Computers are just as dumb as they've always been. They can't grow or reproduce or learn on their own or optimize themselves for survival or expansion. And noone has any idea how to endow them with these capabilities.
We're just waiting and hoping it somehow happens on it's own. This is about as naive as waiting for your car to drive itself. It is just not how the universe works.
Today, progress in machine learning is almost entirely empirical, with limited theoretical advances following later to make sense of the empirical findings. Our theoretical underpinnings are so weak we don’t even have a rough estimate of how much harder it is to make a self-improving AGI than e.g. GPT-3. Maybe it’s many orders of magnitude harder, and not even remotely solvable by taking iterative steps from where we are now. Maybe it’s just one unifying theoretical advance away.
As for waiting and hoping it happens on its own, that’s not a great description of what’s happening. A huge number of people (possibly too many, leading to short-term incentives) are trying to make improvements in any way they can think of. Progress is happening at a staggering pace —- we just don’t have any idea how much progress is needed to reach the goal of AGI.
There are a huge number of people involved but as you point out, it is mostly empirical. In other words, there is no real plan --- we're just hoping for a happy accident.
It’s because, if you admit there is even a slight possibility of progress being made, or future potential, suddenly you’re in the other camp - because it’s suddenly quite important to think about the ramifications. And you have self-categorized that you are not a part of all that mumbo-jumbo. So, retroactively, you have to think there is no possibility.
It is demonstrably false that no progress is being made. I can type `imagine “a bluebird”` and my computer generates one. Many people listen to AI generated music now. More still are directed to artists they like by one. Spot is incredible. Compared to 10 years ago we are in a different world of progress here. It might take a while, but it’s coming.
The fact that you feel compelled to ask that question suggests to me that you won't approve of the answer.
What currently passes for "learning" is mostly just data collection and pattern matching --- as strictly prescribed by software. This is certainly useful but any sign of "intelligence" in this process still resides in the mind of the software developer --- not the computer.
Not only do we not know how to build truly "intelligent" software, we don't know if it's even possible.
Theoretically, we do know how to build intelligent agents, e.g. AIXI. My bet is on something like [1] for the logic and [2] as the decision theory for a computable equivalent.
Humans aren't even generally intelligent in the same way as AIXI (and to be fair AIXI requires sufficient perception and outputs to be effective, but the choice is not as hard-wired as humans are); we are very environment dependent and need augmentation with tools, education, etc. in order to make intelligent decisions.
[1] https://intelligence.org/2016/09/12/new-paper-logical-induct... [2] https://intelligence.org/2017/10/22/fdt/
Your second statement (if it’s possible) sounds like a “no true intelligence” argument. It’s definitely possible since we exist but might take any number of years.
Your first statement (don’t know how to build it): We don’t need to completely understand intelligence to create it. We need to create conditions from which it can arise. I’m not saying we will completely not understand it either! Just that a partial state of understanding like what we are working with permanently, may be enough. And some people do have ideas about how to do this.
Intelligence is anti-formal-understanding. People have formal models of intelligence and they’re totally impractical. “True” intelligence has messy heuristics everywhere and was evolved naturally.
So you really have no idea if this will work but you have faith.
Godel's Incompleteness Theorem suggests this may not be possible within the confines of a binary logic Turing machine; i.e. a modern computer. See my reference.
> his result shows that either (i) the human mind is not a Turing machine or (ii) there are certain unsolvable mathematical problems.
Well, I choose option (ii). Some problems, such as the halting problem, are unsolveable. Furthermore, for any set of axioms, there are problems undecidable with that set of axioms.
This entire proof is based on "artificial intelligence is not possible, because it could not solve some mathematical problems". But the fact is that humans also can't solve some mathematical problems, and they exist regardless.
You could have an artificial intelligence that is million times smarter than humans, and still can't solve some extremely abstract mathematical problem (that humans can't solve either). So what?