Knowledge Seeker https://lorenzopieri.com/
lorepieri(Lorenzo Pieri)
Karma: 184
What fact that you know is true but most people aren’t ready to accept it?
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.
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.
On Falsifying the Simulation Hypothesis (or Embracing its Predictions)
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.
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.
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.
The newly-created AGI will immediately kill everyone on the planet, and proceed to the destruction of the universe. Its sphere of destruction will expand at light speed, eventually encompassing everything reachable.
Why?
In fact, if not consensus, then at least the majority opinion amongst those mathematicians, computer scientists, and AI researchers who have given the subject more than a few days thought.
Is this true, or you have asked only inside an AI-pessimistic bubble?
And if True, why should opinions matter at all? Opinions cannot influence reality which is outside human control.
Overall I don’t see a clear argument about why should we worried about AGI. Quite the contrary, building AGI is still an active area of research with no clear solution.
A pragmatic metric for Artificial General Intelligence
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.
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!
Matthew, Tamay: Refreshing post, with actual hard data and benchmarks. Thanks for that.
My predictions:
A model/ensemble of models achieves >80% on all tasks in the MMLU benchmark
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.
A credible estimate reveals that an AI lab deployed EITHER >10^30 FLOPs OR hardware that would cost $1bn if purchased through competitive cloud computing vendors at the time on a training run to develop a single ML model (excluding autonomous driving efforts)
This may actually happen (the 1B one, not the 10^30), also due to inflation and USD created out of thin air and injected into the market. I would go for no in 2026 and yes in 2030.
A model/ensemble of models will achieve >90% on the MATH dataset using a no-calculator rule
No in 2026, no in 2030. Significant algorithmic improvements needed. It may be done if prompt engineering is allowed.
A model/ensemble of models achieves >80% top-1 strict accuracy on competition-level problems on the APPS benchmark
No in 2026, no in 2030. Similar to the above, but there will be more progress, as a lot of data is available.
A gold medal for the IMO Grand Challenge (conditional on it being clear that the questions were not in the training set)
No in 2026, no in 2030.
A robot that can, from beginning to end, reliably wash dishes, take them out of an ordinary dishwasher and stack them into a cabinet, without breaking any dishes, and at a comparable speed to humans (<120% the average time)
I work with smart robots, this cannot happen so fast also due to hardware limitations. The speed requirement is particularly harsh. Without the speed limit and with the system known in advance I would say yes in 2030. As the bet stands, I go for No in 2026, no in 2030.
Tesla’s full-self-driving capability makes fewer than one major mistake per 100,000 miles
Not sure about this one, but I lean on No in 2026, no in 2030.
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.
Apparently many records have been subjected to cheating:
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.
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 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.
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.
Unpopular opinion (on this site I guess): AI alignment is not a well defined problem, there is no clear cut resolution to it. It will be an incremental process, similar to cybersecurity research.
About the money, I would do the opposite: select researchers that would do it for free, just pay them living expenses and give them arbitrary resources.