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“Above the Trend Line” – Your Industry Rumor Central for 3/18/2019

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.

Are B2B Marketers Leave Money on the Table by Ignoring B2C Data?

In this contributed article, Collin Dayley, Senior Vice President Sales and Strategic Partnerships at Versium, suggests that typically, B2B data focuses on role, and firmographic information. While B2C data can reveal information providing clues to the emotional reasons and process your customers use when making buying decisions. By combining both B2C and B2B data, marketers can develop more relevant content and experiences that meet individual buyer needs. This is proven to increase the ability to contact and engage B2B buyers.

High School Students Beat Trained Data Scientists at UC Berkeley, Solve Real Healthcare Problems with Aible AI in Minutes

Aible, the innovators of AI for business impact, announced the UC Berkeley Real World AI Challenge winners — the top scorers include two high school students, a history major and no data scientists. Nearly 30 high school and college students competed to create a custom Artificial Intelligence (AI) based on a real-world healthcare data set […]

Kinetica Launches Active Analytics Platform

Kinetica, a leader in active analytics for the Extreme Data Economy, announced the release of the first complete active analytics platform, dramatically simplifying the architecture to deliver smart analytical applications at massive scale. The platform unites the key elements of active analytics: historical analytics, streaming analytics, graph analytics, location intelligence, and machine learning-powered analytics.

AnotherBrain Reveals a New Generation of Artificial Intelligence

AnotherBrain is bringing its Organic AI™ technology that can transform every sensor into a smart sensor for use in industrial automation, automotive and IoT markets. AnotherBrain’s technology is explainable by design, uses low energy, and low data in order to make the deployment of artificial intelligence (AI) in factory and logistics more practical and effective.

A Blueprint for Preparing Your Own Machine Learning Training Data

Download the new guide from Alegion that acts as a pre-flight checklist for data science teams that are contemplating preparing their own maching learning training data.

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

How to Plan and Launch Your Modern Data Catalog

Implementing a data catalog helps every member of your data community discover and use the best data and analytics resources for their projects, achieve faster results, and make better decisions. They illuminate tribal knowledge and spur collaboration, both of which are key elements of collective data empowerment. Are you ready to plan and launch your modern data catalog? Data.world says, let’s get started.

Strategies for Obtaining Patents on AI Inventions in the U.S. and Europe

In this contributed article, Maria Anderson – Partner, Kimberly A. Kennedy – Associate, and Alexander J. Martinez – Associate at Knobbe Martens, discuss how AI has exploded in the last decade. Adopted in many business sectors, AI is rapidly becoming an integral part of society. Thus, obtaining patent protection for AI technology is more important now than ever before.

How to Get to the Data-Enabled Data Center

Despite their many promising benefits, advancements in Artificial Intelligence (AI) and Deep Learning (DL) are creating some of the most challenging workloads in modern computing history and put significant strain on the underlying I/O, storage, compute and network. An AI-enabled data center must be able to concurrently and efficiently service the entire spectrum of activities involved in the AI and DL process, including data ingest, training and inference.