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Turning IT Upside Down In a Machine Learning World

In this special guest feature, Chris Heineken, CEO and Co-founder of Atrium, suggests that as Machine Learning (ML) is growing in the IT and cloud space, understanding how to best utilize its capabilities will change the approach to implementing new IT investments. The systems of intelligence characterized by augmented ML, robust analytics, and workflow-based frameworks biased towards action, will dominate the mindset of those looking to place their organizations at the top end of the IT systems ‘bell curve.’

“Above the Trend Line” – Your Industry Rumor Central for 10/7/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

DataRobot Enhances Enterprise AI Platform, Further Automating the Path from Data to Value

DataRobot, a leader in enterprise AI, unveiled new features to its Enterprise AI platform designed to automate the entire end-to-end data science process, introducing an AI Catalog and next-generation automated feature engineering.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3

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.

Arm Treasure Data Introduces TreasureBoxes and Custom Scripts to Enable Faster Time-to-Value for Customers

Arm® Treasure Data™ announced new product capabilities and features for its Customer Data Platform (CDP), including Treasure Boxes and Custom Scripts; each helps speed business results customers will see from the platform. Treasure Boxes are the industry’s first CDP solutions library of prebuilt sets of code and applications. Treasure Boxes can be easily deployed in a customer’s Arm Treasure Data account to seamlessly leverage customer data for specific use cases. Custom Scripts provide companies with the ability to run AI/ML-based analytics on their data to drive actionable insights for marketing campaigns.

Why Data Annotation is the Secret to Hacking AI

In this special guest feature, Michael Goldberg, VP of Marketing at Innodata (NASDAQ: INOD), indicates that even the most technically advanced algorithm cannot address or solve a problem without the right data. We know having access to data is quite valuable, but having access to data with a learnable ‘signal’ consistently added at a massive scale is the biggest competitive advantage nowadays. That’s the power of data annotation.

Despite $6.1 Billion in Funding, China Not Leading in AI Innovation

Over the past four years, Chinese artificial intelligence (AI) startups have received $6.1 billion in funding. However, despite immense amounts of investment and being second only to the U.S. in number of companies focusing on AI, China is not leading in cutting-edge AI innovation. To better understand the progress of AI in China, our friends over at Lux Research, a leading provider of tech-enabled research and advisory services for technology innovation, took a deep dive into China’s AI patents, research papers, venture capital (VC) funding, and other innovation data in their new report, “Will China Take Over the Global AI Industry?”

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.