Machine learning has been proven to be so effective that many make the mistake of assuming it applies to all situations and can solve every single problem. As with any technology, there is a time and a place for machine learning – particularly when it comes to existing problems you simply couldn’t tackle due to a lack of resources. If you plan on leveraging ML at any point in the future, you’ll have much greater success if you cut through the noise, avoid the following misconceptions and gain a more accurate understanding of what machine learning can and can’t do.
Myth #1 – Machine learning and AI are one and the same.
The terms machine learning and artificial intelligence are often used interchangeably, but the reality is, they aren’t synonyms. To break it down to simplest of explanations, machine learning is a technique that’s being applied in real-world scenarios. AI is actually a much broader expression that encompasses a spectrum of areas including robotics, natural language process and computer vision. While the results may appear “intelligent,” machine learning is really about learning patterns, applying statistics and predicting outcomes based on data.
Myth #2 – Machine learning will replace people.
It’s a common fear that artificial intelligence technology and its many applications (including machine learning) will ultimately eliminate the need for human workers. While it will most certainly change the jobs being performed and how they are handled, the main purpose of ML isn’t to replace but rather to augment personnel. In actuality, it’s predicted to create more new roles than it will make obsolete. This means greater opportunity for human workers to learn new skills and apply their cognitive and creative talents to more meaningful initiatives.
Myth #3 – Anyone can build a machine learning platform.
Google “how to build machine learning” and you’ll inevitably get pages of results featuring various open source tools and courses. But the fact remains that machine learning is a highly specialized technique. For it to be successful, you must understand exactly how to prepare and partition data for testing and training, know how to choose the most appropriate algorithm and – most importantly – know how to turn that information into a productive system. Furthermore, you must also monitor that system to ensure consistently relevant results.
Getting machine learning right takes time and lots of experience. If you’re just getting started, your best bet is to work with partner that already specializes in this advanced technology and can handle the complexities and nuances on your behalf. Ayehu NG features built-in, highly sophisticated machine learning algorithms and can have you up-and-running in just minutes. Try it free today!