A decentralized organization is more than the sum of its parts
This is the 2nd article of a 4-part series introducing Abraham, an open project to study and build an autonomous artificial artist. The full series is as follows:
• Artist in the Cloud — Towards the summit of AI, art, and autonomy
• The Spirit of Decentralization
• The Collective Imagination — How a machine shows us what it means to be human [eta October]
• A Path Towards Genesis — An initial agenda and timeline for the Abraham project [eta November]
The first time I read about decentralized autonomous organizations (DAOs), I realized that autonomy was the missing piece…
Towards the summit of AI, art, and autonomy
Abraham is an open project to make an autonomous artificial artist, a crowd-sourced AI that generates art.
This article, the first in a 4-part series, gives an overview of the basic idea. Follow-up articles will examine each of the technical components in greater depth. The full series is as follows:
• Artist in the Cloud
• The Spirit of Decentralization — Why decentralized organizations are more than the sum of their parts
• The Collective Imagination — How a machine shows us what it means to be human (eta October)
• A Path Towards Genesis — An agenda and timeline…
An excavation of mathematics reveals the continuity of our knowledge
If deep learning is what used to be called machine learning, machine learning is what used to be called computational statistics. After all, few would have been familiar with the term in the early 1980s when they were already writing programs for logistic regression, principal component analysis, and other techniques that would be taken for granted a generation later. Maybe they’d heard it at some upstart conference but certainly not in common usage. They had been busy building upon the work of their own predecessors who had been solving those same equations by hand, anticipating that in the future…
What I’ve learned from one year of workshops
Over the past year, I’ve been teaching a workshop I’ve usually called “machine learning for artists,” which is also the name of a free, online book I’m building with collaborators, containing supporting materials and educational resources about a subject I’ve been interested in for a long time which has received a groundswell of public interest in the last two years.
I’ve been very lucky to be able to turn this into nearly a full-time job. I didn’t intentionally set out to do that, but each workshop seemed to bring about another invitation for one elsewhere. Over the past 14 months…
This spring I will be teaching a course at NYU’s Interactive Telecommunications Program (ITP) called “Machine Learning for Artists.” Since the subject is fairly uncommon outside of the realm of scientific research, I thought it would be helpful to outline my motivations for offering this class.
There have been a handful of courses introducing machine learning to students in creative fields, including ones by Heather Dewey-Hagborg, Patrick Hebron, and Rebecca Fiebrink. A few classes geared toward people in journalism, STS, and related disciplines exist as well. But in general, the topic is taught mostly to computer scientists and engineers. This…
How artistic experiments with deep learning guard us from hype
In recent months, a raft of art projects concerned with deep learning [1][2] have appeared, helping to popularize a branch of machine learning that has rapidly gained traction within the research community. Characterized by more complex architectures than previous generations of machine learning, large-scale deep learning methods have only recently become practical with modern computing power. As with all labels, no one can guess how long this particular one will stick, and even some of its proponents dislike certain claims made about it. Nevertheless, a number of algorithms associated with it have set new benchmarks in image, sound, and natural…
