Point taken on the “we” , it can sound patronising in the wrong light. Just think of that meaning “my immediate colleagues and I” - it is not to mean a universal or authoritative we, rather an anecdotal plurality.
It’s possible that others imagined human level as the base for “generalized intelligence”, but my colleagues and I were taking the term general to mean generalised, as in not narrowly defined (like expert systems are). A type of intelligence that could be applied broadly to different categories of problems, including ones not foreseen by the designer of the system. That these problems might be very basic ones was immaterial.
That this concept of general intelligence is not necessarily life transforming to possess on your smartphone doesn’t mean it isn’t a huge step forward. It is a very hard problem to solve. Transformer networks are the best tool we have for this task, and they work by inferring meaning to patterns and outputting a transform of that meaning. With LLMs, the pattern is the context text string, and the output is the next likely text fragment.
The surprising effectiveness of LLMs is due, I think, not to any characteristic of their architecture that makes them “intelligent”, but rather due to the fundamental nature of language itself, and especially the English language because of its penchant for specificity and its somewhat lower reliance on intonation to convey meaning than most languages. (I’m not a linguist, but I have discussed this with a few and it is interesting to hear their ideas on this)
Language itself captures meanings far beyond the words used in a statement. Only a tiny fraction of information is contained in words, the rest is inferred knowledge based on assumptions about shared experiences and understandings. Transformers tease out this context and imbed it through inferences constructed by billions of textual inputs. They capture the information shadows cast by the words, not just the words themselves.
This way of decoding cultural data, as an n-dimensional matrix of vast proportions rather than just the text of a culture itself, turns out to be a way to access both the explicit and the implicit knowledge imbedded in that culture, especially if that culture is codified using very specific and expressive tokens.
It turns out that this capture of shared experiences and understanding enables a great deal of abstract general problem solving, precisely the kind of problem solving that is vexingly difficult to solve using other methods.
LLMs, not just transformers, are actually a really big deal. In effect, they create a kind of probabilistic expert system where the field of expertise is a significant fraction of the sum total of human thought and experience.
But there are numerous and significant shortcomings to this approach, of course…. Not the least of which is the difficulty to effectively integrate new information or to selectively replace or update existing data in the model. And hallucinations (which are not a malfunction , but rather completely normal operations) are a basically unsolvable problem, though they can be mitigated.
But anyway…
I think the real insight to be had here , or at least my personal takeaway is that we owe a great deal of what we think of as intelligence to our culture than to our individual intellectual prowess. As we solve the problems of general intelligence, we both construct marvellous machines and confront the suggestion that we aren’t nearly as clever as we thought we were.
As for “AGI” I will still call that a crossed threshold , but certainly not to the level that humans have solved “biological GI” with full inculturation.
If people want AGI to mean human level competence, I’m ok with that. It won’t be the first term of art to have drifted in meaning. I would personally choose a more descriptive term for that, but 3 letter terms are not all that expressive, and adding another letter or two just gets awkward, so I get it. And I think it’s a lost battle anyway, it’s mostly old cranks such as myself that are impressed with things that most animals manage easily to do.
As for the SOTA, I think there is huge room for improvement both in narrowing down competencies for specific tasks, and for expanding capabilities for applications that need human level abstraction and creativity. But I doubt that the next steps toward human level competency will be nearly as fruitful as the ones in the recent past, and I have serious doubts about the ability of current paths to lead to superhuman intelligence. Superhuman capabilities, yes, but there’s nothing new about that. Superhuman intelligence will require things which we don’t understand, by definition.
We have mastered flight, but I’m still waiting for my practical flying car.
Meanwhile, we suffer on with our earthbound wheels and idiot-savant AI.