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Artificial Intelligence Pioneer Says We Need To Start Over (axios.com)
Steve LeVine, writing for Axios: In 1986, Geoffrey Hinton co-authored a paper that, four decades later, is central to the explosion of artificial intelligence. But Hinton says his breakthrough method should be dispensed with, and a new path to AI found. Speaking with Axios on the sidelines of an AI conference in Toronto on Wednesday, Hinton, a professor emeritus at the University of Toronto and a Google researcher, said he is now "deeply suspicious" of back-propagation, the workhorse method that underlies most of the advances we are seeing in the AI field today, including the capacity to sort through photos and talk to Siri. "My view is throw it all away and start again," he said. Other scientists at the conference said back-propagation still has a core role in AI's future. But Hinton said that, to push materially ahead, entirely new methods will probably have to be invented. "Max Planck said, 'Science progresses one funeral at a time.' The future depends on some graduate student who is deeply suspicious of everything I have said."
Expert systems aren't AI, and pattern-matching algorithms aren't AI. AI is something that can creatively solve problems based on unreliable inputs and abstracting specific experience to general cases.
The problem there is we don't even understand how that works in theory, so modeling and developing an actually AI based on that model is impressively difficult.
Personally, I think we'll get there (understanding intelligence) faster by trying to replicate a mammalian brain in silicon that we will trying to bash out new algorithms.
>Likely he is not right either, because AI beyond statistical classification ("weak AI") may well be impossible
Nature did it with meat. Meat is not special. We have to learn how to replicate the mechanisms - which involves first understanding the mechanisms. Both of those are daunting tasks, but not fundamentally impossible.
If you think they are, then you must believe intelligence is a product of a supernatural process, and your theories are not appropriate for a science-based discussion site.
AI is a joke. There has been no real progress in AI since the 60s. What you see now is parlor tricks and a byproduct of Moores Law. Now that Moores Law is over, we need to find some other way to do computing. We will never have AI with digital computing.
If you think that, then you have no clue what the limits on software complexity that can still be handled are. Sure, we are hardware-limited and we will be that for the foreseeable future. But the little overlooked fact here is that we have no clue what the software actually should do in order to simulate a brain, so even if we had the hardware, we would not be any closer to the result.
Also, why assume that just scaling the thing up makes it suddenly be intelligent? That is a baseless assumption as that has never been experimentally verified and there is no theory that has been verified and could be applied either.
At this time, the workings of intelligence, consciousness and free will are "magic", i.e. nobody has a clue how they work. Assuming a purely physical apparatus could attain all these is neither supported by our current understanding of Physics nor does it have any scientific base. It is a belief. And, as it turns out, the follower of this belief ("physicalists") use pretty much the same faulty argumentation techniques so common with religious fanatics. A pathetic fail on their part.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
Well, "God" is a transparent pseudo-explanation for those weak of mind, but the physicalists (fundamentalists that believe everything is just matter and energy) are not much better. Both use belief-based strategies of dealing with the unknown and both are anti-science.
When it comes to consciousness, intelligence and free will, the scientific state-of-the art is "nobody has a clue". Anybody actually thinking scientifically is able to live with that, but that approach is beyond a great many people. Hence they invent stupid pseudo-explanations.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
> Assuming a purely physical apparatus could attain all these is neither supported by our current understanding of Physics nor does it have any scientific base. It is a belief. And, as it turns out, the follower of this belief ("physicalists") use pretty much the same faulty argumentation techniques so common with religious fanatics.
There you go again - the third time in this discussion by my rough count. You deride the idea that physical processes could create intelligence as a product of the faith of religious fanatics. This universe runs on the laws of physics. Claiming anything else is... the product of the faith of a religious fanatic.
If you don't believe in physical laws, you should be having this discussion with your preferred religious authority, and not with us here on Slashdot.
>Using silicone semi brute strength to emulate "meat" may be infeasible as we are rapidly reaching silicone's physical limit.
Have a look into memristors, a new toy that could be very useful for making artificial brains.
Then consider that nature 'figured out' how to be more efficient by using more switches with lower thresholds and taking the average, while we tend to juice transistors to ensure a strong '1' or '0'.
And finally... silicon. Silicone is not particularly useful in computers except as a sealant and sometime adhesive or vibration damper. :)
Deep learning and other related machine learning techniques are proving very useful for a wide range of tasks. We don't need to "start over" to advance useful machine learning techniques.
Hinton seems to mean to get "strong AI". Yes, I read TFA, but the strength of Axios articles is that they are very short, but that is also their weakness. Very little is actually said in TFA.
We are a long, long way from anything that emulates a natural neural system at any level.
Consider Caenorhabditis elegans. Every cell in this simple worm has been mapped, also the development of every cell from a single cell has been mapped (male worms have 1031 cells). We know every cell in its nervous system (there are 302), and every cell that each cell is connected to, and we know the type of connections for all. What's more we have completely sequenced its genome. We know more about this little multi-cell organism than any other multi-cell animal on the planet.
Since we know every cell in its nervous system, and every connection between every cell, we must be able to emulate this worm's "brain"! Heck we must be able to "upload" the worm's brain to a computer! Right? Right?
No.
We are still working on understanding the functioning and capabilities of a single neuron in its brain. That has proven so complex as to defy characterization thus far. We are essentially nowhere in understanding how this 302 cell brain works despite decades of effort.
Meanwhile Kurzweil has changed his prediction of "when computers will have human-level intelligence" from 2020 to 2029. I guess believing it was going to happen in the next 26 and a half months was cutting it a little too close. I have been reading about his predictions about AI for a couple of decades now and have yet to see any explanation of how he imagines this is going to happen - other than his expectations about hardware capabilities, and that there is still an unspecified "software issue" that needs to be solved. Indeed.
Second class citizen of the New Gilded Age
Just imagine what the human mind's distributed representation of the "intelligence" concept would look like. Clever animate entities (and most associations therewith) are way off in their own private corner of vector space compared to just about everything else.
When the gap is this large, the enormous void in between somehow becomes a non-object (to superficial cognition) and so people just begin to presume that we need to jump the gap, rather than slowly filling the gap in.
It's almost like the travelling moon illusion when you're driving in a car and the moon is low in the sky, off to the side (which children find amazing, but adults have learned to ignore).
I was thinking about the sun this morning and about relative illumination at different latitudes. The correct physical model is parallel rays, which immediately suggests that for a perfect sphere, the poles get no direct radiation at all during equinox, the eternal kiss of sunrise=sunset.
Then I looked outside through the window, and realized that the human brain—which knows the sun is far away—still doesn't think it's as far away as the earth is wide (very wide, if you believe in a flat earth model) or even a few multiples (but it's actually thousands), and so the intuition from our eyes never says parallel rays.
We've been nibbling away at the giant AI void quite successfully, but the travelling moon illusion still makes us think we need to jump.
The reason we keep reclassifying our victories as "not really AI" is because we know for a moon fact that the void never actually changes size. But it does, and it has, and it will continue to shrink, and I really don't think we're going to spring generalized intelligence all at once out of scary clown box.
First we must learn to perceive the void as a continuum of many way points, mapped out by many generations of technical improvement, like Vancouver and Cook or Lewis and Clark.
For me, recent results with LSTMs have made the void seem just a little bit smaller than it was before. I'm now at the very beginning of an ability to perceive the moon as being at a great, yet finite distance.
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With something so thoroughly hived off in its own corner of distributed-representation hyperspace as intelligence, what's to define, anyway? Definitions are street signs erected in conurbia, which one resorts to after Toronto and Hamilton and Niagara Falls have all become built-structure indistinguishable as you skirt the horseshoe.
There are many conurbations in distributed-representation vector space where definitions are the last gasp at forestalling cognitive Gangs of New York. Definitions are less important under open skies of Boise, Idaho or Butte, Montana; even less important still when you've wandered out into the green grid-lines of the entirely unpainted Matrix.
Here's a quick test: if your frontier town's "population #,### sign" (there is only one sign) and it has at most one comma, definitions are premature.