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AI Critical Measures: Time to Value and Insights

AI is a game changer for industries today but achieving AI success contains two critical factors to consider — time to value and time to insights.  Time to value is the metric that looks at the time it takes to realize the value of a product, solution or offering. Time to insight is a key measure for how long it takes to gain value from use of the product, solution or offering.

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.

AI Goes Mainstream

According to a recent Gartner survey, Artificial intelligence (AI) learning has moved from a specialized field into mainstream business use with 37 percent of respondents reporting their enterprises either had deployed AI or would do so shortly. WekaIO’s Barbara Murphy explores the path of artificial intelligence from the fringe to mainstream business practices. Find out what is driving AI growth and adoption.

Alegion Outlines the 4 Most Prevalent Types of AI Bias

AI systems are becoming more and more of the norm as machine and deep learning gain grown — especially within the data center and colocation markets. That said, Artificial Intelligence systems are only as good as their underlying mathematics and the data they are trained on. Download a new report from Alegion to further understand the bias behind machine learning and how to avoid four potential pitfalls.

insideBIGDATA 2019 Annual Executive Round Up

Our annual insideBIGDATA Executive Round Up showcases the insights of thought leaders on the state of the big data industry, and where it is headed. In our annual 2019 round up, we examine five topics: the importance of AI explainability in 2019, what industries are making the best competitive use of AI in 2019, how enterprises are seeking to improve technological infrastructure and cloud hosting processes for supporting AI, how AI-optimized hardware solves important compute and storage requirements, and how AI plays important roles at 3 leading companies.

Labeled Training Sets for Machine Learning

It’s no secret that machine learning success is derived from the availability of labeled data in the form of a training set and test set that are used by the learning algorithm. Labels are the values of the response variables (what’s being predicted) that are used by the algorithm along with the feature variables (predictors). One consistent problem faced by data scientists is how to obtain labels for a given data set for use with machine learning. In this article we’ll see a variety of techniques used down in the trenches.

Four Types of Machine Learning Bias

AI models comprise algorithms and data, and they are only as good as their underlying mathematics and the data they are trained on. When things go wrong with AI it’s because either the model of the world at the heart of the AI is flawed, or the algorithm driving the model has been insufficiently or incorrectly trained. Download the new whitepaper from Alegion that can help AI project leads and business sponsors better understand the four distinct types of bias that can affect machine learning, and how each can be mitigated.

Robots Keeping Shelves Stocked: How Machine Learning and AI is Helping the CD Industry Stay in the Game

In this special guest feature, Amjad Hussain, founder and CEO of Algo.ai,, observes that whereas 20 years ago, labels were able to flood stores with CDs in the knowledge surplus inventory would inevitably sell over time, doing so now could lead to huge losses. As such, companies are increasingly turning to new smart technology which harnesses the power of machine learning AI to accurately predict future demand using existing data.

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