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Three Examples of the use of Artificial Intelligence in Business

Published August 5, 2021
Doug Rose
Author | Agility | Artificial Intelligence | Data Ethics

Machine learning plays a key role in artificial intelligence. Machines can be fed large volumes of data and, through supervised and unsupervised learning, analyze that data in various ways to predict outcomes and reveal deeper insights into the data. Four types of machine learning algorithms are commonly used to perform these different types of analysis:

  • Classification: Assigning items to different labeled classes
  • Regression: Identifying the connection between a dependent variable and one or more independent variables
  • Clustering: Creating groups of like things
  • Association: Identifying associations between things

The ability of machines to learn by engaging in different types of analysis is impressive, but machine learning becomes truly amazing when these abilities are applied to real-world tasks. In this post, I describe three examples of the use of artificial intelligence in business.

Intelligent Robots

When most people hear the term "artificial intelligence," they think of robots — C-3PO and R2D2 in Star Wars; Commander Data in Star Trek 🖖; the T-800 played by Arnold Schwarzenegger in The Terminator; Robot in Lost in Space; and Hal, the Heuristically programmed Algorithmic computer that runs the spaceship Discovery in 2001: A Space Odyssey.

The robots of today are not quite as impressive, but they are certainly moving in that direction. Today's robots are designed primarily to perform physical labor — assembling products, painting vehicles, delivering packages, and vacuuming floors.

Although robots are still highly specialized, they were even more so in the past. Older robots needed highly specific programming to tell them exactly what to do. These older robots are still in use today; for example, in computer-aided manufacturing (CAM), a program may instruct a drill press to drill holes in specific locations to specific depths on a part. 

Today's robots are more sophisticated. With the development of physical symbol systems and machine learning, robots can now adapt to changing environments. For example, many robotic vacuum cleaners use a form of symbolic AI to map different rooms and determine the most effective paths to take to vacuum the entire floor. When they’re losing their charge, they can return to home base and dock with the charging station. To prevent accidents, they know to avoid stairs and other obstacles.

A more complex example is the self-driving car. The newest vehicles employ an artificial neural network and are outfitted with a host of complex sensors that feed data into the network. These driverless cars can figure out how to navigate from point A to point B as many people already do — by following directions from a program like Google Maps. However, they must also be able to read and interpret street signs, avoid running over pedestrians, adjust to driving conditions, and much more. 

Nobody can program into a self-driving car all of the possible variables the car may encounter, so it must be able to learn. Early versions of self-driving cars have steering wheels, an accelerator, and a brake pedal and require a human in the driver’s seat who can override the automated system when necessary. This approach provides a form of supervised learning in which the driver corrects the neural network when it makes a mistake.

Natural Language Processing (NLP)

If you set out to make a sophisticated robot like those you see in the movies and on TV, one of the first things you need to do is enable the robot to understand and communicate in spoken language. This has always been a challenge for AI developers, and they are meeting this challenge through the technology of natural language processing (NLP). If you’ve met Siri, Alexa, or Cortana or used talk-to-text on your smartphone, you’ve already experienced NLP. As you speak, the computer identifies the words and phrases and appears to understand what you said. 

NLP makes computer-human interactions more human. For example, if you have an Amazon Fire TV Stick for streaming movies and TV shows, you can hold down the speaker button and say "Find movies directed by Stanley Kubrick," and the Fire Stick will list that director's movies — at least the movies available for viewing. Using traditional programming, you would never be able to develop software that could anticipate this question, figure out what you wanted, and deliver a complete list of movies. Just getting the computer to recognize different voices asking the same question would be a monumental task.

However, big data combined with machine learning is up to the task. Big data feeds the machine everything it needs to identify words and phrases, along with an enormous database of movie and TV show titles, actors, directors, plot descriptions, and more. Machine learning provides the means for understanding words and phrases spoken in different ways and evolving when corrected for making mistakes. You may notice that the longer you use a natural language processing system or device, the better it gets at understanding what you say.

The Internet of Things (IoT)

The Internet of Things (IoT) refers to the large and growing collection of everyday objects that connect to the Internet and to one another. These devices include smart thermostats that learn your daily habits and adjust automatically to keep you comfortable, smart watches that can track your daily activity and let you know when you’re meeting your fitness goals, and smart refrigerators that can tell you when your milk is past its expiration date.

And because smart devices are connected to the Internet, you can control them remotely. For example, you can put a casserole in the oven before you leave home in the morning and then turn on the oven from your smartphone 15 minutes before leaving work, so that the casserole is done by the time you get home.

Certain devices can even communicate with one other. For example, your alarm clock can tell the coffee machine when to start brewing. Your smart watch can tell your smart locks to unlock the doors when you approach your home or turn on music when you enter your living room.

On the Horizon

It is hard to imagine AI expanding because it is already being applied so broadly in businesses, homes, government, and more. Instead of looking to the future and seeing more AI, I look to the future and see better AI — better robots, much better natural language processing, and fewer glitches in IoT devices. Perhaps, someday far into the future, we will begin to see robots that can do more than just follow us around with our luggage and deliver groceries.

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