Anastasis Germanidis (b. 1991) is a transdisciplinary artist exploring identity crisis in an age of rapid technological change. His artwork has been shown internationally across the US and Europe, and his recent project Antipersona was one of Wired UK's Best Apps of 2016. He's happiest when he works in public and hopes to one day have every part of his behavior and personality be generated by computer programs that he's written.
Welcome, Programmable Human is an experimental performance in which all my actions, which include giving art critiques of student works, having conversations about today's news stories with visitors, and visualizing the stock market with my body, are generated by a computer program.
A set of Python modules are sending instructions telling me the exact words to say and actions to take to my phone, and then, through text-to-speech, to my earphones. The instructions generated are a function of a variety of online data sources scraped in real time; for instance, recent tweets containing the keyword "TFW" dictate what feelings I will express to the visitors.
For the duration of the performance, the source code of the program is projected on the wall, with the line currently being evaluated highlighted, so as to enable complete algorithmic transparency of my behavior.
Antipersona simulates the experience of using Twitter as if you're signed in from any user account of your choice, providing a window into someone else's social media point-of-view. When you "become" an account on Antipersona, you can see the same timeline they see and receive the same notifications (for follows, mentions, and retweets) they receive, for 24 hours.
The advent of social media has turned our personal identities into discrete entities that we can mold to our wishes. At the same time, it has made us feel closer to other people's identities, enabling us to live a low-resolution version of their lives alongside them day by day. The boundaries separating our own identities from those of other people are quickly becoming irrelevant.
Perhaps being confined to a single identity is not how we want to exist in the world anymore. If that's the case, we need to come up with novel social and technological arrangements for sharing and adopting identities, turning them into a new kind of commons.
Emotions Folder is an OS X application that uses your webcam to continuously monitor your facial expression. When it recognizes any display of a basic emotion (happiness, sadness, fear, anger, disgust, surprise), it captures a GIF snapshot of your face alongside with what’s on your screen and stores it inside a folder named “Emotions” on your home directory.
Emotions Folder is meant to be companion in self-reflection, allowing you to inspect your emotional past anytime you want and answer questions such as “what kind of stuff tends to make me sad?” or “what day of the week do I feel angry the most?”
If you're in a confessional mood, you now get to quickly share your recent emotions with your friends or on social media by dragging + dropping snapshots from your Emotions Folder. And if you ever want a clean break from the past, you can throw the Emotions Folder in the Trash Can. (Don't worry, the Emotions Folder will be regenerated when you start feeling things again.)
Emotions Folder constructs a digital trail of your emotional past, proving that you're not just someone who consumes and produces content, but someone who feels things while doing so. It’s a first step towards a new kind of software that doesn’t treat you as a one-dimensional, utilitarian user but as a complex human being!
Thingscoop is a command-line utility for analyzing videos semantically - that means searching, filtering, and describing videos based on objects and places that appear in them.
When you first run thingscoop on a video file, it uses a convolutional neural network to create an "index" of what's contained in the every second of the input video by repeatedly performing image classification on its frames. Once an index for a video file has been created, you can search (i.e. get the start and end times of the regions in the video matching the query) and filter (i.e. create a supercut of the matching regions) the input using arbitrary queries.
Thingscoop uses a very basic query language that lets
you to compose queries that test for the presence or absence
of labels with the logical operators ! (not), || (or) and &&
(and). For example, to search a video for the presence of the sky
and the absence of the ocean:
thingscoop search 'sky && !ocean'.
Right now two models are supported by thingscoop:
vgg_imagenet uses the architecture described in "Very Deep
Convolutional Networks for Large-Scale Image Recognition" to
recognize objects from the ImageNet database, and
googlenet_places uses the architecture described in Going
Deeper with Convolutions" to recognize settings and places
from the MIT Places database.
Thingscoop is based on Caffe, an open-source deep learning framework.
Grouping scenes from various films by setting
All instances of "highway" (MIT Places label) in the movie Koyaanisqatsi
All instances of "military uniform" (ImageNet label) in the movie Moonrise Kingdom
All MIT Places labels appearing in the movie Clockwork Orange in alphabetical order
Videodigest is a command-line utility for generating condensed versions of videos. It does so by applying an automatic text summarization algorithm to the subtitles of the input video to find the N most important sentences, then compiling the video regions where those sentences appear using moviepy.
The following summarization algorithms are supported:
luhnfrom "The Automatic Creation of Literature Abstracts".
edmundsonfrom "New Methods in Automatic Extracting".
lex-rankfrom "LexRank: Graph-based Centrality as Salience in Text Summarization".
lsafrom "Using Latent Semantic Analysis in Text Summarization and Summary Evaluation".
3 minute summary of The Empire Strikes Back
1 minute summary of the congressional hearing on Planned Parenthood
1 minute summary of the first lecture of MIT's Quantum Physics I
The web has become overfilled with user tracking scripts. They’re all there to answer the question: who are you? What kind of things do you like? The assumption is that your personality be reduced to a vector in some kind of hyper-dimensional space of possible personalities. And that, once your unique “personality vector” has been determined, your experience of technology can become better through personalization. Netflix can show you the best movies for your taste, Pandora can play music that you will like, OkCupid can discover the optimal romantic partners for you, and so on.
Fickle News is a news reader web app that uses facial expression recognition to present you news that correspond to your inferred real-time emotional state, thereby implementing a very drastic kind of media personalization. It’s an interface-as-provocation inviting its users to consider the desirability of a future where technology has been ubiquitously designed to expose them exclusively to algorithmically curated information that is compatible with their existing life-outlook and worldview.