Machine learning (ML) is all the rage right now. The subject of artificial intelligence had taken a backseat in recent years, but advancements were still being made. Breakthroughs in machine learning have made the topic resurgent—machines are now getting smarter.
As machines get smarter, more people are interested in what they can do and how they can be of use in almost any industry you can imagine. If you’ve been wondering about machine learning and what it can do for you—now and in the future—you need to know about these five updates.
1. It’s Everywhere
Machine learning harnesses the power of big data to allow machines to “self-teach.” Over time, the machine learns to make predictions based on historical trends.
For most people, machine learning will most affect the apps they’re using; virtually every app in use today has the potential to become an intelligent app, and most new companies are planning to develop intelligent apps.
If your company isn’t already using ML to recommend products to your customers or to predict customer churn, you soon will be.
2. The Algorithm Market
ML is made possible by algorithms that allow the machine to interpret large amounts of data, and analyze the information for trends and anomalies (much like data analysts). The machine essentially “teaches” itself about, say, the habits of certain kinds of customers by collecting data from purchases, then comparing similar purchases and purchasers.
The algorithms these machines learn are already trending toward a “tried and true” method. Instead of writing from scratch each time, companies are turning to a marketplace for algorithms: They simply purchase the algorithms from developers and plug them into their apps. It’s making it easier for companies to get ML into their apps—even those that already exist—instead of trying to reinvent the wheel.
3. Trust and Transparency Are Paramount
ML has come a long way in a short time, and it’s everywhere. Yet the sophistication of the technology also calls into question the ethics of using it. One example is the chatbot teaching assistant at Georgia Tech last year: Students at the end of the semester were shocked (yet delighted) to learn one of their TAs was actually a chatbot, not a human being.
While you may not shock your customers by using ML to make product recommendations, they might feel betrayed by contacting customer service, only to be met with a chatbot—no matter how good it is at helping them.
4. A Human Touch Is Essential
As sophisticated as it’s become, ML isn’t perfect. Even if it were, it would probably still be prudent to involve a human being somewhere along the line—in part because it’s so difficult to replicate the human thought process.
Several companies have demonstrated the need for a human touch when it comes to ML. In an experiment, Redfin found users had a higher click rate on ML-generated recommendations than their own searches. Yet when the company’s human sales reps reviewed the machine’s recommendations and tweaked them before sending them out, users had an even higher click-through rate!
5. Deep Learning on the Rise
There’s still an even longer road ahead for ML. You can see this in talk about unsupervised learning, improving neural networks, and deep learning.
In fact, as good as today’s machine learning is, it’s still very far from the human ability to learn. Deep learning focuses on rectifying these shortcomings, bringing machines closer to human capabilities in the areas of pattern recognition and analysis, learning, and decision making. While still in its infancy, there’s a lot of interest in deep learning as a more sophisticated subset of ML.