Machine learning has already changed the way we work and process information in the modern business environment. It’s helped us become more efficient—make smarter decisions—and target customers better than ever before. But there’s a whole new type of learning—reinforcement learning—that is going to do a lot more.
Unlike machine learning, which uses data analysis to allow computers to learn without being programmed, reinforcement learning allows computers to learn from experience—just like humans do. Much like Thorndike’s well-known experiment where a cat was trapped in a box until it learned to step on a lever to escape, artificial intelligence (AI) is now learning how to solve problems faster and faster using this technique. It’s almost as if technology is crossing over into humanity as we study its behaviors under various methods of conditioning.
Recently, tech giants in the AI space, including Alphabet, have been making huge leaps in advancement in reinforcement learning. It has the potential to change everything from how we drive to how we interact with one another—and there are tons of ethical issues still to be determined. The following are a few things to know about reinforcement learning.
It’s Different Machine learning as we’ve traditionally known it is mostly what you’d call “supervised learning” or programming. In this type of learning, developers create a curated or labeled set of data, and computers learn to associate different shapes, sounds, or words with those curated sets. This process is incredibly labor and time intensive. In reinforcement learning, however, the learning happens much differently. The computer learns by interacting with the world around it. It learns through trial and error what the goal is, rather than being told. In essence, it learns to solve problems—not to look for specific solutions.
Why It Matters In essence, reinforcement learning is teaching computers to think, not just learn. Using their own judgment, they determine their behavior based on feedback from the world around them, just like we as humans would when operating in our physical environments.
