Intro to Artificial Intelligence (AI)

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From smart speakers to self-driving cars, you’ve probably heard a lot about Artificial Intelligence (AI). In this introductory guide, let’s take a closer look and learn more about what AI is all about!

What is AI?

In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

Types of AI

Artificial Narrow Intelligence (ANI)

Narrow intelligence refers to the use of AI algorithms to perform specific tasks. Most of the AI used today falls under this category. Examples of this type of AI include face recognition, Apple’s Siri, Amazon’s Alexa, self-driving cars, and quality control processes in modern factory assembly lines.

Artificial General Intelligence (AGI)

General Intelligence refers to a type of AI that more closely replicates the way the human brain works. It can learn, discover and solve problems on its own, without any human intervention. This type of AI does not yet exist, however it is the subject of many science fiction books & movies and raises many philosophical and ethical questions.

How does AI work?

Most AI today is built using machine learning algorithms. These types of algorithms are based on neural networks, which mimic the way neurons in a human brain work. The advantage of these algorithms is that, once trained, they are very efficient at recognizing patterns.

How is AI trained?

AI is trained by feeding neural networks large amounts of data.

In the case of supervised learning, training data is prepared and labeled by an AI engineer in advance, so that the algorithm can learn the correct relationship between inputs and outputs. Given enough data, the AI will eventually be able to correctly recognize and identify desired patterns with a high degree of confidence. This is how things like face recognition and speech detection work.

In the case of unsupervised learning, an AI engineer doesn’t know in advance what they want the algorithm to do. They feed the AI algorithm lots of data and then allow it to try to find patterns on its own. Data scientists and researchers then examine the output of the AI to see if there are any insights or meaningful relationships to be found. This type of AI can be unpredictable, and not always productive.

Examples of AI (interactive)

Online courses

Software engineering resources

Artist Resources

  • AI Artists (community of artists exploring Artifical Intelligence)
  • (approachable, friendly machine learning for the web)
  • Unfolding AI (MIT Symposium on Art, Computation and AI)

AI Reading List



AI Machine Learning Coding