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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.

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.

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.

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.

How Machine Learning is Improving Business Intelligence

In this contributed article, Lauren Adley, writer and editor at Essay Writing In Australia, discusses how machine learning (ML) is improving BI. The ML ability to streamline operational processes is probably its most important way. With the help of intelligent IT automation, productivity boosts can be massive, and this is why this aspect of ML is attracting a lot of attention.

AgilOne Announces Customer Data Platform Update with New Features for Machine Learning

AgilOne, a leading customer data platform for enterprise B2C brands, announced major advances in machine learning, as well as enhanced reporting capabilities and new features to maximize the profitability and results of couponing programs. These new capabilities further AgilOne’s strengths as the only customer data platform with the ability to support the advanced needs and use cases of the enterprise.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2018

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.