Nov 302015
 

While cruising through talks on youtube I found one titled “I am a legend: Hacking Hearthstone with machine learning“, an interesting application of machine learning in video games:

 

 

Hearthstone is a card game in which each player has a deck of characters, the goal of which is to use those cards to do damage against your opponents. I digress, the essence his talk had two major themes: finding imbalances in cards predicting an opponents moves. The former involved deriving an over determined system and simply applying least squares, the ladder involving machine learning. I think a simple explanation of what his algorithm does is extract sequences of cards played from a rich data set:

Experimenting with various dataset sizes, I started to get consistent results when using over 10,000 game replays. Yet using more than 50,000 games does not improve the results, so I settled for a dataset of 50,000 replays that were played between May and July 2014.

Part of writing a good talk is being able to balance technical and non technical aspects of a subject while maintaining the attention of audiences whose background is varied. I think Elie did a decent job of this, though of course I would be bias and hope for a bit more depth in the machine learning.

I was a bit upset that the speakers said they were going to release their software and then didn’t, but their reasons were sensible after contacting the creators of Hearthstone. However there are parts of their software that I think they should release, such as the front end web application and back end webserver, this would allow people to write their own statistical/machine learning plugin. Providing the machine learning portion of the software is what gives users an unfair advantage, which is what the creators of Hearthstone were concerned about.

Another extremely interesting application of machine learning to video games is MarI/O , an application of creating a neural network which can pass levels in Mario:

 

 

The author of MarI/O, youtube personality SethBling (I’m not sure what his real name is), used an application of neural networks. Essentially the neural network is a mapping of the buttons you can press to move your character to blocks on the screen and then associating a distance travelled based on that mapping. If Mario makes it further in the level then a previous mapping, then his fitness goes up and he is evolving. As an aside, neural networks are the same technology that companies like Nvidia use to identify vehicles. Jonathen Cohen, former director of the deep learning department at Nvidia, describes the methodology in a short demo.

These are only some relatively recent talks on machine learning in video games, there are certainly other applications and more we’ll see in the future.

Nov 252015
 

If you are passionate about your work, your day to day duties in your job can be incredibly pleasant. I personally love being able to code daily, whether it be writing new features, bug fixing, or refactoring. However, all of those tasks become stressful when under pressure. If you’re not in a management role, then you likely have very little control on how projects are managed and how estimates are given. If your team doesn’t have a consistent metric for measuring complexity of feature requests or bug fixes, estimates can be hard if not impossible to give. There is only so many factors we can control, but part of being a professional is identifying those factors. Here is a couple that have become important to me when coding under pressure.

1. Knowing your limits

It’s easy to make mistakes when sleep deprived and mentally exhausted. Working long hours day after day can ultimately be counter productive. Make sure you find a balance or you will burn out. I’ve always found it difficult to “turn off” when a problem hasn’t been solved. Though many times it’s when I stop thinking about a problem that it magically becomes clear what needs to be done.

2. Time management

When faced with a list of things that need to be done, proper allocation of your time is important. For example, performance optimization. In general, I’m speaking of code that sits outside of the realm of high performance computing. Would you rather have something that works slowly and gives you the right answer, or works faster but gives you the wrong answer? Don’t get bogged down trying to optimise if you don’t have time. Note that optimise later doesn’t mean write clean code later, you should always strive to write clean code even if it’s not an efficient implementation.

3. Personal velocity

Part of writing software is estimating how long it will take you. Even if your team doesn’t use velocities, you can still gather data from tracking system and version control software to calculate your own personal velocity. Brian Tarbox and Heather Mardis wrote a thoughtful post about this, “Retrospective Velocity, When Will This Project Be Done?“. If your lead comes to you with a project and a deadline, you should have a quantitative way of estimating how long it will take you and inform them whether or not it is possible and find a compromise in the latter scenario.

Start small, pick something you have control of in your development environment and try to improve it. Whether it be finding a better way to juggle work and life or finding a balance between design and implementation.

Nov 182015
 

I’ve always been intrigued by computer security but I never had a lot of time to integrate the computer science and mathematics into it like the way I have with digital signal processing. In particular, I think I’d like to do a bit of research into wireless communication security. There’s no better way to learn than by doing, so I decided to experiment with Kali Linux and build my own penetration testing tablet. I was watching a Defcon video recently and someone mentioned something called a “Pwn pad”, a penetration testing tablet by PwnieExpress which comes bundled with Kali Linux Nethunter:

The Kali Linux NetHunter project is the first Open Source Android penetration testing platform for Nexus devices, created as a joint effort between the Kali community member “BinkyBear” and Offensive Security. NetHunter supports Wireless 802.11 frame injection, one-click MANA Evil Access Point setups, HID keyboard (Teensy like attacks), as well asBadUSB MITM attacks – and is built upon the sturdy shoulders of the Kali Linux distribution and toolsets.

Why buy one when you can build one yourself? After scowering the internet for instructions (of which most were outdated or just didn’t work), I managed to create a concise set which worked for me. Of course, use the following instructions at your own discretion.

Software:
Team Win Recovery Project 2.8.7.0: twrp-2.8.7.0-manta.img
Kali Linux Nethunter 2.0 for Nexus 10 (Mantaray Kitkat) image http://images.kali.org/kali_linux_nethunter_2.0_mantaray_kitkat.zip

Hardware I use:
TL-WN722N High gain 150mb/s USB wifi adapter
SENA UD100 Industrial Bluetooth USB Adapter


usb-devices

Just a few basic steps to root and flash your Google Nexus 10 to use the Kali Linux Nethunter image.

  1. Transfer the Kali Linux image to the tablet
  2. Reboot your Nexus 10 into the bootloader screen by holding down both the volume buttons and the power button at the same time (3 buttons), hold until the bootscreen shows up. plug it into your computer
  3. From your terminal:
    > sudo apt-get install android-tool-fastboot
    > sudo fastboot oem unlock
  4. Select yes on the device by toggling the volume key and pressing power
    > sudo fastboot flash recovery /path/to/twrp-2.8.7.0-manta.img
  5. Toggle the volume button to Recovery Mode and press power to select it. The TWRP screen will now load.
  6. At this point, I would make a backup through TWRP in case things don’t go as planned.
  7. Do a factory wipe through TWRP. Reboot, it will ask you if you want to install SuperSU, do so.
  8. Finish installing SU after setting up your tablet by opening the SuperSU application. I used TWRP to download and install the application, not Google play. Reboot into recovery mode, select install, and select the Kali Linx image.

These instructions are from memory. I messed around and tried a bunch of things on the internet that didn’t work since they were outdated, this should be the least amount of work to be done to get it running. Voila, here’s a picture of mine:

 

finished-product

Post install list:

  • apt-get update
  • apt-get upgrade
  • apt-get dist-upgrade

It comes with very recent versions of Python and g++ which was nice. Good luck and happy testing!