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Sentience: Reimagine What’s Next for Big Data in the era of AI

In this special guest feature, Oliver Ratzesberger, Chief Product Officer for Teradata, discusses his new book titled “The Sentient Enterprise” which highlights the future of business decision-making, analytics, AI and deep learning.

Quadcopter Navigation in the Forest using Deep Neural Networks

In the video presentation below, a group of deep learning researchers study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; the team proposes a different approach based on a Deep Neural Network used as a supervised image classifier.

Data Governance: Lessons Learned from the Front Lines

In this contributed article, Ken Arnold is Analytics Manager at Covenant HealthCare, says there’s no question that a strong data analytics foundation is critical in today’s healthcare ecosystem. If your organization is considering adopting a data governance capability to support your analytics efforts, keep these best practices in mind to ensure that your data is consistent, accurate and trusted across the entire organization.

How Self-Learning Programs Help Businesses Save Time and Money

In this contributed article, technology writer and blogger Kayla Matthews points out that AI and machine learning can learn from experiences without constant input from programmers, and as a result businesses are increasingly looking for ways to use them to make their operations more efficient and cost-effective. Here is a shortlist of some ways that self-learning programs help businesses save time and money.

Interview: Joe Pasqua, Executive Vice President of Products at MarkLogic

I recently spoke with Joe Pasqua, Executive Vice President of Products at MarkLogic. Our discussion touched on a number of related issues, including the importance of effective data integration as organizations’ work to implement initiatives as broad as digital transformation and as specific as compliance with the new General Data Protection Regulation (GDPR) laws, which go into effect this May.

Pay Attention to Spatial Data, It Is the Next Frontier

In this special guest feature, Madhusudan Therani, CTO at Near, points out that with an almost endless list of sources – including map and satellite data, catchment areas, service points, building and customer locations, land use data, urban data, and communication pathways – spatial data is a valuable global commodity which comes in many forms. So why do businesses need to process spatial data and what are some of the challenges they face in doing so at scale?

The Parallel Universe of Dark Data and Dark Matter

In this contributed article, David Gingell, Chief Marketing Officer for Seal Software, takes a look at how all of the hype around dark data and AI-driven dark-analytics is missing the first crucial step, which is to identify what is real and what is useful, and for what application. While it may sound exciting, using AI to automatically process vast amounts of data in ultra-fast operations is not going to provide much insight.

Book Review: Learning TensorFlow

Deep Neural Networks (DNNs), upon which deep learning is based, are trained with large amounts of data, and can solve complex tasks with unprecedented accuracy. TensorFlow is a leading open source software framework that helps you build and train neural networks. Here’s a nice resource to help you kick-start your use of TensorFlow – “Learning TensorFlow” by Tom Hope, Yehezkel S. Resheff and Itay Leider.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2017

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.

Beyond Big Data: Why AI Requires Getting Small Data Right

In this special guest feature, Maciej Gryka, Head of Data Science at Rainforest, discusses why big data in the context of AI leads us to ask some serious questions about the future of big data. Data scientists often wonder whether we need big data as much as some think. In many cases the answer is “no” and instead of going big, what we really need to be doing is thinking smaller. Read on to learn why.