New Directions

After letting this blog sit idle for several years, it’s time to revive it and send it off in a new direction. Between work and my kids, I haven’t had any time for personal projects for quite some time now. But since turning 63, I have been able to sign up for Social Security. That’s taken some of the financial pressure off my work schedule, so I now have a window of time again in my life to pursue some personal interests.

In the decade and a half since I started working on robotics, the field has matured significantly. Some of the ideas that I was playing with, like distributed processing, which was still unimplemented in any commercially available products 10-15 years ago, are now appearing as off-the-shelf products in the industrial robotics market. So I don’t feel I have anything more to offer in that direction.

As far as homeschool robotics and physics lab support, companies like LEGO, Pasco, VEX and Pitsco/TETRIX have fully taken over and now completely dominate the educational market in the public school, private school and homeschool arenas. So again, I don’t think I have anything as an engineer/designer to offer.

And while it is still something I’m strongly interested in, advancement in agricultural robotics has to wait on machine vision getting a couple of orders magnitude better than it is right now.

Even though I haven’t posted anything new here over the last few years, I’ve still been watching each of those areas in industry listed in my header. And in my wanderings in the fields of robotics and AI, there is still one area that remains open and undeveloped, and that’s where I’m going to focus my energies next and see what I might be able to produce.

This unexplored area falls under the heading of neural networks. This gap in development that I sense can be best expressed by noting that neural networks have, for all practical purposes, become a software/programming field. There is essentially no one working to develop the underlying hardware necessary to implement a practical neural network. The reason I feel I have an advantage is that I’m essentially a hardware designer first and foremost. And when I look at the software side, I see things that could be implemented in hardware much more effectively. But as I search through the literature on Google, I don’t see anyone developing these kinds of ideas at the hardware level.

In all of the hardware examples of neural networks I come across, the individual cells are hardwired together, right from the start. No one seems to have developed an underlying hardware platform that can spawn new connections between individual cells, grow them, and/or prune them when they prove no longer necessary. It would seem that if one could come up with a hardware system that could evolve new neural connections the same way the brain can, this would be the ideal platform for neural networks. I have some ideas on implementing precisely this kind of structure in hardware. So for the next few months, that is the direction I’m going to head in. And I will use this blog as a sort of journal to post my progress (If there is any, that is).

I may not have any luck in this endeavor, but it will be fun nonetheless.