Introducing Artificial Intelligence
The business opportunities are too diverse (and as yet to be discovered) for a simple list. My hope is that you will understand the full scope of the technology and then be able to apply it to the opportunities in your organization or even to start a new business.
This post is organized into three parts. Part one is an overview of artificial intelligence. Part two expands on this overview and deepens your understanding of machine learning. Part three goes into neural networks — computers that simulate the structure and the function of the human brain through the use of layers of interconnected artificial neurons. This area has grown in popularity in recent years due to the increasing availability and decreasing cost of computer storage and processing and accessibility to massive data sets.
In part one you’ll get a look at some of the early theories that drove the design of the first intelligent machines. Most of these theories start with an attempt to understand human intelligence. What does it mean to be intelligent? Is it our ability to connect symbols to concepts? Maybe it’s our creativity?
You’ll see the struggle that early computer scientists had with trying to create the first intelligent programs. At first, many computer scientists focused on symbolic reasoning. They figured that if they could get computers to understand our symbols it would help them better understand our world. So they created systems that identified letters in our alphabet, digits and different graphical representations like stop signs and question marks.
These early ideas still influence AI today. This symbolic approach gave rise to expert systems. These systems went through countless if-then statements to simulate thinking and decision-making; for example, if you see “A,” then make an “ah” sound. If you see stop sign, then “stop.” Each of these decision points had to be painstakingly programmed into a computer.
In the 1990s, expert systems were the dominant form of artificial intelligence. Companies used these systems to help make medical diagnoses, approve or reject loan applications or find a good stock pick. The computer would go through long lists of if-then statements. So for a loan you might have an expert system that goes through a predefined list, such as, “If they have a credit history, then check for missed payments.” “If they missed payments, then how many payments were missed over the past year?” “If they missed more than 10 payments in the past year, then reject this loan application.”
As you can imagine these lists can get pretty long. You need a human to try to imagine every possible if and then. A really complex task, could result in a combinatorial explosion — so many different possibilities that it’s nearly impossible to come up with all the different combinations.
As computer programmers encountered these limitations, they started to revisit the idea of machine learning. Machine learning has actually been around since the early 1950s. It was used to create programs that could beat a human player at checkers. These checker programs were extremely innovative. The machines could come up with their own strategies and learn from their mistakes. Even these early computers were sophisticated enough to learn how to beat a human player.
Machine learning was a huge leap from programmed instructions and if-then statements that merely simulated the very human process of thinking and making decisions. Part two of this post takes a deeper dive into machine learning to reveal how it changed the rules of traditional software development.
With machine learning, the machine no longer needs to be explicitly programmed to complete a task; it can pour through massive data sets and create its own understanding. It can learn from the data and create its own model, one that represents the different rules to explain relationships among data and use those rules to draw conclusions and make decisions and predictions.
With machine learning you might feed a machine all the data on the different inventory it takes to build a car along with blueprints for every car ever manufactured. After pouring through this data, the machine begins to understand certain things about what it means to be a car. It knows that cars need wheels, doors and a windshield. There might be thousands of different kinds of cars, but the machine creates a model to identify them all.
To enable machines to create these models, programmers have developed numerous advanced machine learning algorithms. A machine learning algorithm is a mathematical function that enables the machine to identify relationships among inputs and outputs. The programmer’s role has shifted from one of writing explicit instructions to creating and choosing the right algorithms.
To take machine learning to the next level, computer scientists came up with the concept of an artificial neural network, the topic of part three of this post. Artificial neural networks are patterned after the structure and function of the brain. The machine contains a web of interconnected artificial “neurons,” each of which contains a machine learning algorithm. These neurons make decisions based on inputs from other neurons, the strength of the connections to those other neurons, and the deciding neuron’s own algorithm and internal bias.
The artificial neural network was inspired by the way biological neurons work in the human brain. As humans, we learn new things and create memories based on increasing the strength of the connections between these nerve cells.
Modern artificial neural networks can create machine learning systems consisting of billions of these neurons. Such a complex network has tremendous power to find patterns in massive data sets. You can feed data into such a network, and it will create a model to better understand the larger patterns. For example, you could feed millions of images of dogs into your neural network and let it self adjust and create its own model of what it means to be a dog. That model might not match how humans think of dogs. It may not identify dogs by looking at their shape and color or their ears and nose. Instead, it identifies statistical patterns of the different dots (pixels) in the images of the dogs. In a sense, the neural network develops its own understanding of “dogness.” This way it can learn to correctly identify a dog even if it has never seen this particular dog before.
As you can imagine, the predictive power of neural networks has a wide variety of practical applications and enormous business potential. If you’re in finance, neural networks can spot trends in the market to help you make trades. If you’re in pharmaceuticals, you might have a neural network look for characteristics of existing drugs and compare them to new compounds. If you’re in retail you might look for patterns in what customers buy to figure out what they’re likely to buy next.
Many large companies are already using neural networks for voice recognition, transcription and digital personal assistants. For example, if you subscribe to Netflix, the system recommends movies and shows based on what you’ve watched in the past. Amazon uses neural networks to make targeted product recommendations and to power its personal digital assistants, including Alexa in their digital media strategy.
But you don’t need to go that big to reap the value of neural networks. Think about the data in your organization. Then think about some of the patterns that would be valuable to see in your data. If you can quickly come up with valuable patterns, then AI is probably a good fit for your organization.
If you have no experience with artificial intelligence, it is probably best to read this post from start to finish. By reading the posts in part one, you’ll develop the fundamental understanding of machine learning required to tackle more complex topics. If you are more familiar with artificial intelligence then you could potentially start with part two.
As you read, keep in mind that the ultimate purpose of this post is to get you thinking about the challenges and problems in your business or your area of expertise that AI and machine learning can help you overcome or solve. Think about the data you have and imagine what you could possibly extract from that data to overcome a specific challenge, solve a specific problem or answer a specific question. After all, without your very human ability to ask questions and imagine possibilities, artificial intelligence and machine learning are useless. The power, as it has always been, is in the combination our creativity and our tools.
