Since its creation in 1994, Amazon has grown far beyond books. It has become almost synonymous with online shopping, while building a large physical footprint of warehouses and stores, a workforce of more than 600,000 people and a cloud business used extensively by the U.S. government, among others.
Amazon is not the dominant player in many of its sectors. But its range has helped it become the second-most-valuable U.S. company, behind only Apple. A rumor of Amazon’s interest in a new field can send that industry into a stock market whirlwind. Below is a snapshot of many of the dozens of companies or divisions that Amazon owns and operates, showing its reach is far and wide.
Night Sight is a new feature of the Pixel Camera app that lets you take sharp, clean photographs in very low light, even in light so dim you can’t see much with your own eyes. It works on the main and selfie cameras of all three generations of Pixel phones, and does not require a tripod or flash. In this article we’ll talk about why taking pictures in low light is challenging, and we’ll discuss the computational photography and machine learning techniques, much of it built on top of HDR+, that make Night Sight work.
A job at Facebook sounds pretty plum. The interns make around $8,000 a month, and an entry-level software engineer makes about $140,000 a year. The food is free. There’s a walking trail with indigenous plants and a juice bar.
But the tone among highly sought-after computer scientists about the social network is changing. On a recent night at the University of California, Berkeley, as a group of young engineers gathered to show off their tech skills, many said they would avoid taking jobs at the social network.
“I’ve heard a lot of employees who work there don’t even use it,” said Niky Arora, 19, an engineering student, who was recently invited to a Facebook recruiting event at the company’s headquarters in Menlo Park, Calif. “I just don’t believe in the product because like, Facebook, the baseline of everything they do is desire to show people more ads.”
The Brookings Institution, Jennifer L. Cohen and Homi Kharas
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Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process.
Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.
While Silicon Valley workers continue to protest their employers selling artificial intelligence products to the US military, the US military is still looking to spend money on AI.
The Army Research Lab, the Project Maven team, and the US Department of Defense’s Joint Artificial Intelligence Center will host technology companies later this month in Maryland, where the government will view private demonstrations. According to federal contracting data (free login required for the full list), large tech companies such as Intel, IBM, GE, Oracle, as well as defense company Raytheon, have expressed interest in showing off their AI for the military.
Absent from the list are AI giants such as Google, Microsoft, and Amazon, though the DoD has not responded to an inquiry as to whether the available contracting data is the complete list of attending organizations.
A year ago, the Norwegian Consumer Council commissioned a study into kids’ smart watches, finding that they were incredibly negligent when it came to security and incredible greedy when it came to surveillance: a deadly combination that meant that these devices were sucking up tons of sensitive data on kids’ lives and then leaving it lying around for anyone to take.
At the time, the manufacturers involved both denied any wrongdoing and simultaneously promised to improve anyway. A year later, no such improvements have arrived.
Over the past year, roughly one-fifth of Amazon and Google job postings have asked for machine-learning skills, according to an analysis by Burning Glass for the Wall Street Journal. Nationwide, only 3 percent of job postings ask for machine learning knowledge, which hints at just how hard Amazon and Google are leaning into these technologies.
A.I. and machine learning are still relatively nascent disciplines, at least outside of university laboratories. Research and development costs are high, with many initiatives resulting in commercial products only after many years of work. In light of that, Amazon and Google are among the few tech firms that can even afford to hire and foster the necessary talent.
Battelle, National Ecological Observatory Network (NEON Project)
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Ecological data collected in the field gives researchers a window into how ecosystems and climate dare changing in a particular area. But to see the bigger picture, you have to take a step back—way back. All the way to space.
Satellite data provide an important perspective on Earth’s systems—including land, atmosphere, oceans and cryosphere—and how they are changing as a result of natural and human-induced stresses. MALIBU (a joint project of NASA’s Terrestrial Ecology Program, NOAA/NESDIS/STAR, and BlackSwift technologies) is using NEON data to verify that the satellite-derived time series data we are getting from above are accurate and reliable over time.
Post and Courier (Charleston, SC), Mary Katherine Wildeman
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The College of Charleston will soon offer a new graduate-level degree in data science and analytics that aims to meet the specific needs of the Lowcountry’s expanding technology industry.
The first students will begin class this summer.
The program director said the degree will be the first of its kind in the state.
“In 30 years, our internet bandwidth on campus has grown from 1 megabit per second to 150,000 megabits per second,” Dominick said. “This project is the most significant redesign and reengineering of the campus network since it was established.”
The OIT upgrade also is integral to the physical development of campus.
“Our current network design will not scale to meet the needs of Princeton’s Campus Plan for 2026 and beyond,” Dominick said. “Particularly with the University’s emphasis on data science research, the planned expansion of the School of Engineering and Applied Science, and the development of the new Lake Campus, we need a network that will support future growth.”
Yoshua Bengio, one of the so-called “godfathers of AI,” said universities aren’t getting the recognition they deserve when it comes to AI research.
Bengio, renowned for his early work in the 80s and 90s on a branch of AI known as deep learning, which has become wildly popular again now, believes Silicon Valley tech giants get a lot of praise for AI research compared to academic institutions.
“One thing I don’t like about the reporting around AI is that journalists seem to think the progress is happening in companies, and that’s not true,” said Bengio in an interview. “They are part of it, but a lot of the progress is continuing to happen in academia.”
Computer science jobs are the number one source of all new wages in the United States, but only a small percentage of college graduates study computer science. With the help a $1.7 million grant over the next five years, teachHOUSTON, the College of Natural Sciences and Mathematics STEM teacher preparation program at the University of Houston, will train and educate the next generation of computer science teachers. The award is part of an $8 million dollar Supporting Effective Educator Development (SEED) grant from the Department of Education.
EurekAlert! Science News, American Statistical Association
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The US Department of Agriculture (USDA)’s reorganization plan, which would relocate a key agricultural research agency outside Washington, DC, has drawn increasing criticism from those who know and depend on its economic and statistical analyses. First announced during Congress’s summer recess in August, the reorganization plan for the USDA’s Economic Research Service (ERS) has been closed off to congressional input and public comment–in stark contrast to other recent federal agency reorganization plans.
“Continuing the success of LPIRC at CVPR 2018, OVIC is hosting a winter installment with more categories and tasks. We are targeting NIPS2018 to announce the winners.” Deadline for submissions is November 30.
… Data literacy is an increasingly popular term floating around within business intelligence—and rightfully so. In order to keep pace with the rapid growth of data and increase in BI tools, it’s critical to make data literacy a top priority for your organization. To make data literacy a reality, we’ve created a natural language generation (NLG) platform that generates automated written analytics directly inside your data visualization tools.
When data and insights are presented within your company, how can NLG help employees become data literate? Here are four key stages of data understanding and how NLG can lead the charge in achieving company-wide data literacy bliss.
The Raspberry Pi Foundation “has been able to turn its attention to what it calls one of its ‘most frequently requested ‘missing’ products’: the Raspberry Pi 3 Model A+.”
This Fall I became more active with the Apache Flink community in my role as Technical Evangelist at data Artisans, after almost 8 years in Big Data. At October’s Bay Area Flink Meetup, I discussed my impressions of Flink from the point of view of a technical practitioner who is new to Flink but has been working in the Big Data space for a while. As I was speaking, I was struck by the focus, investment, and curiosity in the room. In retrospect, this fits with my overall impressions of Apache Flink and the Apache Flink community. Below I’d like to cover 5 early impressions of Apache Flink and why companies should explore Flink early in their stream processing journey.
“Have you ever been frustrated with the complicated experience of making charts in Python? We have, so we created Chartify, an open-source Python library that wraps Bokeh to make it easier for data scientists to create charts.”