I have a very vivid memory of being a kid splitting a small bag of jellybeans with my friend. We were very good at sharing the bag. He would eat two, and then I would eat two. We worked together to empty the bag. As we ate our way down, I noticed that my friend […]

Like people, machines can learn through supervised or unsupervised learning. With supervised learning, a human labels the data. So the machine has an advantage of knowing the human definition of the data. The human trainer gives the machine a stack of cat pictures and tells the machine, “These are cats.” With unsupervised learning, the machine […]

Prior to starting an AI project, the first choice you need to make is whether to use an expert system (a rules based system) or machine learning. Basically the choice comes down to the amount of data, the variation in that data and whether you have a clear set of steps for extracting a solution […]

Fueling the rise of machine learning and deep learning is the availability of massive amounts of data, often referred to as big data. If you wanted to create an AI program to identify pictures of cats, you could access millions of cat images online. The same is true, or more true, of other types of […]

You may be wondering why machine learning took so long to catch on. After all, Arthur Samuel created his revolutionary checkers program in 1959. Machine learning was poised to become the dominant form of artificial intelligence. It had the wind at its back. What happened is that machine learning took a backseat to other innovations […]

Machine learning has gotten a big boost recently because it works particularly well with artificial neural networks (often referred to simply as neural networks) — computer systems that are modeled after the neural structure of the human brain. A biological brain is composed of billions of neurons that communicate with one another electrochemically across minute […]

One of the best ways to understand machine learning is to look at the various applications of machine learning in the business world: Data security: In an attempt to avoid detection, people who produce malware constantly change the code, typically two to ten percent, but with machine learning, security software can accommodate this small percentage […]

The symbolic approach and AI planning work great for applications that have a limited number of matching patterns; for example, a program that helps you complete your tax return. The IRS provides a limited number of forms and a collection of rules for reporting tax-relevant data. Combine the forms and instructions with the capability to […]

There’s a big difference between memorizing and learning. Imagine that you were looking at these eight images. You would probably quickly recognize that these are eight different breeds of dogs. Now imagine I showed you this picture. Would you know what it is? How would you know what it is? You’ll notice that it’s not […]

In the late 1980s early expert systems started to disappear, but a symbolic approach remained. Today, you see it in what’s called artificial intelligence planning — a branch of AI that employs strategies and action sequences to enhance the computer’s ability to match symbols and patterns. AI planning attempts to solve the problem of combinatorial […]