Beginning this fall, Tuskegee University students interested in careers in the computer engineering industry will have a new academic pathway to fulfilling their professional goals.
A new bachelor of science degree in computer engineering will focus on computer hardware design and cybersecurity engineering. The degree program, approved by the university’s Board of Trustees at its March 2018 meeting, is based in the newly renamed Department of Electrical and Computer Engineering.
Murray Webb had been a lackluster student more interested in sports than schoolwork while attending a small Virginia college. Then he transferred to Kennesaw State University in suburban Atlanta to pursue a master’s degree in applied statistics and landed four job offers upon graduation.
Webb, 33, now earns $160,000 a year targeting healthcare customers for hospitals and says he is approached weekly by companies and recruiters seeking data scientists.
Webb is part of a national employment trend that has data scientists at tech companies such as Airbnb Inc. and Uber Technologies Inc. adding the words “I’m hiring” next to their LinkedIn.com profiles.
In the early days of machine learning, hiring good statisticians was the key challenge for AI projects. Now, machine learning has evolved from its early focus on statistics to more emphasis on computation. As the process of building algorithms has become simpler and the applications for AI technology have grown, human resources professionals in AI face a new challenge. Not only are data scientists in short supply, but what makes a successful data scientist has changed.
Engineer and adventurer Richard Jenkins has made oceangoing robots that could revolutionize fishing, drilling, and environmental science. His aim: a thousand of them.
The Berkeley Artificial Intelligence Research Blog; Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
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Machine learning systems trained to minimize prediction error may often exhibit discriminatory behavior based on sensitive characteristics such as race and gender. One reason could be due to historical bias in the data. In various application domains including lending, hiring, criminal justice, and advertising, machine learning has been criticized for its potential to harm historically underrepresented or disadvantaged groups.
In this post, we talk about our recent work on aligning decisions made by machine learning with long term social welfare goals. Commonly, machine learning models produce a score that summarizes information about an individual in order to make decisions about them.
Directing academics has been compared to herding cats, animals that famously follow their own path and scorn instruction. So, while worrying, it’s perhaps not surprising that two-thirds of lab heads who responded to a Nature survey this year said that they had received no training in mentoring or managing people. Yet two-thirds of these untrained senior scientists said they thought it would be useful.
They were right. Good-quality training is a key ingredient to building a successful research group. So, too, is the wider academic environment in which researchers work. If a department or institution does not encourage collaboration, celebrate success or value solid work over flashy promotion — as well as training — then group leaders will struggle to create a healthy research culture in their own laboratories.
How institutions can help lab groups to be productive, supportive and rigorous is an essential but often-overlooked topic.
Recruiters regularly visit my campus in Golden, Colo. — the home of both Coors beer and my own Colorado School of Mines. Like those from other engineering and applied-sciences universities, our graduates are in high demand in such critical sectors as energy, aerospace, information technology, manufacturing, and construction. My students earn big starting salaries, and recruiters go to great lengths to woo our top graduates.
That’s why, when hundreds of recruiters descend on my campus twice each year, I make a point of understanding their needs. I ask any I encounter the same thing: “What are you looking for from our graduates?” Without fail, I get a version of the same answer. Yes, they want technical skills. But they also want something broader. They want to hire engineers who can communicate and think critically, who can adapt and create, who can assess the quality of conflicting information, and who can view a problem from multiple perspectives. These are the core skills cultivated by the liberal arts, and I’ve never met an employer who didn’t think they were more important than most other people think.
Indeed, recently I put my question to a senior executive of one of the country’s biggest oil companies. “I’m looking for diversity of thought,” he said, without hesitating.
What does the Facebook–Cambridge Analytica scandal say about the risks of trusting Facebook and other tech companies with our data? We talked with Jack Balkin, Knight Professor of Constitutional Law and the First Amendment at Yale Law School and founder and director of the Information Society Project, whose proposal for making online companies “information fiduciaries” has received new attention in the wake of the scandal.
Today marks the seventh Global Accessibility Awareness Day, a celebration of inclusion and digital access for people with disabilities. Microsoft took the opportunity to unveil the Xbox Adaptive Controller, a gaming controller designed to accommodate a range of special needs, and Apple announced that its Everyone Can Code curricula for the Swift programming language will come to schools with vision- and hearing-impaired students.
Neither of those announcements has much to do with artificial intelligence, but increasingly, tech firms are enlisting the help of AI to build accessible, inclusive products. Last week, Microsoft committed $25 million to its AI for Accessibility program with the aim to “assist people with disabilities with work, life, and human connections,” and Facebook recently said it’s collecting data from disabled users to inform its design decisions.
That’s just the tip of the iceberg. Already, text-to-speech and object recognition AI is improving the lives of the more than roughly 40 million people in the U.S. with eyesight and speech problems. And in the not-too-distant future, self-driving cars will afford house- and wheelchair-bound folks the freedom to travel without the assistance of a caregiver, friend, or family member — some for the first time in their lives.
One of the world’s most visible environmentalists is optimistic about the future of the planet because of technology. Former US Vice President Al Gore believes advances in machine learning, artificial intelligence, connected devices, and other technology will make it possible for society to reach sustainability goals at record speed.
“The world is in the early stages of a sustainability revolution that has the magnitude and scale of the industrial revolution at the speed of the digital revolution,” Gore said at the Bloomberg Sustainable Business Summit in Seattle Thursday.
Gore used Google as an example. The tech giant acquired machine learning startup DeepMind and used its technology to study energy use at data centers. The technology allowed Google to reduce its energy use by 40 percent.
When Jonny Simkin was growing up in San Diego, he, like most Americans, relied solely on his car for transportation. So Southern California’s notorious traffic jams were understandably a source of frustration–so much so that it eventually inspired the now-30-year-old to launch Swiftly, a maker of enterprise software used by transit agencies and cities to improve urban mobility.
By integrating with existing GPS devices aboard buses and trains, the company’s software is able to collect real-time arrival and departure data. In addition to helping cities and agencies leverage that data to identify potential chokepoints and breakdowns, Swiftly also supplies data to customer-facing applications like Google Maps in the 45 cities where the company operates, including Boston and Chicago.
“The algorithm basically collects every GPS record from every vehicle in a transit network in real time,” says Simkin. “We then combine historical trends with [the] real-time data to better predict future performance as well as to understand where and when issues occur so that agencies can prevent them from happening again.”
Federal immigration officials have abandoned their pursuit of a controversial machine-learning technology that was a pillar of the Trump administration’s “extreme vetting” of foreign visitors, dealing a reality check to the goal of using artificial intelligence to predict human behavior.
Immigration and Customs Enforcement officials told tech-industry contractors last summer they wanted a system for their “Extreme Vetting Initiative” that could automatically mine Facebook, Twitter and the broader Internet to determine whether a visitor might commit criminal or terrorist acts or was a “positively contributing member of society.”
But ICE dropped the machine-learning requirement from its request in recent months, opting instead to hire a contractor that can provide training, management and human personnel who can do the job. Federal documents say the contract is expected to cost more than $100 million and be awarded by the end of the year.
Worldwide May 15-July 22. “If you’re a college student and want to take part in the festivity of data this summer where Analytics Vidhya is providing their in-house training courses along with Mega Hackathons for students to compete and win grand Cash prizes.” [free]
Austin, TX “SciPy (Scientific Computing with Python) is excited to once again offer our Teen Track, July 9-10. If you are 14-18 years old, please join us to learn more about the Python language and how developers solve real world scientific problems using Python and its scientific libraries.” [$$, space is limited]
“This worksheet aims to crystalize what a good Distill article looks like into questions that we can ask reviewers (and that authors and editors can refer to). We think it’s very important for the future of Distill. We’d be very grateful for community feedback and thoughts on any part!”
University of Paderborn, DICE Research Group, Geraldo Souza
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Linked Data enables data to beopened up and connected so that people can build interesting new things from it. We present Squirrel, a crawler of Linked Data, in order to exploit all the content of the Linked Web. By looking at initial RDF or Html seeds, Squirrel follows all available links and performs a deep search to crawl everything.
How is a cult-brand made? We analyzed consumer insights to better understand how certain brands develop fiercely loyal customers, and what Wawa’s success story can teach brands about the power of a cult following.
