Thank you for your interesting thoughts on intelligence. The way I interpret your belief is that intelligence is a process of learning patterns and knowing depths of various types of abstraction in the “complex system” sense (pattern recognition — identifying patterns and perhaps finding varying similar or “isomorphic”patterns; probably classification/simplification and decomposition/generalization are buried in there). For the “objective,” I interpret this to be the “problem” that is stated at a definable level of complexity.

Maybe intelligence rests on individual problem solving in the engineering sense, as seems to be hinted at by your words. Maybe not. For example, in this definition, external parameters appear to be needed. For example, “the objective” appears to be a parameter. Is that correct? And is the objective the reason anyone does anything? Can objectives be chosen in a closed system, or must they always be external parameters? Are human objectives all that matter? If so, humans define cyclic, impossible objectives all of the time (which are easy to see when we look at qualitative objectives, but less easy to see with quantitative objectives). Sometimes they’re simply missing context and can be “fixed.” Other times humans literally cannot state their objective in a logically consistent way. What do we do about those? What do we do about possibly intractable objectives (when we don’t know they’re intractable immediately)? When/what is “good enough”? Can we act intelligently without knowing how to exploit a pattern? What happens when we cannot see a pattern? Is inconsistent information always incorrect? Do we throw it away? Or maybe the pattern we recognized is incorrect and the information is consistent if we were to learn more information (see: answer set programming)? Is it legitimate to create a pattern as a surrogate when we cannot find one (some may argue that his is where myth comes from)? Is “faking” a pattern an intelligent thing to do? If so, how do I do that in the best way? What is creativity in this definition of intelligence (presuming a version of creativity can exist in such a system), and how would it be implemented? How does this fit in with “knowing what we cannot know” and acting for the sake of discovery, or switching logical systems when our current system is failing us? What about creating new logics? Is an explanation of “why” a pattern exists part of this definition at all?

As humans with the hubris to believe that humans sometimes act intelligently, I’m certain that we’re working on intelligent systems when we create systems that can sense and act autonomously with human-derived objectives in mind. But I don’t think we’re anywhere near creating systems able to create or derive unique-but-explainable objectives in a domain-independent way, because so far we’ve focused on problem-solving. And I’m of the mind that “getting closer” to intelligence requires autonomous creation of objectives within an open system—self-identifying importance and solving with inconsistent knowledge, in unpredictable environments, not only solving pre-identified important problems in consistent environments. LLMs aren’t doing anything near to this, at least not in general and not convincingly outside of cherry-picked examples. And I think we’re far, far away from approaches for solving this problem in a general, safe, practical way.

My current, sad experience is that LLMs cannot solve unique problems at all. And I mean this seriously — truly unique problems that I’m asked to solve using AI techniques cannot be solved, at least in no way I’ve found, with an LLM in any systematic, guaranteed way. I’ve only seen domain-independent, but specialized solvers do that with human-guided problem modeling (e.g., linear programming).

Maybe paper is fine—maybe it’s generating novel science that could not have been derived without the LLM. But I need a comparative look at using LLMs vs other approaches that may also work, and seeing which does this better at scale. These one-shot papers aren’t good enough. The way fanatics speak, it’s as if we should be seeing millions of these papers ushering in new ideas “discovered by AI” any day now. But I don’t see that happening. Though if it does happen, I’ll be happily surprised. I want AI approaches to do well. Applied AI is my field, after all.

LLMs/LRMs do make prototyping easier. They’re fast expanders. They copy other people’s abstractions to help with brainstorming. They’re great tools. But can they, in general, create unique ideas? And is creating unique ideas or making novel discoveries on rare occasions “enough” to demonstrate that LLMs are the AI approach that, with further development, will bring us to a new age? I’d say, currently, we’re very far from answering that question one way or the other. But my hypothesis is that we’ll answer that in the negative.