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Q&A: Mitchell Shuster on Machine Learning Hype vs. Reality

What questions should I be asking to get the most out of my data with machine learning? What are best practices for using machine learning in my organization?

Our own Informationist and Data Scientist Mitchell Shuster will be tackling these questions and more on an IEEE-sponsored round table webinar tomorrow. Titled “The Business Potential of Machine Learning and Cognitive Computing,” this webinar will cover the next phase of machine learning, including deep learning, analytics and cognitive computing. Click here to register for the webinar.

Prior to the webinar, I sat down with Mitchell to talk about the hype and reality around machine learning:

What is machine learning?

Machine learning is a broad concept representing dozens of approaches and algorithms for improving how technology performs. The most important and distinctive quality that separates it from traditional computing is that the accuracy of the output improves as more data is processed (which is where the “learning” part comes into play).

What are the pitfalls organizations should avoid when looking to leverage machine learning?

Beware the hype! Although machine learning has great potential, it’s not a magic wand to solve all problems. You need the right data, the right infrastructure, the right people with the right skills, and you need a question that your data can answer. And then you still need to carefully consider your results in context before you can do anything with them.

What’s next for machine learning?

The next phase of machine learning is already upon us. It comprises concepts like deep learning (one of the five “new” types of advanced analytics from our perspective) and cognitive computing (think Watson and Siri). It’s definitely an exciting time to be a data scientist!

Mitchell will be guesting on the webinar tomorrow, May 27, from 1-2pm ET. Want more from Mitchell? Make sure to register for the webinar to secure your spot!

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