Knowledge Seeker https://lorenzopieri.com/
lorepieri
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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.
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 should be possible to locate the activation paths for different tasks, as the tasks are pretty well separated. Something on the lines of https://github.com/jalammar/ecco
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.
Pretty disappointing and unexpected to hear this in 2022, after all the learnings from the pandemic.
What’s stopping the companies from hiring a new researcher? People are queueing for tech jobs.
If they leave then only who does not care remains…
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.
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.