Thanks to New Yorker for co-sponsoring the event!
Talk 1: Gene Kogan on ML for Artists (60 min)
Abstract: This talk covers the emerging intersection between machine learning and artistic practice. Recent advances from deep learning have greatly helped us make sense out of complex multimedia, and embed audio, text, and image data into the same space. Now artists are beginning to exploit these new capabilities for creative and expressive applications. We'll discuss the broad potential of machine learning techniques for creativity, show a number of recent projects in this field, and introduce some online resources for getting into it yourself.
Bio: Gene Kogan is an artist and a programmer who is interested in generative systems, artificial intelligence, and software enabling self-expression and creativity. He writes code (http://www.github.com/genekogan) for live music, performance, and visual art. He is a collaborator within numerous open-source software projects, and leads workshops and demonstrations on topics related to code and art. Gene is a contributor to ml4a (http://ml4a.github.io/), a free book about machine learning for artists, activists, and citizen scientists. He regularly publishes video lectures (http://ml4a.github.io/classes/), writings, and tutorials to facilitate a greater public understanding of the topic. He has previously taught classes at ITP-NYU (http://tisch.nyu.edu/itp), Bennington College (http://www.bennington.edu/Academics/AreasStudy/VisualArts.aspx), and SchoolOfMa (http://www.schoolofma.org/), and has been artist-in-residence at SFPC (http://www.sfpc.io/) and Eyebeam (https://www.eyebeam.org/). [Contact]
Talk 2: Trent McConaghy on Blockchains for AI (30 min)
Abstract: This talk describes the various ways in which emerging blockchain technologies can be helpful for machine learning / artificial intelligence work.
Bio: Trent McConaghy did AI research for nearly 20 years, including using AI to help drive Moore's Law. For the last several years, he's been working on blockchain technology. He's working on a shared public database for the internet: the IPDB (http://www.ipdb.foundation/) network & foundation, and BigchainDB (http://www.bigchaindb.com/) blockchain database software.
Before the main talks:
•Ronert Obst will advertise a few job openings at New Yorker, co-sponsor of today's event.
•Antje Lechner and Thomas Krone will solicit help (on a contract basis) to analyze a medical dataset of 210,000 patients from different hospitals in Germany. All data is anonymized and covered by confidentiality agreements. They have already done some exploratory analysis and have specific questions that they hope can be solved by machine learning methods. A first task is to predict hospital-acquired acute kidney failure (AKI), or, more generally, to predict medical risks from the data.