What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) is a technology with human-like problem-solving capabilities. AI in action appears to simulate human intelligence—it can recognize images, write poems, and make data-based predictions.

What is AI?

AI, also known as Artificial intelligence, is a technology with human-like problem-solving capabilities. AI in action appears to simulate human intelligence—it can recognize images, write poems, and make data-based predictions. 

Modern organizations collect large data volumes from diverse sources, such as smart sensors, human-generated content, monitoring tools, and system logs. Artificial intelligence technologies analyze the data and use it to assist business operations effectively. For example, AI technology can respond to human conversations in customer support, create original images and text for marketing, and make smart suggestions for analytics.

Ultimately, artificial intelligence is about making software smarter for customized user interactions and complex problem-solving.

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What are some types of AI technologies?

AI apps and technologies have increased exponentially in the last few years. Below are some examples of common AI technologies you may have encountered.
Image generation involves AI creating new images from scratch or based on descriptions. For example, AI can take a simple text prompt like "a sunset over the mountains" and generate a realistic or artistic image of that scene. This technology is used in art, entertainment, and marketing, allowing creators to visualize concepts quickly and efficiently.
Text generation is when AI writes text automatically, mimicking human writing. It can create anything from simple sentences to entire articles, poems, or stories. This technology is used in chatbots, content creation, and even for writing emails or reports.
Speech generation allows AI to produce spoken words, like how virtual assistants (such as Alexa) talk to you. Speech recognition is when AI understands and processes human speech. This technology is widely used in voice-activated devices, customer service hotlines, and even in helping people with disabilities communicate more effectively.
Multimodal AI combines different data types, like text, images, and sound, to create a more comprehensive understanding of information. For example, a multimodal AI might analyze a video by understanding the spoken words and objects in the video and reading any text that appears on the screen. This advanced form of AI is used in fields like autonomous vehicles, where understanding and interpreting multiple data types simultaneously is crucial for safe operation.

History of AI

In his 1950 paper, "Computing Machinery and Intelligence," Alan Turing considered whether machines could think. In this paper, Turing first coined the term artificial intelligence and presented it as a theoretical and philosophical concept.  However, AI, as we know it today, is the result of the collective effort of many scientists and engineers over several decades.

1940-1980

In 1943, Warren McCulloch and Walter Pitts proposed a model of artificial neurons, laying the foundation for neural networks, the core technology within AI.

Quickly following, in 1950, Alan Turing published "Computing Machinery and Intelligence," introducing the concept of the Turing Test to assess machine intelligence.

This lead to graduate students Marvin Minsky and Dean Edmonds building the first neural net machine known as the SNARC, Frank Rosenblatt developed the Perceptron which is one of the earliest models of a neural network, and Joseph Weizenbaum created ELIZA, one of the first chatbots to simulate a Rogerian psychotherapist between 1951 and 1969.

From 1969 until 1979 Marvin Minsky demonstrated the limitations of neural networks, which caused a temporary decline in neural network research. The first "AI winter" occurred due to reduced funding and hardware and computing limitations.

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1980-2006

In the 1980's, there was a renewed interest and government funding for AI research primarily in translation and transcription.During this time, expert systems, like MYCIN, became popular because they simulated human decision-making processes in specific domains like medicine. With the 1980's revival of neural networks, David Rumelhart and John Hopfield published papers on deep learning techniques showing that computers could learn from experience

From 1987-1997, due to other socio-economic factors and the dot-com boom, a second AI winter emerged. AI research became more fragmented, with teams solving domain-specific problems across different use cases.

Starting in 1997 to about 2006, we saw significant ahcievements in AI including IBM's Deep Blue chess software defeated world chess champion Garry Kasparov. In addition to this Judea Pearl published a book that included probability and decision theory in AI research and Geoffrey Hinton and others popularized deep learning, leading to a resurgence in neural networks. However, commercial interest remained limited.

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2007-Present

From 2007 to 2018, advancement in cloud computing made computing power and AI infrastructure more accessible. It led to increasing adoption. innovation and advancement in machine learning. The advancements included a convolutional neural network (CNN) architecture called AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton winning the ImageNet competition, showcasing the power of deep learning in image recognition and Google's AlphaZero mastered the games of chess, shogi, and Go without human data, relying on self-play.

In 2022, chatbots that uses artificial intelligence (AI) and natural language processing (NLP) to have human-like conversations and complete tasks like OpenAI's ChatGPT became widely known for its conversational abilities, renewing AI interest and development.

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AI in the future

Current artificial intelligence technologies all function within a set of pre-determined parameters. For example, AI models trained in image recognition and generation cannot build websites.

Artificial general intelligence (AGI) is a field of theoretical AI research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to perform tasks for which it is not necessarily trained or developed. 

AGI is a theoretical pursuit to develop AI systems with autonomous self-control, reasonable self-understanding, and the ability to learn new skills. It can solve complex problems in settings and contexts that were not taught at its creation. AGI with human abilities remains a theoretical concept and research goal. It is one possible future of AI.

Read more about artificial general intelligence »

AI in the future

How is AI used today?

AI is everywhere today, working behind the scenes to power your favorite applications.
AI is at work every time you log into your favorite streaming service. Streaming platforms use AI algorithms to analyze your viewing or listening habits and recommend content tailored to your preferences. The algorithms consider factors like your past selections, trending content, and similarities with other users. They ensure you always have something interesting to watch or listen to.
Online retailers utilize AI to personalize your shopping experience. AI suggests items that match your interests by analyzing your browsing history, purchase patterns, and the time you spend looking at specific products; you find what you’re looking for more quickly and can discover new products.
AI is revolutionizing healthcare by assisting in diagnostics, treatment planning, and patient monitoring. For example, AI-powered systems analyze medical images to detect early signs of diseases like cancer. AI systems integrate data from smart wearables, patient records, and family history to help doctors customize treatment plans for chronic ailments.
Forecasting with AI is about predicting future events or trends based on historical data. For example, weather forecasting systems use AI to predict weather patterns, helping people plan for storms or other weather-related events. AI forecasting helps companies anticipate product demand, allowing them to manage inventory better and avoid shortages or surpluses.
AI systems analyze real-time geospatial data to predict traffic patterns, optimize routes, and suggest alternative paths during congestion. AI helps you get to your destination faster and reduces fuel consumption and emissions, contributing to a greener environment.