How is AI used?

While addressing a crowd at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin began his speech by offering the following definition of how AI is used today:
"AI is a computer system capable of performing tasks that usually require human intelligence... Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning, and some of it is powered by really boring things like rules."

Artificial intelligence generally falls into two broad categories:

• Narrow AI:
  Sometimes referred to as "weak AI," this type of artificial intelligence operates in a limited context and simulates human intelligence. Narrow AI often focuses on performing a single task extremely efficiently. While these machines may seem intelligent, they operate under far more constraints and limitations than even the most basic human intelligence.
• General Artificial Intelligence (AGI):
  AGI, sometimes referred to as "strong AI," is the type of artificial intelligence we see in the movies, like the robots from Westworld or Data from Star Trek: The Next Generation. AGI is a machine with general intelligence, and just like a human being, it can apply that intelligence to solve any problem.
• Narrow artificial intelligence:
  Narrow AI is all around us and is by far the best achievement of artificial intelligence to date. By focusing on performing specific tasks, Narrow AI has seen many breakthroughs over the past decade that have had "significant societal benefits and contributed to the nation's economic vitality," according to "Preparing for the Future of Artificial Intelligence," a 2016 Report published by the Obama administration.

Here are some examples of narrow AI:

How does artificial intelligence work?

• ♦ google search
• ♦ Image recognition software
• ♦ Siri, Alexa, and other personal assistants
• ♦ Autonomous cars
• ♦ IBM's Watson
• ♦ Machine learning and deep learning

Much of Narrow AI is powered by breakthroughs in machine learning and deep learning. Understanding the difference between artificial intelligence, machine learning, and deep learning can be confusing. Venture capitalist Frank Chen provides a good overview of how to tell them apart, noting:
"Artificial intelligence is a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques ."
Simply put, machine learning feeds a computer data and uses statistical techniques to help it "learn" how to incrementally improve at a task without having explicitly been programmed for that task, eliminating the need for millions of lines of code written. Machine learning includes supervised learning (using labeled data sets) and unsupervised learning (using unlabeled data sets).
Deep learning is a type of machine learning that performs inputs through a biologically inspired neural network architecture. Neural networks contain several hidden layers through which data is processed, allowing the machine to go "deep" in its learning, making connections and weighting inputs for best results.