Data Science newsletter – August 17, 2017

Newsletter features journalism, research papers, events, tools/software, and jobs for August 17, 2017

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Data Science News



Technology Review 35 Under 35

MIT Technology Review


from

MIT Technology Review, as a rule, focuses on the technology first—the breakthrough, the surprise, the accidental discovery with the potential to upend the way we live. Our annual look at 35 outstanding innovators under 35 is a reminder that behind all those innovations are people with dreams, fears, and ambitions. Sometimes they hack away at a problem for years before figuring out a way forward. Sometimes they stumble on a solution they didn’t know they were searching for. We hope these portraits offer a sense of the variety of work being done in technology, and a sense of what’s coming next.


Diversity Crisis in AI, 2017 edition

fast.ai, Rachel Thomas


from

Deep learning has great potential, but currently the people using this technology are overwhelmingly white and male. We’re already seeing society’s racial and gender biases being encoded into software that uses AI when built by such a homogeneous group. Additionally, people can’t address problems that they’re not aware of, and with more diverse practitioners, a wider variety of important societal problems will be tackled.


AI artist conjures up convincing fake worlds from memories

New Scientist, Daily News, Matt Reynolds


from

Take a look at the above image of a German street. At a glance it could be a blurry dashcam photo, or a snap that’s gone through one of those apps that turns photos into paintings.

But you won’t find this street anywhere on Google Maps. That’s because it was generated by an imaginative neural network, stitching together its memories of real streets it was trained on.

Nothing in the image actually exists, says Qifeng Chen at Stanford University, California, and Intel. Instead, his AI works from rough layouts that tell it what should be in each part of the image. The centre of the image might be labelled “road” while other sections are labelled “trees” or “cars” – it’s painting by numbers for an AI artist.


Google acquires AIMatter, maker of the Fabby computer vision app

TechCrunch, Ingrid Lunden


from

Computer vision — the branch of artificial intelligence that lets computers “see” and process images like humans do (and, actually, often better than us), and then use those images to help run programs — is at the heart of how the next generation of tech is developing, and this week Google made an acquisition this week to help it along with its own efforts in this area.

The search and Android giant has acquired AIMatter, a startup founded in Belarus that has built both a neural network-based AI platform and SDK to detect and process images quickly on mobile devices, and a photo and video editing app that has served as a proof-of-concept of the tech called Fabby.


Andrew Ng is raising a $150M AI Fund

TechCrunch, John Mannes


from

It’s clear now that the turn of Ng’s three part act is a $150 million venture capital fund, first noted by PEHub, targeting AI investments.

Ng, who formerly founded Google’s Brain Team and served as chief scientist at Baidu has long evangelized the benefits AI could bring to the world. During an earlier conversation, Ng told me that his personal goal is to help bring about an AI-powered society. It would follow that education via his deep learning classes is one step of that and providing capital and other resources is another.


Beyond Limits is Spreading NASA’s AI to the World

Huffington Post, Vicky Law


from

In the most desolate of environments, these truly intelligent and autonomous AI systems have learned to analyze situations, think critically, and solve difficult problems with human-like reasoning. Now Caltech and NASA are ready to commercialize this technology. Southern California company, Beyond Limits, which was granted an exclusive license by Caltech to improve and commercialize this technology, recently closed a Series B of $20 million from BP Ventures, and is rapidly establishing itself as the leader in industrial-grade Artificial General Intelligence (AGI) software.


Beyond HAL: How artificial intelligence is changing space systems

Space News, Debra Warner


from

Mars 2020 will need far more autonomy. “Missions are paced by the number of times the ground is in the loop,” said Jennifer Trosper, Mars Science Laboratory mission manager. “The more the rover can do on its own, the more it can get done.”

The $2.4 billion Mars 2020 mission is just one example of NASA’s increasing reliance on artificial intelligence, although the term itself makes some people uneasy. Many NASA scientists and engineers prefer to talk about machine learning and autonomy rather than artificial intelligence, a broad term that in the space community sometimes evokes images of HAL 9000, the fictional computer introduced in Arthur C. Clarke’s 2001: A Space Odyssey.


Apple, Aetna hold meetings to bring Apple watch to millions more

CNBC, Christina Farr and Jeffrey McCracken


from

Apple Watch recently overtook Fitbit as the top-selling wearable tracker, with shipments reaching 22 million for the first three months of 2017, according to research firm Strategy Analytics. The next version will reportedly have a new design and wireless connectivity, allowing it to connect to the internet without a nearby iPhone.

As CNBC previously reported, Apple is quietly developing new health sensors that would make its devices a “must have” for millions of people with chronic disease. It has a secret team working on adding continuous and noninvasive blood sugar monitoring to its hardware, which would be a game-changer for diabetics.

One of the people said Aetna’s proposed timeline is slated for early next year.


NASA is about to find out if a supercomputer can survive a year in space

Popular Science, Rob Verger


from

On Monday, at 12:31 p.m. Eastern time, a SpaceX Falcon 9 rocket lifted off on a resupply flight for the International Space Station, and among its cargo, in addition to ice cream, was something else very cool: a supercomputer.

The machine, made by Hewlett Packard Enterprise and called the Spaceborne Computer, is capable of a teraflop worth of computing power, which puts it roughly in line with a late-1990s supercomputer. Made up of two pizza box-shaped machines in a single enclosure, the HPE supercomputer is a part of a year-long experiment to see how an off-the-shelf computer system can fare in space if protected in the right way by software.


If an AI creates a work of art, who owns the copyright?

World Economic Forum, Quartz, Robert Hart


from

Artificial intelligence is already capable of creating a staggering array of content. It can paint, write music, and put together a musical. It can write movies, angsty poems, and truly awful stand-up comedy. But does it have ownership over what it produces?

For example, an AI at Google has managed to create sounds that humans have not heard before, merging characteristics of two different instruments and opening up a whole new toolbox for musicians to play around with. The company’s DeepDream is also capable of generating psychedelic pieces of art with high price tags; last year two sold for $8,000—with the money going to the artists who claimed ownership over the images.

 
Deadlines



SAGE Campus – Online Courses for Data Scientists from Sage Publications

SAGE Publications has launched SAGE Campus, a suite of online data science courses for social scientists. Building on research SAGE undertook to identify the challenges that social science researchers who want to work with large datasets say they face, SAGE Campus courses have been created with social science academic experts involved at every stage. As a result, the courses are uniquely tailored for social scientists and will help them gain the skills needed to embrace the opportunities that big data and new technology present. SAGE Campus are offering $100 off all of their courses, just enter promo code dsnewsletter at the SAGE Campus checkout (expires October 9).

RF Machine Learning Systems (RFMLS) program

The goal of the RF Machine Learning Systems (RFMLS) program is to develop the foundations for applying modern data-driven Machine Learning to the RF Spectrum domain as well as to develop practical applications in emerging spectrum problems which demand vastly improved discrimination performance over today’s hand-engineered RF systems. Ultimately these innovations will result in a new generation of RF systems that are goal-driven and can learn from data rather than being hand-engineered by experts. Deadline for responses is October 10.
 
NYU Center for Data Science News



NYU researchers develop new paradigm for 5G emulation

RCR Wireless News, Aditya Dhananjay


from

Researchers at CATT and NYU Wireless have built the world’s first wireless emulator suitable for 5G systems that feature massive bandwidths and hundreds of antenna elements. In this unique patented design, the solution emulates not only the wireless channel, but also the beamformers (or phased-arrays) on both the transmitter and receiver devices under test (DUTs). This joint emulation of the beamformer and the wireless channel is the key enabling technology that allows for faster development cycles, while significantly lowering the hardware cost and complexity of the emulator. The project was led by post-doctoral fellow Dr. Aditya Dhananjay, and was supervised by faculty members Dr. Sundeep Rangan and Dr. Dennis Shasha. The project was enabled by a generous hardware donation of commercial off-the-shelf (COTS) components from National Instruments. The NYU researchers are also making the emulator software available to academic researchers for free, along with reference TX and RX DUT software designs.


Probability and Statistics for Data Science

Carlos Fernandez-Granda


from

These notes were developed for the course Probability and Statistics for Data Science at the
Center for Data Science in NYU. The goal is to provide an overview of fundamental concepts
in probability and statistics from first principles. [pdf]

 
Tools & Resources



Making Internet Things, Part 1: Working with Data

The Pudding, Ilia Blinderman


from

This is the first installment of a multi-part series designed to help you familiarize yourself with the tools used to make visual, data-driven essays.


Today I put up a static HTML version of the Python Data Science Handbook so that search engines can index it!

Jake VanderPlas, GitHub, O'Reilly Publishing


from

This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks.

 
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