OpenAI today launched Microscope, a library of neuron visualizations starting with nine popular or heavily neural networks. In all, the collection encompasses millions of images. Like a microscope can do in a laboratory, Microscope is made to help AI researchers better understand the architecture and behavior of neural networks with tens of thousands of neurons. Initial models in Microscope include historically important and commonly studied computer vision models like AlexNet, 2012 winner of the now retired ImageNet challenge. AlexNet has been cited over 50,000 times in research.

In a paper published on the preprint server Arxiv.org this week, researchers at Uber's Advanced Technologies Group (ATG) propose an AI technique to improve autonomous vehicles' traffic movement predictions. It's directly applicable to the driverless technologies that Uber itself is developing, which must be able to detect, track, and anticipate surrounding cars' trajectories in order to safely navigate public roads. It's well-understood that without the ability to predict the decisions other drivers on the road might make, vehicles can't be fully autonomous. In a tragic case in point, an Uber self-driving prototype hit and killed a pedestrian in Tempe, Arizona two years ago, partly because the vehicle failed to detect and avoid the victim. ATG's research, then -- which is novel in that it employs a generative adversarial network (GAN) to make car trajectory predictions as opposed to less complex architectures -- promises to advance the state of the art by boosting the precision of predictions by an order of magnitude.

Latest technologies such as machine learning and deep learning require a colossal amount of data to improve its outcomes' accuracy. However, it is nearly impossible for a local computer to process the vast amount of data. As a result, practitioners use distributed computing for obtaining high-computational power to deliver quick and accurate results. However, effectively managing distributed computation is not straightforward, and this causes hindrance in training and evaluating AI models. To address these challenges, Uber has open-sourced its Fiber framework to help researchers and developers streamline their large-scale parallel scientific computation.

Working at Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

Princeton mathematician John Conway has died of the coronavirus. He was 82 years old. The British-born Conway spent the early part of his career at Cambridge before moving to Princeton University in the 1980s. He made contributions in various areas of mathematics but is best known for his invention of Conway's Game of Life, a cellular automaton in which simple rules give rise to surprisingly complex behaviors. It was made famous by a 1970 Scientific American article and has had a lively community around it ever since then.

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In a major about-face in public health policy, the Centers for Disease Control (CDC), U.S. Surgeon General Dr. Jerome Adams, and state and local health officials around the country recently began urging people to wear homemade face masks when they're out in public. The directive is not meant to replace social distancing, but to reduce the spread of infection and ensure the most effective personal protective equipment goes to health care workers on the front line. But it could also throw a wrench in a number of facial recognition applications, including those used to unlock smartphones. Less than a year old, Google's facial recognition system on Pixel 4 smartphones is built to recognize a person even if they've shaved their beard or are wearing sunglasses, but Face Unlock for Pixel 4 is rendered virtually useless by homemade face masks. A Google spokesperson told VentureBeat that Face Unlock isn't made to recognize people wearing face masks and declined to say whether the company is working to add that capability to its system.

In many patients with diabetes, early signs leading to diagnosis include abnormal levels of glucose, increased urinary excretion, and higher food consumption. While those symptoms may be able to determine likelihood of diabetes diagnosis, machine learning could be the most capable way of accurately predicting future onset of diabetes, a new study has found. As published in the Journal of the Endocrine Society, a research team utilized a form of artificial intelligence known as machine learning, which comprises of computerized algorithms learning and adapting to new patterns when exposed to fresh data. For the study, over 500,000 medical records from more than 130,000 patients were analyzed by the research team, 65,505 of which were regarded as having no prior history of diabetes. The records, dated 2008 through 2018, were based in a populated region of Japan.

The grant will reshape current forecasting methods and will allow the team to develop an open-source, modular, and flexible tool that uses epidemic case incidence data to inform short-term case and epidemic risk projections. It builds on the "Mapping the Risk of International Infectious Disease Spread (MRIIDS)" prototype that the team developed with funding from the U.S. Agency for International Development. Project partners will apply natural language processing and machine learning algorithms to automate the extraction of epidemic case data and will further refine and improve forecasting algorithms through use of artificial intelligence. Integration of other innovative data streams will strengthen the accuracy and validity of these predictive models for impending outbreaks.

My friend Robert John, professor of computer science at the University of Nottingham, who has died of liver cancer aged 64, pioneered the use of "type-2 fuzzy sets" in computational intelligence, to establish ways of reasoning algorithmically about linguistic concepts that involve uncertainty – something humans are good at, but computers are not. In the 1990s, while Rob (as he was known to family, though called Bob by work colleagues) was working at De Montfort University, he became involved in research into solving a community transport scheduling problem using fuzzy logic. Working from the foundations laid by Prof Lotfi Zadeh, Rob, through his PhD in 2000 and subsequent work with Prof Jerry Mendel and others, developed the mathematical techniques to use type-2 fuzzy sets. Two papers on type-2 and interval type-2 that he wrote with Mendel are among the most frequently cited and influential in the world on the topic. Rob was a founder member in 1995 of the Centre for Computational Intelligence at De Montfort and led its growth through the 2000s, established his reputation in the Institute of Electrical and Electronics Engineers' conferences and in journals on fuzzy logic, and was promoted over time to deputy dean.