Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Electronic Health Record (EHRs) systems are now used in 80% of doctors offices and contain a rich source of patient data available to innovate and improve healthcare. A team at New York University's Courant Institute of Mathematical Sciences developed algorithms and a system to extract EHR data to faster diagnose patients and provide a thorough understanding of the patient's health.

This year I ramped up the amount of machine learning I covered in my fall class at Penn. For the final project, I had my students recreate the phenomenally creative restaurant health inspection prediction project from Chicago. There were two components of the project – the development of a predictive model and then the design of an application to convert the predictive intelligence into a application that the health department could use to better allocate its limited inspection resources. The students don't estimate models anymore complicated than logistic regression, but they do spend a great deal of time constructing training and test sets and validating their models. Below is a video from two of the students in the class, Shruthi Arvind and Kristen Coe, presenting their health inspection app.

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Last week, I compiled a list of the 5 jobs robots will take first. Today, let's have a go at the 5 jobs robots will take last. For this article only, let's define "robots" as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. For example, an assembly line worker performs mostly manual repetitive tasks which, depending on complexity and a cost/benefit analysis, can be automated. A CEO of a major multinational conglomerate performs mostly cognitive nonrepetitive tasks that are much harder to automate.

Back in January, Coupa acquired Spend360, a leader in artificial intelligence for spend management founded by Paddy Lawton, the author of today's blog post. What happens when a mathematician falls down a rabbit hole into the world of sourcing and procurement? Artificial intelligence (AI) comes to spend management, that's what. It's a tale that illustrates how AI has been evolving over the past several years, to the point where it's finally starting to deliver real value. I'll tell you all about how that happened and what it means, but first I want to give some background so you understand exactly what it is we're talking about, and why it's happening now. It seems that every third business article you read these days makes some mention of artificial intelligence and machine learning, and how they're going to transform the world.

How worried should we be about artificial intelligence? Recently, I asked a number of AI researchers this question. The responses I received vary considerably; it turns out there is not much agreement about the risks or implications. Non-experts are even more confused about AI and its attendant challenges. Part of the problem is that "artificial intelligence" is an ambiguous term.

That's a tough one – for me it's a moment when the end user goes'wow!' and there have been a number of projects where this has happened. The one that first comes to mind is an application we built for one of our clients in the insurance industry, where the Arria NLG system provides summaries of insurance policy sales from millions of sales records as well as root cause analysis. This would have taken an experienced analyst half a day to produce. Another that comes to mind, is an application that we built for an online advertising company to be used by campaign managers. Our system provided not only analysis of weekly performance but also recommendations on how to improve campaign performance.

They're old ideas, but machine learning and AI are now the communications industry's hot buzzwords, judging from last week's Mobile World Congress. "For mobile operators around the world, this is no longer an experiment," said Patrick Ostiguy, CEO of Accedian, during an MWC panel discussion on machine learning. Machine learning, which involves training a computer by feeding it examples and counterexamples, has been around for decades. The post office's optical mail-sorting machines are one example. True AI and deep learning, which strive to teach a brain how to teach itself, are also long-standing disciplines.

Diabetes can damage peripheral nervous tissue with the consequent loss of protective sensation. Increased risk factors include having diabetes for more than 10 years, being male, having poor glucose control, and having cardiovascular, renal or retinal complications. Patients should be advised to recognize early symptoms including loss of vibration sense, pain sensation, and temperature sensation in the feet. The American Diabetes Association recommends a multidisciplinary approach to management of foot ulcers and high-risk feet, particularly those with a history of ulcers or amputations. High risk patients should be referred to specialists for preventive care and life-long surveillance.

We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good job covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection. Additional bonus appendices cover some elements of linear algebra, dimensionality reduction, probability and statistics, regression analysis, and optimization, in case those concepts are fuzzy for the student. They're by no means thorough enough to learn the topic, merely to remind the reader of salient points they should remember.