Models and Concepts of Life and Intelligence
This chapter begins with a section that examines the theories regarding the mechanics of life, and thought. They begin by talking about how people have historically how we define things that are alive, or not. And, how people have always considered themselves to be both made of living matter, and continuous with inanimate mater. Then, they trying to establish a working definition for what is required for an entity to be alive, and man’s reluctance to accept things they have created to be alive. Ultimately, what they allude to is adaptation.
Next, they begin to example the nature of what it really means to be random. Much of the foundations of self organized systems rely on stochastic adaptation, so they try to determine if anything is really every “random.” They go through multiple examples of various events that we consider to be random, such and computer random number generators. For these types of events, that we know are deterministic, they label as “quasirandom” events. However, more complex events where we cannot observe all of the variables that result in an outcome are, random. Ultimately, what they decided is that random only means “unexpected outcome,” And that nothing truly happens without cause.
The following section examines what Gregory Bateson coined the “two great stochastic systems,” which are evolution, and mind. The section works through the interconnections between evolution, and the mind. Particularly, trying to explain a method of thought based on evolution. So called, “memes” that act like meta-physical genes and behavior in a similar manner. They do make a distinction between the two, stating the evolution removes the less fit from members of the population, while the mind adapts by changing the states of persisting members.
The Game of Life is then examined, as it illustrates a simple form of emergence. The Game of Life is a “game” that is setup on a grid; each cell in a grid has a certain set of rules that dictated its behavior based on the cells around it. A cell can be either “alive” or “dead.” They then try to deal with the slippery issue of what exactly emergence means. They talk about how complex behavior “emerges” from a series of relatively simple systems. Emergence is generally considered a characteristic of complex, or dynamic systems.
Cellular Automata (CA) provided the foundation for the Game of Life mentioned in the last section. Most cellular automata are one dimensional and binary. The book illustrates a simple example where by a center number is affected by its neighbors depending on its current state. This is a seemingly simple situation that can result in eight different outcomes. They discuss the different types of cellular automata: evolution leads to homogeneous state, a simple stable state or periodic structures, chaotic patterns, or complex localized structures. The fourth structure is the one of the most interest. It has been theorized that it can be manipulated in such a way to perform any kind of computation.
The following section began to examine artificial life as it develops within computer programs. They make the assertion that something need not behave like any “real” life to be living. In fact they may follow a set of characteristics completely like anything we have seen on Earth. They use CA’s as their “breeding stock” in a few examples. They introduce “random” mutations by flipping bits in the rule table. The change in the rules, results in a change in the system. This is likened to the difference between genotype, and phenotype. They then go on to multiple examples such as biomorphs, and Sims’ “seed” creatures.
The final section in this chapter examines intelligences, first in people then in machines. Much of what is considered to be human intelligence is based on the premise established by a psychologist named Boring. His idea is that human intelligence is whatever an intelligence test measures. This is actually an ironic situation, considering the current computers can be setup to easily complete current IQ tests with near, or perfect accuracy. Turing created the test to determine computer intelligence. In order for a computer to be intelligent, it has to fool a human into thinking it is communicating with a human. David Fogel contests that intelligence is something that should be measured equally between humans and computers, he defines it as the “ability of a system to adapt its behavior to meet its goals in a range of environments.”