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A practical guide to machine learning in business

A practical guide to machine learning in business

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.

Amazon Machine Learning offers several machine learning models you can use with data stored in S3, Redshift or R3, but you can’t export the models, and the training set size is rather limited. Microsoft’s Azure ML Studio has a wider range of algorithms, including deep learning, plus R and Python packages, and a graphical user interface for working with them. It also offers the option to use Azure Batch to periodically load extremely large training sets, and you can use your trained models as APIs to call from your own programs and services. There are also machine learning features such as image recognition built into cloud databases like SQL Azure Data Lake, so that you can do your machine learning where your data is.

Supervised learning

Many machine learning techniques use supervised learning, in which a function is derived from labelled training data. Developers choose and label a set of training data, set aside a proportion of that data for testing, and score the results from the machine learning system to help it improve. The training process can be complex, and results are often probabilities, with a system being, for example, 30 percent confident that it has recognized a dog in an image, 80 percent confident it’s found a cat, and maybe even 2 percent certain it’s found a bicycle. The feedback developers give the system is likely a score between one and zero indicating how close the answer is to correct.

It’s important not to train the system too precisely to the training data; that’s called overfitting and it means the system won’t be able to generalize to cope with new inputs. If the data changes significantly over time, developers will need to retrain the system due to what some researchers refer to as “ML rot.”

Machine learning algorithms — and when to use them

If you already know what the labels for all the items in your data set are, assigning labels to new examples is a classification problem. If you’re trying to predict a result like the selling price of a house based on its size, that’s a regression problem because house price is a continuous rather than discrete category. (Predicting whether a house will sell for more or less than the asking price is a classification problem because those are two distinct categories.)

If you don’t know all the labels, you can’t use them for training; instead, you score the results and leave your system to devise rules that make sense of the answers it gets right or wrong, in what’s known as unsupervised learning. The most common unsupervised learning algorithm is clustering, which derives the structure of your data by looking at relationships between variables in the data. Amazon’s product recommendation system that tells you what people who bought an item also bought uses unsupervised learning.

With reinforcement learning, the system learns as it goes by seeing what happens. You set up a clear set of rewards so the system can judge how successful its actions are. Reinforcement learning is well suited to game play because there are obvious rewards. Google’s DeepMind AlphaGo used reinforcement learning to learn Go, Microsoft’ Project Malmo system allows researchers to use Minecraft as a reinforcement learning environment, and a bot built with OpenAI’s reinforcement learning algorithm recently beat several top-ranked players at Valve’s Dota 2 game.

The complexity of creating accurate, useful rewards has limited the use of reinforcement learning, but Microsoft has been using a specific form of reinforcement learning called contextual bandits (based on the concept of a multi-armed slot machine) to significantly improve click-through rates on MSN. That system is now available as the Microsoft Custom Decision Service API. Microsoft is also using a reinforcement learning system in a pilot where customer service chatbots monitor how useful their automated responses are and offer to hand you off to a real person if the information isn’t what you need; the human agent also scores the bot to help it improve.

Combining machine learning algorithms for best results

Often, it takes more than one machine learning method to get the best result; ensemble learning systems use multiple machine learning techniques in combination. For example, the DeepMind system that beat expert human players at Go uses not only reinforcement learning but also supervised deep learning to learn from thousands of recorded Go matches between human players. That combination is sometimes known as semi-supervised learning.

Similarly, the machine learning system that Microsoft Kinect uses to recognize human movements was built with a combination of a discriminative system — to build that Microsoft rented a Hollywood motion-capture suite, extracted the position of the skeleton and labelled the individual body parts to classify which of the various known postures it was in — and a generative system, which used a model of the characteristics of each posture to synthesize thousands more images to give the system a large enough data set to learn from.

Predictive analytics often combines different machine learning and statistical techniques; one model might score how likely a group of customers is to churn, with another model predicting which channel you should use to contact each person with an offer that might keep them as a customer.

Navigating the downsides of machine learning

Because machine learning systems aren't explicitly programmed to solve problems, it’s difficult to know how a system arrived at its results. This is known as a “black box” problem, and it can have consequences, especially in regulated industries.

As machine learning becomes more widely used, you’ll need to explain why your machine learning-powered systems do what they do. Some markets — housing, financial decisions and healthcare — already have regulations requiring you to give explanations for decisions. You may also want algorithmic transparency so that you can audit machine learning performance. Details of the training data and the algorithms in use isn’t enough. There are many layers of non-linear processing going on inside a deep network, making it very difficult to understand why a deep network is making a particular decision. A common technique is to use another machine learning system to describe the behavior of the first.

You also need to be aware of the dangers of algorithmic bias, such as when a machine learning system reinforces the bias in a data set that associates men with sports and women with domestic tasks because all its examples of sporting activities have pictures of men and all the people pictured in kitchens are women. Or when a system that correlates non-medical information makes decisions that disadvantage people with a particular medical condition.

Machine learning can only be as good as the data it trains on to build its model and the data it processes, so it’s important to scrutinize the data you’re using. Machine learning also doesn't understand the data or the concepts behind it the way a person might. For example, researchers can create pictures that look like random static but get recognized as specific objects.

There are plenty of recognition and classification problems that machine learning can solve more quickly and efficiently than humans, but for the foreseeable future machine learning is best thought of as a set of tools to support people at work rather than replace them.

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