What we have with Watson (the Jeopardy model, because the name is used by IBM as an umbrella for staff) etc is the same kind of number-crunching, dumb-smart AI we always hand.
Without any qualitative steps that wont fly.
I wrote a summary here of the main achievements of 2014: https://www.reddit.com/r/Futurology/comments/2qq993/developm...
Stuart Russel recently said "The commercial investment in AI the last five years has exceeded the entire world wide government investment in AI research since it's beginnings in the 1950's."
All those machine learning tricks have been available for the longest time, the two orders of magnitude speed-up that we have received (courtesy of the gaming industry) should be seen as such a qualitative step and yet we are no closer to a general AI than we were before that speed up took place. If anything we've learned how incredibly hard the problem really is and the predictions on how close we are from a decade ago have already slipped significantly.
Unfortunately, much of current statistical machine learning does not help its developers to see, unlike a *-scope. It's just a blackbox. See Chomsky vs Norvig http://norvig.com/chomsky.html
No matter what accomplishments are made, there will always be someone shouting that it's not really AI or it isn't really progress. It's impossible to argue against. Until we finally pass the very last goal post, and then it's too late.
Generalized pattern matching is a tool in the toolbox of an AI but it is not AI by itself and there may be work-arounds to AI which do not require generalized pattern matching (that's an interesting one, requiring a bit of a trick in that you if you could generate a specialized pattern matcher on demand that you don't need a generalized one).
Define "programmer directed". There are neural networks that can do reinforcement learning and play video games which are very general.
Do you have any reason to believe the human brain is any different? We just have more neurons.
As someone (Chalmers?) once said about the problem of consciousness: we didn't need to replicate the flapping of wings or the locomotion of sea creatures before taking to the air or underwater. Might it not also be the case with AI that there's some fundamental principle we've yet to discover that just so happens to have expression in the substrate of 1200cc's of fatty tissue, but could possess the same fidelity (and greater) in silicon and looks nothing like the architecture of the human brain?
Recurrent models (where some outputs are connected back to the inputs) are one possible way to account for time, but the work is still really early on these methods, and it's not clear what architecture (e.g. which and how many inputs and outputs should connect) would be efficacious.
It is not all or nothing. There are analogies at different abstraction level.
Yes we probably shouldn't be plugging in continuous differential equations to mimic chemistry of neuroreceptors, cell sodium channels etc. to replicate it at that level. So in that respect we agree, airplanes are not like birds. Far from it. No flapping. Not composed of cells. Not biological in nature.
On the other hand, there is another way to look at systems -- look at higher functional components and how they are connected. So maybe there is a language processing area connecting to memory. And so on. This is called the connectome of the brian as well. Which identifies what parts are connected to what.
In this regards airplanes are similar to birds. They both have wings. Fuselage. A tail. They are built with similar structural material contraints -- light and durable. Aluminum, titanium for aircraft, and porous bones for birds.
Another way to look at it is in so many decades of AI, we haven't yet come up with another model. So while having to wait for enlightment to hit us one day why not learn from an already existent example.
One hypothetical path discussed is that of tool AI. That is, robust search processes - things we are already quite adept at (genetic algorithms, deep learning, etc) - purposed towards AGI-related goals.
It's not hard to imagine these existing methods being used in the pursuit of a recursively self-optimizing agent (seed AI) that then snowballs into AGI.
Such an approach may not require any fundamental knowledge concerning the nature or architecture of AGI. It would simply be an application of brute computational force using existing tools and knowledge.
Yet even if all we need is this sort of "seed AI", we still need new architectural insights to be able to create it in the first place. Otherwise someone would have surely demonstrated it by now? If nothing else, such a system would need to evaluate effects of its outputs on its inputs over time (e.g. if I shoot a basketball, it takes seconds before it either goes in or doesn't; if I plant a seed in the ground, months will pass before it sprouts -- or not, depending on conditions). Research into recurrent networks, one possible avenue for doing this, is still pretty primitive.
And I'm not sure I'm convinced that this core "seed AI" is sufficient to emulate human cognition. Such a system might effectively integrate audio and visual senses, for example (in order to combine both for prediction tasks), but could such a system ever emulate the sort of continuous verbal inner monologue we all have which narrates our experience? That we have this inner monologue which seems to run alongside our other senses but yet makes use of them (along with stored memories) suggests, at least to me, that some more complicated pathways are involved which link together these various "component systems" (senses, stored memories, emotional states, linguistic synthesis, etc.) beyond just the simple prediction/reward circuitry which I presume the "seed AI" would encapsulate.
Not necessarily. That was my entire point, that robust search processes using existing tools and knowledge may yield a seed AI.
>Otherwise someone would have surely demonstrated it by now?
Again, not necessarily. AGI may not yet exist primarily due to dumb luck.
The computational requirements, especially when you consider most computing capacity on the planet is networked, may already be adequate or even far exceed adequate.
>And I'm not sure I'm convinced that this core "seed AI" is sufficient to emulate human cognition.
It probably won't be. It will most likely be completely alien when compared to human cognition. At the same time, that doesn't preclude it from being vastly more powerful.
I've been hearing that since I started using computers, 36 years ago.
36 years ago the argument that AGI was coming soon could be made in tandem with the argument that we'd make some fundamental advance that allowed computers to express intelligence with less computational capacity than humans (by orders of magnitude). Today we can make an argument that we'll achieve it (at least initially) by leveraging computational capacity on par with or orders of magnitude greater than a human mind.
How much, yes, how, not so much and definitely not at the powerbudget the brain has.
> and how long it will take to build computing machines operating at that scale.
We don't actually know that. There have been some WAGs but so far those appeared to be totally off based on the developments since.
> 36 years ago the argument that AGI was coming soon could be made in tandem with the argument that we'd make some fundamental advance that allowed computers to express intelligence with less computational capacity than humans (by orders of magnitude).
Yes, that was a crucial mistake and it led directly to the AI winter.
> Today we can make an argument that we'll achieve it (at least initially) by leveraging computational capacity on par with or orders of magnitude greater than a human mind.
Chances are that we're missing a very important piece of the puzzle for which there is no known solution even in theory. The problem is that there are many candidates for that important piece none of which have currently proposed workable solutions no matter what the computational budget or the accepted slowdown (they are equivalent).
So I think some caution when throwing around projections numbering 'just a couple of years' is warranted, after all it's 'merely a matter of programming' but in this case we don't have a working model that we understand.
For now - as far as I can see - we are no closer to the goal than where we were 35 years ago but we know better how much we are still missing and all the hard parts are still in front of us.
There were perfectly educated people that said there would never be flying machines, 2 years before the Wright brothers succeeded.
It's not about saying it can't be done it is all about saying it can be done within a specific time-frame.
Key inventions don't happen 'on command' unless the only thing required is a brute force search for something that is already possible in principle (say electric light).
ANI and AGI are qualitatively different. Progress in making cars faster will not lead to teleportation or warp drive. It's not even applicable.
There are many unknown unknowns in AGI. I could not even pretend to give you an estimate.
Russian classification traditionally also have distinction between terms "artificial intelligence" and "artificial mind" ("искусственный разум") to keep the problems of autonomy/agency/consciousness out of field of AI.