Simply put, machine learning builds algorithms that allow computer programs to track user behavior, recognize patterns and learn from experience so programs can continually improve decisions or recommendations without having to be reprogrammed.
Consider the autofill and autocorrect feature of texting. When you type “I” and “l”, odds are that the words “love” and “like” are automatically presented. That is machine learning based on the habits of other text users who speak the same language or live in the same region as you do. Now, start typing the name of friend with an unusual name. Assuming you’ve used that person’s name into a text message in the past, it’s likely to come up after the first two or three letters. Again, that’s a model of machine learning. The same is true of other deep learning practices: Face recognition, internet search engines, and shopping and music suggestions, to name a few more examples, are all data-driven models.
At the organizational level, machine learning is already improving how individuals work, how they work together, and how teams and entire organizations analyze and act on vast amounts of data.
For small-to medium-size businesses (SMBs), moreover, the benefits of this learning at an enterprise level can be particularly profound. Smart applications of machine learning improve the user experience and help make the most of what is increasingly the most valuable and scarce asset: Time.
Machine learning – like any form of artificial intelligence – mimics what humans do constantly: Build heuristics. Humans use past experiences or observations to come to logical conclusions about the future. But whereas humans are limited by the amount of information they can process at any point, the machine and its algorithms make it possible to analyze massive and multiple data sets simultaneously.
As with any technology, machine learning should be implemented with a specific goal to improve and streamline current applications. This technology should simplify processes, not add a layer of complexity.
It’s also important to note that machine learning has its limitations. Though it can spot patterns (and build models around them), it also lacks human insight. AI algorithms can help you find the best driving route to the office, for example, but it won’t tell you that taking public transportation and walking will improve your health and reduce your carbon footprint.
Similarly, it may be able to point you to sales leads or flag strong job candidates, but machine learning still has blind spots when it comes to variables that can ultimately close a deal or identify the best person for a position. In other words: The machine has limits.
It's essential to remember that machine learning does not take away from the human experience; it improves it. By expediting or automating rote actions and decisions, machine learning frees up individuals and organizations to focus more on relationships, making big-picture decisions, and thinking strategically and creatively.
Which, for every SMB, automatically translates into more time to be more strategic and more competitive.
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