Consciousness is like the ability to get a date. We all know, on some level, what it involves. And we’ve all definitely seen examples of it. We could probably all define what it is, and we know it when we see it. However, trying to define what it means to be conscious: to think for one’s self, to make decisions, to formulate opinions, even to feel happiness or sadness is even more complex than getting a date. No, I don’t get out that much, Why do you ask?…
Because of this, computational intelligence has always been a matter of storytelling. This, by itself, doesn't make it terribly exceptional. There are many things, including several things I’ve learned about in English Class, that have been relegated to science-fiction but have a root in some possible science fact. For thousands of years, people have been wondering how to create a machine that retains in some way the thinking capacities of a human being. It is an interesting question for literature to attempt to answer, because, in describing the methods by which intelligence can be synthesized artificially, we must among other things create a set of definitions that describe all of ethics and morality. This has several implications for me, The most unfortunate being that I have to research the Greeks.
I would like to say that the Greeks research of artificial intelligence discussed undecidability, Turing Complete problems, and had its roots firmly planted in computational theory. Of course, it didn't. In fact, unfortunately for me (and maybe fortunately for everyone else), it isn’t necessary to speak very much about computational or programmatic paradigms in order to discuss the basics of artificial intelligence. Really, all that we require are a few useful examples.
I would like to say that the Greeks research of artificial intelligence discussed undecidability, Turing Complete problems, and had its roots firmly planted in computational theory. Of course, it didn't. In fact, unfortunately for me (and maybe fortunately for everyone else), it isn’t necessary to speak very much about computational or programmatic paradigms in order to discuss the basics of artificial intelligence. Really, all that we require are a few useful examples.
There are several examples that are commonly used, and indeed, I have a few that I like to refer to myself. First, there is the everything machine. This is a machine that has in its memory all information in the universe. This information is accessible in the dictionary, so that all you need to do in retrieving the information is ask for it. And with all the information in the universe it's trivial to predict any situation or make any conclusion. Of course, we can't build and everything machine, but we have to remember that we're talking about fiction and anything that provides useful context in the real world or about which we can make real world conclusions, is useful and worth discussing.
The next step is the Little Baby Machine as I call it, or the learning machine as everyone else calls does. The learning machine has one job: it takes information for the world and puts it in a place where can be used to make decisions in the future. This means that it effectively starts from zero, and has no limit on the amount it can learn.
This distinctly separates it from the third type of artificial intelligence that is, as I call it, the scripted AI. In many ways, scripted AI is the polar opposite of the Everything Machine. The Everything Machine knows about the universe everything that it might need to make any decision or predict any outcome. On the other hand, the scripted AI only knows what it has been told by the person who made it. This makes the scripted AI most similar to the Learning Machine, because the Learning Machine only knows what it has had access to through experience. However, we might also say that, even though it is impossible, the everything machine is most similar to the scripted AI, since everything in the universe has been scripted within it.
We cannot write an infinite number of lines of programming code, and if it if we could that infinity would simply be countable, meaning that we cannot mathematically describe the nature of the entire universe even if we were able to write an infinite number of lines every second of every hour, to the end of time.
It is possible to find examples of all three types in fiction over the last 3000 years. My question is whether, or how much, we can learn about real artificial intelligence, (that is, something that we could really make, and play with), from fictional, or even impossible depictions in literature. Where should we start?
This distinctly separates it from the third type of artificial intelligence that is, as I call it, the scripted AI. In many ways, scripted AI is the polar opposite of the Everything Machine. The Everything Machine knows about the universe everything that it might need to make any decision or predict any outcome. On the other hand, the scripted AI only knows what it has been told by the person who made it. This makes the scripted AI most similar to the Learning Machine, because the Learning Machine only knows what it has had access to through experience. However, we might also say that, even though it is impossible, the everything machine is most similar to the scripted AI, since everything in the universe has been scripted within it.
We cannot write an infinite number of lines of programming code, and if it if we could that infinity would simply be countable, meaning that we cannot mathematically describe the nature of the entire universe even if we were able to write an infinite number of lines every second of every hour, to the end of time.
It is possible to find examples of all three types in fiction over the last 3000 years. My question is whether, or how much, we can learn about real artificial intelligence, (that is, something that we could really make, and play with), from fictional, or even impossible depictions in literature. Where should we start?
How about Star Wars?
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