Every year, NYU's nonprofit, critical activist group AI Now releases a report on the state of AI, with ten recommendations for making machine learning systems equitable, transparent and fail-safe (2016, 2017); this year's report just published, written by a fantastic panel, including Meredith Whittaker (previously -- one of the leaders of the successful googler uprising over the company's contract to supply AI tools to the Pentagon's drone project); Kate Crawford (previously -- one of the most incisive critics of AI); Jason Schultz (previously -- a former EFF attorney now at NYU) and many others. Read the rest

BB pal Lissa Soep of YR Media (formerly Youth Radio) writes:

Our Interactive team delved into Spotify's algorithm to discover how songs on the platform are scored for their "danceability." We were intrigued by this use of Artificial Intelligence to quantify something as personal and cultural as what makes us want to move our bodies. So we built a tool that invites users to rate a curated playlist for each song’s “danceability” and compare that rating against the one Spotify produced algorithmically. Our writer Deborah Raji uses the project to raise fascinating questions about what it means for AI to be making its way into so many corners of our lives.

"Can You Teach AI to Dance?" (YR Media)

(Image: detail of illustration by Symone Woodruff-Hardy) Read the rest

Ganbreeder uses a machine learning technique called Generative Adversarial Networks (GANs) to generate images that seem like photos, at least a first glance. Read the rest

The ever-useful Gartner Hype Cycle identified an inflection point in the life of any new technology: the "Peak of Inflated Expectations," attained just before the sharp dropoff into the "Trough of Disillusionment"; I've lived through the hype-cycles of several kinds of technology and one iron-clad correlate of the "Peak of Inflated Expectations" is the "Peak of Huckster Snakeoil Salesmen": the moment at which con-artists just add a tech buzzword to some crooked scam and head out into the market to net a fortune before everyone gets wise to the idea that the shiny new hypefodder isn't a magic bullet. Read the rest

China's war on jaywalking went to the next level last spring when AI-based facial recognition systems were integrated into some crosswalks, to punish jaywalkers by squirting them with water, sending them texts warning them about legal consequences of jaywalking, and/or publicly shaming them by displaying their pictures and names on large digital billboards. Read the rest

Phrenology (the fake science of predicting personality from the shape of your cranial bones) is like Freddy Kruger, an unkillable demon who rises from the grave every time some desperate huckster decides they need to make a few extra bucks. Read the rest

CIT computer scientist Milan Cvitkovic conducted 46 in-depth interviews with "scientists, engineers, and CEOs" and collated their machine learning research needs into an aptly named paper entitled "Some Requests for Machine Learning Research from the East African Tech Scene," which presents an illuminating look into the gaps in the current practice of machine learning, itself an example of how rich-world priorities shape our ability to understand, compute and predict the world. Read the rest

Researchers at NYU and U Michigan have published a paper explaining how they used a pair of machine-learning systems to develop a "universal fingerprint" that can fool the lowest-security fingerprint sensors 76% of the time (it is less effective against higher-security sensors). Read the rest

In 1975, Noam Chomsky and Jean Paiget held a historic debate about the nature of human cognition; Chomsky held that babies are born with a bunch of in-built rules and instincts that help them build up the knowledge that they need to navigate the world; Piaget argued that babies are effectively blank slates that acquire knowledge from experiencing the world (including the knowledge that there is a thing called "experience" and "the world"). Read the rest

When you train a machine learning system, you give it a bunch of data -- a simulation, a dataset, etc -- and it uses statistical methods to find a way to solve some task: land a virtual airplane, recognize a face, match a block of text with a known author, etc. Read the rest

Last year, Princeton researchers revealed a powerful new ad-blocking technique: perceptual ad-blocking uses a machine-learning model trained on images of pages with the ads identified to make predictions about which page elements are ads to block and which parts are not. Read the rest

The Nairobi neighborhood of Kibera is Africa's largest slum, and it's home to an unlikely, Silicon-Valley-style tech park operated by Samasource (motto: "Artificial intelligence meets human dignity"), who serves clients from Google to Microsoft to Salesforce, using clickworkers who get paid $9/day, compared to the going wage of $2/day in the region's "informal economy" (the company believes that paying wages on par with rich-world clickworkers would "distort the local economy"). Read the rest

In the Siggraph 2018 paper Learning to Dress: Synthesizing Human Dressing Motion via Deep Reinforcement Learning, a Georgia Institute of Technology/Google Brain research team describe how they taught body-shame to an AI, leaving it with an unstoppable compulsion to clothe itself before the frowning mien of God. Read the rest

Jason Antic's DeOldify is a Self-Attention Generative Adversarial Network-based machine learning system that colorizes and restores old images. It's only in the early stages but it's already producing really impressive results, and the pipeline includes a "defade" model that is "just training the same model to reconstruct images that augmented with ridiculous contrast/brightness adjustments, as a simulation of fading photos and photos taken with old/bad equipment." Read the rest

Two of the MIT researchers behind the provocative Deep Angel project, an algorithm that digitally erases objects from photos, have now delivered a strange and beautiful system to "conjure phantasms into being." Read the rest

Deon is a project to create automated "ethics checklists" for data science projects; by default, running the code creates a comprehensive checklist covering data collection and storage, modeling and deployment: the checklist items aren't specific actions, they're "meant to provoke discussion among good-faith actors who take their ethical responsibilities seriously. Because of this, most of the items are framed as prompts to discuss or consider. Teams will want to document these discussions and decisions for posterity." Read the rest

Robin Sloan is a programmer and novelist whose books like Sourdough and Mr Penumbra's 24-Hour Bookstore are rich and evocative blends of self-aware nerdy playfulness and magical speculation. Read the rest