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Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

HPE Accelerates Artificial Intelligence Innovation with Enterprise-grade Solution for Managing Entire Machine Learning Lifecycle

Hewlett Packard Enterprise (HPE) announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days.

Sophisticated AI Will Make The Deepfake Problem Much, Much Worse

In this contributed article, front end developer Gary Stevens suggests that the deep fake video issue is still in its infancy. As AI advances, discerning real from unreal news will become exponentially harder.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.

Interview: KDD2019 Co-General Chairs Ankur Teredesai & Vipin Kumar

During my trip to KDD2019 in August, I had the pleasure of sitting down to chat with the co-chairs of the conference, Ankur Teredesai and Vipin Kumar. In the interview that follows, we discuss the growth of the KDD conference over the years, and also it’s changing focus. KDD is touted as being “the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.”

AI in the Enterprise: Trends & Insights on Vendor Selection and Implementation

A new report was released by our friend over at Leverton, a data extraction startup recently acquired by MRI Solutions, titled “AI in the Enterprise: Trends & Insights on Vendor Selection and Implementation.” The report lends insight into the experiences and preferences of “leaders,” “lookers,” and “laggards” (defined below) when it comes to AI deployment.

TOP 10 insideBIGDATA Articles for August 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

Interview: Terry Deem and David Liu at Intel

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel® Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel® Distribution for Python*, both from Intel, to discuss the Intel® Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution’s use in scientific computing, and a look to the future with respect to IPD.

Using Artificial Intelligence to Track Birds’ Dark-of-Night Migrations

On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services’ network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers.

Composable Multi-Threaded Parallelism in Julia

JuliaCon 2019, held July 22-26, 2019 at the University of Maryland in Baltimore, was the biggest and best JuliaCon to date. The JuliaCon session below, “Composable Multi-Threaded Parallelism in Julia,” was presented by Jeff Bezanson and Jameson Nash (Julia Computing). The talk discusses the release of a preview of an entirely new threading interface for Julia programs: general task parallelism.