Paper: https://arxiv.org/abs/2311.02462
Abstract:
We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy. It is our hope that this framework will be useful in an analogous way to the levels of autonomous driving, by providing a common language to compare models, assess risks, and measure progress along the path to AGI. To develop our framework, we analyze existing definitions of AGI, and distill six principles that a useful ontology for AGI should satisfy. These principles include focusing on capabilities rather than mechanisms; separately evaluating generality and performance; and defining stages along the path toward AGI, rather than focusing on the endpoint. With these principles in mind, we propose ‘Levels of AGI’ based on depth (performance) and breadth (generality) of capabilities, and reflect on how current systems fit into this ontology. We discuss the challenging requirements for future benchmarks that quantify the behavior and capabilities of AGI models against these levels. Finally, we discuss how these levels of AGI interact with deployment considerations such as autonomy and risk, and emphasize the importance of carefully selecting Human-AI Interaction paradigms for responsible and safe deployment of highly capable AI systems.
The way the current agent experiments are going it would seem Competent AGI can be built from Emerging AGI modules.
AGI is like the dumbest, most childish concept that is considered mainstream and acceptable to talk about. It’s like if CEOs and tech folks suddenly got an interest in dinosaurs or something.
I doubt that any model currently is in the “emerging AGI” category (even by there own metric of “general ability and metacognitive abilities like learning new skills”).
The model(s) we currently have are fundamentally unable to update their own weights so they do not “learn new skills”. Also I don’t like how they use “wide range of tasks” as a metric. Yes, LLMs outperform many humans at things like standardized tests, but I have yet to see an LLM who can constantly play tiktaktoe at the level of a 5 year old without a paragraph of “promt engineering”
I’m not the most educated on this topic (still just a student studying machine learning) but imo I think that many researchers are overestimating the abilities of LLMs
In context learning allows the model to learn new skills to a limited degree.
This was going to be my point as well. LLMs on their own probably aren’t there yet. But creative uses of in context learning can get you there. By having the LLM interact with the world in some way, judge it’s response against some objective, and then store the response and score in a vector db so that the next time the LLM encounters a similar scenario it can retrieve that example and use it to improve its response.
That process can take you a long way to AGI with tech we have today
To be fair, 5 year Olds don’t have the innate ability to know how tic tac toe works. Someone had to teach them. We just chose not to teach that to llms.
Well, you sort of answered the matter yourself - the fact that prompting works in some cases means you don’t strictly need weight updates for new skills to be learned. It doesn’t mean prompting is an end-all solution, but for DeepMind, this seems enough to consider LLMs “emerging AGI”.
Most people entering in the field now (in the literal sense, aka academia, not some random r/singularity ramblers) disregard current LLM capabilities, but their current level of reasoning was deemed almost a fantasy 5 years ago.
and current LLMs are pretty great for automating simple, easily defined tasks that would drive a human insane (labelling datasets etc). i’m really optomistic about their use in online moderation in the short term, lots of horror stories of facebook employees having mental breakdowns
When I mentioned prompt engineering, I more so meant that people where explaining what to do in a if/else manner to get the LLM to play tiktaktoe (not chain of thoughts or any of those techniques).
In my opinion, learning is both 1) acquiring new skills, and 2) improving upon those skills with repetition. I think it’s very debatable if an LLM could learn something truly novel (or even something like an existing game with some new rules, I.e., chess but with the game pieces in different positions) with in context learning. Secondly, no matter how much you play tiktaktoe with an LLM, it will never improve at the game.
This is just my two cents on why I don’t believe LLMs to fit the criteria of “emerging AGI” that the researchers laid out. Imo I think that to fit that criteria they would need to implement some type of online learning but I definitely could be wrong.
Not designed or intended to- is not the same “fundementally unable”. There are quite simplistic architectures that are very able of updating their weights, which does not make them AGI or any more intelligent. The discussion is about general capability in intellectual tasks, not the training mechanisms.
I more so meant that to learn something new the model would have to update its own weights (I have my reasoning for this in another reply in this thread).
When I said “fundamentally unable to” I meant that current LLM architectures do not have the capability to update their own weights (although I probably should’ve worded that a bit differently)
They don’t have it because it wasn’t programmed into it, because it’s risky business (see chatbot Tay), not because it’s currently impossible. There’s nothing preventing you from running backprop weight updates based on user interactions, e.g. with reinforcement from user sentiment.
You can use a pretrained LLM as the core of a system capable of learning though. Like in the MemGPT paper
On the other hand, I asked ChatGPT-4 to build a table of specific production and GDP contribution information for the 2 dozen most important raw materials production industries and the results were well reasoned and fairly accurate. Don’t think the average person on the streets would be able to do this, let alone know what the answer is off the top of their heads like GPT-4 knows right away.
If I write out a one paragraph text on how to play a game I’ve just made up called “Madeupoly,” and you read it, we’d say that you learned a new skill. If we prompt an LLM with the same text, and they can play within the rules after, couldn’t we say they’ve also learned a new skill?
!RemindMe in 1 day
Machine learning is becoming sociology 2.0 in academia as opposed to remaining firmly in the grasp of STEM and it really sucks. These papers are completely meaningless beyond padding the resumes of grifters and deserve pushback. AI as a field is software, coding and maybe some math, not smart sounding essays and research papers from the same folks who built their careers around the cryptocurrency/NFT/Web3.0 dungpile while having zero hard skills aside from talking themselves into cushy jobs at startups.
grifters
same folks who built their careers around the cryptocurrency/NFT/Web3.0 dungpile while having zero hard skills
Insults like this are completely uncalled-for. All of the authors of this paper are accomplished researchers in ML. Not at all related to what you’ve called the “cryptocurrency/NFT/Web3.0 dungpile”.
It’s a mixed bag. I see a few researchers who have dozens of very similar papers to this one co-authored and others that actually seem to program models to progress machine learning. I still hold to the belief that anyone who doesn’t have copious amounts of programming experience should not be involved in academia related to machine learning. It’s not a space suited for people who don’t have a depth of hands on experience with the topic.
Jascha Sohl-Dickstein invented diffusion models, he’s a pretty big name in the field.
anyone who doesn’t have copious amounts of programming experience should not be involved in academia related to machine learning
ML research is very heavy on math and statistics. In general, the skills necessary for ML are not very similar to the skills necessary for programming.
Wow, I have a lot of respect for many of the Deep Mind Folks but this is a fairly blatant rip-off of the Pathwai (www.pathwai.org) taxonomy proposed over a year ago by Alex Foster ! The pathwai model is speculative but much more thorough and specific. This should be recognized considering they did not seem to cite the original author!
While the paper has limitations, I like the their approach to keeping things to output focused and avoiding the traps of processes like meta cognition, emotions.
AGI is something that big orgs and research labs are keeping going to invest resources into and don’t see any harm in trying to define it properly
TBH, even though I appreciate the effort in creating a research roadmap, if you put the AGI sticker in it, this feels more like creating some landmarks to make some buzz down the road. Conversely, there are a lot of features from other AGI definitions such as autonomous agency, multimodal/sensory learning, world modeling and interactivity that are conveniently left out (“non-physicial” tasks, hey, our lab doesn’t work with those eh, but we can tots do AGI). This caters neither to the academics who are tired of loaded monikers in the field, nor to the futurology enthusiasts who have a much wider definition for AGI.