SLIM: An open, transparent, hand-computable sentencing algorithm

Machine learning companies are making big bucks selling opaque, secretive sentencing algorithm tools to America's court systems: the vendors of these systems claim that they are too sophisticated to explain, and use that opacity to dismiss critics who say the algorithms oversentence black and poor people. Read the rest

Chinese State Council Guidelines for Artificial Intelligence

The Chinese government's wish-list for AI researchers is pretty ambitious: "Breakthroughs should be made in basic theories of AI, such as big data intelligence, multimedia aware computing, human-machine hybrid intelligence, swarm intelligence and automated decision-making." Read the rest

Techniques for reliably fooling AI machine-vision classifiers

The Open AI researchers were intrigued by a claim that self-driving cars would be intrinsically hard to fool (tricking them into sudden braking maneuvers, say), because "they capture images from multiple scales, angles, perspectives, and the like." Read the rest

Robot wisdom from a deep learning system trained on ancient proverbs

Janelle Shane trained a recurrent neural network with a data-set of more than 2000 ancient proverbs and asked it to think up its own: "A fox smells it better than a fool’s for a day." Read the rest

Pictures of dinosaurs, by a flower-drawing algorithm

Chris Rodley fed some pictures of dinosaurs to a "style transfer" machine-learning system that had been trained to draw flowers, and this was the gorgeous result. (via Kottke) Read the rest

Algorithmic decision-making: an arms-race between entropy, programmers and referees

Nesta's Juan Mateos-Garcia proposes that "entropic forces" make algorithmic decision-making tools worse over time, requiring that they be continuously maintained and improved (this is also a key idea from Cathy O'Neil's Weapons of Math Destruction: a machine-learning system is only honest if someone is continuously matching its predictions to reality and refining its model based on the mistakes it makes). Read the rest

Put on Your Corbyn Face: a game you win by displaying empathy

Games for the Many sends us Put on Your Corbyn Face, "A web game where you are challenged to match the emotions of a photo Jeremy Corbyn. Possibly the first web game you play with empathy and emotion." Read the rest

Entrancing avant-garde music video generated by algorithm

Damien Henry, co-inventor of Google Cardboard, trained a machine learning algorithm using footage shot from a moving vehicle and then had the machine generate this beautiful video.

"Graphics are 100% generated by an algorithm in one shot. No edit or post-processing," Henry writes. "Except the first one, all frames are calculated one by one by a prediction algorithm that tries to predict the next frame from the previous one."

The soundtrack is the Steve Reich masterpiece "Music for 18 Musicians."

Read the rest

AI has a legibility problem

I first encountered the idea of technological "legibility" in the work of Natalie Jeremijenko (previously) who insists on the importance of humans being able to know why a computer is doing what it does. Read the rest

Train your AI with the world's largest data-set of sarcasm, courtesy of redditors' self-tagging

Redditors' convention of tagging their sarcastic remarks is a dream come true for machine learning researchers hoping to teach computers to recognize and/or generate sarcasm. Read the rest

Kevin Kelly: "superhuman" AI is bullshit

Kevin Kelly argues that the core premises that underlie the belief that artificial intelligence will overtake human intelligence are "more akin to a religious belief — a myth" than a scientific theory. Read the rest

The next iteration of Alexa is designed to watch you while you get dressed

The Echo Look is the next version of the Alexa appliance: it has an camera hooked up to a computer vision system, along with its always-on mic, and the first application for it is to watch you as you dress and give you fashion advice (that is, recommend clothes you can order from Amazon). Read the rest

Neural network comes up with crazy food recipes

In her spare time, University of California, San Diego engineer Janelle Shane trained a neural network to generate recipes for new dishes. Informed by its reading of existing recipes, the neural network did improve over time yet it's clearly not quite ready for Iron Chef. Here are two recipes from her Tumblr, Postcards from the Frontiers of Science:

Pears Or To Garnestmeam

meats

¼ lb bones or fresh bread; optional½ cup flour1 teaspoon vinegar¼ teaspoon lime juice2  eggs

Brown salmon in oil. Add creamed meat and another deep mixture.Discard filets. Discard head and turn into a nonstick spice. Pour 4 eggs onto clean a thin fat to sink halves.

Brush each with roast and refrigerate.  Lay tart in deep baking dish in chipec sweet body; cut oof with crosswise and onions.  Remove peas and place in a 4-dgg serving. Cover lightly with plastic wrap.  Chill in refrigerator until casseroles are tender and ridges done.  Serve immediately in sugar may be added 2 handles overginger or with boiling water until very cracker pudding is hot.

Yield: 4 servings

This is from a network that’s been trained for a relatively long time - starting from a complete unawareness of whether it’s looking at prose or code, English or Spanish, etc, it’s already got a lot of the vocabulary and structure worked out. This is particularly impressive given that it has the memory of a goldfish - it can only analyze 65 characters at a time, so by the time it begins the instructions, the recipe title has already passed out of its memory, and it has to guess what it’s making.

Read the rest

Award-winning robot rappers perform "Robot's Delight"

These Japanese robots' performance of "Robot's Delight" -- an extended, braggadocios riff on the state of AI learning-through-imitation research, with break-dancing -- won Best Video at the 2017 ACM/IEEE International Conference on Human Robot Interaction. (via 4 Short Links) Read the rest

The "universal adversarial preturbation" undetectably alters images so AI can't recognize them

In a newly revised paper in Computer Vision and Pattern Recognition, a group of French and Swiss computer science researchers show that "a very small perturbation vector that causes natural images to be misclassified with high probability" -- that is, a minor image transformation can beat machine learning systems nearly every time. Read the rest

UC Berkeley nuked 20,000 Creative Commons lectures, but they're not going away

A ruling about a DC university held that posting course videos to the open web without subtitling them violated the Americans With Disabilities Act (while keeping them private to students did not) (I know: weird), and this prompted UC Berkeley to announce the impending removal of 20,000 open courseware videos from Youtube. Read the rest

A confusatorium for self-driving cars

James "New Aesthetics" Bridle (previously) wants to confuse your autonomous vehicle, so he's designed this Autonomous Trap 001, a sequencing scheme with lots of room for growth. Read the rest

More posts