My house has too many fucking railings

My house is essentially done now. Little thanks to me. But I did have to build the railings. Or at least I chose to. Because I'm both a cheap skate and dumb ass. Here are some random pictures of that.

But what are random pictures without random stats?

• 7.8 ft of 1/4" holes drilled
• 116 square inches (0.81 square feet) of steel cuts on the saw
• 65 ft of welding
• 13 1/4" drill bits destroyed
• 6 band saw blades destroyed
• ~70 cubic feet of argon lost
• 2,394 individual acts of machining (not including installation)

Experiment: How much light penetrates a welding helmet?

Summary

Problem: How much light does a welding helmet block? They come in shades like '9' or '12' but the only documentation I can find defines what jobs to use what shade on. It never seems to say what the shades mean.

Conclusion: A basic 'shade 10' welding glass will block all but one part in 20,000 of low-frequency light.

A given shade number seems to allow 1/3 the light of one shade below it. So, for example, a shade 10 will allow 1/3 the light of a shade 9. Also, a shade 8 will allow 3x the light of a shade 9. A 'shade 1' appears to be equivalent to 'no shade'.

This appears to hold true only for light of wavelength green or longer. When viewing blue laser light we anecdotally saw the light appeared much dimmer than viewing green laser light of the same non-shaded intensity.

The experiment

Big thanks to Woody and Erin for helping design and do this experiment.

Original data:

Setup: We measured the shades of a Hobart welding helmet with a variable shade and a regular no-brand set-shade welding helmet. We used a LX1010B light meter inside the helmet, with cloth packed around it to shield light. Without any of the lights on, the sensor read '0' on it's most sensitive setting.

For each measurement, we took one of two flashlights and shone them through the helmet and centered on the light meter sensor. We also shone them directly on the sensor to get a no-shade measurement. For the variable-shade welding helmet we also needed to use an extra flashlight to activate the shade (normally it stays low-shade in order to allow the welder to see before they start welding and then changes it's shade when welding begins; it uses a light sensor on a different part of the face of the helmet to do this.)

We were only able to take a few measurements but they're a highly linear pattern, consistent from one light to the next, and the measurements from the variable-shade helmet closely matched the static-shade-of-10 helmet.

Results

The relationship between lux and shade looks very linear when the lux is plotted on the log scale. At a glance, it's about 1/3 the lux for every additional 1 of shade.

What's most interesting is if we assume this 1/3rd relationship going back down the shade levels, we predict that we'll see about the same as the 'unshaded' lux measurement when we reach shade level 1. This makes sense and reinforces that we've done the measurement at least approximately correctly since I can imagine an engineer/scientist choosing the shade level 0 or level 1 to be the unshaded level.

AIWorld6: The Tree Of Life

The Mechanics

I almost want to get immediately sentimental. But let's for a moment talk about how this tree of life was created and thus how to read it.

To explain how this is build, let's talk about what happens with a single creature. This creature will have a number that represents their species. When the creature replicates, their offspring will have a number that's either +1 or -1 from their parent's number. Using this simple scheme, over many generations different species will have numbers that drift away from each other.

By chance there will also be a lot of cross over of the numbers as species' numbers wander. To fix this I bias the probability of picking -1 or +1 based on where the other creatures are. So for example if there are 1000 creatures with the number 40 and this creature has the number 41, their offspring will likely get a +1 and be 42. This has the effect of spreading out the numbers.

Next I want to use these species numbers to see which creatures are which on the map. So I assign that number to a color. On a 0 to 255, RGB color scale I get to have 1530 different full-brightness colors; so I'll just mod the number of the species with 1530 and plot it on the color scale.

Now I have colors that represent species. How to build the tree? Every X turns I can take a census of what species there are and plot a single line where the intensity is proportional to the percentage of the population with that color. Stacking those slices on each other for tens of millions of turns gives us the full and complete tree of life.

The meaning

Now for the fun part. More accurately, now for the amazing part.

At a high level, it's the first time I've ever seen every single species ever in the tree of life. No stumpy branch or leaf in this tree is left un-turned. We don't have to rely on some spotty fossil record that we piece together by comparing physical structures. We don't even have to parse the DNA. We have a tree of life based on real heritage data. And crazier yet, this is a tree of life playing out just like all our evidence said ours did. Right in front of our eyes in a repeatable way.

Look at the long stretch of yellow and orange. Notice how the yellow is longer lived (ave age ~5000 turns vs less than 1000 turns for orange) and branching less often because replication rates are slower. If we look at the real-time video of the world we can actually see yellow being killed off by the more aggressive pink who are themselves overtaken by the more stable red species.

Look at the times when a branch fans out and then suddenly tightens. That's a small set of individuals or even a single individual which turned out to be more fit than the others and out-competed them. That property alone is what keeps the lines of the species from drifting farther from each other.

I built this system with the intention of building true causational (not correlational) studies of life. The first step of that is getting life. This looks a lot like life to me. Maybe not life just like ours. Maybe without temperature-based homeostasis and carbon. But are we so sure that's the right definition of 'life'? :)

AIWorld6: Without the ability to be functionally different, there is no predation

Normally the predator species would evolve quite quickly (first 1000 generations) but this world goes to 50,000 generations and we never see it.

The difference is a change I made. It's clear that predation relied on a feature I just removed: When a creature attacks they steal a certain amount of energy from the creature they attack. Normally the amount of energy is a function of how much energy they already have (I was trying to model the idea of being able to eat more because you're bigger). In this world, I made it a constant (20 energy).

In retrospect, I was foolishly doing something I said I wanted to avoid: allowing the agents to be functionally different in any way. I want them to compete on brain power alone.

But it does make me wonder if somehow forcing them to compete on brain power will actually result in a less complex and less advanced world. Luckily, I have a simulator to test such things. :)

First then, I'll try to generate speciation without allowing them to be functionally different in any way; I'm going to modify the terrain. Some places will be harder to move over. Some will have less food. We'll see if that causes it. (The simulation for this is running now)

AIWorld6 GreenVsPurple

AIWorld6: New UI and abilities

New abilities

I fixed a bug that prevented them from using the signal/communication system. So now you'll be seeing a lot more signaling than they did before. As reminder, the way this works is that they can write three floating point numbers to the location they're standing on. Those numbers persist until another agent re-writes them. Any agent nearby can read the numbers from that location.

Also, I gave all the agents memory. Twenty of their outputs are saved and then fed back in as inputs the next turn.

In both cases I haven't figured out if they're using those features. Even if they are saving data to memory and communication, it could be just noise.

The new UI

The new UI shows a lot of things. For starters, I sized the world down to 200x200 and then made the UI map 600x600 so it's easier to see what each individual agent is doing.

I also gave them shapes that represent their decisions. So a 'C' shape is attacking, 'O' is replicating, '+' is growing, 'x' is turning, '^' is moving. I made the growing/turning/moving shapes small and the attacking/replicating shapes big because I want to highlight those less common activities.

The speciation is also dramatically improved. Before I'd parse their brains and then make species judgement from that. Now I just simply give an agent a number, when they replicate their spawn gets that number + or - 1. It turns out most of the time there's so much selection going on, the current generation is only 10 or 20 generations from having a single common ancestor. Thus, the numbers stay quite tight around a species. I then simply map that number to a color to make it easy to see.

This speciation improvement also made parsing species stats a lot easier. You'll notice the system now differentiates and gets statistics on predatory behaviour far easier.

AIWorld6 - The Joy of Small Data - Follow up

I got some great feedback from people, props go to rolisz for suggesting a stack exchange conversation that lead me to Kernel density estimation.

You can see from the plot of evaluated and the plot of the derivative of the plot that it'll be easy to pick out what the species are. A naive parser that just looks at the local maximums as indicating a species and local minimums as indicating the difference between species did quite well on this data. I would have guessed [14900, 15050, 15300, 15500, 15650, 15900, 16100] and the KDE with this naive parsing went with [14732, 14861, 15054, 15674, 15725, 16087]. With the exception of 15674 and 15725 being too close to each other I think it was a total win. I'll be implementing this algorithm shortly. And perhaps even showing the histogram to let a viewer second guess the automatic stats gathering if they feel the need.