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

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

Google knew that Project Maven, its contract to supply AI to US military drones would be unpopular, but they were chasing hundreds of millions of dollars in follow-on contracts, and even though dozens of engineers quit over the project, at least they got a snazzy mission patch. Read the rest

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

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

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

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

At this year's Conference on Computer Vision and Pattern Recognition, researcher Chen Chen presented a cool project that vastly improves the quality of images captured in low-light conditions.

Via his presentation:

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fully-convolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work.

Here's the full project page for more information.

Let's enhance!

• CVPR 2018: Learning to See in the Dark (YouTube / Chen Chen) Read the rest

Amazon bills its Rekognition image classification system as a "deep learning-based image and video analysis" system; it markets the system to US police forces for use in analyzing security camera footage, including feeds from police officers' bodycams. Read the rest

Janelle Shane (previously) is a delightful AI researcher who likes to use machine learning systems to produce absurd, inhuman outputs, such as a list of AI-created notional ice-cream flavors generated by merging a list of real ice-cream flavors with a list of metal band names and pressing "go." Read the rest

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

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

Feng Xiang is a prominent Chinese legal scholar with an appointment at Tsinghua University; in a new Washington Post editorial adapted from his recent speech at the Berggruen Institute’s China Center workshop on artificial intelligence in Beijing, he argues that capitalism is incompatible with AI. Read the rest

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

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

Machine learning's reproducibility crisis is getting worse, and the massive shortage of qualified researchers has driven top salaries over $1,000,000, bringing in all kinds of cowboys and pretenders. Read the rest

It's 2018, and the Open Access debate has been settled: institutions, researchers, funders and the public all hate paywalled science, and only the journal publishers -- whose subscription rates have gone up several thousand percent in recent decades, despite the fact that they don't pay for research, review, editing, or (increasingly) paper -- like locking up scholarship. Read the rest