well, fuck.

DeepTingle

I’m temped to just leave this without comment. But there’s a serious point here too:

There’s no denying that many of these systems can provide real benefits to us, such as faster text entry, useful suggestion for new music to listen to, or the correct spelling for Massachusetts. However, they can also constrain us. Many of us have experienced trying to write an uncommon word, a neologism, or a profanity on a mobile device just to have it “corrected” to a more common or acceptable word. Word’s grammar-checker will underline in aggressive red grammatical constructions that are used by Nobel prize-winning authors and are completely readable if you actually read the text instead of just scanning it. These algorithms are all too happy to shave off any text that offers the reader resistance and unpredictability. And the suggestions for new books to buy you get from Amazon are rarely the truly left-field ones—the basic principle of a recommender system is to recommend things that many others also liked.

What we experience is an algorithmic enforcement of norms. These norms are derived from the (usually massive) datasets the algorithms are trained on. In order to ensure that the data sets do not encode biases, “neutral” datasets are used, such as dictionaries and Wikipedia. (Some creativity support tools, such as Sentient Sketchbook (Liapis, Yannakakis, and Togelius 2013), are not explicitly based on training on massive datasets, but the constraints and evaluation functions they encode are chosen so as to agree with “standard” content artifacts.) However, all datasets and models embody biases and norms. In the case of everyday predictive text systems, recommender systems and so on, the model embodies the biases and norms of the majority.

It is not always easy to see biases and norms when they are taken for granted and pervade your reality. Fortunately, for many of the computational assistance tools based on massive datasets there is a way to drastically highlight or foreground the biases in the dataset, namely to train the models on a completely different dataset. In this paper we explore the role of biases inherent in training data in predictive text algorithms through creating a system trained not on “neutral” text but on the works of Chuck Tingle.

In a world where recommender systems try to sell us things we already own and AI projects are trying to revive phrenology and sell it to police departments, it is worth remembering that no dataset is truly neutral.

the remote, secluded and little known rice terraces of yuanyang county in china’s yunnan province were built by the hani people along the contours of ailao mountain range five hundred years ago. during the early spring season, the terraces, once planted, are irrigated with spring water from the forest above, reflecting sunlight to create these stain glass like landscapes

Light Field Video Capture Using a Learning-Based Hybrid Imaging System

Computational Photography research from UC Berkeley and UC San Diego have developed system of capturing Light Field video allowing editing of depth, position and focus:

Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings after capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time. 

holgerlippmann 3688_radial_division #codeArt #Processing #generative #minimal #HolgerLippmann #algorithmic #generativeart #genart #creativecoding #abstract #pixelsorting #glitch #procedural #mathart #landscape #iteration #circulation