AI Articles and Analysis about Business intelligence and analytics

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.
At Emerj, the AI Research and Advisory Company, we research how AI is impacting the pharmaceutical industry as part of our AI Opportunity Landscape service. Global pharma companies use AI Opportunity Landscapes to find out where AI fits at their company and which AI applications are driving value in the industry.
In this article, we use insights from our research to provide a breakdown of several of the pioneering applications of AI in pharma and areas for continued innovation.

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.

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.

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.

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.

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.

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?

Episode Summary: In this episode, we speak with Senior Editor for The Economist in digital and data products and Co-author of "Big Data: A Revolution that Will Transform How We Work, Live and Think", Kenneth Cukier, who speaks on the technologies that underlie big data and make it what it is today. Cukier addresses common misconceptions about machine learning and dives into how companies can catch up with this technology by thinking through, assessing ROI, and making sense of the dynamics of data assets for business. Listen for Cukier's apt analogy in comparing machine learning technology to the dynamics of computing from decades ago.