Charles Simon, Author of Brain Simulator II – Interview Series

Charles Simon is the Author of Brain Simulator II, a companion book to the Brain Simulator II, a free, open source software project aimed at creating an end-to-end Artificial General Intelligence (AGI) system

The original Brain Simulator software was released in 1988, an enormity of time in the software world. How much of a leap forward is Brain Simulator II compared to its predecessor?

Today’s system is over a million times faster. The original was written in FORTRAN, ran on an IBM AT clone, supported a fixed array of 1,200 neurons, and computed about two cycles per second. Today’s program can run on a network and process 2.5 billion synapses per second on a powerful desktop CPU.

This book is about the Brain Simulator II, an open-source software project aimed at creating end-to-end artificial intelligence, what type of coding experience is needed to run this software?

No experience needed. If you aren’t a programmer, you can spend time with the Brain Simulator and come away with an understanding of the capabilities and limitations of neurons, a bit about knowledge representation, and even build your own limited networks. If you are a programmer, you’ll follow the more in-depth technical explanations and build your own modules to extend the system to more advanced AGI strategies.

Why is returning to the biologically inspired roots of AI important to achieving AGI?

In the 1980s the thinking was that if we could just build a big enough neural network, it would spontaneously become intelligent. Over the intervening forty years, this scenario has become increasingly implausible. So, if classic AI approaches haven’t panned out for AGI, let’s look at some different approaches, and the only working AGI model we have is the human brain.

At the same time, there’s no reason for slavish adherence to biological plausibility. For example, we know that our brains can estimate distances to objects based on slight differences in the images received by our two eyes, the basis for 3D movies. We don’t know how this works in the brain so instead, I’ve programmed this functionality in a module which estimates distances using a few lines of trigonometry. We can be pretty sure your brain doesn’t work this way, but the trig approach is likely faster and more accurate.

You state in the book that an AGI requires robotics, why is this so important?

Consider trying to explain color to a blind person or music to a deaf person. If a prospective AGI is just a program on a computer, how can it get a basic understanding of things any three-year-old knows? The child has a point of view and is surrounded by reality. The child knows that objects exist in that reality and that many of them can be manipulated. By playing with blocks a child can learn about shape, size, solidity, gravity, visual occlusion, distance, and on and on. With autonomous motion, vision, and manipulators, an AGI can learn about reality on a more fundamental level than any program which relies only on mountains of text and image data.

After a robotic AGI has acquired a fundamental grasp of objects in reality, that knowledge can be cloned into nonrobotic thinking machines and the understanding will persist. Just as someone who loses their senses of sight or hearing can understand things in a different way than a person who has never had these senses.

One important aspect of the Brain Simulator II is that it uses no backpropagation, what is the rationale for not adopting this methodology?

Your brain operates without backpropagation so AGI must be possible without it. In fact, backpropagation is fundamentally incompatible with a biological model because it relies on being able to sense and modify synapse weights with considerable precision. After some time with the Brain Simulator, you’ll conclude setting synapse weights with any degree of precision is very difficult and accurately sensing what those synapse weights are is impossible. The fundamental problem is that…