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A practical guide to machine learning in business

Mary Branscombe | Sept. 11, 2017
Machine learning is poised to have a profound impact on your business but the hype is sowing confusion. Here’s a clear-eyed look at what machine learning is and how it can be used today.

This is the technique the Clutter feature in Outlook uses to filter messages that are less likely to be interesting to you based on what messages you’ve read, replied to and deleted in the past. It was built with Infer.NET, a .NET framework you can use to build your own probabilistic systems.

Cognitive computing is the term IBM uses for its Watson offerings, because back in 2011 when an earlier version won Jeopardy, the term AI wasn't fashionable; over the decades it’s been worked on, AI has gone through alternating periods of hype and dismissal.

Watson isn't a single tool. It's a mix of models and APIs that you can also get from other vendors such as Salesforce, Twilio, Google and Microsoft. These give you so-called “cognitive” services, such as image recognition, including facial recognition, speech (and speaker) recognition, natural language understanding, sentiment analysis and other recognition APIs that look like human cognitive abilities. Whether it's Watson or Microsoft's Cognitive Services, the cognitive term is really just a marketing brand wrapped around a collection of (very useful) technologies. You could use these APIs to create a chatbot from an existing FAQ page that can answer text queries and also recognise photos of products to give the right support information, or use photos of shelf labels to check stock levels.

Many “cognitive” APIs use deep learning, but you don’t need to know how they’re built because many work as REST APIs that you call from your own app. Some let you create custom models from your own data. Salesforce Einstein has a custom image recognition service and Microsoft’s Cognitive APIs let you create custom models for text, speech, images and video.

That’s made easier by transfer learning, which is less a technique and more a useful side effect of deep networks. A deep neural network that has been trained to do one thing, like translating between English and Mandarin, turns out to learn a second task, like translating between English and French, more efficiently. That may be because the very long numbers that represent, say, the mathematical relationships between words like big and large are to some degree common between languages, but we don’t really know.

Transfer learning isn't well understood but it may enable you to get good results from a smaller training set. The Microsoft Custom Vision Service uses transfer learning to train an image recognizer in just a few minutes using 30 to 50 images per category, rather than the thousands usually needed for accurate results.

 

Build your own machine learning system

If you don’t want pre-built APIs, and you have the data to work with, there’s an enormous range of tools for building machine learning systems, from R and Python scripts, to predictive analytics using Spark and Hadoop, to specific AI tools and frameworks.

 

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