In the last five years, AI/ML has gotten better at some things it was previously mediocre/good at, but what progress towards creativity or singularity has there been? None. DL is not an enabling technology for creativity.
Creativity is much more likely a human way of understanding the ability to create new new things with some sort of a purpose or intent.
But humans have limits to their ability to create things too. There are things that only machines can create and perform.
The state of human creativity is the accumulation of hundreds of thousands of years of trial and error.
It can be put into an evolutionary context and so in my belief, is accessible to computers who now can also learn.
Additionally, there is a transformation that takes place in our brains from information to knowledge. Our understanding of this process is also pretty weak.
Both of these problems are fundamental to the creation of true, autonomous AI.
What makes you say that? I could see your statement being self-evidently true only if you meant "fundamental to the creation of an anthropomorphized AI." Of course, even that glosses over the fact that there are already strong indications within the AI community that the first general purpose AI will likely not "think" even remotely in the same fashion as humans do.
I don't agree that this is true in any way that precludes the creation of "true, autonomous AI". We may not understand the specific mechanisms of how our brains give rise to creativity and the resulting "implementation artifacts", but the fundamental part of creativity (and I'd argue, the part that's relevant to AI) is the ability to efficiently search huge but structured hypothesis spaces and inductively learn new structure (very likely with some stochastic element involved).
That is something that we not only have an understanding of, but have implemented, more than once. By way of examples:
http://www.popsci.com/scitech/article/2006-04/john-koza-has-... http://aliciapatterson.org/stories/eurisko-computer-mind-its... https://psmag.com/triumph-of-the-cyborg-composer-620e5aead47...
I have rather the impression that lots of people who write this kind of papers rather want some computer program who is just like them - just a little bit more clever or creative (call it narcissism) instead of some computer program that is "intelligent" or "creative". These people don't understand that one shouldn't judge a fish by its inability to climb a tree.
Hero of Alexandria, who lived from 10AD to 70AD, is famous for his mechanical inventions. One that I recall was a mechanism that opened the doors to a temple when a fire was lit in a brazier outside. To the people of the time, who didn't understand mechanics, this was a sign of the gods and the everyday miracles they would perform. But to anyone who knew how it worked, it was a simple mechanism.
A computer that plays a complex game, or creates art, or understands spoken queries and answers verbally, or identifies things in photos, or drives a car, seems like magic to most people. But to software developers who understand the algorithms behind those actions there's no magic, and no intelligence. It's just software.
The promise, and claim, of AI boils down to this: that what's going on inside of our own heads is also just software. Maybe that's true, maybe it isn't. Even if it's true, there's no guarantee that equivalent software can be run on the artificial hardware we've produced. So yes, we keep raising the bar, but I think that's because we keep discovering that the bar was actually already higher than we thought it was.
But isn't a question of creativity rather different from the matter of intelligence? I'd love to see recommendations on papers that try to formalize creative AI systems -- but I'd expect something with less focus on exploitation and more on exploration than a goal-directed game engine. Maybe using some novelty signals or the like.
The fact that people redefine what AI whenever reached is is a valid point; but that does not mean one should lump all sistems into the same basket.
Something like a pop music composing and light novel/screenplay writing bot at least seems like a doable goal, yes, but it def isn't AlphaGo.
>But at the same time, those of us who watched the match inside Seoul’s Four Seasons hotel could feel the beauty of that one move, especially after talking to the infectiously philosophical Fan Hui. “So beautiful,” he kept saying. “So beautiful.”
(Fan Gui is the three-time European Go champion)
If any human played that move it would be described as very creative. https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-re...
A human player might have made that move in a burst of creative inspiration. Or they might have made it after careful analytical deliberation. Or it could have been random desperation. If they don't say why they did it, observers are free to interpret it any way they like. That has no bearing on what actually happened.
To try to see it in a grossly simplified step by step fashion: in the abstract, does seem similar to how a human would think of the game though; consider promising moves, where promising is some fuzzy intuition based on the pattern of the board -> for each of those, read how the responses might look like (which the computer does way way more than the human), and what you discover using this suggests other promising moves -> and at some depth of each such read, evaluate the board position; who looks ahead, which too is some fuzzy-ish intuition based on the board patern (though there are various heuristics humans would use when learning this). Computer also evaluates further, a number of fast playouts to the end and mixes this with that pattern-based evaluation.
Is this the kind of thought process you'd associate with creativity? Seems search-based, analytic and strategic; only funny thing is how evaluations are pattern-based nebulous things, but that seems an implementation detail. In some situations, you may come up with evaluation procedures like say material advantage in chess and code the relevant decision procedure, in others, you'd "code" the decision procedure implicitly, by specifying a dataset that exemplifies the desired function, and use statistics to come up with that function. Increases the kinds of decisions you can implement tremendously as you can leverage implicit rather than just explicit knowledge, yes -- but conceptually kinda the same.