Much of our daily life is quietly being reshaped by machine learning. We believe the best way to understand how an algorithm works is to write it. The Machine Learning Literacy Workshop is a hands-on series focused on using machine learning tools for artistic expression. The workshop is open to all skill levels - from beginners to more advanced developers. A few things we are will explore in this workshop: collecting and building databases from scratch, reading and writing electronic text, image recognition and processing, methods for using deep neural networks for the creation of generative art, and deconstructing scientific terminology. The goal of this workshop is to get students to prototype and practice building small projects in a week, explore the social and ethical conditions of machine learning, and create a community of artists and students who would like to work on more machine learning projects to support each other and collaborate in the future.
While the workshop is for all skill levels / designed to be beginner friendly, some familiarity with code is encouraged. We can offer some suggestions for tools for brushing up prior the workshop.
Molmol Kuo, Zach Lieberman, Gene Kogan
Hosted by / Location
the School for Poetic Computation
155 Bank st, New York City
February 12-18, 2018
Monday to Friday: 5pm-9pm
Sunday: 12pm-3pm Panel
1200$ General rate
750$ teacher and student rate
limited scholarships available
email zach at sfpc io
Nothing is more essentially human than linguistic communication. But when programmers, data scientists, and computational linguists work with language, the abstractions they work with sometimes don't line up with your intuitive understanding of spelling and grammar. In this workshop, we'll investigate the state of the art of natural language processing, including: a whirlwind tour of spaCy for parsing English into syntactic constituents; a discussion of techniques for classifying and summarizing documents; and an explanation and demonstration of "word vectors" (like Google's word2vec), an innovative language technology that allows computers to process written language less as discrete units and more like a continuous signal. Workshop participants will develop a number of small projects in text analysis and poetics using a public domain text of their choice. In becoming familiar with contemporary techniques for computational language analysis, critics and researchers will be able to reason better about language-based media on the Internet. Artists and writers, meanwhile, might just learn a few new techniques to add to their creative palette.
No previous programming experience is required. But students will need to have Anaconda installed on their laptops before the session begins (instructions will be sent prior).
This session will be on emotional/subjective data and dataset creation, with coding in Python. We’ll talk about what subjective data means, and how to capture abstractions like emotions and other subjective experiences in datasets. We’ll talk about identifying subjectivity in otherwise “objective” datasets, looking at how the datasets were created, who tagged them if they were tagged, who funded the dataset, what the dataset leaves out, etc. We’ll talk about “artisanal data” and creating explicitly subjective datasets for artistic purposes. We’ll talk about how a culture’s current set of values is a type of bias that gets captured in datasets, and how we can avoid bias retention over time. We’ll talk about labels and vocabulary for these issues - how we can create these, and use them with regularity. Finally, we’ll create and explore our own subjective dataset. We’ll talk about how to be cognizant during the collection, analysis, and usage stages. We’ll also examine currently available emotional datasets, and explore them critically.
Basic programming knowledge, particularly in Python, would be helpful, but this will be an introductory session aimed at beginners.
Hannah Davis is a programmer and generative musician from NYC. Her work falls along the lines of music generation, data sonification, natural language processing, and sentiment analysis. Her algorithm TransProse, which translates novels and other large works of text into music, has been written up in TIME, Popular Science, Wired, and others. A human-computer collaboration, where she analyzed the sentiment of articles talking about technology over time, was performed by an orchestra at The Louvre this past fall. Hannah is currently working on creating unique datasets for art and machine learning, and is also working on a project to generatively score films. She is a 2017 AI Grant recipient. see also musicfromtext.com
Generative art with neural networksThis will be a crash-course on a number of methods for using deep neural networks for the creation of generative art. Students will be given a tutorial on how to use several general-purpose tools, including image transformation networks (pix2pix, style transfer), visualization of convolutional neural networks (deepdream), as well as generative models for text and audio generation. Emphasis will be on prototyping and practice; students will be given temporary virtual machines on a cloud-based service with all the tools already installed. Additionally, we will cover "learning how to learn," that is learning about the fundamental skills and landscape of resources for learning how to keep up to pace with this fast-moving field.
Ideally, students can have basic familiarity with the command line / terminal. how to change directories, execute scripts, and so on. If you are a beginner, we will go over this, too.
Gene Kogan is an artist and programmer who is interested in generative systems and the application of emerging technology into artistic and expressive contexts. He writes code for live music, performance, and visual art. He contributes to open-source software projects and gives workshops and demonstrations on topics related to code and art. He is a contributor to openFrameworks, Processing, and p5.js and a former resident at Eyebeam.
Deep Learn Web
Cristóbal Valenzuela Cristóbal Valenzuela is a technologist and software developer interested in building digital tools and interactive experiences. His work has been sponsored by Google and the Processing Foundation. He is currently a M.P.S Candidates at the Interactive Telecommunications Program (ITP).
Yining Shi is a creative technologist and a software engineer. Her research interests lie in developing novel ways of learning and teaching computational topics through various media like machine learning, creative coding, physical computing, and data visualization. Yining is also a contributor to various open source projects from the Processing Foundation and New York University’s Interactive Telecommunications Program.
Sunday Brunch Panel
On Sunday, February 18th, SFPC is hosting an afternoon of panels and discussions around machine learning, ethics, and systems. Moderated by Caroline Sinders and featuring multideciplinary panelists ; this panel and round table discussion how machine learning affects journalism, art, and the greater public at large. Thinking about how algorithms are utilized for advertising, and how wrong and invasive that can go, how do we critically create while maintaining self-preservation and skepticism inside of these large, moving systems?
Caroline Sinders is a machine learning design researcher and artist. For the past few years, she has been focusing on the intersections of natural language processing, artificial intelligence, abuse, online harassment and politics in digital, conversational spaces. Caroline is a designer and researcher at the Wikimedia Foundation, and a Creative Dissent fellow with YBCA. She has held fellowships with Eyebeam, the Studio for Creative Inquiry and the International Center of Photography. Her work has been featured at MoMA PS1, the Houston Center for Contemporary Art, Slate, Quartz, the Channels Biennale, as well as others. Caroline holds a masters from New York University's Interactive Telecommunications Program.