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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."
great! new way to find even more porn.
The problem with this idea of storing images is that it could be pointless to store that much data. Your brain does not store images of say a tank. Your brain says "Does it have tracks?", "Does it have a turret?", "Does the turret rotate?". Well to figure out if it has tracks it would then search characteristics about tracks, then search for information about turrets etc. Once that is done you know it is a tank, now you need to know what kind. So you would need to know stuff about specific types of tanks like the Russian T-34 has a slanted front. Maybe search for a Russian flag, well then you have to figure out if the symbols on the tank are a Russian flag etc.
Most everything is made up for basic shapes. Some of those shapes may only be described by mathematics like for example, circles, triangles, squares etc. Now you just have to save how those shapes fit together to make a bigger shape but a shape in 3 dimensions.
Say for example you wanted to find a trash can. You would look for something that looks like a cylinder or a rectangle with a hole in the top. It would then have to be sitting on a flat surface. The top of the can may not be well defined due to the bag. Thus you have found a trash can. So rather then searching for images search for common shapes and combine those shapes to find what you are looking for. The problem is picking out the shapes.
All of the robotics problems you described - cleaning your floor, carrying your groceries, navigation, etc. - are AI problems. In fact, even the "(1) high-speed visual similarity search using two-step search clustering technology" that Hitachi is promoting could be considered AI. You see, in computer science, if a problem has some kind of real world application (even in theory) it goes under the moniker of AI, and this gets papers published. In my opinion, AI's just a rebranding of the rest of computer science; object orientation becomes "frames," complicated parsing problems become "natural language processing," robotic navigation is "pathfinding." But I digress!
Anyway, more to the point, Hitachi's technology doesn't do any learning, and thus, doesn't require training. It's also not particularly new technology, they abstract out some features for the images in their database, then search for images with similar features. This is probably oversimplification, I'm not a computer vision guy, but I'm relatively confident that Hitachi has not furthered the state of the art in computer vision. The reason I guess it's newsworthy is the second part: "(2) faster reading through optimized data allocation on an HDD." This isn't a new technology either, though. I'm not sure if TFA is really that newsworthy.
Robots have a lot of trouble trying to match the environment around them to stored records of objects unless the environment is severely constrained. This is mainly because robots are specialized. For navigation, infrared/laser/sound sensors are better suited, as they tell the robot how far away obstacles are. Robots that are concerned with identifying objects usually do not need to navigate.
Describing the algorithms qualitatively is different than defining what they actually are. Evolutionary psychologists really don't do anything essentially different than say something like "Human beings are able to perceive a difference between indoors and outdoors, and clean and dirty. This allows them to keep their house clean, which provides a selective advantage in evolutionary terms, over and above an ape that can't tell the difference between clean and dirty, or indoors and outdoors."
They aren't really *defining* the algorithm ( and I don't think it's safe to assume that it *is* an algorithm as we know them, given the spectacular failure of AI so far ) , just describing it. Saying that "Humans can and do perceive 'indoors and outdoors' is different than describing the actual mechanism of how they do it. The actual functioning of the algorithm remains a black box. Which means then that you don't have any plans or blueprints for building a robot that can wash floors.
Computers are useless. They can only give you answers.
-- Pablo Picasso
Your implication is that the human mind cannot be reduced to a Turing machine. I am in the other camp--who believe that the mind is subject to rigorous physical law, and that physical law can be expressed arithmetically (in principle), and so the human mind is a Turing machine.
I'm not saying that the mind is not subject to physical law, or is not based on math. All I'm saying is that the mind is not a Turing machine ( though it probably would have to have a Turing machine in it somewhere ). It's a different *kind* of machine, not a super-powerful Turing machine.
Goedel basically showed that a Turing machine cannot do *all* the kinds of math that a human mind can do ( though it can do some). Not that a Turing machine lacks a certain amount of power, but just that it never will. It's just quantitavely the wrong tool for the job. It doesn't matter how much power you give it; the 'weakest' Turing machine is essentially the same as the 'strongest' one; it just simply can't do certain things. If a human is able to perceive and understand this, to know something that a Turing machine can't know, then the mind cannot *solely* be a Turing machine. This does not mean that the mind is not a different *kind* of machine, based on physical law, instead of some mystic hocus-pocus; it's just that it's not a Turing machine. My claim is that the mind is a qualitatively different kind of machine, not a Turing machine.
Goedel's theorem says that a consistent arithmetic system will contain unprovable truths. Put otherwise, such a system cannot be both consistent and complete. Thus the Goedel counterargument to Strong AI (that human minds and computers are not fundamentally different) is that humans (e.g. mathematicians) can prove things like Godel's theorem, so we are able to "rise above" the arithmetic and exist in states of full proof and full consistency.
But I think there is a flaw in that logic (note: I am not a mathematician). The theorem doesn't preclude that a given arithmetic system (e.g. human mind) will be able to prove a truth that a weaker system ignored. Thus our ability to see certain truths doesn't mean that there are not other truths that are unprovable to us.
I don't think the implication of Goedel's theorem shows that we 'rise above' the Turing machine, but rather that we have a qualitatively different awareness or knowledge that a Turing machine doesn't have.
Goedel's theorem is recursive. Any human mathematician can see that no matter how powerful the symbolic system is, the Turing machine will never be complete; there will be truths that the system can't prove. No matter how much you expand a particular system to show any truth a weaker system missed, there will be more truths that the newer, more powerful system missed. This process can go on ad naseum into infinity. A human mind can perceive this foray into eternity, but the Turing machine has no way of proving it. How could a human mind perceive something that a Turing machine couldn't, unless we had some component that was fundamentally different than a Turing machine?
What we seem to have that the Turing machine doesn't is meta-knowledge. We can see that any attempt to create a complete and consistent arithmetic system on a Turing machine will just lead to an endless series of more powerful systems that produce ever more elusive truths, and the process never ends. In this sense the Turing machine is 'myopic' -- it will never stop and say "Hey, I'm not getting anywhere with this; this is an infinite loop. No matter how powerful the system is, there will always be more truths that it cannot express." It's unable to know what it can't know, so to speak. However, as humans, we can somehow see the 'big picture', that no matter how powerful a system you make, there will always be another level of truths out there.
More fundamentally, no one has actually shown that the human mind is either consistent or complete (proving both would be required to sh
Computers are useless. They can only give you answers.
-- Pablo Picasso
It knows, and can examine, its complete internal state better than you do, for starters. As you add peripherals, it can obtain information on the world outside of its hardware. The sophistication of this awareness grows both in complexity and in abstraction as the algorithms that deal with the data become more complex themselves. There's absolutely no reason to presume there is anything magical about awareness. A thermometer is more aware of the temperature than you are most of the time, and better than you are at it all of the time. That's a result of design. You can expect the same from AI, should we manage to cobble some up.
We need make no leap whatsoever. Nature has produced human intelligence through a process of incremental improvements. Reproducing or modeling natural systems is something we've turned out to be very good at once we understand them; there is every reason to presume that this is just one more of the same types of challenges. The leap of superstition is entirely yours — it is that awareness and consciousness are not perfectly natural consequences of particular combinations of processes and structures. In order to reach that conclusion, you have to imagine a being whose existence cannot be substantiated by the facts at hand.
And what might that be? I see only beauty, intriguing challenges, mysteries to be plumbed, problems to be solved. What do you see that is so ugly, eh? And why? What are you afraid of?
I've fallen off your lawn, and I can't get up.
No, you don't "know" any such thing. Barring the possibility of projects so black we've never even heard of them, no one seems to know what intelligence is. Least of all you or I. Claiming you know its nature at this point in the development of science is absurd.
First of all, it isn't a definition of intelligence at all. It's just a set of conditions that take into account intelligence or the lack of it, and natural systems as opposed to manufactured systems. I will say that even if I've picked a wrong example, these conditions still stand, you just need a right example to replace my error.
Having said that, though, I don't know what intelligence is. I am under the impression that I can sometimes identify its presence by manifestation of actions, however, at least within the realm of nature. So I sometimes know when it is. Plus there are organic hints; all my experience points to it being an emergent property of at least a moderate degree of complexity of neural systems. So if an animal has a decent collection of nerves - we're back to mice and higher animals - then the hardware may be there, and they are worth watching for displays of tool using, emotional outbursts, nurture, intellectually moderated defense and aggression, sharing, mutual support, empathy, self-sacrifice, and so on. For instance, when one fish continually pushes another to the surface because the other is paralyzed and cannot swim up to its food, that gets my attention. When a cat wants my attention and comes to meow at me because it is out of food, again, I pay attention. When a cat uses a mirror to locate and clean crud off its coat, I pay attention. When apes can learn to use signboards and computer systems to communicate complex concepts, I pay attention. Combinations of these things, especially in large numbers, make me fairly confident that what I am witnessing is a manifestation of intelligence. What is intelligence itself? No idea. What does it cause? Now that I have some ideas about.
Ants are borderline; ant colonies less so. Plants are not. Fungi are not. Bacteria are not. Organic molecules, in large summary collections, may be, depending on various factors; after all, that's one description of a brain, depending on just how disorganized you are implying. My middle son is pretty damned disorganized. :-) Once you get too general, you fall into the "can groups of atoms be intelligent?" trap; of course they can, that's what our brains are.
People use the term AI as a pointer to research seeking various aspects of the goal of AI, and I have no problem with that. That in no way should be confused with the mistaken idea than anyone has made any public announcements of having actually created anything even remotely resembling the target. Again, I except the vague possibility of black projects we may not hear about for decades.
I've fallen off your lawn, and I can't get up.
Gödel's incompleteness theorem is only true for non-trivial formal systems. A trivial formal system on the other hand can prove just about anything, very much like we humans do with emotions and totally whacko notions related to religion, political conviction, and such. If we decide that emotions are trivial formal systems, we humans escape the restrictions discussed here.
All rites reversed 2010