Sign up for our newsletter and get the latest big data news and analysis.

Digital Transformation Projects Continue to be at Risk, Couchbase Research Finds

Despite rising optimism in digital transformation, the vast majority of organizations are still suffering failure, delays or scaled back expectations from digital transformation projects, research from Couchbase has found. In the survey of 450 heads of digital transformation in enterprises across the U.S., U.K., France and Germany, 73 percent of organizations have made “significant” or better improvements to the end-user experience in their organization through digital innovation. Twenty-two percent say they have transformed or completely “revolutionized” end-user experience, representing a marked increase over Couchbase’s 2017 survey (15 percent).

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.

Algorithms: Not Evil, Helpful

INFORMS member Gah-Yi Ban of the London Business School breaks down what algorithms are and how they are useful in a unique talk at a TEDx event. She compares algorithms to evil beings and says it’s a common misconception. Ban helps people understand the role of algorithms in our present lives and how we can shape their role in our future.

How Would You Explain AI to a Second Grader?

Artificial Intelligence (AI) is a hot topic and articles written on the subject are often tailored to a tech audience. But what about people who are not technically proficient? How would you explain AI to them? Cupid Chan is CTO of Index Analytics and a Fellow at the University of Maryland. Recently, Cupid gave a presentation to the group called “How do I teach my second grader about AI?”

Prepare for Production AI with the HPE AI Data Node

In this video from GTC 2019 in San Jose, Harvey Skinner, Distinguished Technologist, discusses the advent of production AI and how the HPE AI Data Node offers a building block for AI storage. Commercial enterprises have been investigating and exploring how AI can improve their business. Now they’re ready to move from investigation into production. […]

Using Bayesian Optimization to Tune Machine Learning Models

The presentation below, “Using Bayesian Optimization to Tune Machine Learning Models” by Scott Clark of SigOpt is from MLconf. The talk briefly introduces Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive.

Better, Faster Graph Processing

A team from MIT CSAIL has developed a new programming language for graph processing that could help. Dubbed “GraphIt,” the new domain-specific language has been shown to outperform existing frameworks by a factor of nearly 5x while also reducing the lines of code by almost an entire order of magnitude.

Big Data Made Simple

The animation below from our friends over at WHISHWORKS explains in simple terms what is Big Data and when it’s time for a company to consider moving to a Big Data environment.

Data Science at Microsoft – Interviews with Practitioners

In this technical brief I wanted to pass along some great resources in support how data scientists approach their profession and illustrate the kind of background a typical data scientist might have to become successful. insideBIGDATA previously featured four compelling podcast interviews with Microsoft data scientists.

State of the Art Natural Language Processing at Scale

The two part presentation below from the Spark+AI Summit 2018 is a deep dive into key design choices made in the NLP library for Apache Spark. The library natively extends the Spark ML pipeline API’s which enables zero-copy, distributed, combined NLP, ML & DL pipelines, leveraging all of Spark’s built-in optimizations.