Pink Noise - Leonid Korogodski [48]
In a normal waking brain, synchronization must be transitory. The waking (or dreaming) brain is always in a phase change state, like a ball at the top of a hill in an unstable equilibrium, choosing which way to fall—the state of maximum complexity, driven by and driving constantly the butterfly effect.
I suggest that the idea that we do not need to know how the brain works in order to simulate its functionality, currently prevalent in the ai research community, is misguided. We would do better to learn from the brain.
Until we harness deterministic chaos, we will never create a true artificial intelligence. Let’s call this the neuromorphic principle.
BUT HOW DID THE BRAIN EVOLVE? AND WHY? NEURONS are extremely hungry, energetically expensive cells, yet the brain kept growing in size, from one species to another. What is the evolutionary advantage of consciousness?
One of the fathers of modern neuroscience, Rodolfo Llinás (b. 1934) proposed that the brain evolved in actively, purposely moving organisms in order to predict results of movement. Plants don’t move purposely, so they don’t need—and there-fore, don’t have—a brain. Tellingly, sea squirts spend the first brief stage of their lives as actively moving larvae—animals, with tiny brains. But as soon as they find a good place to settle down, they turn into plants, digesting their own brains.
We tend to underestimate the complexity of our movement. If you take into account the number of muscle groups in just one hand, and the number of motor neurons activated every tenth of a second in various sequences, then the number of degrees of freedom in moving just that hand becomes so enormous that a CPU-based computer would need to have a truly astronomical CPU frequency to handle it, and at 100% CPU, besides. Yet our brain performs the task effortlessly, with only a small portion of its neurons, leaving a lot of processing power for other things—like thinking.
The computational power of the brain is staggering. It may not be adding numbers very fast, but as a movement and decision making processor it beats a computer anytime. Robots can be programmed to perform well, with repeatable precision in predictable environments. In contrast, the brain never repeats itself exactly, thanks to its evolution-driven architecture. But, for the same reason, it is capable of reacting reasonably fast in any situation in various environments that the members of the species may find themselves in over many millions of years.
Faps—fixed action patterns—and emotions are certain necessary “optimizations” of the brain’s predictive engine. Consciousness is necessary to survive in an unpredictable world, taking over from the auto-pilot when something unexpected happens. Thus, neither emotions nor consciousness are limited to humans. Many animals must have them simply to be functional. I suspect that our first ai children will be more ruled by emotions than we are, because emotions come first, well before reason.
In order to predict, the brain builds an internal model of the world. In the course of action, the observed results are compared to the prediction, and the model is spontaneously modified, via plasticity, to predict better the next time around.
It is important to understand that this model is internally generated. Sensory input from the outside world modifies but doesn’t fully define the model, which can function based on internal input (like it does in dreams or, say, in planning for the future), even in the absence of any sensory input from the outside. The brain is a virtual reality machine.
How, then, can we understand each other? Why aren’t the internal models of different brains so different as to be mutually incomprehensible? Well, they are incomprehensible across different species. But within a species, the foundation of the model has the same evolutionary history. The model, after all, must adequately reflect the shared outside world for survival.