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The Benefits of Reinforcement Machine Learning

Artificial intelligence and machine learning go together like water and ice. You simply can't have one without the other. Machine learning is how data scientists train artificial intelligence systems. But as artificial intelligence continues to grow, so do its training models. So we now have three broad categories of machine learning:

  • Classic machine learning
  • Deep learning
  • Reinforcement learning

All three are subsumed under the term machine learning. But each one has specific characteristics that set them apart. In this post, we're going to look at all three, but our focus will be on the new kid on the block: reinforcement learning - specifically, the benefits of this novel approach to AI training.

Classic Machine Learning

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to train a machine to mimic the way humans learn and gradually improve its accuracy without being explicitly programmed. The expression "machine learning" was coined by Arthur Lee Samuel, an American pioneer in artificial intelligence. 

There are two types of machine learning:

  • Supervised
  • Unsupervised

The difference between the two is that with supervised machine learning, a human provides labels for every training data input that's fed into the machine. Whereas in unsupervised machine learning, there are no such tags. The AI must dig through all the data on its own to find its structure and relationships within the data points.

While classic machine learning can be quite vast and complex, it's nonetheless the most "superficial" type of training an AI model can receive.

Deep Machine Learning

Deep learning is a subset of machine learning based on artificial neural networks. It is a lot like unsupervised classic machine learning but with several layers of neural networks designed to perform more sophisticated tasks. We use the term "deep" because the structure of the neural networks consists of multiple input, output, and hidden layers. Each layer processes its input data and outputs structured information that the next layer can use for predictive tasks. That allows the machine to train itself through its own data processing.

Reinforcement Machine Learning

Now we get to the top of the hill. Reinforcement machine learning trains an AI model to learn from experience by assigning them rewards and punishments in response to their actions. One of the goals of reinforcement learning is to teach the AI model to make a sequence of decisions based on a complex data set. Reinforcement learning trains the AI in a game-like manner. The AI model will use trial and error to devise a solution to the problem. To coax the AI model in the "right" direction, the programmer provides it with rewards or penalties according to the outcomes it provides. The idea is that the reward/penalty system will guide the AI into producing the desired results.

Reinforcement machine learning can hence be applied to classic machine learning and deep machine learning.

It's powerful stuff. And, if you think about it, the reward/penalty system brings AI training closer to how humans actually learn.

So now that we understand what reinforcement machine learning is, what are its benefits?

Benefits of Reinforcement Machine Learning

No Massive Labeled Data Sets Required

With reinforcement learning, you don't need large labeled datasets. It's a massive advantage because data sets can pretty much only grow, and the cost of labeling them grows with them. Having the machine trained with unlabelled data sets makes data scientists' lives easier while providing better training to your machine.

More Original Outcomes

In supervised machine learning, the AI unwittingly imitates whoever labeled the data set. The algorithm can only learn to do the task as intended by the labeled data. It can never learn an entirely new approach. Conversely, with reinforcement learning, the AI may come up with a new way of completing a task that the programmer never anticipated.

More Resistant to Bias

Labeling data sets will invariably introduce bias. The AI model trained on labeled data sets will inevitably inherit the biases it contains. While bias and discrimination can be introduced in other ways, reinforcement learning at least limits the amount of artificially introduced bias by working from unlabelled data sets.

Learning Complex Behavior

Because reinforcement learning is goal-oriented, it can train the AI model to learn sequences of actions rather than simple input-output tasks (supervised machine learning). With reinforcement learning, the AI is given a goal that requires multiple judgment calls and actions. The AI is left to its own devices to figure out how to achieve it, and the solution will be a multi-pronged sequence of steps.

Wrap Up

So that was a high-level view of machine learning in general and reinforcement learning, specifically. It's particularly interesting to see how the different approaches to machine learning build off each other to inch ever closer to "thinking" like human beings. Reinforcement learning is the latest development. But it's certainly not the final word. We've got a long road ahead of us in terms of AI development. It's really just started. And the next learning model is likely to provide novel experiences we can't even imagine today.

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