The first time Josh Silverman addressed the staff of Etsy as their newly installed chief executive, he tried to connect with a work force known for its diversity, idealism and sincerity.
“Hello,” he said. “My name is Josh. I identify as male. My preferred pronouns are ‘him’ and ‘he.’ Most people just call me Josh.”
It was May 3, and Mr. Silverman was speaking to a roomful of traumatized employees. The day before, Etsy had fired 80 people, the first big layoffs at the online marketplace for handmade and vintage arts and crafts. Among those ousted was Etsy’s beloved chief executive of six years, Chad Dickerson.
Now Mr. Silverman — an Etsy board member but an unknown to most employees — stood in the Etsytorium, trying to win over a hostile crowd. His earnest introduction was an olive branch of sorts, an effort to signal that he was attuned to Etsy’s vibrant gay and transgender community, and would be respectful of the company’s distinctive culture. But to many in attendance, his remarks came off as tone deaf, and his inability to read the room foreshadowed sweeping changes that would soon transform Etsy.
“Imagine discovering that a small group of people had invented a fully functioning jet airplane capable of flying long distances at hundreds of miles per hour, decades before the Wright brothers”. So writes Brian Dear in The Friendly Orange Glow, his history of a computer system that most people have never heard of, but perhaps should have. That system, Programmed Logic for Automated Teaching Operations, or PLATO, brought together dreamers, gamers and engineers in a network at the dawn of the 1960s, long pre-dating the Internet. But was this collective venture really as ahead of its time as Dear claims?
The story he tells is both intriguing and a familiar one in the history of technology: a set of determined visionaries break down barriers to make way for a brilliant advance. What differentiates The Friendly Orange Glow is that the vision behind PLATO ultimately failed. The product created was overshadowed, forgotten by all but its most devoted users, and shut down many years later.
Wildlife conservation groups have made a lot of strides in raising awareness of animals whose populations or natural habitats are endangered, and what we can do to help. Now a startup out of Kenya is tapping into advances made in augmented reality, mapping and app-based games to further the cause.
Internet of Elephants, a startup based out of Nairobi, is building an app-based game of the same name that lets users learn more about different species of wildlife in Kenya, as well as other countries and regions, by letting users select the animals and “place” them into their real-world environments to follow them around. Users can learn more about the animals through a reference guide in the app, as well as by walking around the physical world and playing games based on the migratory paths of each creature.
Your days of peeking at someone else’s text conversations on the train might be numbered.
At the Neural Information Processing Systems conference in Long Beach, California, next week, Google researchers Hee Jung Ryu and Florian Schroff will present a project they’re calling an electronic screen protector, where a Google Pixel phone uses its front-facing camera and eye-detecting artificial intelligence to detect whether more than one person is looking at the screen. An unlisted, but public video by Ryu shows the software interrupting a Google messaging app to display a camera view, with the peeking perpetrator identified and given a Snapchat-esque vomit rainbow.
• The right to explanation should be interpreted functionally, flexibly, and should, at a minimum, enable a data subject to exercise his or her rights under the GDPR and human rights law.
Last week, ESA approved an extension of Gaia operations for an additional 18 months, so it will keep scanning the sky until at least 2020. One of the key science drivers for the extension was the study of proper motions of stars in dwarf galaxies, which requires observations taken over as long a time baseline as possible.
New England Journal of Medicine; Robert Kiley, Tony Peatfield Jennifer Hansen, and Fiona Reddington
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The Wellcome Trust, the Medical Research Council, Cancer Research UK, and the Bill and Melinda Gates Foundation share a common vision for maximizing the value of data that are generated through the trials we fund. We are committed to ensuring that the data from published clinical trials can be accessed by researchers so they can validate key findings, stimulate further inquiry, and ultimately deliver lifesaving results.
The sharing of data during the outbreak of Ebola virus disease in West Africa that began in 2014 helped researchers to trace the origins of the final few cases and bring the epidemic under control.1 And the challenge organized by the Journal to encourage researchers to use data from the Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated the vast potential for those data to be reused to develop new applications and uncover new knowledge.2
The recent announcement by the International Committee of Medical Journal Editors (ICMJE) on data-sharing statements for clinical trials3 is a step in the right direction but falls short of realizing our vision.
The ambitious and integrated framework of the United Nations (UN) Sustainable Development Goals (SDGs) demonstrates that complex global problems span the natural and social sciences and that solutions to such problems demand a joint approach of the two. Despite decades of efforts toward better integration, much of society still presumes a stark divide between the disciplines, and most scientists continue to be trained, evaluated, and rewarded in disciplinary silos. The recent merger of the International Council for Science (ICSU) and the International Social Science Council (ISSC)—leading international councils of the natural and social sciences, respectively—sends a powerful message that the future of science depends on collapsing the walls between academic disciplines.
Raleigh, NC Friday, December 8, starting at 3 p.m. Featured speaker: Gary Lackmann. Part of the North Carolina State University Research Computing Event series. [free]
Researchers Brenden Lake and Todd Gureckis, assistant professors at NYU, as well as Anselm Rothe, a graduate student, translated questions a human would ask to find the ships using a programming language. The questions included such inquiries as “How long is the blue ship?” and “Does the blue ship have four tiles?” or “Do the blue and red ships touch?” The machine then used a probabilistic model to determine which questions were the most useful and how to construct new questions to win the game.
This method of programming AI to generate smart questions is different from the usual approach where machines are simply fed a ton of data from which to come up with their own examples. The researchers see an application of their technology in fields like customer service.
Artificial intelligence is increasingly a part of our daily lives. It helps run our search engines, populating our Facebook feeds. It lets us interact with Siri and Alexa. But the powerful algorithms and predictive systems that make up AI are playing a deeper role in society with some serious implications. Professor Kate Crawford is co-founder of New York University’s new AI Now Institute. She says, you’re seeing AI at work in health care, education, even criminal justice. [audio, 4:56]
My expectations for Firefox Quantum, the new browser from Mozilla, were not particularly high. Mozilla made big promises about Quantum’s speed and efficiency, which are what everyone makes big promises about when they launch a new browser, and they never really make a difference in the experience. Sure, a couple dozen Chrome tabs can bring even the beefiest computer hardware grinding to a beach-balling halt, but Chrome does the job. What could Firefox even do to win me over?
It turns out there are lots of things Firefox Quantum could do to improve the browser experience, and it did many of those things. The new Firefox actually manages to evolve the entire browser experience, recognizing the multi-device, ultra-mobile lives we all lead and building a browser that plays along. It’s a browser built with privacy in mind, automatically stopping invisible trackers and making your history available to you and no one else. It’s better than Chrome, faster than Chrome, smarter than Chrome. It’s my new go-to browser.
Most search applications assume a query-response paradigm: the searcher submits a search query, and the search engine responds with results. The query-response paradigm works well for simple search needs that the search engine understands.
The query-response paradigm breaks down, however, when searchers have more complex needs, or when the search engine struggles with query understanding. In those cases, it’s better to model search as a conversation.
The newly redesigned TripAdvisor.com emphasizes traveler photos throughout the site, but not all of these photos are useful in every situation. Deep Learning networks provide an excellent opportunity for us to improve our users’ experience by highlighting the most attractive and useful photos for varying presentation contexts. This post will discuss our approach for gathering training data, developing a model, and scaling it up to over 110 million photos and 7 million places of interest.
“The 2nd edition of D3.js in Action was officially published today. It’s eleven chapters of full-color, in-depth exploration of the most popular data visualization library in JavaScript.”
Journal of the American Medical Informatics Association; Hua Xu et al.
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“Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications.”
“We are excited to announce the Amazon ML Solutions Lab, a new program that connects machine learning experts from across Amazon with AWS customers to help identify novel uses of machine learning inside customers’ businesses, and guide them in developing new machine learning-enabled features, products, and processes.”