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Why Small Businesses Need to Pay Attention to What Machine Learning Is
What is machine learning? Artificial intelligence? Deep learning? Ask a group of small business owners what those terms mean, and most can put together a working definition. The results you get from such an informal survey, however, could be as varied as, “They’re in the CRM I use,” to apocalyptic images, inspired by movies like The Terminator, of robots taking over the world. Either way, most people would likely say that artificial intelligence (AI), machine learning, and deep learning are basically synonymous. The truth is machine learning is related to both AI and deep learning, but this technical concept is distinct.
Machine learning is one of those concepts, along with social media marketing and publishing a blog, that many business owners hear they need to invest in, but aren’t really sure why except they’ve heard that “it’s the future.” (Machine learning really is the future, but that doesn’t mean it has to be as confusing as Skynet seems.) As Greg Corrado, a senior research scientist at Google, told The Guardian, “It’s not magic. It’s just a tool. But it’s a really important tool.” Corrado also believes machine learning will soon be something “everybody can do a little of.”
Even so, few small business owners truly understand machine learning, and only the most tech-savvy have a detailed plan for implementation.
Here’s what machine learning is, what makes it different from AI, and why small businesses should keep an eye on this technology.
Machine Learning vs. AI vs. Deep Learning: What’s the Difference?
While sometimes used interchangeably, machine learning, artificial intelligence, and deep learning are not the same thing. Calum McClelland writes that AI “[involves] machines that accomplish tasks normally associated with human intelligence.” Those tasks usually require planning, recognizing patterns, understanding language, or problem solving.
The term “artificial intelligence” isn’t as new as the hype would have us believe. In fact, John McCarthy, a computer scientist known as “The Father of AI,” first coined the term in 1956. At the time, computers weren’t fast or powerful enough to accomplish tasks anywhere near what humans could do, but that’s changed in the last 60 years.
Today’s developers can achieve AI in several ways. One is to write every single bit of code necessary for a machine to accomplish a specific task. For simple tasks, such as finding webpages containing the precise keyword typed in by a user types (as in the early days of Google), this is a great way to create AI. As user expectations increase, however, search engines such as Google must code more efficiently in order to accomplish a growing list of more complex tasks, such as returning search results using synonyms, spoken language, or similar keywords. Otherwise, it would simply take too much time.
Instead, Google and other companies are writing code designed to allow computers to learn how to pick up on language cues, recognize patterns, and draw conclusions about what users are looking for. Enabling computers to process the information necessary to come to their own conclusions is, in a nutshell, machine learning.
“Read a Book”
Machine Learning Examples
Other Everyday Uses
Machine Learning in Business
How CRM Platforms Use Machine Learning
Seventy percent of shows watched on Netflix are the result of a personalized recommendation by the platform. Those recommendations are possible because of machine learning. For Netflix and other companies that recommend products and services, an algorithm tracks individual customer behaviour, as well as patterns among many similar customers. This algorithm helps businesses personalize their web shopping experience as well as increase revenue per transaction.
With that said, sometimes machine learning doesn’t get it right. Personalized recommendations can be hit or miss, as Netflix and Amazon have both shown. This is just one aspect of machine learning that still needs tweaking — and more human guidance.