Jim Carnicelli's AI Blog

[ Audio Version ] I've been away from this too long, distracted by other things in my life. I've missed it. Lately, I've been finding myself getting excited again to the point of getting distracted from those other things and back in this world. The most interesting development in the world of artificial intelligence of late, to my thinking, is the recent release of Numenta's Hierarchical Temporal Memory algorithm, the brainchild largely of Dileep George and inspired largely by Jeff Hawkins, author of On Intelligence . Having been so disappointed by artificial neural networks, expert systems, and various other "traditional" approaches to AI, I found the ideas presented by Hawkins refreshing and exciting, so I joined Numenta's mailing list and eagerly awaited the arrival of its promised products. Now that the NuPIC platform and related tools have been released, Numenta has also authored various white papers on how it actually works. In refreshing contrast...

[ Audio Version ] I've been thinking more about perceptual-level thinking and how to implement it in software. In doing so, I've started formulating a model of how cortical neural networks might work, at least in part. I'm sure it's not an entirely new idea, but I haven't run across it in quite this form, so far. One of the key questions I ask myself is: how does human neural tissue learn? And, building on Jeff Hawkins' memory-prediction model , I came up with at least one plausible answer. First, however, let me say that I use the term "neuron" here loosely. The mechanisms I ascribe to individual neurons may turn out to be more a function of groups of them working in concert. Let me start with the notion of a group of neurons in a "neural bank". A bank is simply a group of neurons that are all looking at the same inputs, as illustrated in the following figure: Perhaps it's a region of the input coming from the auditory nerves. Or perhaps...

[ Audio Version ] I've been getting too lost in the idiosyncrasies of machine vision of late and missing my more important focus on intelligence, per se. I'm changing direction, now. My recent experiences have shown me that one thing we haven't really done well is in the area of perceptual level intelligence. We have great sensors and cool algorithms for generating interesting but primitive information about the world. Edge detection, for example, can be used to generate a series of lines in a visual scene. But so what? Lines are just about as disconnected from intelligence as the raw pixel colors are. Where do primitive visual features become percepts? Naturally, we have plenty of systems designed to instantly translate visual (or other sensory) information into known percepts. Put little red dots around a room, for instance, and a visual system can easily cue in on them as being key markers for a controlled-environment system. This is the sort of thinking that is used in ...

[ Audio Version ] I don't normally do reviews of ordinary products. Still, I tried out an interesting one recently that makes practical use of a fairly straightforward machine vision technique that I thought worth describing. The product is called EyeTwig ( www.eyetwig.com ), and is billed as "head mouse". That is, you put a camera near your computer monitor, aim it at your face, and run the program. Then, when you move your head left and right, up and down, the Windows cursor, typically controlled by your mouse, moves about the screen in a surprisingly intuitive and smooth fashion. Most people would recognize the implication that this could be used by the disabled. I thought about it, though, and realized that this application is limited mainly to those without mobility below the neck. And many of those in that situation have limited mobility of their heads. Still, a niche market is still a market. I think the product's creator sees that the real potential lies in an...

[ Audio Version ] I've been experimenting further with stereo vision. Recently, I made a small breakthrough that I thought worth describing for the benefit of other researchers working toward the same end. One key goal of mine with respect to stereo vision has been the same as for most involved in the subject: being able to tell how far away things in a scene are from the camera or, at least, relative to one another. If you wear glasses or contact lenses, you've probably seen that test of your depth perception in which you look through polarizing glasses at a sheet of black rings and attempt to tell which one looks like it is "floating" above the others. It's astonishing to me just how little disparity there has to be between images in one's left and right eyes in order for one to tell which ring is different from the others. Other researchers have used a variety of techniques for getting a machine to have this sort of perception. I am currently using a combin...

[ Audio Version ] While engaging in some stereo vision experiments, I found myself a little stuck. I stopped working for a while and started staring at a wall on the opposite side of the room, pondering how my own eyes deal with depth perception. I crossed my eyes to study certain facets of my visual system. I got especially interested when I crossed my eyes so that the curtains on either side of the doorway were overlapped. I wasn't surprised to find my eyes were only too happy to lock the two together, given how similar they looked. I was, however, surprised to see how well my visual system fused various differences between the two images together into a single end product. It even became difficult to tell which component of the combined scene came from which eye without closing one eye. I thought it worthwhile to create some visual illusions based on some of these observations. To view them, you'll need to cross your eyes so that your right eye looks at the left image and vi...

[ Audio Version ] Once again, I've forgotten to announce a sub-site I created recently that I call Topics in Machine Vision (click here) , back on August 28th. Unlike my earlier Introduction to Machine Vision , it does not set out to give a broad overview of the subject matter. Instead, it's geared toward the researcher with at least some familiarity with the subject. Also, whereas I intended the introduction to stand complete on its own, Topics is more organic, meaning that I'll continue to add content to it as time passes. Knowing that this could get to be difficult to read and manage, I've broken down Topics into separate sections and pages. The first section I've fleshed out is on the Patch Equivalence concept I introduced in an earlier blog entry here. In fact, once I introduced this topic in detail, I went back and ran some experiments in application of the PE concept to stereo vision and published the results , including tons of example images that demonstrat...