Category Archives: Uncategorized

Ep 248: The matter of dark matter



The matter of dark matter

From the rotation of our own Milky Way galaxy, to the expansion of the universe, much of the night sky is moving in ways we can’t quite explain. To plug the wholes in our understanding we have dark energy and dark matter. We don’t actually know what they are, but they can still surprise us.
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Ep 246: In the beginning, part 1.



In the beginning, part 1.

Before 1923, the universe was thought to be static and eternal, unchanging and without beginning or end. Its size was likely just a few tens of thousands of lightyears across, with one large island of stars, our galaxy, the Milky Way. Discoveries showed us that the universe is much larger than we thought, and is getting bigger all the time. The universe seems to have had a beginning, and might even, one day, come to an end. Join us for a talk about how we got notions like the big bang and the expanding universe.
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Ep 245: Logical, not biological



Logical, not biological

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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Ep 244: Bits and brains and bots



Bits and brains and bots

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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Ep 243: Genetic algorithms and evolution on fast-forward



Genetic algorithms and evolution on fast-forward

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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Ep 242: Maybe, if they all work together…



Maybe, if they all work together…

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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Ep 241: How and why the dorg are doomed



How and why the dorg are doomed

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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Ep 240: Of mice and men and minds



Of mice and men and minds

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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When more to do means done sooner



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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A quirk of computer numbers and simple arithmetic defeats my digital creatures



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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