Lukas Biewald
There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I’ve heard that old joke “Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it” about 20 times in the past week alone.
But from where I sit, running a company that enables a huge number of real-world machine-learning projects, it’s clear that machine learning is already forcing massive changes in the way companies operate.
It’s not just futuristic-looking products like Siri and Amazon Echo. And it’s not just being done by companies that we normally think of as having huge R&D budgets like Google and Microsoft. In reality, I would bet that nearly every Fortune 500 company is already running more efficiently — and making more money — because of machine learning.
So where is it happening? Here are a few behind-the-scenes applications that make life better every day.
Making user-generated content valuable
The average piece of user-generated content (UGC) is awful. It’s actually way worse than you think. It can be rife with misspellings, vulgarity or flat-out wrong information. But by identifying the best and worst UGC, machine-learning models can filter out the bad and bubble up the good without needing a real person to tag each piece of content.
A similar thing happened a while back with spam emails. Remember how bad spam used to be? Machine learning helped identify spam and, basically, eradicate it. These days, it’s far more uncommon to see spam in your inbox each morning. Expect that to happen with UGC in the near future.
Pinterest uses machine learning to show you more interesting content. Yelp uses machine learning to sort through user-uploaded photos. NextDoor uses machine learning to sort through content on their message boards. Disqus uses machine learning to weed out spammy comments.
Finding products faster
It’s no surprise that as a search company, Google was always at the forefront of hiring machine-learning researchers. In fact, Google recently put an artificial intelligence expert in charge of search. But the ability to index a huge database and pull up results that match a keyword has existed since the 1970s. What makes Google special is that it knows which matching result is the most relevant; the way that it knows is through machine learning.
But it’s not just Google that needs smart search results. Home Depot needs to show which bathtubs in its huge inventory will fit in someone’s weird-shaped bathroom. Apple needs to show relevant apps in its app store. Intuit needs to surface a good help page when a user types in a certain tax form.
Successful e-commerce startups from Lyst to Trunk Archive employ machine learning to show high-quality content to their users. Other startups, like Rich Relevance and Edgecase, employ machine-learning strategies to give their commerce customers the benefits of machine learning when their users are browsing for products.
Engaging with customers
You may have noticed “contact us” forms getting leaner in recent years. That’s another place where machine learning has helped streamline business processes. Instead of having users self-select an issue and fill out endless form fields, machine learning can look at the substance of a request and route it to the right place.
That seems like a small thing, but ticket tagging and routing can be a massive expense for big businesses. Having a sales inquiry end up with the sales team or a complaint end up instantly in the customer service department’s queue saves companies significant time and money, all while making sure issues get prioritized and solved as fast as possible.
Understanding customer behavior
Machine learning also excels at sentiment analysis. And while public opinion can sometimes seem squishy to non-marketing folks, it actually drives a lot of big decisions.
For example, say a movie studio puts out a trailer for a summer blockbuster. They can monitor social chatter to see what’s resonating with their target audience, then tweak their ads immediately to surface what people are actually responding to. That puts people in theaters.
Another example: A game studio recently put out a new title in a popular video game line without a game mode that fans were expecting. When gamers took to social media to complain, the studio was able to monitor and understand the conversation. The company ended up changing their release schedule in order to add the feature, turning detractors into promoters.
How did they pull faint signals out of millions of tweets? They used machine learning. And in the past few years, this kind of social media listening through machine learning has become standard operating procedure.
What’s next?
Dealing with machine-learning algorithms is tricky. Normal algorithms are predictable, and we can look under the hood and see how they work. In some ways, machine-learning algorithms are more like people. As users, we want answers to questions like “why did The New York Times show me that weird ad” or “why did Amazon recommend that funny book?”
In fact, The New York Times and Amazon don’t really understand the specific results themselves any more than our brains know why we chose Thai food for dinner or got lost down a particular Wikipedia rabbit hole.
If you were getting into the machine-learning field a decade ago, it was hard to find work outside of places like Google and Yahoo. Now, machine learning is everywhere. Data is more prevalent than ever, and it’s easier to access. New products like Microsoft Azure ML and IBM Watson drive down both the setup cost and ongoing cost of state-of-the-art machine-learning algorithms.
At the same time, VCs have started funds — from WorkDay’s Machine Learning fund to Bloomberg Beta to the Data Collective — that are completely focused on funding companies across nearly every industry that use machine learning to build a sizeable advantage.
Most of the conversation about machine learning in popular culture revolves around AI personal assistants and self-driving cars (both applications are very cool!), but nearly every website you interact with is using machine learning behind the scenes. Big companies are investing in machine learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.
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