Emerging tools such as artificial intelligence and natural language processing are being used in the insurance sector, but costs remain high and there are questions about bias being introduced into machine learning, according to a speaker at the Public Risk Management Association's annual meeting Monday. "Everything is smart these days," said Brian Billings, vice president of predictive analytics in Ballwin, Missouri, for Midwest Employers Casualty Co., part of W.R Berkeley Corp., and such devices as cell phones and televisions now collect data from their users. "All of that technology is being driven by the use of data." Machine learning, including artificial intelligence and natural language processing, takes the data being collected and tries to predict some kind of outcome, Mr. Billings said, such as a numerical value or, in the case of the insurance sector, a claims scenario. With natural language processing, a model is trained to read text, Mr. Billings said.

If you would like to learn artificial intelligence and natural language processing you might be interested to know that NVIDIA has created three introductory courses. To provide developers and those interested an introduction on how to use modern development tools to quickly create conversational artificial intelligent (AI) and natural language processing (NLP), GPU-accelerated applications. "Text classification answers the question: Which category does this bit of text belong in? For example, if you want to know whether a movie review is positive or negative, you can use two categories to build a sentiment analysis project. Take this one step further, and classify sentences or documents by topic using several categories. In both use cases, you start with a pre-trained language model and then "train" a classifier using example classified text to create our text classification project."

Wherever that will lead is, at the time of the writing of this article, still not certain, but regardless of the direction, it's clear that advancing progress with artificial intelligence is a key strategic element for both major parties. Over the course of the past few years, governments around the world have taken strong positions on advancing their strategies around AI adoption. Certainly heading into the new year it seems that the pace of adoption won't be slowing any time soon. At the recent Data for AI conference, we had an opportunity to get insights into how the government plans to continue and accelerate its adoption of AI in an interview with Ellery Taylor, Acting Director of the Office of Acquisition Management and Innovation Division, at the US General Services Administration (GSA). In this article he shares his outlook for the future of AI and how it is being adopted in the government.

The concept of artificial intelligence is becoming more common, not only in the technological field but in daily life. It basically refers to the ability to simulate processes of human intelligence developed by computer software, acquiring the abilities to reason, learn and self-correct. All this that may seem futuristic we can see represented in our daily technology, as is the case of chatbots with which we can communicate so easily in our smartphones. On the other hand, we find natural language that is nothing more than the language that we humans use, it can be spoken, written or gestural. This language, which may sound the most basic, requires many neural connections and brain and body processes to understand others and express ourselves.

There's been a huge amount of focus on the improvements artificial intelligence can make to individual consumers, Dave Damer, CEO and Founder of Testfire Labs, explores the strategic productivity gains that could be recognized by SMEs, corporates, and other organizations. Spend any time reading about artificial intelligence (AI), and you might think all the innovation is in the consumer space. While there's plenty of exciting development -- the growing skills of Alexa, the natural language processing (NLP) of Google Assistant, or the ubiquity of Siri, that's only a fraction of what AI and NLP can do. There's another, seemingly intractable problem where AI and NLP could be revolutionary -- business productivity, specifically the area that gets a big collective eye roll from employees -- meetings. The idea of most meetings is to draw people together, discuss ideas and options, create a consensus, and develop actions to achieve business tasks. That's the ideal, the reality is a little different: This is compounded as workforces become more culturally and geographically diverse.

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