Skip to content
Menu
PyCon Hong Kong
  • Schedule
  • Sponsors
  • Ticket
  • Staff
  • Code of Conduct
  • About
    • PyCon HK
    • 2020 Fall
      • Photos
    • 2020 Spring
      • Photos
    • 2018
      • Photos
    • 2017
      • Photos
      • Videos
    • 2016
      • Photos
      • Videos
    • 2015
      • Photos
PyCon Hong Kong

How to Evolve Life using Python

Posted on October 28, 2018November 7, 2020

In this talk I will demonstrate how to create Artificial Life in a computer using Python and its wonderful libraries.

The Python program, called “Lenia”, is developed to discover/evolve new life species, using its interactive UI to facilitate Evolutionary Algorithms (EA). A video recorded from the program won the Virtual Creatures Contest in GECCO conference 2018, Kyoto.

It utilizes the following libraries:
– NumPy for matrix calculation
– PyOpenCL/PyCUDA and Reikna for GPU acceleration – SciPy and PIL/Pillow for image processing
– Tkinter (and potentially Matplotlib) for interactive UI – Subprocess and FFmpeg for real-time video

Lenia is now open source at https://github.com/Chakazul/Lenia

Buzzwords: Artificial intelligence, interactive UI
Level: Intermediate: Target audiences with intermediate experience in python programming
Requirements to Audiences: Nil
Language: English

Speaker: Bert Chan (Hong Kong)

Speaker Bio: Big data consultant at ASL, award-winning AI researcher & font designer

GitHub: http://github.com/Chakazul
LinkedIn: https://www.linkedin.com/in/bertchan/
Twitter: https://twitter.com/BertChakovsky

Sponsors

Recent Posts

  • PyCon HK 2020 Fall Photos
  • MySQL speaks at PyCon HK
  • Microsoft speak at PyCon HK 2020 Fall
  • Clover Health speaks at PyCon HK 2020 Fall
  • Sponsors – PyCon HK 2020 Fall

Categories

  • 2020 Fall
  • 2020 Spring
  • 2018
  • Conference Highlights

Archives

Meta

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org
©2021 PyCon Hong Kong | Powered by WordPress and Superb Themes!
← Collaboration hack with slackbot ← Distributed System in Deep Learning