An Executive’s Guide to Machine Learning
Staying ahead in the accelerating artificial-intelligence race requires executives to make nimble, informed decisions about where and how to employ AI in their business. One way to prepare to act quickly: know the AI essentials presented in this guide.
Artificial intelligence: A definition
AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.
Machine learning: A definition
Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.
Understanding the Major Types of Machine Learning
Supervised Learning
What Is It? An algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output (eg, how the inputs “time of year” and “interest rates” predict housing prices).
When To Use It? You know how to classify the input data and the type of behavior you want to predict, but you need the algorithm to calculate it for you on new data.
How It Works?
- A human labels every element of the input data (eg, in the case of predicting housing prices,labels the input data as “time of year,” “interest rates,” etc) and defines the output variable (eg, housing prices)
- The algorithm is trained on the data to find the connection between the input variables and the output.
- Once training is complete— typically when the algorithm is sufficiently accurate—the algorithm is applied to new data.
Unsupervised Learning
What Is It? An algorithm explores input data without being given an explicit output variable (eg, explores customer demographic data to identify patterns).
When To Use It? You do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you.
How It Works?
- The algorithm receives unlabeled data (eg, a set of data describing customer journeys on a website)
- It infers a structure from the data
- The algorithm identifies groups of data that exhibit similar behavior (eg, forms clusters of customers that exhibit similar buying behaviors).
Reinforcement Learning
What Is It? An algorithm learns to perform a task simply by trying to maximize rewards it receives for its actions (eg, maximizes points it receives for increasing returns of an investment portfolio).
When To Use It? You don’t have a lot of training data; you cannot clearly define the ideal end state; or the only way to learn about the environment is to interact with it.
How It Works?
- The algorithm takes an action on the environment (eg, makes a trade in a financial portfolio).
- It receives a reward if the action brings the machine a step closer to maximizing the total rewards available (eg, the highest total return on the portfolio).
- The algorithm optimizes for the best series of actions by correcting itself over time.
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