Hans Moravec introduced the idea of the "landscape of human competence" , a topology representing the peaks and valleys of human capabilities. Art, writing, coding, game playing. Elevation corresponds to cognitive difficulty, and the landscape maps to everything humans are capable of doing. AI is represented as the rising waterline - when Moravec created the idea, AI was more or less constrained to a few scattered lakes, with humans clearly demonstrating superiority nearly everywhere. After transformers, the waterline began to rise, and today we no longer have a vast contiguous majority, but are left with a scattered handful of islands, and the waterline continues to rise.
It's not arrogant or incurious to acknowledge the flood, but it might be to deny that flood is happening.
If you think there are fundamental human qualities or capabilities that AI can't ever have, you might put in the work to articulate that, instead of projecting negativity onto people who have watched the vast majority of the human competencies landscape get completely submerged over the last 10 years. The islands we have remaining don't really suggest any unifying principle underlying things that AI is still bad at, but instead they highlight the lack of technical capabilities and various engineering tracks to solve for. Many of the problems are solved in principle, but are economically infeasible; for all intents and purposes, you might consider those islands completely submerged as well.
I think you would need to work very hard to prove that the topology you are describing is well-formed enough for this analogy to make sense. For one: "cognitive difficulty" is not really a crisply defined quantity such that expressing it as a function of some input vector makes obvious sense (to me anyways). What's the cognitive difficulty of deciding what to have for dinner? What's the cognitive difficulty of making my 5 year plan? What's the cognitive difficulty of imagining a nice gift to get my wife for her birthday? There are so many things humans do which are heavily 'contingent' (in the sense of having sensitivity to the local culture, history, personal experience, etc) that the idea of being able to assign everything a single, decidable scalar to represent 'difficulty' seems like an extremely tall order to me. And that's setting aside whether the ambient vector space of 'human capabilities' is even really a sensible construct (a proposition that I also doubt quite heavily).
All this to say that describing what's happening as a 'rising tide' seems misleading to me. Techno-sociological development is super messy already, let's not make it more complex by pinning ourselves to inaccurate and potentially misleading analogies. The introduction of the car did not 'push humans higher onto a set of capability peaks', it implied a total reorganization of behavior and technologies (highways, commuting, and suburban sprawl); using the terms of your analogy humans built new landmasses on top of the water.
1. Implying that there are only "a few islands left" shoes a strong bias towards assuming that only thins humans do in the digital realm is relevant, when in fact, the vast majority of things humans do are not in the digital sphere at all.
2. It's pretty clear when most people say that machine intelligence is close, right now, they are alluding to LLM or Deep Learning based approaches. I don't think you should assume they mean machines will catch up in a 100 years. They seem to imply it will be by 2030 or sowmthing.
To address both points - there appear to be no individual, well defined tasks that humans can do that you cannot train a machine to do. Some tasks are inefficient, uneconomical, and other impractical, but there appear to be no tasks that in principle machines cannot do. What is missing is broad generalization, human equivalent time horizons, continuous learning, and embodiment.
Robotics has passed the point of superhuman performance for any given task. Software has passed the point of superhuman performance for any given task.
Regardless of the particular technique or embodiment, the constraints aren't "is it possible in principle" but "is it too expensive" and "is this allowed by the pertinent principles and regulations and laws"
We don't have AGI that learns and adapts in real time like humans. We do have incredibly powerful algorithms that can learn from whatever data we throw at them, but many domains where it's impractical, ruinously expensive, illegal, or otherwise not possible to use AI for some other good reasons.
The few islands left to humanity are not fundamental barriers. We haven't solved intelligence, or achieved RSI or ASI or AGI yet; those were never the important thresholds.
AI has always been a question about good enough, and it looks like we've gone solidly past the good enough line into "we can probably automate everything" even if we don't solve the big problems over 5 or 10 years or beyond. I think it's very unlikely we don't solve intelligence by 2030, but even if AI stalls out where it's at right now, and all we get is the incremental improvements and engineering optimizations on current SOTA, we have enough to automate anything humans do at levels exceeding human capabilities.
What AGI and ASI do is make humans economically obsolete. Good enough AI means there might be some places where humans are needed for generalization and adaptability until the exhaustive tedious work gets done for a particular application that enables a robot or software system to be competent enough to handle the work.
A hiker on a mountain might as well imagine that at the end of their journey they will step off onto the moon. But it's just a mirage. As us humans have externalized more and more of our understanding of the world into books, movies, websites and the like, our methods of plumbing this treasury for just the needed tidbits have developed as well. But it's still just working off that externalized collective understanding. This includes heuristics for combining different facts to produce new ones, sure, but still dependent on brilliant individuals to raise the "island peaks" which ultimately pulls up the level of the collective intelligence as well.
While a 2 dimensional projection of intelligence may be a satisfying rhetorical device, I think it’s an extremely mathematically naive interpretation.
Not only is intelligence probably most accurately modeled as something extremely high dimensional, it’s probably also extremely nonlinearly traversed by learning methods, both organic and artificial. Not a topology very easily “flooded”.
It wasn't a formal model or a theorem, it was an observation about reality. Humans are indeed gradually being overtaken on almost all fronts by AI. But by all means, if you want to take issue with Moravec's framing of the issue, feel free.
Explaining it as something like "realizable instantiation of physical computation occurring in the universe mapping to an ultra-sparse, discrete point cloud embedded in the Euclidean parameter space of all computable functions" could definitely be more precise, but you're either going to need a topology like a landscape or a bumpy sphere to visualize it, and then you're going to need to spend more time showing the effects of things like scaling laws, available compute, where the known boundaries of human intelligence lie, and so on, and so forth, and by then you've lost everyone, probably even the ML professor.
It's a good enough metaphor that maps to a real thing.
> It's a good enough metaphor that maps to a real thing.
My entire point, which I’m not sure you addressed is that no, it’s not a good metaphor. Water “floods” a 3d topology in a predictable manner with regards to the volume the topology can contain. The entire argument is that progress is observable, predictable, and limitless, and the “islands” are a rhetorical device. My argument was turning the rhetorical device around and pointing out that we know so little about intelligence and AI that describing it in this way is not meaningful beyond sounding intellectual.
It's not arrogant or incurious to acknowledge the flood, but it might be to deny that flood is happening.
If you think there are fundamental human qualities or capabilities that AI can't ever have, you might put in the work to articulate that, instead of projecting negativity onto people who have watched the vast majority of the human competencies landscape get completely submerged over the last 10 years. The islands we have remaining don't really suggest any unifying principle underlying things that AI is still bad at, but instead they highlight the lack of technical capabilities and various engineering tracks to solve for. Many of the problems are solved in principle, but are economically infeasible; for all intents and purposes, you might consider those islands completely submerged as well.