Dank Learning: teaching a machine learning algorithm to generate memes

A physics student and an engineering student from Stanford fed 400,000 memes to a Long Short-Term Memory Recurrent Neural Network and asked it to generate more memes of its own. Read the rest

Ranking the most influential computer security papers ever published

Konrad Rieck has data-mined the nine top security conferences, compiling a decade-by-decade list of the papers most often cited in the presentations delivered at these events: top of the pile is Random Oracles are Practical: A Paradigm for Designing Efficient Protocols (Sci-Hub mirror), from the 1993 ACM Conference on Computer and Communications Security. Rieck has also produced a "normalised" ranking that tries to offset the seniority effect, whereby older papers collect more citations. (via Four Short Links) Read the rest

The first "portable" computer fit in two trailer vans and weighed 20 tons

The first electromechanical computers occupied whole buildings, making them rather unwieldy; in the 1950s, an effort to create a "portable" computer called the DYSEAC bore fruit in the form of a computer on wheels that could be relocated, provided you had the trucking logistics to move two trailers with a combined weight of 20 tons. Read the rest

The new Deep Fakes are in and they're spookily good

SIGGRAPH is coming, when all the amazeballs graphics research drops, and the previews are terrifying and astonishing by turns (sometimes both!). Read the rest

Machine learning may be most useful in tiny, embedded, offline processors

The tiny embedded processors in smart gadgets -- including much of the Internet of Shit -- are able to do a lot of sensing without exhausting their batteries, because sensing is cheap in terms of power consumption. Read the rest

Using saccade-tracking to trick VR users into walking in circles, giving the illusion of "infinite walking"

"Saccades" are the phenomenon where your eyes flick momentarily from one place to another; during saccades, you don't consciously register visual input, creating tiny moments of blindness (AKA "saccadic suppression"). Read the rest

The most interesting thing about the "Thanksgiving Effect" study is what it tells us about the limits of data anonymization

Late last year, a pair of economists released an interesting paper that used mobile location data to estimate the likelihood that political polarization had shortened family Thanksgiving dinners in 2016. Read the rest

Count your bees with a Raspberry Pi and machine learning

Sure, you worry about your bees, what with colony collapse disorder, but they're hard to count! Read the rest

Garbage In, Garbage Out: machine learning has not repealed the iron law of computer science

Pete Warden writes convincingly about computer scientists' focus on improving machine learning algorithms, to the exclusion of improving the training data that the algorithms interpret, and how that focus has slowed the progress of machine learning. Read the rest

The paleocomputing miracle of the 76477 Space Invaders sound effects chip

In 1978, the 76477 Complex Sound Generation chip was foundational to creating the sound effects in many popular games, notably Space Invaders; it was also popular with hobbyists who could buy the chip at Radio Shack -- it could do minor miracles, tweaking a white noise generator to produce everything from drums to explosions, using an integrated digital mixer to layer and sequence these sounds. Read the rest

Debugging AI is a "hard problem"

Writing code is a lot easier than fixing code! For a lot of well-understood reasons, code requires a lot of debugging to run safely and property, and different code structures and practices make debugging harder or easier. S. Zayd Enam, an AI researcher at Stanford, writes about the specific problems of debugging AI code, which is extremely difficult. Read the rest

A hard look at the wastefulness of "proof of work," the idea at the core of the blockchain

David Gerard is a technically minded, sharp-witted, scathing critic of Bitcoin and other cryptocurrencies; his criticism is long, comprehensive and multipartite, but of particular interest is is critique of "proof of work" (an idea that is central to the blockchain, but which many cryptographers are skeptical of). Read the rest

A dozen googlers quit over Google's military drone contract

Google's "Project Maven" is supplying machine-learning tools to the Pentagon to support drone strikes; the project has been hugely divisive within Google, with employees pointing out that the company is wildly profitable and doesn't need to compromise on its ethics to keep its doors open; that the drone program is a system of extrajudicial killing far from the battlefield; and that the firm's long-term health depends on its ability to win and retain the trust of users around the world, which will be harder if Google becomes a de facto wing of the US military. Read the rest

Enhance enhance: Using machine learning to recover lost detail from upscaled photos

A team of researchers from Twitter have published a paper detailing a machine learning technique that uses a generative adversarial network to make shrewd guesses about how to up-res small images by up to 400%, into crisp, large images, with eye-popping results. Read the rest

See in the Dark: a machine learning technique for producing astoundingly sharp photos in very low light

A group of scientists from Intel and the University of Illinois at Urbana–Champaign have published a paper called Learning to See in the Dark detailing a powerful machine-learning based image processing technique that allows regular cameras to take super-sharp pictures in very low light, without long exposures or the kinds of graininess associated with low-light photography. Read the rest

Welsh police deployed facial recognition tech with a 92% false positive rate, but they're sure it's fine

The South Wales Police deployed a facial recognition technology at the June 2017 Champions League soccer final in Cardiff, and 92% of the people identified by the system as matches for suspiciousness were false positives. Read the rest

Should I use an algorithm here? EFF's 5-point checklist

The Electronic Frontier Foundation's Jamie Williams and Lena Gunn have drawn up an annotated five-point list of questions to ask yourself before using a machine-learning algorithm to make predictions and guide outcomes. Read the rest

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