A Simple Guide to Machine Learning

Introduction
Lawyers know a lot about a wide range of subjects—the result of constantly dealing with a broad variety of factual situations. Nevertheless, most lawyers might not know much about machine learning and how it impacts lawyers in particular. In this article, I provide a short and simple guide to machine learning at a level understandable to the typical attorney.
The phrase “artificial intelligence” usually refers to machine learning in one form or another. It might appear as the stuff of science fiction, or perhaps academia, but in reality machine learning techniques are in broad use today. Such techniques recommend books for you on Amazon, help sort your mail, find information for you on Google, and allow Siri to answer your questions.
In the legal field, Westlaw and Lexis use machine learning tools in their natural-language search and other features. ROSS Intelligence is an “AI” research tool that finds relevant “phrases” from within cases and other sources in response to a plain-language search. Kira Systems uses machine learning to quickly analyze large numbers of contracts. These are just two of dozens of new, machine-learning-based products. On the surface, these tools might seem similar to current legal products, but you will see by the end of this article that they do something fundamentally different, making them not only potentially far more efficient and powerful, but disruptive as well. For example, machine learning is the “secret sauce” that enables ride-sharing services like Uber, allowing it to efficiently adjust pricing to maximize both the demand for rides and the availability of drivers, predict how long it will take a driver to pick you up, and calculate how long your ride will take. With machine learning, Uber and similar companies are rapidly displacing the traditional taxicab service. Understanding what machine learning is and what it can do is key to understanding its future effects on the legal industry.
What Is Machine Learning?
Humans are good at deductive reasoning. For example, if I told you that a bankruptcy claim for rent was limited to one year’s rent, you would easily figure out the amount of the allowed claim. If the total rent claim were $100,000, but one year’s rent was $70,000, you would apply the rule and deduce that the allowable claim is $70,000. Now reverse the process. Assume I told you that your client was owed $100,000 and that the annual rent was $70,000, and then told you that the allowable claim was $70,000. Could you figure out how I got that answer? You might guess that the rule is that the claim is limited to one year’s rent, but could you be sure? Perhaps the rule was something entirely different. This is inductive reasoning, and it is much more difficult to do.
Machine learning techniques are computational methods for figuring out “the rules,” or at least approximations of the rules, given the factual inputs and the results. Those rules can then be applied to new sets of factual inputs to deduce results in new cases.
Here is an example that is easy to understand. You all know the old number series games. For example:
2  4  6  8  10  _?_
The next number is 12, right? Here, the inputs are the series of numbers 2 through 10, and from this we induce the rule for getting the next number—add 2 to the last number in the series. Here is another one:
1   1   2   3   5  _?_
The next number is 8. This is a Fibonacci sequence, and the rule is that you add together the last two numbers in the series.
These games illustrate the use of inductive reasoning to figure out the rule. You then apply that rule to get the next number. Broken down a little, the prior game looks like this:
Input                         Result
1   1                                2
1   1   2                           3
1   1   2   3                      5
1   1   2   3   5               _?_
We look at the group of inputs and induce a rule that gives us the shown results. Once we have derived a workable rule, we can apply it to the last row to get the result “8,” but more importantly we can apply it to any group of numbers in the Fibonacci sequence. This is a simple (very simple) example of what machine learning does.
Naturally, real-world problems are more complex. Instead of a short series of numbers as i