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“Above the Trend Line” – Your Industry Rumor Central for 2/2/2023

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.

Heard on the Street – 2/1/2023

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Why AutoML Isn’t Enough to Democratize Data Science 

In this contributed article, Noam Brezis, co-founder and CTO of Pecan AI, explores that because AutoML was born out of academia, in its current incarnation it is only built to simplify the model building process. This is likely the reason why existing AutoML solutions are finding challenges with scaling. Plus, these types of solutions are not incorporating the aspects of data prep and feature engineering, nor the model training, deployment and monitoring, which as a result slowing down adoption of AI within the enterprise and curtailing the impact it can deliver.

AI Under the Hood: Interactions

We asked our friends over at Interactions to do a deep dive into their technology. Mahnoosh Mehrabani, Ph.D., Interactions’ Sr. Principal Scientist shared some fascinating information about how Interactions’ Intelligent Virtual Assistants (IVAs) leverage advanced natural language understanding (NLU) models for “speech recognition” and “advanced machine learning.” The company uses NLU models to help some of today’s largest brands to understand customer speech and respond appropriately.

Heard on the Street – 1/23/2023

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Interview: David Willingham, Principal Product Manager, MathWorks

I recently caught up with David Willingham, Principal Product Manager, MathWorks to discuss the evolution of data-centric AI and how engineers can best navigate – and benefit from – the transition to data-focused models within deep learning environments. As research into data-centric AI continues, we can expect best practice to evolve to suit a growing list of applications. Greater levels of data optimization and collaboration between multi-disciplinary teams is also likely to follow in the near future.

The insideBIGDATA IMPACT 50 List for Q1 2023

The team here at insideBIGDATA is deeply entrenched in keeping the pulse of the big data ecosystem of companies from around the globe. We’re in close contact with the movers and shakers making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List.

What is “Hyper Data Collection” and Why is More Sophisticated Data Protection Required as a Result of This?

In this contributed article, Arti Raman and Debbie Reynolds discusses “Hyper data collection,” the ability organizations have to access many different data types from individuals due to their ability to leverage wide-ranging access to an individual’s data through offering multiple products, services, and experiences that can be combined to create more robust profiles of individuals.

Heard on the Street – 1/4/2023

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

What Is Federated Learning in Health Care? And How Should Health IT Teams Prepare?

In this contributed article, Ittai Dayan, co-founder and CEO of Rhino Health, believes that while traditional machine learning has huge potential for medical researchers, its major shortcoming is the vast amount of centralized data collection that’s required, and the privacy issues this creates. Federated learning has been suggested as a potential solution to this problem. This is a novel ML technique that is able to access data held across numerous decentralized servers (such as data held by individual hospitals), with the data never leaving these servers and remaining completely anonymous.