We have two dogs. One, Nini, has all the physical features of a Black Labrador Retriever, and the other, Kalena, is golden in color and has the features of a Heinz 57.
Nini's non-Lab features include longer hair and white markings on her chest and feet.
When we go outside, I carry a throwing toy to give them exercise. The funny thing is that Nini does not have the genetic memory (instinct) of a retriever. Nini has never cared whether I throw the toy, or not, but Kalena will eye it and jump up and down and beg me to throw it. Then, the 57 will become the retriever and find the toy and bring it back and dare me to throw it, again.
Nini's behavior, on the other hand, is bizarre. Nini will run interference once Kalena has the ball, and do all in her power to keep Kalena from getting it to me. She will nip at Kalena's legs, try to trip her, and growl and do everything in her power to keep Kalena from bringing me the toy.
Today, it hit me that Nini's hair length and foot markings look like those of a sheepdog we once had. And her behavior with Kalena is similar to Colonel's behavior when he was herding sheep. (Colonel was my son's sheepdog when we raised sheep.)
It saddens me that these unique dogs' traits end here. All their mixed-up ways will never be repeated because the animal shelter required they be neutered before we could adopt them. Neither of them will ever mother a litter of strange and wonderful puppies.
Some people want to do the same thing with humans. The focus of Margaret Sanger (mother of Planned Parenthood) was eugenics, good genetic breeding. (http://bit.ly/nTf5B9) Their goal is to keep the un-productive, un-genetically pure, un-fit, un-loved, and un-perfect from procreating, whether by advance planning (birth prevention) or afterthought (abortion).
The role of a gospel preacher, in contradistinction, is to demonstrate to un-lovely and un-loved people that God loves them and can give them the power to rise above whatever circumstances into which they were born. Our history need not determine our destiny. Our past need not determine our future.
Nobody is so bad that they should not have been born. Everybody is somebody in God's economy, and all can be made perfect through God's power indwelling them through the Spirit of Christ.
Even mixed-up people, like our mixed-up dogs, have their own unique gifts they can share, and all of us benefit from each other's gifts. All of us suffer when some of us aren't given the chance to exist and grow into the potential God can make for them.
The wonderful, mixed-up, joy-giving instincts of Nini and Kalena will die with them, but, in the meantime, I will enjoy their erratic behavior!
Saturday, August 20, 2011
Tuesday, April 26, 2011
Pattern Recognition
One of the technical things I am interested in is called "dimensionality reduction." Some real world problems involve such a large amount of information that it is difficult for computers to process them, either because of their computational complexity or the humongous amount of storage space required to examine the problem.
Hyperspectral Imaging in Remote Sensing is one such problem. A satellite passing over an area of the earth's surface can take pictures and record the information that is in view. Because most of these images are composed of reflected light from the sun, each picture element (pixel) in a picture can measure that reflection with sensors that respond to different wavelengths (colors) of light, including both those that are visible to humans, and also those that are not visible, e.g., ultraviolet, infrared, etc.
When you consider that the pixel represents a small area (depending on the optics of the camera), you can imagine that one small section of the earth's surface as small as one square foot would take an array of thousands of elements to record the reflected wavelengths at each pixel. A similar array of elements would be needed for each pixel in the picture, resulting in millions and billions of data elements, for even a small picture of the earth's surface.
You can view something with Google Earth, for example, to get an idea of the amount of information, but remember that Google Earth just shows points in the visible spectrum. Hyperspectral images represent much more information. Every point on the surface map can be represented as an array of points in another dimension, each representing a different wavelength in the spectrum.
Now, consider the pattern recognition problem. How do we recognize what we are seeing? How do we know when something is a building, a tree, a truck, or a person? In military applications, how do we distinguish between a friend and a foe? And how do we get a computer to do the same thing a human would?
The current state of the art involves training the program with known data, and then testing it with unknown data.
Pattern recognition requires training. We cannot recognize things we have not already learned something about.
Now, why is this issue in a web page focused on epistemology?
The most notable form of pattern recognition for people is fault-finding. We recognize faults in others. Why? Because we are so intimate with those faults in ourselves. Unfortunately, we don't like to acknowledge them in ourselves. Something to think about.
Hyperspectral Imaging in Remote Sensing is one such problem. A satellite passing over an area of the earth's surface can take pictures and record the information that is in view. Because most of these images are composed of reflected light from the sun, each picture element (pixel) in a picture can measure that reflection with sensors that respond to different wavelengths (colors) of light, including both those that are visible to humans, and also those that are not visible, e.g., ultraviolet, infrared, etc.
When you consider that the pixel represents a small area (depending on the optics of the camera), you can imagine that one small section of the earth's surface as small as one square foot would take an array of thousands of elements to record the reflected wavelengths at each pixel. A similar array of elements would be needed for each pixel in the picture, resulting in millions and billions of data elements, for even a small picture of the earth's surface.
You can view something with Google Earth, for example, to get an idea of the amount of information, but remember that Google Earth just shows points in the visible spectrum. Hyperspectral images represent much more information. Every point on the surface map can be represented as an array of points in another dimension, each representing a different wavelength in the spectrum.
Now, consider the pattern recognition problem. How do we recognize what we are seeing? How do we know when something is a building, a tree, a truck, or a person? In military applications, how do we distinguish between a friend and a foe? And how do we get a computer to do the same thing a human would?
The current state of the art involves training the program with known data, and then testing it with unknown data.
Pattern recognition requires training. We cannot recognize things we have not already learned something about.
Now, why is this issue in a web page focused on epistemology?
The most notable form of pattern recognition for people is fault-finding. We recognize faults in others. Why? Because we are so intimate with those faults in ourselves. Unfortunately, we don't like to acknowledge them in ourselves. Something to think about.
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