Constructing the ApeWorm World

In this draft I will try to avoid using any particular programming language to illustrate the mechanisms of action. If necessary, I will use pseudocode. When the analysis is finished and it is time to try to run simulations I will try to remember to insert links to the code samples.

The ApeWorm world (or dual worlds) does not need to be defined explicitly. The upper and lower limits for the ApeWorm coordinates will suffice. As shown in Figure 1, the allowable coordinates run from 0 to 4 on both axes.


Figure 1

ApeWorms themselves have 4 segments, but require five coordinate points to describe fully. Each point has an x1 component and an x2 component. The points will be called A0(x1,x2), A1(x1,x2) , A2(x1,x2) , A3(x1,x2) , and A4(x1,x2).

The data we gods will use to track ApeWorms are not the same data that they keep track of themselves. An ApeWorm will know where its head, or segment one, is on the world grid. In other words, it will know A0(x1,x2) and A1(x1,x2). Each intersegment node (joint) will be able to convey to the control system one of three states: left, center, or right (L, C, R). Handedness will be determined from the higher numbered segment. In other words for the 1 joint, we look towards the 1 segment from the 2 segment. Thus in the curled ApeWorm example in Figure 1 the joints are all in the right or R configuration.

The joints are controlled by sets of two opposing virtual muscles. When there is no signal to a muscle, it is relaxed, and it contracts as signals increase. An algorithm will determine which of the three states the joint is in based on the relative strengths of the control signals to the muscles.

The ApeWorm's ability to "view" another ApeWorm is stereoscopic but otherwise simple. The virtual retinas coincide with the x1 and x2 axes. Each segment of these axes can only "see" a segment in its row or column, and if there are two segments in a column can only see the closest one. [But a second segment in a column might be inferred by the control system.] The sensor can see the segment number, if any, and can see the distance, or what cross-coordinate the segment lies on. Thus in the upper-left example in Figure 1, the sensors on the x1 (horizontal) axis will record nothing. The x2 sensor closest to the origin will record a segment 4 that is at x1 = 1. The x2 sensor between 1 and 2 will record a segment 3 also at x1 = 2, etc.

Next: ApeWorm Brain Overview (to be constructed)

The Cortex, Dogs, and Changes of Coordinates

I finished my second read-though of On Intelligence by Jeff Hawkins several weeks ago. I got a lot out of this reading, and even was able to follow the details of Chapter 6 which eluded me the first time. Despite good intentions I neglected to write down my much-provoked thoughts until now. I have been spending most of that time indexing the new edition of Windows Internals by Mark Russinovich et. al., which is interesting in an entirely different way: one sees the products of human intelligence, but it is obvious that there is no danger of a Windows operating system of the current style becoming intelligent or conscious, ever.

In addition to following Jeff's suggestions about noticing how my own mind works, I have been thinking about these matters while watching my dog, Hugo. Let's say he represents mammals in general. He may not have the big old cortex that Jeff admires so much, but he seems to be constantly using his little one to make predictions. Hugo has to make a lot of decisions, and he often freezes in place while making them. Come when called? Maybe, maybe not. A treat in hand might just mean being captured and taken indoors, or left out of a car ride. To make such decisions, I believe, Hugo has to predict outcomes.

Like most dogs, Hugo likes to chase thrown toys. He has come to associate arm movements with probable outcomes. He knows if you are throwing in a particular direction, and begins his run in that direction without waiting for the toy to be released. He expects the toy to appear in front of him. If it does not, he looks back. Will I go ahead and throw the toy past him, or throw it in another direction.

Do this a few times, and he stops dashing as soon as my arm is moving. He waits to see if and where where I actually throw the toy.

I also believe Hugo has a construct of the world very similar to our human construct. He navigates the real world with an ease that can only come from having an internal map of the world. He understands the three-dimensional nature of the world, and in particular that obstacles like a tree or a house can have space behind them.

All this means is that if we want to build cortex like machine designs, we can do a lot without having to recreate a human brain. A car as smart as Hugo could go anywhere it wants on roads without smashing into other cars. This reminds me of science fiction stories where human brains are disembodied and plugged directly into space ships.

So maybe our first goal should be to create animal-brain equivalents and see what can be done with them.

I just happen to be reviewing the branch of mathematics that deals with changes of coordinates. I've always wanted to understand quantum physics and general relativity better, so I occasionally break open a math book, because at some point you must do the math to know what the smart guys are talking about. It is clear to me that human brains, and probably mammal brains too, are pretty good at changes of coordinates. The fact that we construct a mental map of the world and so easily map visual, audio, and tactile coordinates to it and back again is pretty remarkable.

I am again about to review how invariants are treated in tensor mathematics. Maybe that has nothing to do with the ability of the cortex to navigate the world, but it just might. Our brains certainly are good at creating invariant memories and comparing them to real world experiences.

Using the cortex to analyze the cortex: now that is a wonder.