Notes for the Lifebox, the Seashell, and the Soul - Rudy Rucker

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Notes for The Lifebox, the Seashell, and the Soul, by Rudy Rucker neurons, and positive values as activating them. Given that we’ll often start out with random weight values in our neurons, and that some neurons have quite a few input lines, it can well happen that in some cases the weighted sum can be a fairly large positive or negative number. In order to keep the output value in range between negative one to positive one, we “squash” the sum to a value between zero and one as indicated in the image of the artificial neuron. Figure: An Artificial Neuron A neuron computes the weighted sum of its inputs and uses this sum to calculate its output. Each neuron also has a “threshold weight” w 0 that it adds into the weight sum as well. In order to keep the output signal in the zero-to-one range, the neuron “squashes” the weighted sum before sending the result as an output.. My Anti-Beige Agenda It’s possible that electronic computers are only a passing fad. Brain Surgery In a human being, changing the microcode is analogous to undergoing brain surgery or (in a temporary way) dropping acid; flashing the BIOS is akin to having a peak conversion experience; and changing the operating system is comparable to adopting a behavioroutput neurons, the networks become bigger and harder to train. p. 72
Notes for The Lifebox, the Seashell, and the Soul, by Rudy Rucker modifying regimen such as meditation classes or group therapy. A pathological neurosis can be so deeply ingrained a system bug that one has to dig down quite far to change it. Good News Bad News Depending on how you look at it, this may seem like either good news (we won’t be replaced by smart robots) or bad news (we won’t figure out how to build smart robots). Back Propagation as a Computation In a way, a neural net is something very simple. The net-training process itself has a simple and deterministic description: pick a network architecture, pick a pseudorandomizer and assign starting weights, and do back-propagation training on a sample set. The only complicated part here is the details of what lies in the sample set examples. *** Again thinking back to evolution, you might wonder why we don’t tune a neural net by using a genetic algorithm approach. You could look at a whole bunch of randomly selected weight sets, repeatedly replacing the less successful weight sets by mutations and combinations of the more successful weight sets. You could do this, but in practice the neural net fitness landscapes are smooth enough that the back-propagation hill-climbing method works quite well ⎯ and its faster and simpler. But what about local maxima? Mightn’t a hill-climbing method end up on the top of a foothill rather than on the top of a mountain? In practice this tends not to happen, largely because so many weights are involved in a typical real-world neural net. Each additional weight to tweak adds another dimension to the fitness landscape, and these extra dimensions can act like “ridges” that lead towards the sought-for global maximum, sloping up from a location that’s maximal for some but not all of the weight dimensions. Also, as mentioned in the last chapter, we don’t really need absolute optimality. Reasonably good performance is often enough. Technical Problem with Running BZ on a Network Instead of on a CA A network doesn’t have the neighborhood structure of a CA, like if A connects to B and A connects to C, that doesn’t imply B is “near” C on a network, though it does in space? Well, actually, if A will promise to act as a relay station, then B and C would be near in that sense. To go more towards network, how about giving each cell A its nearest neighbors NA plus one remote neighbor rA. And say we pick the remote neighbors so they aren’t near each other. That is, if A and B are close then rA and rB aren’t close. Maybe there could be a nice canonical way to do this. If I wanted to program this, I could have a CA with an extra real number field dist, and it would take the usual nearest neighbor inputs plus an input from a cell whose index is dist*N where N is the size of the CA array. I could fill the dist fields at startup with a randomizer. Oh, I did this years ago with John Walker, when I wrote the ZipZap and XipXap series of CAs in assembly language. The remote neighbors didn’t change much. p. 73
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Notes for the Lifebox, the Seashell, and the Soul - Rudy Rucker

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