Beating benchmarks, even very difficult ones, is all find and dandy, but we must remember that those tests, no matter how difficult, are at best only a limited measure of human ability. Why? Because they present the test-take with a well-defined situation to which they must respond. Life isn't like that. It's messy and murky. Perhaps the most difficult step is to wade into the mess and the murk and impose a structure on it – perhaps by simply asking a question – so that one can then set about dealing with that situation in terms of the imposed structure. Tests give you a structured situation. That's not what the world does.
Consider this passage from Sam Rodiques, "What does it take to build an AI Scientist"
Scientific reasoning consists of essentially three steps: coming up with hypotheses, conducting experiments, and using the results to update one’s hypotheses. Science is the ultimate open-ended problem, in that we always have an infinite space of possible hypotheses to choose from, and an infinite space of possible observations. For hypothesis generation: How do we navigate this space effectively? How do we generate diverse, relevant, and explanatory hypotheses? It is one thing to have ChatGPT generate incremental ideas. It is another thing to come up with truly novel, paradigm-shifting concepts.
Right.
How do we put o3, or any other AI, out in the world where it can roam around, poke into things, and come up with its own problems to solve? If you want AGI in any deep and robust sense, that's what you have to do. That calls for real agency. I don't see that OpenAI or any other organization is anywhere close to figuring out how to do this.
Yes, the matching of "mental content" between one mind and another is perhaps the central issue in semantics. You might want to take a look at Warglien and Gärdenfors, Semantics, conceptual spaces, and the meeting of minds:
Abstract: We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.
You can find those ideas further developed in Gärdenfors' 2014 book, Geometry of Meaning, chapters 4 and 5, "Pointing as Meeting of Minds" and "Meetings of Minds as Fixpoints," respectively. In chapter 5 he develops four levels of communication.
2Around the corner I've got a post that makes use of this post in the final section: Relationships among words, metalingual definition, and interpretability.
YES.
At the moment the A.I. world is dominated by an almost magical believe in large language models. Yes, they are marvelous, a very powerful technology. By all means, let's understand and develop them. But they aren't the way, the truth and the light. They're just a very powerful and important technology. Heavy investment in them has an opportunity cost, less money to invest in other architectures and ideas.
And I'm not just talking about software, chips, and infrastructure. I'm talking about education and training. It's not good to have a whole cohort of researchers and practitioners who know little or nothing beyond the current orthodoxy about machine learning and LLMs. That kind of mistake is very difficult to correct in the future. Why? Because correcting it means education and training. Who's going to do it if no one knows anything else?
Moreover, in order to exploit LLMs effectively we need to understand how they work. Mechanistic interpretability is one approach. But: We're not doing enough of it. And by itself it won't do the job. People need to know more about language, linguistics, and cognition in order to understand what those models are doing.
Whatever one means by "memorize" is by no means self-evident. If you prompt ChatGPT with "To be, or not to be," it will return the whole soliloquy. Sometimes. Other times it will give you an opening chunk and then an explanation that that's the well known soliloquy, etc. By poking around I discovered that I could elicit the soliloquy by giving it prompts that consisting of syntactically coherent phrases, but if I gave it prompts that were not syntactically coherent, it didn't recognize the source, that is, until a bit more prompting. I've never found the idea that LLMs were just memorizing to be very plausible.
In any event, here's a bunch of experiments explicitly aimed at memorizing, including the Hamlet soliloquy stuff: https://www.academia.edu/107318793/Discursive_Competence_in_ChatGPT_Part_2_Memory_for_Texts_Version_3
I was assuming lots of places widely spread. What I was curious about was a specific connection in the available data between the terms I used in my prompts and the levels of language. gwern's comment satisfies that concern.
By labeled data I simply mean that children's stories are likely to be identified as such in the data. Children's books are identified as children's books. Otherwise, how is the model to "know" what language is appropriate for children? Without some link between the language and a certain class of people it's just more text. My prompt specifies 5-year olds. How does the model connect that prompt with a specific kind of language?
Of course, but it does need to know what a definition is. There are certainly lots of dictionaries on the web. I'm willing to assume that some of them made it into the training data. And it needs to know that people of different ages use language at different levels of detail and abstraction. I think that requires labeled data, like children's stories labeled as such.
Panksepp is also known for discovering "laughter" in rats: "Laughing" rats and the evolutionary antecedents of human joy?
Abstract: Paul MacLean's concept of epistemics-the neuroscientific study of subjective experience-requires animal brain research that can be related to predictions concerning the internal experiences of humans. Especially robust relationships come from studies of the emotional/affective processes that arise from subcortical brain systems shared by all mammals. Recent affective neuroscience research has yielded the discovery of play- and tickle-induced ultrasonic vocalization patterns ( approximately 50-kHz chirps) in rats may have more than a passing resemblance to primitive human laughter. In this paper, we summarize a dozen reasons for the working hypothesis that such rat vocalizations reflect a type of positive affect that may have evolutionary relations to the joyfulness of human childhood laughter commonly accompanying social play. The neurobiological nature of human laughter is discussed, and the relevance of such ludic processes for understanding clinical disorders such as attention deficit hyperactivity disorders (ADHD), addictive urges and mood imbalances are discussed.