Chicken Breeder Reactor

Posted on 2024-03-31 by M. Eric Carr

Minecraft chickens (or maybe they’re ducks?) are strange birds. They’re functionally unisex, for one: all adults lay eggs which can hatch into chicks. Oddly, there doesn’t even need to be another chicken present for this to happen, so presumably they’re all clones.

They drop chicken eggs, which can be used for a few things like making cakes. If your gameplay style allows killing passive mobs (I don’t), they can also be a source of chicken meat and feathers, both of which are also useful.

Oh, yeah. And chickens can more or less bring about the end of a Minecraft world.

I saw the idea online somewhere, years ago, that you can make an automatic chicken breeder machine in vanilla Minecraft. It’s even possible to make a version where no chickens are harmed. The idea is, you corral one or more chickens (any chicken will do; they’re all identical) in a small area with a floor made of Hoppers (a single hopper will do.) These hoppers should all feed into a Dispenser powered by a Redstone oscillator and pointed at a wall not far away. When the chicken lays an egg, it is fed into the Dispenser, which shoots it at the wall, breaking it. Some percentage of these become baby chickens.

A Chicken Breeder Reactor.
This is a Really Bad Idea (but it isn’t in my main world, so why not?)

The neat part is, baby chickens pathfind to any adults nearby. Since you have one or more adults in the corral, the baby will try to find its way there. Give it a one-way stairs (with a drop of two or more blocks at the end), and it will end up alongside its parent.

Now, a few minutes later (they grow up so fast!), you’ll have two adults laying eggs. Soon, you’ll have a lot of chickens. And the more you have, the more eggs per hour they lay. The more eggs per hour that they lay, the more egg-laying chickens you have. The process feeds on itself exponentially, and can eventually render a Minecraft world unplayable. And you don’t even kill any chickens (or anything else) to do it.

It should be fun to see it speed up and go wild.

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Generative Adversarial Networks (GANs)

Posted on 2024-02-28 by M. Eric Carr

It’s an interesting idea, and kind of amazing that it actually works (and works well). Generative Adversarial Networks (GANs) are, as the name implies, a generative form of Machine Learning that attempts to generate content such as images, based on nothing more than lots of labeled examples of that data.

With GANs, two models evolve in competition with each other: the discriminator network is trained on real images (from a source dataset) and fake images (from a generator network). It is scored on how well it can distinguish real from fake. The generator network, meanwhile, starts out knowing nothing at all about what it should be producing. It learns when its output is fed into the discriminator, which decides how likely it is to be real or fake (or how likely it is or isn’t to be each of a family of possible objects it has been trained on, for instance.)

With a PyTorch model set to record the generator’s output for each generation (including the static-like output of the initial, completely untrained generator model), the model can be seen to learn the images, epoch by epoch. Each frame (epoch) after the first image represents the output of the updated generator network when prompted to produce the digits 0 through 9.

To show the learning process clearly, the learning rate has been decreased by a factor of 20 from the original (quite effective) setting of 0.002.

When training, each epoch (each frame of the movie) takes about 15 seconds to train, on a single GeForce RTX2060 GPU (host PC is a Core i9/9900 with 128GB running Windows 10.) If you view the individual movie frames at one image per second, you’re essentially watching them at the same rate that the network could train, if the learning rate were not artificially slowed.

What does this mean, in practice? Machines can now learn from data rather than being told specifically what is what — sometimes, even if that data isn’t labeled. They can learn categories on their own, too.

And oh, by the way, GPT4 wrote 95% of this code, from just a few prompts. It took a little working with it to get the images right, but my role was essentially just that of test engineer, copying code that GPT4 provided, running it, and reporting back with the results. That’s easy to automate! No doubt, we will soon see coding AIs that do just that. (Some of them can already execute Python code.)

So my computer has taught itself (marginally) better handwriting than mine.
That was bound to happen at some time, but 2024 looks like it will be a fun year.

I love living in the future.

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“It’s 2024!”

Posted on 2024-01-24 by M. Eric Carr

Back when I was in first or second grade (so, sometime in the 1979-1981 timeframe), the sixth graders put on a musical skit about life in The Future. I don’t remember much about it, except everybody was dressed in shiny “futuristic” clothes, mirrored sunglasses, and had flying cars. They sung about what life was supposedly like — I don’t remember the details, except they were very excited to live in The Future, and kept enthusiastically singing, “It’s Twenty-Twenty-Four!!”

Well, here we are. It’s actually 2024. It’s officially The Future, at least as 1980 or so saw it. What would my 1980 counterpart think about modern life, if he got to spend a day in the real 2024?

Well, first, I’d have to coax him away from staring at the 4k monitor I picked up last Fall. “TV” screens are much larger and brighter — but far lighter — than anything 1980 had imagined. Even the expensive projection TVs from back then don’t come close to a $300 OLED monitor.

And then there’s the content. YouTube is better TV than 99% of TV, easily. I’m going to have a better idea of what I want to watch than some TV network executive. Whatever you want to learn — from playing the ukulele to deep learning — has tutorials ready to be watched, 24/7.

To distract my younger self before he finds out about Reddit, I show him a 3D printer and a laser cutter/engraver. Some similar technology did exist back then, but only in industry — the catalyst for the 3D printer hobby explosion was the expiration of some key patents around 2010. “So you have toys that can make other toys,” he observes, as a Benchy starts to print. It’s a great time for hobby electronics in general, I point out, as I show him all of the local aircraft that I can track with my home ADS-B setup.

So do we have flying cars? Well, not really. Honestly, even 1980 would understand that it’s dangerous enough to trust people with driving in 2D, and without the careful training that pilots have to pass in order to be licensed, flying cars would not end well. We do know how to make flying cars (some of them have been around for decades), but the old wisdom that cars make poor airplanes and airplanes make poor cars is still generally true.

We do have some amazing tech in “normal” cars, though. I pull out my Android smartphone (a 2022 model, but still so far beyond any tech 1980 had that it would look like magic) and reserving an Uber. A few minutes later, a sleek-looking white car pulls up, making very little noise. 1980-me comments that the engine is very quiet; I tell him that’s because it doesn’t have one. The driver obliges us by giving us a quick demonstration of its acceleration — comparable to the huge, powerful muscle cars of the 1960s, only almost silent. And we’re probably close to having self-driving cars (full autopilot) commonly available.

“It knows where it is,” 1980-me comments, looking at the moving map display. I explain about GPS and satellite navigation, and how “being lost” is generally very easy to fix, now. He asks if privacy is a concern, and is happy to hear that GPS is receive-only — the satellites don’t know and don’t care who’s using the signals. I decide not to mention all the many ways in which we are tracked, if maybe not geographically.

We head back home. “So, how fast are modern computers compared to what we have in 1980?” I explain about multi-core CPUs, cache memory, gigahertz clock speeds, pipelining, and such. He mentions that he wrote a program in BASIC to calculate prime numbers, and remembered that it took four minutes to find the prime numbers up to 600.

I code the same problem up in FreeBasic on my Windows PC. It takes ten milliseconds. After explaining what a millisecond is (and how it’s actually considered fairly long, in computing terms), we calculate that my modern PC is more than 24,000 times faster. To be fair, this is pitting compiled 64-bit code on the Core i9 against interpreted BASIC on the 1982-era Sinclair — but on the other hand, we’re not making use of the other fifteen logical processors, or the GPU. We race a Sinclair emulator (“your computer can just pretend to be another one?”) to compute the primes up through 10,000. The i9 does even better — some 80,000x faster.

I do the calculation in my head, but don’t mention the more than eight million times difference in memory size between the Sinclair and the i9’s 128GB. (It would be 64 million times, without the Sinclair’s wonky, unreliable add-on memory pack!) First graders don’t yet really have a good intuition for just how big a million is, anyway.

“So, what are video games like?” I put the Quest 2 headset on him and boot up Skyrim VR. He enjoys it — at least right up until he gets too close to the first dragon and gets munched. He loves Minecraft, of course — “It’s like having an infinite Lego set and your own world to build in!”

Then I show him Flight Simulator 2020 and the PMDG 737. He hasn’t found the power switch yet (it’s not super obvious), but he hasn’t stopped grinning, either. And he has yet to meet GPT4 and friends. Or to listen to my mp3 collection.

I love living in the future.

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KVL and KCL

Posted on 2023-12-31 by M. Eric Carr

Sometimes, it helps to state the obvious.

Kirchhoff’s Voltage Law (KVL) and Kirchhoff’s Current Law (KCL) are two of the most important ideas in electronics — right up there with Ohm’s Law itself. When they are first encountered, they seem almost too simple to be useful, and you could be forgiven for thinking that Kirchhoff moonlighted as Captain Obvious. But useful these ideas are, when used to write equations that describe the behavior of an electronic circuit.

KVL states that the sum of voltage changes around a closed loop — any closed loop — is zero. With just a little reflection, it’s obvious that this must be true. Otherwise, you could simply go around the loop as many times as you want, producing arbitrarily high voltages. Voltage-wise, if you get back to the same point, you’re also back to the same voltage.

Voltage rises and drops around a closed loop sum to zero.
The voltage across the resistors will add to 10V.

Kirchhoff’s Current Law is similarly straightforward. For DC circuits, the current in to any given node must equal the current out of the node. (That is, we’re not allowed to store, create, or destroy electrons.) Equivalently, the total current flowing in to (or out of) any node must be zero.

Current flowing in to a node (or out of it) sums to zero.
Three currents flow in to the center; their sum flows out.

By expressing these ideas as equations relating voltage, current, and resistance, we can solve systems of equations to find currents and voltages in each part of the circuit.

For example, consider the following circuit:

We can describe any DC current flow in this circuit in terms of two quantities: I1, which we will consider to be the current flowing clockwise in the left loop; and I2, which we will consider to be the current flowing clockwise in the right loop. (We may well get a negative number — or zero — for one or both; this would mean the current is flowing counterclockwise, or not flowing, respectively.)

With this convention, KVL, and Ohm’s Law, we can write the following equations:

(Left loop) 12V – 1000R*I1 -1000R*(I1-I2) = 0

(Right loop) 1000R*(I1-I2)-1000R*I2-8V=0

Solving these equations (by algebraic methods, or these days by an app using linear algebra), we get: I1=5.33mA and I2=-1.33mA. So I2 is actually flowing counterclockwise, which makes sense when you think about it — if V2 were disconnected, the center node would be at 6V. Since we’re connecting an 8V Thévenin source to it, current will flow into it.

Math isn’t a secret code. It’s the language of Nature.

Posted in Drexel, EET201, EET202, Electronics, Fundamentals | Tagged , , , , , , , , | Leave a comment