Hitachi Develops New Visual Search
Tech.Luver writes to tell us that Hitachi has developed a new visual search engine that can supposedly find similar images from within millions of video and picture data entries in around 1 second. "The technology assesses the similarity of images based on image characteristics presented as high-dimensional numeric information. The information is acquired by automatically detecting information regarding the images, such as color distribution and shapes."
Usually, HDD = Hard Disk Drive
Using these words, search engine style indices and techniques can be used to make searching -- by supplying an example image area which can have its words computed -- quite fast.
The key bottle neck here is the clustering stage: reducing the original input of typically hundreds of features per frame -- multiplied by 25 frames per second by minutes, or hours, of video -- to a much smaller set of clusters. It looks like the work in the linked article is using a modified clustering algorithm which does not require all of the data to be in memory at once.
The TRECVID project is a challenge style exercise where groups compete to provide the best search results for a given set of queries where the search material is hours of video.
The field of evolutionary psychology is attempting to do just that: to deduce what algorithms are working in the human brain. One of the end-goals of such research is of course to be able to generate artificial versions of those algorithms. If you're interested in such things, Steven Pinker's "How the Mind Works" is a fantastic and accessible read. It describes how things like vision are optimized for environments we evolved from, and tend to fail when put in contrived situations (like optical illusions). It also tackles the "adaptive advantage" of having emotions, and so forth.
Of course, it's easier said than done to actually transplant a biological algorithm into a computer. Even when you figure out the basic strategy ("it seems to be a neural net that responds to edges in a visual image and passes shape information along to the next module"), it turns out that the details are difficult (millions of years of evolution have adjusted the exact "weighting factors" to very specific values!).
Goedel basically showed that a Turing machine cannot do *all* the kinds of math that a human mind can do
No. Nobody, so far, has found anything the human brain can do that a Turing machine, in principle, can't. Godel's theorem isn't really relevant here, unless you can show that humans could eventually determine the truth of any statement in mathematics.
I've seen similar arguments made about the halting problem, and the argument is equally flawed. People like to think that, while no computer can solve the halting problem in general, that they can. But I see no evidence for it --- and in fact, there are several very small Turing machines (which compute Collatz-like functions) and we haven't determined if they halt or not.
Basically, as far as we know, the human brain is subject to Godel's incompleteness theorem, and the non-solvability of the halting problem, as much as any Turing machine is. The fact that we can do things easily that computers so far haven't says nothing; for all we know, tomorrow someone will program a Turing machine to do them.
Gödel's theorems have nothing to do with representing the human mind in any form. They cannot be applied to the human mind for the purposes of answering the question of strong AI. Basically, the only thing that Gödel's theorems do is carry the Liar's Paradox ("This sentence is false.") to the level of basic arithmetic. There is no magical process that proves or disproves anything about the human mind. The confusion stems from the fact that the mathematical terms "incomplete" and "inconsistent" seem to imply so much more when quoted in a non-mathematical context.
For anyone who is interested in reading further, Gödel's Theorem: An Incomplete Guide to Its Use and Abuse contains a thorough discussion of the issue.
I would like to believe that we will achieve strong AI one day. However, referencing Gödel's incompleteness theorems just because they sound appropriate at first glance does not give any argument scientific credibility.
Here's a couple of articles I read:
Facts about the brain
Rods and Cones
There are around 125 millions rods and 6 million cones in each eye, with the percentages of each color/wavelength (red = 64%, green=32%, blue=2%)
No Sense
The human eye has 100 million neurons per per eye of five types, but there are only around 1 million neurons per optic nerve (arranged in bundles of 1000).
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