Category Archives: digital organisms

Ep 200: will robots rule the world?

will robots rule the world?

If we were to create an artificial intelligence that’s more intelligent than we are, would it take over and force us into extinction? Can a machine have a mind equal to or even better than our own? Today, we take a look at such questions, along with some side trips to sharks and whales and monkeys, and rocks and chocolate. I swear the candy thing really was relevant; I just got distracted.
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Damn local minima!

So, here’s the game, implemented in A certain number of cycles is chosen, between a minimum amount, 10 at the moment, and a maximum, 50. Then a coin is flipped that determines whether the light is on or off for this round. One port will, when called, set b to 1 if the light is on, and -1 if it’s off. So far so good. This is enough for the figures to have the simplest eye imaginable. They can detect whether or not the light is on, something that even single cells are capable of. Next, we attempt to get them to react to whether or not the light is on, again, something that some of the simplest life forms can do.
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Their first task was easy

Their first task was easy. Can the digital organisms I wrote, called “figures,” learn to call a good command and avoid a bad one?

My first job was easy. It only took 6 lines of code. Each digital creature, each “figure,” has a certain amount of energy. = 12,168;

Each time a figure has a turn, (when they execute one command,) the energy level drops.


When a figure is out of energy, it dies. It is completely deleted from the system, leaving behind it’s children, if it had any.

If (

It’s about time I tried using my artificial life as an artificial intelligence

It’s about time I tried using my artificial life as an artificial intelligence. It’s been the goal since day one. Before I could do that, I had to come up with the algorithm in the first place, and then make sure that the figures could actually evolve. To evolve, they need to change, and some of those changes should help them survive and reproduce, passing on their successful tricks to their offspring.
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It bloody well works!

Written on Wednesday October3, 2018

Small sample sizes and all those caveats.

I’m tossing out the number of figures being born as a threshold. That will pop out of any of them at any time, and it’s just luck. One of them starts reproducing very quickly, and happens to find a window of relatively few mutation’s, and then skate on through to the finish line.

I retested the first five populations, snagged with a threshold of 100 and 100,000, if memory servs me. Looking at the most mutations given to a population, side by side results, first five and latest five.
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I was going to say that’s going nowhere, but looking at my notes just now.

Written on Tuesday October 2, 2018

It seemed like a good idea. Let evolution solve the mutation problem for me. This is procrastination, as what I really need to do next is update the documentation and archive this version. It’s time to clean up the code and concentrate on making the system run faster. Still, I had a few days, and I’d notice that some populations were much more resistant to mutation than others.
Continue reading I was going to say that’s going nowhere, but looking at my notes just now.

Think I was trying for the wrong thing.

Written on Thursday September 27, 2018

mutation each extinction
figures1 skipcom
5.pop average 73
figures just fat
6.pop average 48
x.pop average 460 just fat longest with mu 131616 without only 38453
61.pop keeps doing too well to tell

I was trying to make s.pop into a six figures steady pop size stable population. I wanted to recreate 6.pop whose magic children have done so well that they climb off the measurement scale. I set things up so that s.pop was read from disc and stored in memory. Then, when s.pop died out, she’d be restored from memory rather than from disc. Mutations would happen unless the population was within a certain size range. Once it was all ready and as tested as I was willing to bother with, I let er rip.
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Solving the mutation problem

This might solve everything. I’ll need to do some testing to make sure I’m right, but there’s a beautiful way around the mutation problem.

It’s not the type of mutation; it’s the type of population. We’ve got two main types: the quick and the slow. The quick ones, like m1.pop, do their best to fill up all available space and to make new figures as quickly as possible. The slow ones generally find a particular population size, and stay there, no matter how much more room is available; and they only make new figures slowly. I’ve said several times, and it turns out to be true—speed isn’t everything.
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