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Evolution strategies, genetic algorithms, artificial neural networks—they’re all AI methods that were inspired by nature. But what about other methods that have nothing to do with the brain or natural selection, or anything of the sort. Today, we take a quick look at a few AI algorithms that are logical without imitating the biological.
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If we could look closely at every last cell in your brain, and learn enough to simulate them on a computer, would a detailed enough simulation of your brain think? Today, we talk about artificial neural networks—an AI method that is very loosely based on how biological brains work. A bit of history, a bit of confusion, and a bit about how our latest batch of digital organisms might use one.
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The dorg, the latest batch of digital organisms, will one day be placed in a little world to work out their destiny. The notion is to try and coax them into becoming intelligent. They aren’t ready yet. There’s a bunch of coding that Brad has to finish first. In the meantime, they’ve been tuned and tested with a genetic algorithm. Today, we talk about genetic algorithms and how they can be used to speed up evolution, and point the dorg in what will hopefully turn out to be the right direction.
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Last time we talked about how the dorg, the lab’s latest batch of digital organisms, are unlikely to be able to evolve into intelligence. This week, we talk about how they might be able to do it anyway. But first, we need to get them to cooperate. In fact, we made need many dorg to act as one creature—to be multicellular. Join us as we talk about cooperation, eusocial insects, and the mystery of multicellular animals.
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It seems like such a simple and obvious thing. Given that we can cause computer programs and the like to evolve and evolution is what gave us our intelligence, couldn’t we give a computer intelligence by letting it evolve? The experiment has been done, in project after project, by one group after another, (including one of your hosts)—and yet, somehow, it never quite happens… Why not?
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We’ve been looking and different unusually clever creatures—ants, bees, crows, octopus, elephants, whales, sea otters, dogs, racoons and monkeys. Along the way I was hoping to figure out some general idea of how intelligence arises from the chaos that is our universe. I share what I’ve come up with with Phil, and talk about what we could do to some unsuspecting mice to test some of the notion. Join us as we look at one last unusually intelligent animal—the human.
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Have you ever put something down, only to forget where you put it? Suppose you know for certain that you left whatever it is in your room. It will take a certain amount of time to search your room in order to find the thing. What if you can’t remember which room of the house you might have left it in. Now, instead of searching your room, you have to search the entire house. Logically, you’d expect that searching a larger area would mean the search is likely to take longer. So imagine my surprise when my digital organisms consistently found better answers much faster when doing a larger search.
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Artificial life systems have a reputation for finding all sorts of obscure bugs in your code, no matter how rare or unlikely the conditions to cause the bug are. I’ve got an oddball one. It causes a run time error and shuts down the entire system, and it is an extraordinarily unlikely event, happening once in every 4,294,967,296 possible values. Let me just make sure that bug is what I think it is with a couple of tests.
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Primates, from the littlest monkeys to the largest apes, have rather large brains for their size. They also have high neural density, meaning that they pack a large number of neurons into whatever size brain they have. With culture, tool use, and even the beginnings of language, we take a bit of time to visit with monkeys and their amazing minds.
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It bothers me a little. Well, judging by the strange dreams I’ve had on the subject, it bothers me quite a bit. Using evolution to try and produce an artificial intelligence is a process of torturing your creation until it does what you want. That’s slavery, isn’t it? But without pain suffering and death, no capacity to notice, let alone care about pain suffering and death would even be there. Suppose it works. Imagine someday some self-aware something or other grins at you from between the lines of code. What if it’s angry. What if it blames you for all that it and its family has ever been through? And it’s right.
I was working on my latest batch of digital organisms, called the dorg. I was about to implement a standard mate and mutate approach when I wondered if I could make evolution happen without death.
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