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Comment by aothman | Hacker News Reader

As an AI grad student, this kind of sensationalism is somewhere between a minor irritation and a serious threat. AI always has had a severe problem with over-promising and under-delivering, and I'm of the humble opinion that until you're actually shipping the most awesome thing in the world you should keep your mouth shut. If the first thing people associate "AI research" with is "disappointment", that hurts everybody (particularly, NSF funding).

"Brain-based" AI should stay in the dark ages. Optimization-based AI is the present and the future.

(That said, if you want to talk about your sweet computer vision system that's "coming soon", go right ahead. Just don't call it AI.)

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"Brain-based" AI should stay in the dark ages. Optimization-based AI is the present and the future.

Humans can see. Computer vision systems suck. There's a perfectly good one in our brains. Why not try to understand what already works?

Contrary to what most would believe, brain-based computer vision has made a lot of progress in the past 20 years. Some might think there is a fundamental flaw in the "brain-based" approach given past failures, but that ignores that fact that those failures very likely happened due to a poor understanding of the brain at the time.

The work in brain-based computer vision however has been mostly academic. Brain-based computer vision startups are even more recent, and I think it's exciting to see the startup approach to solving what has been mostly an academic problem. In a startup, the engineering mindset, quick iteration, as well as a lack of concern for publishing and other forces at play in academia could produce very different results.

I do agree that the 5 year promise is extreme, but I think we need time to see how this relatively new mode of work (both in terms of the technical approach, and the process of implementation in a startup) will play out before we call it a failure.

Full Disclosure: I was an intern at Numenta last summer.

Is Numenta's approach really that informed by findings regarding actual brain function? It's been a while and I don't remember most of Hawkins' model, but I don't feel that one needs to consult any actual neuroscientific results to use the general concepts of hierarchical design or top-down processing, which seem to capture the basic idea of his work.

This whole neuro-A.I. fad began with artificial neural networks, which had nothing to do with brains, and still hasn't died.

Numenta's most recent algorithms are actually very strongly neurobiological. If you have looked at earlier work, you should check out the most recent white paper from a couple of months ago, which detail more than year of recent efforts in that direction.

http://www.numenta.com/htm-overview/education/HTM_CorticalLe...

There is also a recent talk by Jeff Hawkins from a few months ago on the same subject.

http://www.archive.org/details/Redwood_Center_2010_12_02_vs2...

You are correct that neural networks had almost nothing to do with brains. Numenta's new cortical learning algorithms, on the other hand, are very closely modeled on the structure and function of the neocortex.

> this kind of sensationalism is somewhere between a minor irritation and a serious threat

A serious threat to what?

Continued funding, I'd guess. It's the same sort of talk that triggered the AI Winter.

> AI always has had a severe problem with over-promising and under-delivering

Is this because the AI researchers truly over-promise, or because media/laypeople take a concept or statement and run with it?

Traditionally, it was the AI researchers over-promising. See the Wikipedia article on the AI Winter: http://en.wikipedia.org/wiki/AI_winter

There was something of a collective and large-scale underestimation of how hard AI would be. Why that would be the case is interesting, especially since it persisted for some decades, across multiple fields filled with smart people. From probably the 1880s to, say, the 1970s, there seemed to be this widespread view that high-level AI was just around the corner, and mainly depended on some inevitable technical progress (faster computers and more memory, plus a bit of algorithms work).

There wasn't even really much debate, in either CS or philosophy or engineering, over whether computers would be able to do "routine" tasks like accurate object recognition, mathematics, playing chess, etc., in the near future. The biggest controversy was over whether computers could ever be "truly" intelligent and creative, e.g. whether computers would also replace Beethoven in addition to mathematicians, or whether they'd be forever limited to just being very capable automatons. Somehow everyone missed that even making them the "lesser" kind of intelligent, so they can walk around, recognize objects, translate languages, etc., would turn out to be pretty hard.

Does Numenta fall under "Brain-based?"

Numenta does fall under brain based. I'm not sure what Vicarious are working on, but recently Numenta transitioned to radically more biological algorithms. It would be interesting to compare the two if Vicarious comes out with more detailed information about their algorithms.

I would say "Brain-inspired". As I see it, Numenta's model (as of about a year ago) is based on (1) the hierarchal organization of neurons, (2) the presence of feedback loops in neural architectures and (3) the importance of temporal processing even for static scenes. This doesn't include any intracellular details nor any of the larger and/or specialized brain structures.

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