Tag: videogames

Building a new desktop!

I recently decided to buy a new computer.

While looking for laptops, it struck me that they can be so expensive for the hardware you get. I actually don’t need to my computer to be mobile, as most of the time it just sits in my study.

Hence, I opted for buying a desktop. And even better, I decided to build one myself!

I thought building a PC was going to be all complex and technical, but it’s actually really easy! I hope I can inspire you to try out for yourself as well.

Basically, you need only need 6 parts to build a computer:

Casing
Power supply
Motherboard
Processor (CPU)
Hard drive (SSD)
Memory (RAM)
Optional: Graphics card (GPU)
Optional: (extra) Fans

Desktop Computer Components (With images) | Computer history, Old ... — Via Pinterest (look at that old school case & speakers)

So I did some research into what hardware to buy. Specifically, I wanted a PC that could handle some deep learning and some of the newer video games. Hence, I decided on this setup:

Casing: Be Quiet! Base with pre-installed fans
Power supply: Cooler Master V550 Gold
Motherboard: MSI B450-A Pro Max
Processor (CPU): AMD Ryzen 5 3600X
Hard drive (SSD): Crucial P1 1TB
Memory (RAM): Crucial Ballistix 3200MHz 2x8GB (I got grey ones)
Graphics card (GPU): MSI GeForce RTX 2060 Super Armor OC

Note: these are affiliate links.
If you buy a similar setup, it will generate a few bucks used to keep my website live!

My setup totalled to about €1100 or $1200, but it may depend on the vendors you pick. Nonetheless, the CPU and the GPU are definitely the most expensive (and important).

I did not buy any additional fans, as the Be Quiet base already had some pre-installed. However, I think it might be better to install extra’s.

Actually, it’s very easy to upgrade (or downgrade) your system. You can easily switch out modules to decrease or increase the performance (and cost). For instance, you can install another two memory cards on your motherboard, or simply spend more on a GPU.

After everything was delivered to my house, I thought the hard part started: building the desktop and putting everything together. But actually, this only took me about an hour or two, with the help of some great tutorials on Youtube:

I hope this convinces and helps you to build your own system at home!

Bellwoods: A procedurally generated game in only 13 kilobytes

JS13K Games is a competition where developers are challenged to create an entire game using less than 13 kilobytes of memory. Creative developer Matt Deslaudiers participated and created Bellwoods: an art game for mobile and desktop that you can play in your browser.

The concept of the game is simple: fly your kite through endless fields of colour and sound, trying to discover new worlds. To remain under 13kb, all of the graphics and audio in Bellwoods are procedurally generated. The game was mostly programmed in JavaScript with minimal custom HTML/CSS. Matt’s motivation and the actual development you can read about in his original blog. The source code the game, Matt also shared on GitHub.

Mélissa Hernandez, a French UX and Interaction Designer, helped Matt design this beautiful game. Together, they even versed a haiku that not only evokes the mood of the game, but also provides some subtle gameplay instructions:

over the tall grass
following birds, chasing wind
in search of color

Play the game yourself, or have a quick look and feel with the video below.

Neural Networks play Super Mario Bros & Mario Kart

Seth Bling calls himself a video game designer, a hacker and an engineer. You might know him from MarI/O: his neural network that got extremely good to at playing Super Mario Bros. The video below shows the genetic approach Seth used to train this neural network. Seth randomly generated a starting population of neural networks where the inputs – the current frame in the Mario video game – were randomly connected to the outputs – the eight buttons to press (jump, duck, up, down, right, left, etc). By giving the neural nets that made it furthest into the game a larger chance to pass on their genes (their input-output relations) to the next generation with slight mutations, Seth automatically generated neural networks that were more and more proficient in completing the game. In short, by evolution, Seth’s neural network learned the most effective response to the changing video game environment.

After MarI/O, Seth this week posted his newest creation: MariFlow. Here, Seth trained a neural network on 15 hours of training data, consisting of Seth himself playing Super Mario Kart. The neural network thus learned what buttons (output) Seth would most likely push when he encountered a certain Mario Kart parcours piece (input). However, due to random chance, the neural net would often get itself stuck in situations that Seth had not encountered in his training sessions (e.g., reversed, against a wall). The neural net would fail miserably in such situations because it had not learned how to behave. Accordingly, Seth had to generate new training data for these situations and he did so using Human-Computer Interactions in Machine Learning: Seth and the neural net would play alternatively for a while, thus generating training data for situations that Seth would not have encountered on its own. After the neural net was trained with these additional data, it became quite proficient in playing Mario Kart (like Seth) often even winning matches! If you want to know more, you can read the manual here or watch Seth’s video below. If you want to replicate or just play with the data, Seth made everything available here.