Machine Learning for Human Creative Practice – Dr. Rebecca Fiebrink (Eyeo 2018)

Dr. Rebecca Fiebrink dives into the intersection of machine learning and creativity, showing how AI can become a partner rather than a replacement for artists. She explores its use in music, visual arts, and performance, where musicians and creators train models with custom datasets to map gestures, sounds, and movement into dynamic artistic outputs. The talk highlights the importance of making AI tools accessible and adaptable so that artists can integrate them seamlessly into their creative processes.

A Brief History of Neural Nets and Deep Learning – Andrey Kurenkov

Andrey Kurenkov takes us on a journey through the evolution of neural networks, starting from their theoretical roots in the 1940s to their transformative impact today. He covers key milestones that shaped AI:

• Perceptrons (1950s-60s): Early artificial neurons that sparked initial interest in machine learning.

• Backpropagation (1980s): A game-changing technique that allowed neural networks to learn efficiently.

• Deep Learning Boom (2000s-Present): The rise of sophisticated architectures like CNNs and RNNs, fueled by massive datasets and computational advancements.

  This article makes it clear how neural networks evolved from abstract concepts to powerful tools shaping fields like computer vision, natural language processing, and generative art.

Points of Reflection

How Can Machine Learning Support and Expand Creative Practices?

AI as a Creative Partner, Not a Replacement

• AI enhances human creativity by acting as an interactive collaborator rather than replacing artists.
• Artists can train AI models with personal data, creating highly customized and unique creative experiences.

Making AI More Accessible for Artists

• Dr. Fiebrink emphasizes the need for AI tools that are user-friendly, even for those without programming expertise.
• Open-source frameworks are breaking barriers, allowing more creators to experiment with AI-driven art.

Blending Data Science and Artistic Expression

• Machine learning bridges the gap between structured data analysis and freeform creativity.
• Real-time AI interpretation of movement, sound, and images is unlocking new ways of artistic expression.

Ethical Considerations: Bias and Fair Representation

• AI models are only as unbiased as the data they learn from, making it crucial to use diverse datasets.