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Artificial intelligence enables computers and machines to mimic the perception, learning, problem-solving, and decision-making capabilities of the human mind.

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 mindlearning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problemsand combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

After decades of being relegated to science fiction, today, AI is part of our everyday lives. The surge in AI development is made possible by the sudden availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. AI is completing our words as we type them, providing driving directions when we ask, vacuuming our floors, and recommending what we should buy or binge-watch next. And its driving applicationssuch as medical image analysisthat help skilled professionals do important work faster and with greater success.

As common as artificial intelligence is today, understanding AI and AI terminology can be difficult because many of the terms are used interchangeably; and while they are actually interchangeable in some cases, they arent in other cases. Whats the difference between artificial intelligence and machine learning? Between machine learning and deep learning? Between speech recognition and natural language processing? Between weak AI and strong AI? This article will try to help you sort through these and other terms and understand the basics of how AI works.

The easiest way to understand the relationship between artificial intelligence (AI), machine learning, and deep learning is as follows:

Let's take a closer look at machine learning and deep learning, and how they differ.

Machine learning applications (also called machine learning models) are based on a neural network,which is a network of algorithmic calculations that attempts to mimic the perception and thought process of the human brain. At its most basic, a neural network consists of the following:

Machine learning models that arent deep learning models are based on artificial neural networks with just one hidden layer. These models are fed labeled datadata enhanced with tags that identify its features in a way that helps the model identify and understand the data. They are capable of supervised learning (i.e., learning that requires human supervision), such as periodic adjustment of the algorithms in the model.

Deep learning models are based on deep neural networksneural networks with multiple hidden layers, each of which further refines the conclusions of the previous layer. This movement of calculations through the hidden layers to the output layer is called forward propagation. Another process, called backpropagation, identifies errors in calculations, assigns them weights, and pushes them back to previous layers to refine or train the model.

While some deep learning models work with labeled data, many can work with unlabeled dataand lots of it. Deep learning models are also capable of unsupervised learningdetecting features and patterns in data with the barest minimum of human supervision.

A simple illustration of the difference between deep learning and other machine learning is the difference between Apples Siri or Amazons Alexa (which recognize your voice commands without training) and the voice-to-type applications of a decade ago, which required users to train the program (and label the data) by speaking scores of words to the system before use. But deep learning models power far more sophisticated applications, including image recognition systems that can identify everyday objects more quickly and accurately than humans.

For a deeper dive into the nuanced differences between thesetechnologies, read AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference?

Weak AIalso called Narrow AI or Artificial Narrow Intelligence (ANI)is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow is a more accurate descriptor for this AI, because it is anything but weak; it enables some very impressive applications, including Apple's Siri and Amazon's Alexa, the IBM Watson computer that vanquished human competitors on Jeopardy, and self-driving cars.

Strong AI, also called Artificial General Intelligence (AGI), is AI that more fully replicates the autonomy of the human brainAI that can solve many types or classes of problems and even choose the problems it wants to solve without human intervention. Strong AI is still entirely theoretical, with no practical examples in use today. But that doesn't mean AI researchers aren't also exploring (warily) artificial super intelligence (ASI), which is artificial intelligence superior to human intelligence or ability. An example of ASI might be HAL, the superhuman (and eventually rogue) computer assistant in 2001: A Space Odyssey.

As noted earlier, artificial intelligence is everywhere today, but some of it has been around for longer than you think. Here are just a few of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

IBM Watson products and solutions give enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore IBM's portfolio of managed services and solutions.

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What is Artificial Intelligence (AI)? | IBM