AI Articles and Reports about Business intelligence and analytics

Explore articles and reports related to artificial intelligence for business intelligence and analytics, including applications in forecasting, predictive analytics, text analysis, and more.

Jay Perrett (Aria Networks) - Genetic Algorithms Interview

Genetic Algorithms Evolve Simple Solutions Across Industries

Episode Summary: As it turns out, survival of the fittest applies as much to algorithms as it does to amoebas, at least when we're talking about genetic algorithms. While we've explored other types of machine learning algorithms in business in past articles, genetic algorithms are newer territory. We recently interviewed Dr. Jay Perret, CTO of Aria Networks, a company that uses genetic algorithm-based technology for solving some of industry's toughest problems, from optimization of business networks to pinpointing genetic patterns that are correlated with specific diseases.

machine learning in pharma and medicine

7 Applications of Machine Learning in Pharma and Medicine

When it comes to effectiveness of machine learning, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments. Hence, the present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals.
Burgeoning applications of ML in pharma and medicine are glimmers of a potential future in which synchronicity of data, analysis, and innovation are an everyday reality. We provide a breakdown of several of these pioneering applications, and provide insight into areas for continued innovation.

NLG in business

Fundamentals of NLG in Business Intelligence – Yseop’s Matthieu Rauscher

Episode Summary: You might be aware that some of the articles online about sports or financial performance of companies are article written by machines; this machine learning-based technology is the burgeoning field of natural language generation (NLG), which aims to create written content as humans would—in context— but at greater speed and scale. Yseop is one such enterprise software company, whose product suite turns data into written insight, explanations, and narrative. In this episode we interview Yseop's Vice President Matthieu Rauscher, who talks about the fundamentals of NLG in business, and what conditions need to be in place in order to drive business objectives. Rauscher also addresses the difference between discover-oriented machine learning (ML) and production-level ML, and why different industries might be drawn to one over the other.

Machine Learning and Location Data Applications for Industry

Machine Learning and Location Data Applications for Industry

There is a certain level of stigma that exists around using machine learning and location data in business applications, understandably due to risks inherent in exploitation of individual privacy. But if we look under the hood of society's daily web of interactions, we see that the location information economy—from GPS to radio signal based-triangulation to geo-tagged images and beyond—is now almost ubiquitous, from the moment we track our morning commute to the end-of-day search for healthy and convenient take-out for dinner.

Machine Learning in Human Resources - Applications and Trends

Machine Learning in Human Resources – Applications and Trends

Human resources has been slower to come to the table with machine learning and artificial intelligence than other fields—marketing, communications, even health care. But the value of machine learning in human resources can now be measured, thanks to advances in algorithms that can predict employee attrition, for example, or deep learning neural networks that are edging toward more transparent reasoning in showing why a particular result or conclusion was made.

Natural Language Processing – Business Applications

Natural Language Processing – Business Applications

Executives worry about their businesses.

They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process.

Deep Learning Applications in Medical Imaging 9

Deep Learning Applications in Medical Imaging

In 1895, the German physicist, Wilhelm Röntgen, showed his wife Anna an X-ray of her hand. “I have seen my death,” she said. Medical imaging broke paradigms when it first began more than 100 years ago, and deep learning medical applications that have evolved over the past few years seem poised to once again take us beyond our current reality and open up new possibilities in the field.

Deep Learning Applications in Medical Imaging 8

Machine Learning Algorithms for Business Applications – Complete Guide

With the development of free, open-source machine learning and artificial intelligence tools like Google’s TensorFlow and sci-kit learn, as well as “ML-as-a-service” products like Google’s Cloud Prediction API and Microsoft’s Azure Machine Learning platform, it’s never been easier for companies of all sizes to harness the power of data. But machine learning is such a vast, complex field. Where do you start learning how to use it in your business?

Business intelligence and analytics

Explore articles and reports related to artificial intelligence for business intelligence and analytics, including applications in forecasting, predictive analytics, text analysis, and more.