Even so, I fully expect memristors to deliver strong AI.
Accelerating change. Basically Kurzweil's view. Exponential improvement of machines, which will eventually reach then exceed human intelligence in every single domain, or something like that.
Even horizon. If we ever build something that achieve greater-than-human intelligence, we cannot predict whatever it will do to the world, because we're just not as smart as that thing.
Intelligence explosion. If we ever build an AI (or something similar) that is more effective at doing AI research than we are, then that AI would be even more effective… and foom you have something that would leave Skynet in the dust, so it'd better be our friend[1]. Note that the first iteration of that thing may not need to be smarter than us: it just have to be able to build something smarter than itself[2] (and of course, the self-improvement cycle must not hit a ceiling too soon).
[1]: https://en.wikipedia.org/wiki/Friendly_AI [2]: https://en.wikipedia.org/wiki/Seed_AI
Why would they?
Is strong AI a function of storage capacity or speed?
An AI running at 1/100th of what a future AI may be capable of is still an AI and I can't see how a mere improvement of a couple of orders of magnitude would do what decades of Moore's law have failed to do so far.
If strong AI was just a matter of speed then we could theoretically take any of the large clusters available today and run it at an appreciable fraction of its normal speed leading at a minimum to a validation of the fact that it is indeed a strong AI that's been created.
The barrier seems to be more that we don't know how to go about building one from a software perspective than that we wouldn't have the capability to design the hardware.
So how would an advance in hardware suddenly fix that?
Yes, absolutely! I actually think the most appropriate benchmark is memory bandwidth, which hasn't been improving as fast as FLOPS or storage capacity. It's not a matter of running a strong AI at 1/100 speed on today's fastest supercomputer. It would be more like 1 billionth or trillionth speed.
The reason for our disappointingly slow progress in AI over the years is that our hardware is still nowhere near powerful enough to usefully implement the same algorithms as the brain, and we likely won't even develop the right algorithms until we have hardware closer to the requirements, so we can test and iterate.
Shouldn't it be possible to have artificial AI mimicking human brain algorithms at 1/100th the speed, where perhaps a single thought based on learned information takes hours, instead of seconds?
I find it hard to agree, that despite the nanosecond latency times and the terabytes of throughput we can wring out of single computing devices(gpu's etc), we couldn't simulate brain-like AI faster than a billionth of what it should be.
You're probably right though.
That's a possibility.
Now what algorithm do you propose to use?
Here's an overview which references some very impressive results: http://www.youtube.com/watch?v=AY4ajbu_G3k
Now, that's entirely different from purposeful design of an AI such that it speaks English and knows what I like for breakfast. I don't think we'll ever know how to sit down and write one in Notepad.
Instead, memristor-based AIs will be evolved using genetic algorithms or other evolutionary approaches. Yeah, maybe that's wishful thinking too, but that's how I see it playing out.
In spite of our ability to simulate this sort of process for decades we have not succeeded in building a strong AI, the difference in having a hardware version is one of performance (just like Neural Network chips are typically a lot faster than their software simulated counterparts). There is no real difference in capability here, just a (possibly very large) speedup.
Now, I'm not ruling out that such a speedup will cause us to be able to create things that so far were not possible but I have a hard time convincing myself that this will almost certainly be the case.
Given that biological examples of a minimum speed limit for cognition exist, and that the behavior of computational networks at various speeds seem to follow the same behavior (slower=worse), then it seems reasonable to assume that a similar lower limit for cognition exists for silicon-based networks.
Therefore, faster devices such as memristors might be just the thing necessary to get our machines thinking, and moreover, that we may never see intelligent behaviors in slower simulated environments.
Therefore, it isn't such a stretch to think that (i) insights may continue to pop up reliably for "a while", and (ii) not just in the domain of hardware speed-ups.
Faster hardware also allows you to get by without fully understanding how the strong AI algorithms work by using evolutionary methods. You use brute force evolution to evolve algorithms that are even more efficient than their parents. Some people might frown upon using evolution to create AI but that's what was used to create human intelligence so why not use evolution guided by humans to create machine intelligence?
But couldn't we accomplish this with distributed processing rather than faster hardware?
Later algorithms can be run on lesser hardware because they don't have to retread the same territory as their ancestors; they only have to search the space that hasn't already been examined. As the saying goes, they search "smarter, not harder".
Storage capacity.
The brain is relatively slow. Ignoring microtubules, we're just dumb chemical reactions. But we are a lot of dumb chemical reactions. A human brain is ~ 100 billion neurons with each neuron having 100 to 7000 connections to other neurons.
So, just estimating here, 100 giganeurons * 1000 connections = 100 trillion neuron connections.
To simulate that, we use matrices of floats for each connection. So 100 trillion 4 byte floats = 400 terabytes of memory (with lots of hand-wavy assumptions). The fanciest GPUs we have today top out around 3 GB of memory (but they have 1k great run-in-lockstep cores) and the largest whole non-proprietary system I've seen tops out at 1 TB of local RAM: http://www.siliconmechanics.com/quotes/212240?confirmation=1...
We can simulate parts (vision and hearing have advanced on neural networks in the past 5 years), but higher level cognition/motivation is still an unknown quantity.
That's not the singularity, else we'd have had several singularities across several generations as certain groups of people fail to grasp the utility or value of printing presses, steam engines or atomic bombs.
The notion of the singularity suggests a point past which it is impossible to predict the future.
Global networking, in its basic form, was entirely predictable to certain technologists for decades before its existence. In fact, the original Spaceship Earth ride at Epcot nailed many peaces of later commonplace technologies – even multiplayer gaming. Hell, I read of a B'ahai priest who predicted the world wide web in the 30's.
Meanwhile, a singularity posits a confluence of technologies, connectedness and social change that renders all events past its arrival entirely impossible to predict.