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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.
>stop pretending your anti-science quasi-religious fundamentalist beliefs are science
Project much? What the hell is wrong with you? You're the one making supernatural claims, not I.
>"Nature did it with meat" has no scientific basis.
Wow. So the fact that we can observe evolution and our fellow humans, make predictions, test them... 'no scientific basis'.
> All Science has is interface observations. And even a child these days knows that what you can observe on the outside of a box is not necessarily created on the inside.
Right back to YOUR belief of 'magic inside'.
I shall quote you back to yourself: "Seriously, stop pretending your anti-science quasi-religious fundamentalist beliefs are science. They are not."
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