There are not many new technologies that are truly game-changers. Machine learning is one of those technologies. In a nutshell, machine learning is a form of artificial intelligence in which computers learn to recognize patterns over time and are then able to make complex decisions without human input. Sounds simple enough, right? But it actually has very profound implications for social media.
Bringing the ultimate pattern recognition machine to social mediaThe classic example of machine learning at work involves image recognition. How, exactly, are you supposed to teach a computer to recognize images of, say, a dog? To a human, a dog is a dog, even a young child can’t mess that up. But it’s much more complex for a machine – you need to show a computer millions of images of dogs, in various poses, positions, and situations before it starts to come up with a set of rules for recognizing dogs. With a strong enough algorithm, a machine can spot images of dogs everywhere. In fact, if the algorithm is powerful enough, a machine can spot images of just about anything you want it to.
That’s the reason, for example, that Facebook can now spot images of your friends in photos – it has a very sophisticated machine learning algorithm at work scanning faces in images. It can recognize your friends wherever they are – in any pose, situation or position – even in crowds. So image recognition was really the “Trojan horse” that enabled machine learning to enter the social media realm. From there, the number of applications for machine learning has skyrocketed.
Chat bots and machine learningOne of the most popular applications for machine learning involves chat bots, which are essentiallyAI-powered bots that can converse with humans. In this case, machine learning is used to “teach” these chat bots how to recognize certain natural language queries, even with improper syntax and grammatical errors. But it’s difficult – and it’s why some chat bots can only answer a limited range of questions – they just haven’t “learned” how to answer more complex questions when they aren’t phrased a particular way.
You can immediately see why chat bots that can converse with humans on a broad range of topics can be such a game-changer – they essentially enable brands to have one-on-one conversations with millions of fans. And, in many cases, humans really don’t care that they are conversing with a bot rather than a human. Would you rather wait on hold for 30 minutes to ask a human when your package is going to arrive, or would you rather chat with a bot, which can deliver a response within 30 seconds?
Social media monitoring and machine learningOther uses for machine learning include social media monitoring. According to some estimates, there are 1.5 million pieces of user-generated content added to Facebook every single day. A human can’t possible read all that – but a machine can. If you’re a brand, that gives you a very unique way to monitor all the conversations taking place around you.
And here’s where the “learning “ aspect comes into play – machines eventually become smart enough to recognize nuances of sarcasm or humor, as well as the tell-tale conversations that might be the very beginning of an online brand crisis. Wouldn’t it be nice if a machine could give you an early heads-up before disgruntled customers show up at your business the next day?
Taking the big picture view, machine learning can be the key to streamlining your overall social media presence and finding the proverbial “needle in the haystack.” That will free up your social media team to focus their time and energy on what really matters, and not just scrolling through pages and pages of Facebook posts and Instagram images.
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*This post originally appeared on socialmedia hq.
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