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Category: Technical

Auto generating game engines

Auto generating game engines

 

Game studios and enthusiasts may soon have a new tool at their disposal to speed up game development and experiment with different styles of play. Georgia Institute of Technology researchers have developed a new approach using an artificial intelligence to learn a complete game engine.

Their AI system watches less than two minutes of gameplay video and then builds its own model of how the game operates by studying the frames and making predictions of future events, such as what path a character will choose or how enemies might react.

Learn more here:
https://gvu.gatech.edu/ai-uses-less-two-minutes-videogame-footage-recreate-game-engine

 

Shiny Pokemon and iPhone screen recording

Shiny Pokemon and iPhone screen recording

I was fooling around with Pokemon Go and ended up getting a very rare shiny Magikarp. I also wanted to know how hard it was to record the iPhone screen and the sound. There are lots of different ways from both PC and Mac, but I used my Mac Mini and it was very easy:

  1. Connect your iPhone or iPad to your Mac via the lightning cable.
  2. Open QuickTime player.
  3. Click File then select ‘New Movie Recording’
  4. A recording window will appear (with you in it, most likely). …
  5. Select the Mic of your iPhone if you want to record music/sound effects.
  6. Click the Record button.

So, I recorded the evolution from the rare shiny Magikarp to the rare shiny Gyarados. Results were very good:

YOLO!

YOLO!

YOLO is a real-time object detection system. On a Titan X it processes images at 40-90 FPS, and it has a pretty nifty demo reel too. 🙂

Mixed Reality Room

Mixed Reality Room

THIS is compelling. No bulky headsets/goggles/etc.

Using projection mapping and a mix of tracking systems, creative studio THÉORIZ shows off a slick prototype which projects 3D images that dynamically adapt to movements. Everything you see was captured live, with no post-production.

Gradient Descent using Flocking algorithms

Gradient Descent using Flocking algorithms

One of the key mathematical foundations of machine learning is using gradient descent to find maxima and minima in a multi-dimensional data set. Gradient descent is good, but getting the most out of it can sometimes leave you wringing your hands or doing a lot of painful mathematical investigation and analysis. Investigations that can quickly tax even a mathematics major.

Sergui Puscas shows us a different, more intuitive way to find maxima and minima by using swarming and flocking techniques. It’s a pretty fun read.