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Student Maps Brain to Image Search
StonyandCher writes to mention that a University of Ottawa grad student is creating a search engine for visual images that will be powered by a system mapped from the human brain. "Woodbeck said he has already created a prototype of the search engine based on his patent, which apes the way the brain processes visual information and tries to take advantage of currently-available graphics processing capabilities in PCs. 'The brain is very parallel. There's lots of things going on at once,' he said. 'Graphics processors are also very parallel, so it's a case of almost mapping the brain onto graphics processors, getting them to process visual information more effectively.'"
turn out like a bad nightmare after watching A clockwork Orange ??
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Car keys are always in the last place you look, so we know that brain based searching is inefficient at best.
No weapon in the arsenals of the world is so formidable as the will and moral courage of free men.-Ronald Reagan
I worked in visual brain research for years, and can vouch there are lots of skeletons in the closet, or elephants in the drawing room: there is no accepted model of the statistics of real images (corners, occlusion, shading), nor of the algorithms necessary to infer them from inputs, nor of the learning process to infer those algorithms. Yes the brain is parallel, and yes it involves robust, fuzzy processing and analog values, but we not only don't know how the brain does it, we don't even know what problem it's trying to solve. The good news is that if this student does indeed have a business model and a real-world problem people will pay to solve, then the ratchet of engineering evolution could give us some real traction into understanding and solving this mystery. Good luck!
You might end up with a search engine that just looks for pr0n.
If a baby duck is a "duckling," why would anyone want to eat "dumplings?"
Web search does not immediately reveal any details of his algorithm or any relevant papers, just media publicity. He does not even seem to have a web page.
You're making quite an assumption, there. They might be in the last place you look, but I make sure to keep looking after I find 'em every so often, just to avoid that awful cliché.
The best part is, you can put "finding the keys" in any percentile you want, just by looking some more. Heck, you can really screw with the average by looking for 'em occasionally when you already know where they are.
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If you can, the patent system is more than a little bit broken, though I guess we all know that by now. I would think that the existance of the brain would constitute prior art...
I absolutely agree with you. Even the Computerworld (admittedly not the pinnacle of scientific reporting) article starts by saying "University of Ottawa student Kris Woodbeck is combining the neural processes we use to understand image data with the features of graphics processors." I don't even know where to begin with that statement. So he's come up with a model of neural image processing (a feat in itself)...and is mapping it to a GPU? This is like saying "we've figured out how to isolate stem cells from a source other than human embryos...and we used plastic petri dishes to do it!."
Second: 'The brain is very parallel. There's lots of things going on at once," he said. "Graphics processors are also very parallel'. OK, is this a science finding (i.e. a new image processing ALGORITHM based on the brain), or a systems paper (we came up with a parallel GPU version of an algorithm). I really hope he was misquoted, because otherwise it sounds like vaporware or untested hypotheses.
And then: 'For images, it might be when you took it, with what camera, with what exposure, that's about it. Then you're stuck with a red barn in rolling hills and I might know it was taken in California, but no one else does. How do you surface that metadata so it becomes much more searchable?' OK, now where did this come from again? neural processing? parallel GPU? and now inferring metadata?
Sorry, but getting a provisional patent is hardly a difficult thing (most universities in the US will file one for free if *YOU* think you might be on to something). Furthermore, all this would be more credible if they published some results and at least a brief description (which is allowable by patent law). Until I see some numbers, this is non-news.
An old-timer with old-timey ideas.
The solution to many of the questions like the very good parent of this post is to understand several things about the brain that a 100% map will not disclose. Please understand, the mapping of the brain will be of value though it will be of far less value than anticipated. The reason it will be of little value relative to brain function. We actually already know the processes and the number of steps involved. Also there are several features of the circuitry that are not at all contained in our silicon models.
Here is an abbreviated attempt to point out the differences in brain circuitry and why a map will not be of much value. The first problem is that the brain is dealing with an unfamiliar data type structure to our current digital structures. The data has no absolute value. All data coming in is relative in value to previous data. This produces a linear calculus where answers are arrived at in a single XOR subtraction step. The data form coming in will be of more value to the model than any model as all the computational steps are known past the data entry point. They have been known for a long time. The next problem is that the brain has "ghost circuits." These are analogous to the old time "Cross Talk" functions in analog telephonic circuits. Unlike silicon and other circuits where great effort is made to produce separation of data and isolation of circuits, the brain operates because signals can and do quite intentionally affect adjacent computational results. This is a 3 dimensional space effect. Another reason that a map will not be of much value is that the circuitry is in a chemical bath that is altered by reaction sums. The result is another step in the computation by making relational conclusions.
If I haven't confused everyone by now, it isn't by lack of trying to be as simple as can be. The calculus is similar to slide rule operations. (Something forgotten today.) The other functions produce a structural ability for the circuitry to produce results even with highly error ridden data and with outright gaps in data. Data purity above about 17% is sufficient for nearly perfect operations. The unique feature of the data form is that it also allows data which is arrived at by completely type unrelated sensors to be applied to derive intelligent results. This means you can take the output of an ear and match it to a visual field and get useful data! The differential data structure also allows nearly infinite memory compression and use of broad band differential sums to control responses and a gross filter. You use this driving a car.
The basic problem we have in producing an analog to the brain in computers (Artificial Intelligence Type) is that we attempt to do with absolute value sensors what is being done with relative sensors. The result is that computations form a geometrically increasingly difficult solution set when differential data would have produced a linear solution set. To be plain this allows as little as 4 or 5 computational steps to arrive at a very intelligent solution when a googleplex of steps are required using absolute value processing with a lessor result.
There is also a sensor reality that is missing. All natural sensors are motor controlled to center point damping which is a time delay cancellation producing only differential data for output. None of our synthetic sensors do this function and it is why we do not get the results we want. It is really a pretty simple fact that if you want to reverse engineer something you should actually reverse engineer it. Mapping the brain will disclose logical circuits we already know exist and the results of their calculations. Only the data form will tell what is going on.
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