Artificial Intelligence (AI)

Artificial intelligence (AI) is a wide-ranging branch of computer science in which smart machines are built to perform tasks that typically require human intelligence. In simpler terms, it is making computers think like humans. The term is most frequently used to describe machines that mimic cognitive functions such as learning, communication, and problem-solving. Read on to learn more about how AI works and how it is applied to different business use cases.

What Is Artificial Intelligence (AI)?

Artificial intelligence is the application of computer science and complex data algorithms to create human-like behaviors in machines. While the term was coined in 1956, AI has since advanced by leaps and bounds thanks to advanced algorithms, increased data volumes, and improvements in computing power and technology. 

In the 1950s, early AI research delved into topics such as problem-solving and symbolic methods. Ten years later, the U.S. Department of Defense expressed interest in the concept and began to train computers to mimic basic human reasoning. By 2003, intelligent personal assistants were produced long before Siri or Alexa were introduced.

Popular examples of artificial intelligence include AI autopilots on commercial flights, spam filters, mobile check deposits, and voice-to-text features on mobile devices.

Watch and Learn: What Is AI?

AI has a variety of consumer and enterprise applications that continue to expand over time. Learn more from Webo, the Webopedia encyclopedia bot, in this explanatory AI video:

[su_youtube url=”https://youtu.be/iw5xbUWWN0g” title=”A video explanation of artificial intelligence (AI) and how it works from Webopedia’s YouTube channel.”]

How Does AI Work?

To understand how AI works, it’s important to first understand AI’s subcategories and how they can be applied to various industries and business needs:

• Machine learning (ML): ML teaches a machine to make inferences and decisions based on past experiences. It’s a type of data analysis that uses algorithms to learn from data. This ability to reach a conclusion by evaluating historical and live data saves time and helps the machine “learn” how to make better decisions.
• Deep learning: Deep learning is a subset of ML that processes data and creates patterns for use in decision-making. Deep learning models are frequently image-based. A growing use case for deep learning is the deepfake, or creating an image or video to so closely mimic a person that the fake media cannot easily be distinguished from reality.
• Neural networks: Neural networks work to imitate how a human brain functions. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain would.
• Natural language processing (NLP): NLP analyzes, understands, and generates text or vocal responses based on languages that humans use naturally. This helps AI applications, such as chatbots and other conversational AI, to interface with computers (and their human users) in both written and spoken contexts.
• Natural language generation (NLG): NLG automatically generates combinations of words in text format, based on human language patterns and formation.
• Computer vision: A computer vision algorithm attempts to understand an image by breaking down and studying different parts of it. This helps the machine classify and learn from a set of images to create a better output decision based on previous observations.
• Cognitive computing: Cognitive computing algorithms mimic a human brain by analyzing text, speech, images, and objects in a human manner and working to give the desired output.
• DevOps automation: AI can perform technological processes, such as software testing, quickly and reliably, freeing development and operations teams to do fewer manual tasks on their projects.

In all of these AI methodologies, detailed big data sets are collected and used as a source for intelligent algorithms. As these algorithms and data sets are used more, AI applications learn how to improve their performance of human tasks and behaviors.

Examples of AI Across Industries

AI is being used in every industry, and the demand for AI capabilities only continues to grow. These are some of the most common business use cases for AI today:

Healthcare

AI provides personalized medicine and X-ray readings. Personal health care assistants can remind patients to take medicine, exercise, or eat healthier with the power of AI automation and communication.

Retail

AI provides virtual shopping capabilities that offer personalized recommendations. Stock management and website layout technology are also improved with AI.

Manufacturing

AI analyzes Internet of Things (IoT) data as it streams from connected equipment; this is done to forecast expected load and demand using recurrent networks.

Banking and financial services

AI enhances the speed, precision, and effectiveness of human efforts in the finance and Fintech industries. It can be used to identify which transactions are likely to be fraudulent and automate manual data management tasks.

Automotive

AI-powered software helps vehicles to identify their immediate environment and safely navigate any hazards or obstacles. Self-driving cars are becoming a popular topic of interest in the AI sphere.

Blockchain

Developing AI models through blockchain technology allows users to view different kinds of progress records in a secure environment.

Read next: Guide to the Best Artificial Intelligence (AI) Software

This article was reviewed and updated by Shelby Hiter in 2022.