Machine learning has been driving innovation in product features for several years, becoming a vital component of enhancing the consumer experience.
Machine learning has been driving innovation in product features for several years, becoming a vital component of enhancing the consumer experience. However, in terms of what () — and specifically, () — can do, we’ve only seen the tip of the iceberg.
As a business leader, you may feel a bit like Chevy Chase in Fletch trying to flub your way into appearing knowledgeable about (it’s all ball bearings nowadays…). Nevertheless, it’s vital to understand what is and how to get the most out of it for your business.
While it sounds technical, , in the simplest sense, is a branch of that uses computer algorithms to learn from data and make better predictions. By utilizing large data sets, machines apply and automatically learn through the analysis of patterns and use that knowledge to improve the algorithm. The techniques behind have been around for decades, but it’s only recently that we’ve had the computing power to put them to the test. Now that applications are more ubiquitous, is coming into its own as an affordable technological tool that even small businesses can leverage.
Machine learning has been behind many significant innovations in the last decade, including self-driving cars and effective web search. Several years ago, we at Rosetta Stone saw an opportunity to utilize (), which is the behind recognition, to transform the language learning experience. Through our patented recognition technology, users could practice correct pronunciation and get feedback in real time.
Here’s how you can start to power innovative product features through .
You’ve Got Data — Use It
If there’s one thing companies have no shortage of, it’s data — user data, search trends, product analysis. You can put these massive amounts of data to work with . Machine learning is a specific way to use a large amount of data to analyze a complex problem without a formula or equation. Using your treasure trove of data for applications is a process that involves making the data available, analyzing it and splitting it into groups to train and hone your computer models. Some companies hire data scientists to help with the more sophisticated aspects of this work, but there are also a wealth of tutorials online to help you take the first steps toward preparing your data for applications.
Some examples of algorithms that use data are Netflix’s movie recommendation engine or the way Amazon discerns what you might be interested in based on your order history. As Netflix and Amazon collect more user data, the personalized recommendations they make grow increasingly targeted and insightful.[…]