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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.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

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.”

Research Highlight: Compressing Neural Networks with Minimal Sacrifice in Accuracy

Most data scientists soon realize that deep learning models can be unwieldy and often impractical to run on smaller devices without major modification. Our friends over at deeplearning.ai recently communicated about a group of researchers at Facebook AI Research determined a new technique to compress neural networks with minimal loss in accuracy.

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

Field Report: KDD 2019

As a very long time member of the ACM and their SIGKDD group, I’d always wanted to attend a KDD conference (first one occurred in 1995). This year I received a gracious invitation to attend KDD2019 in Anchorage, Alaska, August 4-8. It satisfied two of my bucket list items: witnessing a KDD first-hand and also […]

AI for Legalese

Have you ever signed a lengthy legal contract you didn’t fully read? Or have you every read a contract you didn’t fully understand? Contract review is a time-consuming and labor-intensive process for everyone concerned — including contract attorneys. Help is on the way. IBM researchers are exploring ways for AI to make tedious tasks like contract review easier, faster, and more accurate.

Using Camera Data Effectively Without Facial Recognition

Expanded use of privacy-invasive facial recognition technology is a hot-button issue. But there are solutions that can preserve and improve the value of cameras in the enterprise without the drawbacks of privacy-invasive facial recognition. Escalating push-back against facial recognition technology that is perceived as privacy-violating, inaccurate and biased, has sparked a search for solutions to this question: Can camera data be used effectively without facial recognition?