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Matt Coatney

Matt Coatney
Matt Coatney is an Artificial Intelligence expert, data scientist, entrepreneur, technology executive, author and speaker. For the last 15 years he has brought advanced technologies to market, including chemical mining software LeadScope, legal text analytics products, and a predictive modeling tool for the US Air Force. He now leads Exaptive's consulting team. Matt has coauthored a chapter of the book Data Mining: Next Generation Challenges and Future Directions, published by the Association for the Advancement of Artificial Intelligence and MIT Press. He has also published technical papers in respected journals including Knowledge and Information Systems and the International Conference on Data Mining. He also co-authored and published the book The 72 Hour Law Firm: How to Start Your Own Firm in Three Days. He is a frequent blogger and speaker at conferences including the International Legal Technology Association Annual Conference and the Chief Information & Technology Officers Forum.
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Recent Posts

Rapid Data Products: Kicking the Tires on IBM Watson in One Day

Late last year I turned the venerable age of 40, and graying and balding jokes aside, I've spent a good bit of time reflecting on the accelerating pace of change in technology. It's not just that things are getting faster, better, cheaper. It's that whole new capabilities are now possible that we could only dream about even a few decades ago. Mail is electronic. A TV and a computer are basically the same thing. And you can talk to your phone.

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Gleaning Insight from Content with IBM Watson

In recent years, machine learning as a service has come of age, with robust capabilities from Amazon, Google, Microsoft and others now available through REST APIs for a fraction of the cost of deploying or developing your own capabilities. One of the better known – if not easy to separate hype from reality – is IBM Watson. While Watson gained fame as the Jeopardy!-winning supercomputer, IBM now uses the brand for a wide variety of machine learning capabilities from speech-to-text and conversational bots to text mining algorithms for understanding the concepts, references and tone of text-based content.

In this post, we'll cover how to integrate one such IBM service – Natural Language Understanding – and rapidly prototype an application that you can try on your own content. It includes how to get started with IBM's hosted service Bluemix and the Python code to connect to the REST API. I've also included a working data application that you can run with your own text.

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A Data Application to Foretell the Next Silicon Valley?

Can we predict what the next hub of tech entrepreneurship will be? Could we pinpoint where the next real estate boom will be and invest there? Thanks to advances in machine learning and easier access to public data through Open Data initiatives, we can now explore these types of questions.

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Affecting Change Using Social Influence Mapping

If you've ever tried to get a company to adopt new software you know how challenging it can be. Despite what seem to you like obvious benefits and your relentless communication, people selectively ignore or, worse, revolt against the change. Change efforts will even stumble in the face of this wisdom of the ages:

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Data Science Wanderlust: Analyzing Global Health with Protein Sequences

Fifteen years ago, I had the unique opportunity to go on Semester at Sea, an around-the-world trip on a converted cruise ship that combined college coursework stops at nine countries on four continents. This once in a lifetime trip instilled in me a strong sense of wanderlust and a deep desire to give back to the global community.

Every Journey Begins with a Single Step

Fast-forward to a few months ago, when I joined Exaptive on an exciting new project. A large NGO enlisted us to analyze a massive set of historical data for countries. The goal: to develop a better, more granular means of grouping countries than the outdated and crude approach of "developed" and "developing." This large, complex, messy dataset and thorny problem were a great fit for my background in artificial intelligence and data science.

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