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In the Time of Big Data and Machine Learning, It’s Important to Ask “Why?”

In this special guest feature, Sundeep Sanghavi, Co-founder and CEO of DataRPM, believes that “The Five Whys” interrogation technique developed by Sakichi Toyoda, founder of the Toyota car company and hero of the Japanese industrial revolution, is still very relevant in today’s age of big data and machine learning.

UMass Amherst Computer Scientists Develop New Techniques to Measure Social Bias in Software

Today, banks are increasingly using software to decide who will get a loan, courts to judge who should be denied bail, and hospitals to choose treatments for patients. These uses of software make it critical that the software does not discriminate against groups or individuals, say computer science researchers at the University of Massachusetts Amherst. […]

A Developer’s Guide to Launching a Machine Learning Startup

Frameworks, applications, libraries and toolkits—journeying through the world of deep learning can be daunting. If you’re trying to decide whether or not to begin a machine or deep learning project, there are several points that should first be considered.

Breakthrough Argyle Data™ Application Successfully Predicts Mobile Subscriber Creditworthiness in Multiple Operator Trials

AI/machine learning company Argyle Data™ has successfully concluded a series of trials with European and Latin American operators, using new algorithms and neural network architectures analyzing real carrier data to accurately predict subscribers’ intention and ability to pay monthly service bills.

AI vs. Human SEO: How to Keep the Balance?

In this contributed article, Areg Vardanyan, the founder and COO of AYYO Marketing, examines the intersection of artificial intelligence (AI) and search engine optimization (SEO) and how the optimal solution may be a combination of human and machine efforts.

Launch a Machine Learning Startup

Launch a Machine Learning Startup – In this report, we’ll address everything from how to choose a framework and pick the tools you need to get started, to the questions you’ll be asking yourself, and the benefits of immersing yourself in the machine and deep learning communities. This report also untangles the jargon and explores what these terms actually mean. Download this special report now.

Driving Reliability and Improving Maintenance Outcomes with Machine Learning

In this special guest feature, Mike Brooks, Senior Business Consultant at AspenTech, discusses how companies can no longer rely solely on traditional equipment maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions using machine learning.

Virtualitics: Caltech & NASA Scientists Build VR/AR Analytics Platform using AI & Machine Learning

Virtualitics is a transformative start-up company that merges artificial intelligence (AI), big data and virtual reality (VR), and augmented reality (AR) to gain insights from big and complex data sets. Furthermore, Virtualitics leverages AI and easy-to-use machine learning tools so even non-expert users can uncover multidimensional relationships present in complex data sets with the click of a button.

Machine Learning: the Power and Promise of Computers that Learn by Example

25 April 2017

The many potential social and economic benefits from advances in AI-based technologies depend entirely on the environment in which these technologies evolve, says the Royal Society. According to a new report from the UK’s science academy, urgent consideration needs to be given to the “careful stewardship” needed over the next ten years to ensure that the dividends from machine learning – the form of artificial intelligence that allows machines to learn from data – benefit all in UK society.

Predicting and Preventing Power Outages Using Big Data

Texas A&M University researchers have developed an intelligent model that can predict a potential vulnerability to utility assets and present a map of where and when a possible outage may occur. Dr. Mladen Kezunovic, along with graduate students Tatjana Dokic and Po-Chen Chen, have developed the framework for a model that can predict weather hazards, vulnerability of electric grids and the economic impact of the potential damage.