Companies are increasingly relying on machine learning to make predictions about the future. Sometimes that means predicting the future of consumer behavior. Sometimes that means anticipating changes in the stock market. Sometimes, it’s something simpler, like making guesses about the ebb and flow of inventory.
In any case, today’s machine learning is incredibly advanced—but still limited in terms of what predictions it can make. So just how limited is our predictive potential, and how can we eliminate or move past those limitations?
Predicting the future isn’t easy. Even after hundreds of years of trying to understand the weather, it’s nearly impossible to predict weather patterns more than a few days in the future because weather is dependent on so many variables. The economy is similarly complex; it’s hard to predict how inflation will develop, or how economic conditions will change in the near future, just because there are so many confounding variables in place. A sufficiently complex AI may be capable of processing this level of complexity, but if human beings aren’t familiar with how those variables interact, or which variables to study, it’s a lost cause.
We’re also limited by the quality and quantity of data we’re able to gather. To make accurate predictions, most systems need as much data as possible; many businesses aren’t able to provide the sheer quantity necessary to get a reasonable result. On top of that, it takes a serious commitment to data quality to avoid things like data duplication, or inaccurate records. Machines can’t tell which data is good or bad; they can only process the data given to them.
Reasoning and Justification
Let’s say your machine learning algorithm gives you a prediction about your customers in the near future. How did it come to this conclusion? What were the key insights that led it here? How confident is it, and what could be responsible for this prediction being wrong? Machines are good at giving you a final answer, but they aren’t good at showing you the processes or reasoning behind those answers. For example, machine learning programs have become exceptional at playing games like chess and Go, but while we can look at the moves they make, it’s almost impossible to study why and how they made them.
All machine learning algorithms are created and modified by humans, which means they’re naturally subject to human biases. Often without realizing it, even the most talented machine learning and AI developers will include their implicit biases in the final versions of their algorithms, resulting in work that provides unreliable conclusions for a certain portion of the data set, or a prediction that fails to take into account an important variable that the creator is likely to overlook.
The best achievements in human history have come about due to collaboration—brilliant individuals working together, or drawing inspiration from the work of their contemporaries to make meaningful advancements. But unfortunately, we’re not at a level of development that allows machine learning algorithms to engage with and learn from each other. Accordingly, they’re all limited by their own conceptualizations and assumptions.
If you’re interested in learning how far machine learning can go for your business, be sure to take advantage of SAP’s machine learning products. Featuring some of the most advanced AI in the industry, and some of the most innovative applications, you can make intelligent predictions—and create a unique customer experience your competitors won’t be able to match.