People ask me all the time, can a machine really learn? The answer is an absolute yes! Machines can be programmed to learn by studying data to detect patterns and by applying known rules to:
• Categorize or catalog people or things
• Predict likely outcomes or actions based on identified patterns
• Identify hitherto unknown patterns and relationships
• Detecting anomalous or unexpected behaviors
The processes machines use to learn are known as algorithms and functions. As new observations or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
With the advent of big data and lower costs of storage and processing, the amount of data available and our ability to process it has increased exponentially. The ability of machines to learn and thus appear ever more intelligent has increased proportionally. Even so, machines aren’t independent thinkers (yet). Yes, machine learning may identify previously unidentified opportunities or problems to be solved. Machines are not autonomously creative and they will not spontaneously develop new hypotheses from facts (data) not in evidence. Nor can the machine determine a new way to respond to emerging stimuli. Remember: the output of a machine learning algorithm is entirely dependent on the data it is exposed to. Change the data, change the result. Machine-learning tools can also show false positives, blind alleys and mistakes since many of the algorithms are so complicated that it is impossible to inspect all the parameters or to determine how the inputs have been manipulated. As these algorithms begin to be applied ever more widely, risks of misinterpretations, erroneous conclusions and wasted scientific effort will spiral.
Companies are better than ever at understanding why customers buy their products, use their services, or engage their expertise. We can point the “machine” at a lake of consumer data to detect patterns and preferred channels for consumption. It can use historical and real-time data to determine that I, a frequent business traveler and coffee addict, may welcome a real-time message that my favorite coffee shop is around the corner. My dad would not welcome this interaction. He brews his coffee at home and will respond to a coupon in the mail. Which can also include incentives for other items he might buy on his next grocery outing. The machine is optimizing activities for each customer across known channels (digital, paper, brick and mortar). It won’t, however, independently create a new interaction channel that doesn’t already exist.
In simple terms, machine learning is particularly suited to problems where:
• Applicable associations or rules might be intuited, but are not easily codified or described by simple logical rules
• Potential outputs or actions are defined but which action to take is dependent on diverse conditions which cannot be predicted or uniquely identified before an event happens.
• Accuracy is more important than interpretation or interpretability
• The data is problematic for traditional analytic techniques. Specifically, wide data (data sets with a large number of data points or attributes in every record compared to the number of records) and highly correlated data (data with similar or closely related values) can present problems for traditional analytic methods.