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natural machines

NATURE, GENERATIVE ART, AND MACHINE LEARNING

How can nature inform our understanding of machines?

• 29. June - 24. July 2020
• Based in ACUD in Berlin, Germany
• Four weeks, full-time
• Small class of participants


Pricing
Artist / Student (Full Time)*
€1825 until 15. Apr., €2195 regular fee

Freelancer*
€1955 until 15. Apr., €2395 regular fee

Professional*
€2125 until 15. Apr., €2595 regular fee


course
description

In striving towards a holistic approach to technology wherein technology and nature are part of a singular whole, with nature serving as active inspiration, healing presence, and powerful subject, this program offers a way of re-framing the physical and digital world through the lens of nature and ecology. Together we will learn and explore nature and science, and generative and machine learning algorithms as we experiment and apply them to various forms of art-creation.

Machine learning (ML) is a branch of artificial intelligence concerned with the design of data-driven programs that autonomously demonstrate intelligent behavior in a variety of domains. ML systems are all around us. That said, this is a program for a generation of pro-ecology generalists interested in technology, arts and nature.

During the course of the four weeks, participants will be introduced to a series of machine learning and generative art tools in a direct, hands-on manner that focuses on building workflows and toolkits. Participants will be encouraged to engage and interact with the non-human so as to explore a more inclusive approach to nature, machine learning and how we position ourselves as artists.

The goal is to explore a variety of tools and approaches, jumping from microscopy to machine learning, from field-trips to datasets, from the observational to the generative. We aim to learn computational thinking through the observation of nature and learning to see it not only as a subject, or object, but a system of interacting, natural algorithms and organic components. Furthermore we hope to inspire reflection upon how we can engage with nature from the perspective of human creativity.

Each participant will leave this month-long program with a wide array of techniques to continue exploring for their own digital art practice. Final results of the program will be showcased as part of a group exhibition open to the public on the final day of the program.


in this course,
you will be
introduced to

  • An approach to nature through the lens of new technologies
  • Theory, tools and frameworks for producing machine learning and generative artworks
  • •Using variety of instruments (microscopes, sensory input, collected images, etc.) to produce datasets and how to format and process them for use in machine learning
  • Generating images and videos using neural networks, machine learning and creative coding implementations (using the created datasets and more).
  • Fabrication, processing works as printable/plotable, etc.
  • Professional development for artists and creatives
  • Exciting humans working in the field
  • Critical and conceptual development of projects to be exhibited in a final group showcase
  • An amazing network and community of like-minded creative beings and potential future collaborators

course outline

Week 1: Introductions playful experiments and crtiical discourse on nature, science, and technology.

Week 2: Runway ML, p5.js & ml5.js, teachable machine, Google Colab (python), Intro to computer vision.

Week 3: Google Colab (python), remote/local setup (Google cloud), GPT-2 finetuning.

Week 4: Professional development for creatives. Documentation and project development in prepartion for final exhibition showcase open to the public.


who is this
program for?

This is a course made by artists for artists and will focus on building a foundation for further practice in the realm of generative- and digital art, and machine learning. The goal is that each participant leaves this month-long program with a wide array of tools to continue exploring for their own creative practice. No previous experience necessary.