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The standard model of cosmology has been worked out, slowly and painfully and oh so very carefully. Only trouble is, some recent observations don’t quite match what the theory predicts. Join us for a timeline of the early universe, and some of what doesn’t quite match what we were expecting.
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From the surface of Earth, the sun and moon look to be about the same size. Other planets are tiny specks of light, and everything seemingly spins around us. But that’s not what our solar system is really like. So, how did we find out what was where, and which way things were moving?
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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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After covering history and background in the last episode, we can relax and play with the strange ideas and concepts surrounding the theory of the big bang. The event horizon around our universe, the way that time flows differently depending on where you are, how parts of the early universe may have run away and become impossible to observe, and even a theory we propose with no math and fewer reasons.
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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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Podcast: Play in new window | Download (Duration: 19:47 — 13.2MB) | Embed
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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Podcast: Play in new window | Download (Duration: 19:15 — 12.8MB) | Embed
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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