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 al...
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,...
I'm working on these lines to create an easy to understand numeric evaluation scale for AGIs. The dream would be something like: "Gato is AGI level 3.5, while the average human is 8.7." I believe the scale should factor in that no single static test can be a reliable test of intelligence (any test can be gamed and overfitted).
A good reference on the subject is "The Measure of All Minds" by Orallo.
Happy to share a draft, send me a DM if interested.
When you say "switching" it reminds me of the "big switch" approach of https://en.wikipedia.org/wiki/General_Problem_Solver.
Regarding to how they do it, I believe the relevant passage to be:
Because distinct tasks within a domain can share identical embodiments, observation formats and action specifications, the model sometimes needs further context to disambiguate tasks. Rather than providing e.g. one-hot task identifiers, we instead take inspiration from (Brown et al., 2020; Sanh et al., 2022; Wei et al., 2021) and use prompt conditioning.
I guess it shoul...
Fair analysis, I agree with the conclusions. The main contribution seems to be a proof that transformers can handle many tasks at the same time.
Not sure if you sorted the tests in order of relevance, but I also consider the "held-out" test as being the more revealing. Besides finetuning, it would be interesting to test the zero-shot capabilities.
A single network is solving 600 different tasks spanning different areas. 100+ of the tasks are solved at 100% human performance. Let that sink in.
While not a breaktrough in arbitrary scalable generality, the fact that so many tasks can be fitted into one architecture is surprising and novel. For many real life applications, being good in 100-1000 tasks makes an AI general enough to be deployed as an error tollerant robot, say in a warehouse.
The main point imho is that this architecture may be enough to be scaled (10-1000x parameters) in few years to a useful proto-AGI product.
If by "sort of general, flexible learning ability that would let them tackle entirely new domains" we include adding new tokenised vectors in the training set, then this fit the definition. Of course this is "cheating" since the system is not learning purely by itself, but for the purpose of building a product or getting the tasks done this does not really matter.
And it's not unconcievable to imagine self-supervised tokens generation to get more skills and perhaps a K-means algorithm to make sure that the new embeddings do not interfere with previous knowledge. It's a dumb way of getting smarter, but apparently it works thanks to scale effects!
I would agree with "proto-AGI". I might soon write a blog on this, but ideally we could define a continuous value to track how close we are to AGI, which is increasing if:
-the tasks to solve are very different from each other
-the tasks are complex
-how well a task have been solved
-few experience (or info) is fed to the system
-experience is not directly related to the task
-experience is very raw
-computation is done in few steps
Then adding new tasks and changing the environment.
I have always been cautios, but I would say yes this time.
With the caveat that it learns new tasks only from supervised data, and not reusing previous experience.
The fact that adding new tasks doesn't diminuish performance on previous tasks is highly non trivial!
It may be that there is a lot of room in the embedding space to store them. The wild thing is that nothing (apart few hardware iterations) stop us to increase the embedding space if really needed.
Possibly the first truly AGI paper.
Even though it is just exploiting the fact that all the narrow problems can be solved as sequence problems via tokenisation, it's remarkable that the tasks do not interferee distructively between each other. My gut feeling is that this is due the very high dimensional space of the embedding vectors.
It leaves ample room for grow.
My main point is that there is not enough evidence for a strong claim like doom-soon. In absence of hard data anybody is free to cook up argument pro or against doom-soon.
You may not like my suggestion, but I would strongly advise to get deeper into the field and understand it better yourself, before taking important decisions.
In terms of paradigms, you may have a look at why building AI-software development is hard (easy to get to 80% accurate, hellish to get to 99%), AI-winters and hype cycles (disconnect between claims-expectations and reality), the development of dangerous technologies (nuclear, biotech) and how stability has been achieved.
Geniuses or talented researchers are not that impactful as much as the right policy. Contribute creating the right conditions (work environment, education, cross contamination, funding, etc.) to make good research flourish. At the same time if fundamentals are not covered (healthcare, housing, etc.) people are not able to focus on much more than suvival. So pretty much anything that makes the whole system works better helps.
As an example, there are plenty of smart individuals in poor counties which are not able to express their potential.
The bottom line is: nobody has a strong argument in support of the inevitability of the doom scenario (If you have it, just reply to this with a clear and self contained argument.).
From what I'm reading in the comments and in other papers/articles, it's a mixture of beliefs, estrapolations from known facts, reliance on what "experts" said, cherry picking. Add the fact that bad/pessimistic news travel and spread faster than boring good news.
A sober analysis enstablish that super-AGI can be dangerous (indeed there are no theorems forbidding this either...
The downvotes are excessive, the post is provoking, but interesting.
I think you will not even need to "push the fat man". The development on an AGI will be slow and gradual (as any other major technology) and there will be incidents along the way (e.g. an AGI chatbot harassing someone). Those incidents will periodically mandate new regulations, so that measurements to tackle real AGI related dangers will be enacted, similarly to what happens in the nuclear energy sector. They will not be perfect, but there will be regulations.
The tricky part is...
Matthew, Tamay: Refreshing post, with actual hard data and benchmarks. Thanks for that.
My predictions:
No in 2026, no in 2030. Mainly due to the fact that we don't have much structured data and incentives to solve some of the categories. A powerful unsupervised AI would be needed to clear those categories, or more time.
This is a possible AGI scenario, but it's not clear why it should be particularly likely. For instance the AGI may reason that going aggressive will also be the fastest route to be terminated. Or the AGI may consider that keeping humans alive is good, since they were responsable for the AGI creation in the first place.
What you describe is the paper-clip maximiser scenario, which is arguably the most extreme end of the spectrum of super-AGI behaviours.
This would not be a conclusive test, but definitely a cool one and may spark a lot of research. Perhaps we could get started with something NLP based, opening up more and more knowledge access to the AI in the form of training data. Probably still not feasible as of 2022 in term of raw compute required.
For an argument against the sim hypothesis see https://lorenzopieri.com/sim_hypothesis/ or the full article https://philpapers.org/rec/PIETSA-6 (The Simplicity Assumption and Some Implications of the Simulation Argument for our Civilization).
In a nutshell:
0- Suppose by absurd that we are in a simulation.
1- We are equally likely to be in one of the many simulations.
2- The vast majority of simulations are simple [see paper to understand why this is reasonable].
3- Therefore, we are very likely to be in a simple simulation.
4- Therefore,... (read more)