Mainstreaming Machine Learning, Part 1

Stephen DeAngelis

August 26, 2015

“There is pressure in the enterprise software space,” writes Jorge Garcia (@jgptec), “to incorporate new technologies, both in hardware and software, in order to keep pace with modern business. It seems we are approaching another turning point in technology where many concepts that were previously limited to academic research or very narrow industry niches are now being considered for mainstream enterprise software applications. Machine learning, along with many other disciplines within the field of artificial intelligence and cognitive systems, is gaining popularity, and it may in the not so distant future have a colossal impact on the software industry.”[1] Business leaders’ eyes are just opening to the possibilities machine learning applications can have in the commercial world. Using information to improve processes, gain insights, and market more effectively pre-dates the digital age; but, Garcia recognizes that we sit on the cusp of a new era — an era in which cognitive computing systems will enhance decision-making in ways unimaginable in previous eras. Dr. Jeff Karrenbauer, co-founder and president of the supply chain consultancy INSIGHT, observes, “We can solve bigger, more complex problems. Problems that we couldn’t touch 10 or 15 years ago.”[2]

Machine learning is a particularly useful branch of artificial intelligence (AI). Rebecca Merrett (@Rebecca_Merrett) explains, “Machine learning executes AI in that algorithms — which are fed with big data — enable computers or machines to pick up on patterns, predict future outcomes and train themselves on how to best respond in certain situations.[3] It really doesn’t take much imagination to understand why those capabilities are useful in a business environment. Merrett notes, “The technology is making its way into a broad range of industries from marketing with behavioural targeting, to healthcare with accurate and early detection of complex diseases, to infrastructure with smarter urban planning.” Mike Yeomans, a post-doctoral fellow in the Department of Economics at Harvard University, adds, “It seems as though every week companies are finding new uses for algorithms that adapt as they encounter new data. … Machine learning has tremendous potential to transform companies, but in practice it’s mostly far more mundane than robot drivers and chefs. Think of it simply as a branch of statistics, designed for a world of big data.”[4] Alex Zelinsky, chief defence scientist at Australia’s Defence Science and Technology Organisation (DSTO), told Merrett, “The key to intelligence is learning. Once we master machine learning, then you can start to have artificial intelligence. We are intelligent because we can learn; you can learn lessons from doing things and remember those lessons.” Although a number of scientists have openly suggested that defense-related AI systems could lead to humankind’s destruction, commercial applications of narrow AI systems can be beneficial without being threatening. Remembering lessons learned is particularly useful in a business environment because tribal knowledge has a tendency to be lost as employees change jobs or retire. For more on that subject, read my article entitled “Cognitive Computing can Help Retain and Leverage Tribal Knowledge.”

Persisting tribal knowledge is important because that knowledge helps in decision making and decision making lies at the heart of every business. Bain analysts, Michael C. Mankins and Lori Sherer (), assert that if you can improve a company’s decision making you can dramatically improve its bottom line.[5] They explain:

“The best way to understand any company’s operations is to view them as a series of decisions. People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out. We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”

Adam Coates, ?director of Baidu Silicon Valley AI Lab, told Merrett, “The idea behind machine learning is that there are some decisions … where it’s very hard to write down the instructions, so we would like the machine to learn to make those decisions based on looking at a bunch of examples.” In the years ahead, cognitive computing systems will employ machine learning to make all sorts of routine decisions and alert human decision makers when an anomaly occurs requiring their intervention. As the machine learns how decision makers handle exceptional situations, the range of decisions requiring human intervention will decrease. This management-by-exception approach frees human minds to concentrate on more pressing matters and can dramatically increase efficiency as human error is reduced. Yeomans asserts, “Executives who want to get the most out of their companies’ data should understand what it is, what it can do, and what to watch out for when using it.” He observes that companies now have available a wide variety of data that is both wide and deep. “Most of the tools in machine learning.” he writes, “are designed to make better use of wide data.” He continues:

“The most common application of machine learning tools is to make predictions. Here are a few examples of prediction problems in a business:

  • Making personalized recommendations for customers
  • Forecasting long-term customer loyalty
  • Anticipating the future performance of employees
  • Rating the credit risk of loan applicants

These settings share some common features. For one, they are all complex environments, where the right decision might depend on a lot of variables (which means they require ‘wide’ data). They also have some outcome to validate the results of a prediction — like whether someone clicks on a recommended item, or whether a customer buys again. Finally, there is an important business decision to be made that requires an accurate prediction. One important difference from traditional statistics is that you’re not focused on causality in machine learning. That is, you might not need to know what happens when you change the environment. Instead you are focusing on prediction, which means you might only need a model of the environment to make the right decision. This is just like deciding whether to leave the house with an umbrella: we have to predict the weather before we decide whether to bring one. The weather forecast is very helpful but it is limited; the forecast might not tell you how clouds work, or how the umbrella works, and it won’t tell you how to change the weather. The same goes for machine learning: personalized recommendations are forecasts of people’s preferences, and they are helpful, even if they won’t tell you why people like the things they do, or how to change what they like. If you keep these limitations in mind, the value of machine learning will be a lot more obvious.”

He goes on to explain, “Roughly speaking there are three broad concepts that capture most of what goes on under the hood of a machine learning algorithm: feature extraction, which determines what data to use in the model; regularization, which determines how the data are weighted within the model; and cross-validation, which tests the accuracy of the model. Each of these factors helps us identify and separate ‘signal’ (valuable, consistent relationships that we want to learn) from ‘noise’ (random correlations that won’t occur again in the future, that we want to avoid). Every dataset has a mix of signal and noise, and these concepts will help you sort through that mix to make better predictions.” By now it should be obvious that machine learning can be useful for activities as diverse as targeting marketing and route optimization. As the Internet of Things matures and the amount of data needing to be analyzed increases, machine learning will prove invaluable in the commercial sector. In Part 2 of this article, I explore some of these applications in more depth.

Footnotes
[1] Jorge Garcia, “Machine Learning and Cognitive Systems: The Next Evolution of Enterprise Intelligence (Part I),” Wired Innovation Insights, 10 July 2014.
[2] David Z. Morris, “Big data could improve supply chain efficiency—if companies would let it,” Fortune, 5 August 2015.
[3] Rebecca Merrett, “Intelligent machines part 1: Big data, machine learning and the future,” CIO, 4 June 2015.
[4] Mike Yeomans, “What Every Manager Should Know About Machine Learning,” Harvard Business Review, 7 July 2015.
[5] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.