Four short links: 20 July 2018

Convolutional Architectures, GPU Language, Acoustic Scenes, and Cybersecurity Numbers
  1. DARTS: Differentiable Architecture Search -- our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. And runs on a single GPU. Open source.
  2. The Spiral Language -- a functional language designed for GPUs by emphasizing inlining (GPUs don't have great stacks, so compilers have to handle subroutines carefully and differently than traditional architectures). Inlining is a trade-off that expresses the exchange of memory for computation. It should be the default instead of heap allocating.
  3. DCASE: Detection and Classification of Acoustic Scenes and Events -- workshops and a community for the researchers working on making sense of audio.
  4. Cybersecurity: Data, Statistics, and Glossaries (FAS) -- This report describes data and statistics from government, industry, and information technology (IT) security firms regarding the current state of
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Specialized hardware for deep learning will unleash innovation

The O’Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture. In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specialized hardware for machine learning. Since the release of AlexNet in 2012, we have seen an explosion in activity in machine learning, particularly in deep learning. A lot of the work to date happened primarily on general purpose hardware (CPU, GPU). But now that we’re six years into the resurgence in interest in machine learning and AI, these new workloads have attracted technologists and entrepreneurs who are building specialized hardware for both model training and inference, in the data center or on edge devices. Continue reading Specialized hardware for deep learning will unleash innovation.

Four Short Links: 19 July 2018

Microrobotics, Adaptive Chips, ACM Ethics, and Data Journalism
  1. DARPA's Insect-Scale Robot Olympics (IEEE) -- Yesterday, DARPA announced a new program called SHRIMP: SHort-Range Independent Microrobotic Platforms. The goal is “to develop and demonstrate multi-functional micro-to-milli robotic platforms for use in natural and critical disaster scenarios.”
  2. DARPA Changing How Electronics Are Made (IEEE) -- Step two, to be kicked off at the summit, is something we call “software-defined hardware.” That’s where the hardware is smart enough to reconfigure itself to be the type of hardware you want, based on an analysis of the data type that you’re working on. In that case, the very hard thing is to figure out how to do that data introspection, how to reconfigure the chip on a microsecond or millisecond timescale to be what you need it to be. And more importantly, it has to monitor whether you’re right or not, so that
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Highlights from the O’Reilly OSCON Conference in Portland 2018

Watch highlights covering open source, AI, cloud, and more. From the O'Reilly OSCON Conference in Portland 2018. People from across the open source world are coming together in Portland, Oregon for the O'Reilly OSCON Conference. Below you'll find links to highlights from the event.

Live coding: OSCON edition

Suz Hinton live codes an entertaining hardware solution in front of your eyes.

Drive innovation and collaboration through open source projects

Ying Xiong explains how Huawei collaborates with industry leaders and innovates through open source projects.

Recognizing cultural bias in AI

Camille Eddy explains what we can do to create culturally sensitive computer intelligence and why that's important for the future of AI.

The whole is greater than the sum of its parts

Christopher Ferris says
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Data collection and data markets in the age of privacy and machine learning

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning. Because large deep learning architectures are quite data hungry, the importance of data has grown even more. In this short talk, I describe some interesting trends in how data is valued, collected, and shared.
big data trends

Economic value of data

It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. In the early phases of
cost of training data
company valuation
data added value
data mishap cost
data ethics courses
data commons
data liquidity
data models and features
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Editing our world: CRISPR and the future of genomics

The basic technology behind gene editing and a conversation between Jennifer Doudna and Siddhartha Mukherjee.

The basic behind gene editing and a conversation between Jennifer Doudna and Siddhartha Mukherjee.

In the University of California, Berkeley’s antique-inspired Hertz Music Hall, sit two of contemporary science’s most prominent and of-the-moment figures. Jennifer Doudna, the scientist credited with some of the greatest advancements in CRISPR technology, is a Berkeley native, at least by virtue of occupation. She is the founder of the well-known Doudna Lab as well as the director of the Innovative Genomics Institute, a joint venture between UC Berkeley and UC San Francisco. Siddhartha Mukherjee is a physician, researcher, author, and Assistant Professor of Medicine at the University of Columbia’s Medical Center. Best known for his books, The Emperor of All Maladies: A Biography of Cancer and The Gene: An Intimate History, Mukherjee is well-versed in the intricacies of medical biology
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