Complexity_ A Guided Tour - Melanie Mitchell [87]
HOW DOES INFORMATION ACQUIRE MEANING?
How information takes on meaning (some might call it purpose) is one of those slippery topics that has filled many a philosophy tome over the eons. I don’t think I can add much to what the philosophers have said, but I do claim that in order to understand information processing in living systems we will need to answer this question in some form.
In my view, meaning is intimately tied up with survival and natural selection. Events that happen to an organism mean something to that organism if those events affect its well-being or reproductive abilities. In short, the meaning of an event is what tells one how to respond to it. Similarly, events that happen to or within an organism’s immune system have meaning in terms of their effects on the fitness of the organism. (I’m using the term fitness informally here.) These events mean something to the immune system because they tell it how to respond so as to increase the organism’s fitness—similarly with ant colonies, cells, and other information-processing systems in living creatures. This focus on fitness is one way I can make sense of the notion of meaning and apply it to biological information-processing systems.
But in a complex system such as those I’ve described above, in which simple components act without a central controller or leader, who or what actually perceives the meaning of situations so as to take appropriate actions? This is essentially the question of what constitutes consciousness or self-awareness in living systems. To me this is among the most profound mysteries in complex systems and in science in general. Although this mystery has been the subject of many books of science and philosophy, it has not yet been completely explained to anyone’s satisfaction.
Thinking about living systems as doing computation has had an interesting side effect: it has inspired computer scientists to write programs that mimic such systems in order to accomplish real-world tasks. For example, ideas about information processing in the immune system has inspired so-called artificial immune systems: programs that adaptively protect computers from viruses and other intruders. Similarly ant colonies have inspired what are now called “ant colony optimization algorithms,” which use simulated ants, secreting simulated pheromones and switching between simulated jobs, to solve hard problems such as optimal cell-phone communications routing and optimal scheduling of delivery trucks. I don’t know of any artificial intelligence programs inspired both by these two systems and by cellular metabolism, except for one I myself wrote with my Ph.D. advisor, which I describe in the next chapter.
CHAPTER 13
How to Make Analogies (if You Are a Computer)
Easy Things Are Hard
The other day I said to my eight-year-old son, “Jake, please put your socks on.” He responded by putting them on his head. “See, I put my socks on!” He thought this was hilarious. I, on the other hand, realized that his antics illustrated a deep truth about the difference between humans and computers.
The “socks on head” joke was funny (at least to an eight-year-old) because it violates something we all know is true: even though most statements in human language are, in principle, ambiguous, when you say something to another person, they almost always know what you mean. If I say to my husband, “Honey, do you know where my keys are?” and he replies, simply,