In a sign of blockchain’s growing traction on college campuses, eleven more schools on Thursday formally joined the University Blockchain Research Initiative, a $50 million program backed by the cryptocurrency firm Ripple.
The program, first announced last June, provides funds for research and curriculum development related to blockchain, which is a type of software run across multiple computers to create a permanent, tamper-proof ledger.
The new universities joining the initiative include Cornell, Carnegie Mellon, Duke, and the University of Michigan. They join 17 other schools from around the world that are eligible to receive grants for curriculum development and research by student and faculties.
The Daily Californian student newspaper, Stanley von Ehrenstein-Smith
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UC Berkeley announced Jan. 30 that it has suspended new collaborations with and research funding from Chinese telecommunications giant and electronics manufacturer Huawei as well as its corporate subsidiaries and affiliates after the U.S. Department of Justice, or DOJ, brought criminal charges against the company.
Dieter May, BMW’s top expert on these things, estimates that by 2021 voice will be the main mode of interaction for secondary functions, things that aren’t steering, accelerating and braking. “Right now, we are continuing on a multimodal approach, which means you still have the iDrive controller, you have the touchscreen, then you have gesture and then voice,” said May, senior vice-president of digital products and services. “I think the real killer will be the voice. Because it’s so natural you don’t need to mess around – the distraction is the least.”
There’s a bit of Seattle gaming history threaded through a new artificial intelligence training game unveiled on Tuesday.
Iconary, which pairs humans and software in a drawing-and-guessing game from the Allen Institute for Artificial Intelligence, was inspired by Pictionary, invented in Seattle more than three decades ago by a then-24-year-old Rob Angel and collaborators.
We asked Angel to check out Iconary, which differs in fundamental ways from other games like chess and Go where artificial intelligence can beat the best of humanity.
“I forgot it was A.I. because I got so engaged with it, and that’s a positive,” Angel said.
Ben Hogan’s ball striking was legendary, perhaps the best the game has ever seen. But even his motion had good years and bad, and he’d spend hours on the range trying to, as he famously quipped, “dig answers out of the dirt.” In fact, it took Hogan a decade to notch his first professional solo win — his early game was plagued by a hook, the result of an overly strong lead-hand grip and insufficient body rotation. Weakening his hold after a “series of trial-and-error experiments” was a key step that eventually transformed Hogan’s swing into the one we admire today.
So, to better understand how his swing evolved from this point, I assigned Hogan a Swing Index with the help of the same AI technology powering our Swing Index App.
The scores you see are graded relative to Hogan’s potential, miss tendency (a hook, in this case) and body type. No two “10s” are equal— the “perfect” swing depends on who’s motoring the club. It took Hogan a few years, but eventually, he found his.
“It is nearly impossible for human moderators to go through all posts manually to determine if there is a problem,” says Gilles Jacobs, a language researcher at Ghent University in Belgium. “AI is key to automating detection and moderation of bullying and trolling.”
His team trained a machine learning algorithm to spot words and phrases associated with bullying on social media site AskFM, which allows users to ask and answer questions. It managed to detect and block almost two-thirds of insults within almost 114,000 posts in English and was more accurate than a simple keyword search. Still, it did struggle with sarcastic remarks.
Abusive speech is notoriously difficult to detect because people use offensive language for all sorts of reasons, and some of the nastiest comments do not use offensive words. Researchers at McGill University in Montreal, Canada, are training algorithms to detect hate speech by teaching them how specific communities on Reddit target women, black people and those who are overweight by using specific words.
How big is Twitter’s daily user base? A lot smaller than Snapchat’s, it turns out.
For years, Twitter has been asking investors to judge the company by looking at user growth for its daily active users. But Twitter never shared how many daily active users it actually had, which made the year-over-year growth hard to appreciate.
That changed on Thursday when Twitter shared its daily user total for the first time: Twitter has 126 million daily users, which is 60 million fewer users than Snapchat (and a lot fewer users than the core apps owned by Facebook). That means roughly 39 percent of Twitter’s monthly active users are on the app every day.
If you want to fall head over heels for your career, you may want to check out the business of love.
Online dating companies and matchmakers are making bank. According to a 2016 report by MarketData Enterprises, dating services in the United States alone are worth approximately $2.5 billion.
Patti Stanger, matchmaker, businesswoman and reality TV star says, “The next trend will be niche sites. I’m a dog lover, you’re a dog lover. I like to dance, you like to dance. And then you’ll have, ‘It’s free [but] you’re going to have to pay extra to get to the top of the list.’ ”
In addition, she says, the next technology wave involves a filtration system for specific information such as key demographics. As new apps emerge, so too should new job opportunities.
To demonstrate the technology in their smartphones, Huawei set themselves the challenge of finishing one of the most famous incomplete works in musical history
Schubert’s Symphony No.8 was started in 1822, but for reasons that have never been understood, Schubert never completed the work.
The first two movements are complete – and this two-movement piece is one of the composer’s most famous works – but there are only fragments of the final two movements.
Chinese technology company Huawei decided to try and use AI to complete the work – with the help of composer Lucas Cantor.
The Boston Globe, Ideas, Swathi Meenakshi Sadagopan
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Artificial intelligence is taking up all sorts of tasks that would have been hard to imagine just a few years ago: drafting legal documents, deploying police officers, even writing news articles.
But there are limits to what AI can do. If it’s going to be truly effective, not to mention fair, it’s got to have a steady human hand — a programmer who can spot problems and make the necessary adjustments.
Making those adjustments, though, isn’t just a matter of technical proficiency. Sometimes, it requires cultural competency. A forthcoming paper by Carnegie Mellon PhD student Kenneth Holstein and others makes the point.
Can the minds of machines teach us something new about what it means to be human? When it comes to the intricate story of our species’ complex origins and evolution, it appears that they can.
A recent study used machine learning technology to analyze eight leading models of human origins and evolution, and the program identified evidence in the human genome of a “ghost population” of human ancestors. The analysis suggests that a previously unknown and long-extinct group of hominins interbred with Homo sapiens in Asia and Oceania somewhere along the long, winding road of human evolutionary history, leaving behind only fragmented traces in modern human DNA.
SF Studios, the Scandinavian company celebrating its 100th anniversary this year, is developing an English-language series based on Max Tegmark’s 2017 New York Times bestseller “Life 3.0: Being Human in the Age of Artificial Intelligence.”
The science-fiction series will follow a group of young scientists working at a startup who discover the first sentient artificial intelligence and envision ways in which it could be used to create a better world, which leads to a clash of ideals and morals. The show is a fictionalized treatment of Tegmark’s exploration of the ramifications of AI, which was translated in multiple languages and published around the world.
Eos; Miriam A. Bertram, LuAnne Thompson, James W. Murray, Chris Bretherton, and Cecilia Bitz
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The focus of graduate education is to train students to become experts in their disciplines. However, the current generation of students must also be able to address interdisciplinary problems, including environmental, social, and policy-related challenges related to climate change. To be successful, they will need to engage broadly both within and outside the academy. Although there are federally funded programs to help meet this need, such as the Integrated Graduate Education Research and Training (IGERT) and National Science Foundation Research and Training, resources at hosting institutions are generally not available once funding ends. Another approach is for faculty from distinct disciplines to include students in collaborative programs and support students as they step beyond their research.
The Program on Climate Change (PCC) at the University of Washington (UW) is an example of a collaborative effort that works across disciplines to address climate change. Courses, seminar series themes, and the annual summer institute topic are created in consultation with faculty across departments. T
“We are again happy co-sponsor the second Workshop and Challenge on Learned Image Compression at CVPR 2019 in Long Beach, California.The half day workshop will feature talks from invited guests Anne Aaron (Netflix), Aaron Van Den Oord (DeepMind) and Jyrki Alakuijala (Google), along with presentations from five top performing teams in the 2019 competition, which is currently open for submissions.” Deadline for paper submission is April 8.
Amazon Elastic Inference (EI) is a resource you can attach to your Amazon EC2 instances to accelerate your deep learning (DL) inference workloads. EI allows you to add inference acceleration to an Amazon SageMaker hosted endpoint or Jupyter notebook for a fraction of the cost of using a full GPU instance. It reduces the cost of running deep learning inference by up to 75%.
DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
After two months of unboxing, building, using, testing, and comparing eight of the newest standing desks side by side, we think the Uplift Bamboo Stand Up Desk with 1″ Thick Desktop and V2 Frame is the best desk for people who want a relatively stable surface that looks good, moves quickly and quietly, and should give you the fewest problems in working every day. But several other standing desks are nearly as good, and a few are worth considering.
In our new white paper, co-authored with Debbie Collins at Southampton University, Nicholas Fernando of Grow Learning and Andy Kirk, data visualisation expert and trainer, we present a review of the current literature on approaches to teaching research skills online and tell the story of developing SAGE Campus, a suite of online data science courses for social scientists. Our goal is to share insights and guidance for faculty, librarians, learning technologists and educators who are planning to develop their own online courses in the future, or would like to incorporate online course material into their curriculum.