AI’s Language Problem

Actually thes two parts do exist in tandem, it's just that those engineers/designers (a role you that can do the same) are usually independently successful and have repeatedly built high revenue products or started a company themselves.

The major flaw i see in manager/corp/team analysis of workers is that it misses out on a portion of the population that is genuinely independently functional and creates and shares the value they create. They don't work for companies because they either don't need to or own their own. These are the ideals worth keeping in mind.

> His young mind has not yet recognized the paradox that "infinity" is the biggest number, so what does it mean when I say, "Infinity plus one"?

Paradox? In the extended reals, where infinity is the biggest number, infinity plus one gets you infinity, just as you'd expect. In, say, the study of ordinal numbers, where there are many infinite quantities, it doesn't make any sense to talk about "the biggest number".

image caption is a separate, albeit related problem to what I'm talking about.

Ontologies are much the same; they are interesting for the problems they solve, but it's not clear how well those problems relate to the more general problem of language.

word embeddings are also quite interesting, but again, are typically based entirely off whatever emergent semantics can be gleaned from the structure of documents. It's not clear to me that this is anymore than superficial understanding. Not that they aren't very cool and powerful. Distributional semantics is a powerful tool for measuring certain characteristics of language. I'm not sure how much more useful it will be in the future.

Uunsupervised learning from video and images is a strictly different problem that seems to me to be much lower down the hierarchy of AI Hardness. More like a fundamental task that is solvable in its own universe, without requiring complete integration of multiple other universes. Whether the information extracted by these existing technologies is actually usefully semantic in nature remains to be seen.

I agree that we'll get there, somewhat inevitably; not trying to argue for any Searlian dualistic separation between what Machines can do and what Biology can do. I'm personally interested in the 'how'. Emergent Strong AI is the most boring scenario I can imagine; I want to understand the mechanisms at play. It may just be that we need to tie together everything you've listed and more, throw enough data at it, and wait for something approximating intelligence to grow out of it. We can also take the more top-down route, and treat this as a problem in developmental psychology. Are there better ways to learn than just throwing trillions of examples at something until it hits that eureka moment?

AI as it's defined today is fundamentally reactive. If we applied the AlphaGo methodology to language, it would come up with what a good response would be to words it heard, but the purpose of such a conversation would be the conversation itself.

A real conversation is about conveying understanding, not about the words spoken.

AlphaGo was trained on however many zillions of games and playing against itself, but does it actually understand anything about the game? Or can it simply react to the current state of the game and suggest what the next move should be. It will never have a leap of intuition causing it to say "the only winning move is not to play."

Intelligence is not purely reactive.

How would one prove the opposite? That a human actually understands anything about the game and isn't reacting to the state of the game to suggest the next move? I'm not saying AI as it exists today understands, I'm just saying this "understanding" metric isn't a good metric unless it works in reverse.