Artificial Intelligence.

What Is Artificial Intelligence (AI)? How Does AI Work?

What Is Artificial Intelligence?

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every industry.

Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind. And from the development of self-driving cars to the proliferation of generative AI tools, AI is increasingly becoming part of everyday life.

What AI Is, How It Works, Types of AI
AI Importance, Benefits, Disadvantages
Artificial Intelligence Applications and Examples
AI Regulation and the Future of AI
Artificial Intelligence History
What AI Is, How It Works, Types of AI

What Is Artificial Intelligence?

Artificial intelligence refers to computer systems that can perform tasks commonly associated with human cognitive functions — such as interpreting speech, playing games and identifying patterns. Typically, AI systems learn how to do so by processing massive amounts of data and looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision; for instance, by playing a game over and over until they eventually figure out the rules and how to win.

Strong AI vs. Weak AI

Artificial intelligence is often distinguished between weak AI and strong AI. Weak AI (or narrow AI) refers to AI that automates specific tasks, typically outperforming humans but operating within constraints. Strong AI (or artificial general intelligence) describes AI that can emulate human learning and thinking, though it remains theoretical for now.

Weak AI

Also called narrow AI, weak AI operates within a limited context and is applied to a narrowly defined problem. It often operates just a single task extremely well. Common weak AI examples include email inbox spam filters, language translators, website recommendation engines and conversational chatbots.

Strong AI

Often referred to as artificial general intelligence (AGI) or simply general AI, strong AI describes a system that can solve problems it’s never been trained to work on, much like a human can. AGI does not actually exist yet. For now, it remains the kind of AI we see depicted in popular culture and science fiction.


How Does AI Work?

Artificial intelligence systems work by using any number of AI techniques.

Machine Learning

A machine learning (ML) algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been programmed for that certain task. It uses historical data as input to predict new output values.

Machine learning consists of both supervised learning (where the expected output for the input is known thanks to labeled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).

Deep Learning

Deep learning is a type of machine learning that runs inputs through a biologically inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.

Neural Networks

Neural networks are a series of algorithms and a subset of machine learning that process data by mimicking the structure of the human brain. Each neural network is composed of a group of attached neuron models, or nodes, which pass information between each other. These systems allow machines to identify patterns and relationships within data, plus learn from mistakes. This makes neural networks useful for recognizing images, understanding human speech and translating words between languages.

Natural Language Processing 

Natural language processing (NLP) is an area of artificial intelligence concerned with giving machines the ability to interpret written and spoken language in a similar manner as humans. NLP combines computer science, linguistics, machine learning and deep learning concepts to help computers analyze unstructured text or voice data and extract relevant information from it. NLP mainly tackles speech recognition and natural language generation, and it’s leveraged for use cases like spam detection and virtual assistants.

Computer Vision

Computer vision is a field of artificial intelligence in which machines process raw images, videos and visual media, taking useful insights from them. Then deep learning and convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers discern the difference between visual shapes and patterns. Computer vision is used for image recognition, image classification and object detection, and completes tasks like facial recognition and detection in self-driving cars.


Types of Artificial Intelligence 

Artificial intelligence is often categorized into four main types of AI: reactive machines, limited memory, theory of mind and self-awareness.

Reactive Machines

As the name suggests, reactive machines perceive the world in front of them and react. They can carry out specific commands and requests, but they cannot store memory or rely on past experiences to inform their decision making in real time. This makes reactive machines useful for completing a limited number of specialized duties.

Examples of reactive machines include Netflix’s recommendation engine and IBM’s Deep Blue (used to play chess).

Limited Memory

Limited memory AI has the ability to store previous data and predictions when gathering information and making decisions. Essentially, it looks into the past for clues to predict what may come next. Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data, or an AI environment is built so models can be automatically trained and renewed.

Examples of limited memory AI systems include some chatbots (like ChatGPT) and self-driving cars.

Theory of Mind

Theory of mind is a type of AI that does not actually exist yet, but it describes the idea of an AI system that can perceive and understand human emotions, and then use that information to predict future actions and make decisions on its own.


Self-aware AI refers to artificial intelligence that has self-awareness, or a sense of self. This type of AI does not currently exist. In theory, though, self-aware AI possesses human-like consciousness and understands its own existence in the world, as well as the emotional state of others.

AI Importance, Benefits, Disadvantages

Why Is Artificial Intelligence Important?

Artificial intelligence aims to provide machines with similar processing and analysis capabilities as humans, making AI a useful counterpart to people in everyday life. AI is able to interpret and sort data at scale, solve complicated problems and automate various tasks simultaneously, which can save time, effort and fill in operational gaps missed by humans. AI serves as the foundation for computer learning and is used in almost every industry — from healthcare to manufacturing and education — to help make data-driven business decisions and carry out repetitive or computationally intensive tasks.

Many existing technologies use artificial intelligence to enhance user experiences. We see it in smartphones with AI assistants, online platforms with recommendation systems and vehicles with autonomous driving abilities. AI also helps protect people by piloting fraud detection and robotics for dangerous jobs, as well as leading research in healthcare and climate initiatives. 


Benefits of AI

AI is beneficial for automating repetitive tasks, solving complex problems, reducing human error and much more.

Automating Repetitive Tasks

Repetitive tasks such as data entry and factory work, as well as customer service conversations, can all be automated using AI technology. This lets humans focus on other priorities.

Solving Complex Problems

AI’s ability to process large amounts of data at once allows it to quickly find patterns and solve complex problems that may be too difficult for humans, such as predicting financial outlooks or optimizing energy solutions.

Improving Customer Experience

AI can be applied through user personalization, chatbots and automated self-service technologies, making the customer experience more seamless and increasing customer retention for businesses.

Advancing Healthcare and Medicine

AI works to advance healthcare by accelerating medical diagnoses, drug discovery and development and medical robot implementation throughout hospitals and care centers.

Reducing Human Error

The ability to quickly identify relationships in data makes AI effective for catching mistakes or anomalies among mounds of digital information, overall reducing human error and ensuring accuracy.


Disadvantages of AI

While artificial intelligence has its benefits, the technology also comes with risks and potential dangers to consider.

Job Displacement

AI’s abilities to automate processes, generate rapid content and work for long periods of time can mean job displacement for human workers.

Bias and Discrimination

AI models may be trained on data that reflects biased human decisions, leading to outputs that are biased or discriminatory against certain demographics. 

Privacy Concerns

The data collected and stored by AI systems may be done so without user consent or knowledge, and may even be accessed by unauthorized individuals in the case of a data breach.

Ethical Concerns

AI systems may be developed in a manner that isn’t transparent, inclusive or sustainable, resulting in a lack of explanation for potentially harmful AI decisions as well as a negative impact on users and businesses.

Environmental Costs

Large-scale AI systems can require a substantial amount of energy to operate and process data, which increases carbon emissions and water consumption.

Artificial Intelligence Applications and Examples

Artificial Intelligence Applications

Artificial intelligence has applications across multiple industries, ultimately helping to streamline processes and boost business efficiency.


AI is used in healthcare to improve the accuracy of medical diagnoses, facilitate drug research and development, manage sensitive healthcare data and automate online patient experiences. It is also a driving factor behind medical robots, which work to provide assisted therapy or guide surgeons during surgical procedures.


AI in retail amplifies the customer experience by powering user personalization, product recommendations, shopping assistants and facial recognition for payments. For retailers and suppliers, AI helps automate retail marketing, identify counterfeit products on marketplaces, manage product inventories and pull online data to identify product trends.


AI in manufacturing can reduce assembly errors and production times while increasing worker safety. Factory floors may be monitored by AI systems to help identify incidents, track quality control and predict potential equipment failure. AI also drives factory and warehouse robots, which can automate manufacturing workflows and handle dangerous tasks. 


The finance industry utilizes AI to detect fraud in banking activities, assess financial credit standings, predict financial risk for businesses plus manage stock and bond trading based on market patterns. AI is also implemented across fintech and banking apps, working to personalize banking and provide 24/7 customer service support.


Video game developers apply AI to make gaming experiences more immersive. Non-playable characters (NPCs) in video games use AI to respond accordingly to player interactions and the surrounding environment, creating game scenarios that can be more realistic, enjoyable and unique to each player. 


AI assists militaries on and off the battlefield, whether it's to help process military intelligence data faster, detect cyberwarfare attacks or automate military weaponry, defense systems and vehicles. Drones and robots in particular may be imbued with AI, making them applicable for autonomous combat or search and rescue operations.


Artificial Intelligence Examples

Specific examples of AI include:

Generative AI Tools

Generative AI tools, sometimes referred to as chatbots — including ChatGPT, Gemini, Claude and Grok — use artificial intelligence to produce written content in a range of formats, from essays to code and answers to simple questions.

Smart Assistants

Personal AI assistants, like Alexa and Siri, use natural language processing to receive instructions from users to perform a variety of ‘smart tasks.’ They can carry out commands like setting reminders, searching for online information or turning off your kitchen lights.

Self-Driving Cars

Self-driving cars are a recognizable example of deep learning, since they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals and much more.


Many wearable sensors and devices used in the healthcare industry apply deep learning to assess the health condition of patients, including their blood sugar levels, blood pressure and heart rate. They can also derive patterns from a patient’s prior medical data and use that to anticipate any future health conditions.

Visual Filters

Filters used on social media platforms like TikTok and Snapchat rely on algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing.


The Rise of Generative AI

Generative AI describes artificial intelligence algorithms that can create new content — such as text, images, video or audio — based on a given user prompt. To work, a generative AI model is fed massive data sets and trained to identify patterns within them, then subsequently generates outputs that resemble this training data. Generative AI uses machine learning, neural networks, and deep learning-based large language models to produce its content.

Generative AI has gained massive popularity in the past few years, especially with chatbots like ChatGPT, Gemini and Claude — as well as image generators such as DALL-E 2 and Midjourney — arriving on the scene. These kinds of tools are often used to create written copy, code, digital art, object designs and more. They are leveraged in industries like entertainment, marketing, consumer goods and manufacturing.

AI Regulation and the Future of AI

AI Regulation

As artificial intelligence algorithms grow more complex and powerful, AI technologies — and the companies that create them — have increasingly drawn scrutiny from regulators across the world.

In 2021, the European Union Parliament proposed a regulatory framework that aims to ensure AI systems deployed within the European Union are “safe, transparent, traceable, non-discriminatory and environmentally friendly.” Under this framework, AI systems that can be used to perform real-time surveillance, or to manipulate people, categorize populations or discriminate against vulnerable groups, would be banned from use within the EU (though some limited exceptions may be made for law enforcement purposes).

In 2022, the Biden White House introduced an AI Bill of Rights, outlining principles for responsible use of AI. And in 2023, the Biden-Harris administration introduced The Executive Order on Safe, Secure and Trustworthy AI, which aims to regulate the AI industry while maintaining the United States’ status as a leader in artificial intelligence innovation.

The order requires the companies operating large AI systems to perform safety testing and report results to the federal government before making their products publicly available. It also calls for labeling of AI-generated content and increased efforts to answer questions about the impact of AI on intellectual property rights. Additionally, the executive order calls for several worker protections including against unsafe AI implementation and harmful disruptions of the labor force. The order also calls for the United States government to work alongside other countries to establish global standards for mitigating the risks of AI and promoting AI safety more generally.


Future of Artificial Intelligence 

In the near future, AI is poised to advance in machine learning capabilities and related frameworks like generative adversarial networks (GANs), which can help further develop generative AI and autonomous systems. Inevitably, AI will continue to make an impact across multiple industries, potentially causing job displacement, but also new job opportunities.

Looking ahead, one of the next big steps for artificial intelligence is to progress beyond weak or narrow AI and achieve artificial general intelligence (AGI). With AGI, machines will be able to think, learn and act the same way as humans do, blurring the line between organic and machine intelligence. This could pave the way for increased automation and problem-solving capabilities in medicine, transportation and more — as well as sentient AI down the line.

While likely groundbreaking, future advancements in AI have raised concerns such as heightened job loss, widespread disinformation, unpredictable AI behavior and possible moral dilemmas associated with reaching technological singularity

For now, society is largely looking toward federal and business-level AI regulations to help guide the technology’s future.

Artificial Intelligence History

History of AI

Artificial intelligence as a concept began to take off in the 1950s when computer scientist Alan Turing released the paper “Computing Machinery and Intelligence,” which questioned if machines could think and how one would test a machine’s intelligence. This paper set the stage for AI research and development, and was the first proposal of the Turing test, a method used to assess machine intelligence. The term “artificial intelligence” was coined in 1956 by computer scientist John McCartchy in an academic conference at Dartmouth College.

Following McCarthy’s conference and throughout the 1970s, interest in AI research grew from academic institutions and U.S. government funding. Innovations in computing allowed several AI foundations to be established during this time, including machine learning, neural networks and natural language processing. Despite its advances, AI technologies eventually became more difficult to scale than expected and declined in interest and funding, resulting in the first AI winter until the 1980s.

In the mid-1980s, AI interest reawakened as computers became more powerful, deep learning became popularized and AI-powered “expert systems” were introduced. However, due to the complication of new systems and an inability of existing technologies to keep up, the second AI winter occurred and lasted until the mid-1990s.

By the mid-2000s, innovations in processing power, big data and advanced deep learning techniques resolved AI’s previous roadblocks, allowing further AI breakthroughs. Modern AI technologies like virtual assistants, driverless cars and generative AI began entering the mainstream in the 2010s, making AI what it is today.



  • (1942) Isaac Asimov publishes the Three Laws of Robotics, an idea commonly found in science fiction media about how artificial intelligence should not bring harm to humans.
  • (1943) Warren McCullough and Walter Pitts publish the paper “A Logical Calculus of Ideas Immanent in Nervous Activity,” which proposes the first mathematical model for building a neural network. 
  • (1949) In his book The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.


  • (1950) Alan Turing publishes the paper “Computing Machinery and Intelligence,” proposing what is now known as the Turing Test, a method for determining if a machine is intelligent. 
  • (1950) Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.
  • (1950) Claude Shannon publishes the paper “Programming a Computer for Playing Chess.”
  • (1952) Arthur Samuel develops a self-learning program to play checkers. 
  • (1954) The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English. 
  • (1956) The phrase “artificial intelligence” is coined at the Dartmouth Summer Research Project on Artificial Intelligence. Led by John McCarthy, the conference is widely considered to be the birthplace of AI.
  • (1956) Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program. 
  • (1958) John McCarthy develops the AI programming language Lisp and publishes “Programs with Common Sense,” a paper proposing the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans.  
  • (1959) Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving. 
  • (1959) Herbert Gelernter develops the Geometry Theorem Prover program.
  • (1959) Arthur Samuel coins the term “machine learning” while at IBM.
  • (1959) John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project.


  • (1963) John McCarthy starts the AI Lab at Stanford.
  • (1966) The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government details the lack of progress in machine translations research, a major Cold War initiative with the promise of automatic and instantaneous translation of Russian. The ALPAC report leads to the cancellation of all government-funded MT projects. 
  • (1969) The first successful expert systems, DENDRAL and MYCIN, are created at Stanford.


  • (1972) The logic programming language PROLOG is created.
  • (1973) The Lighthill Report, detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for AI projects. 
  • (1974-1980) Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s Lighthill Report, AI funding dries up and research stalls. This period is known as the “First AI Winter.”


  • (1980) Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first AI Winter.
  • (1982) Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.
  • (1983) In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and AI. 
  • (1985) Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp. 
  • (1987-1993) As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “Second AI Winter.” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.


  • (1991) U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.
  • (1992) Japan terminates the FGCS project in 1992, citing failure in meeting the ambitious goals outlined a decade earlier.
  • (1993) DARPA ends the Strategic Computing Initiative in 1993 after spending nearly $1 billion and falling far short of expectations. 
  • (1997) IBM’s Deep Blue beats world chess champion Gary Kasparov.


  • (2005) STANLEY, a self-driving car, wins the DARPA Grand Challenge.
  • (2005) The U.S. military begins investing in autonomous robots like Boston Dynamics’ “Big Dog” and iRobot’s “PackBot.”
  • (2008) Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.


  • (2011) IBM’s Watson handily defeats the competition on Jeopardy!. 
  • (2011) Apple releases Siri, an AI-powered virtual assistant through its iOS operating system. 
  • (2012) Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in the breakthrough era for neural networks and deep learning funding.
  • (2014) Google makes the first self-driving car to pass a state driving test. 
  • (2014) Amazon’s Alexa, a virtual home smart device, is released.
  • (2016) Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.
  • (2016) The first “robot citizen,” a humanoid robot named Sophia, is created by Hanson Robotics and is capable of facial recognition, verbal communication and facial expression.
  • (2018) Google releases natural language processing engine BERT, reducing barriers in translation and understanding by ML applications.
  • (2018) Waymo launches its Waymo One service, allowing users throughout the Phoenix metropolitan area to request a pick-up from one of the company’s self-driving vehicles.


  • (2020) Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.
  • (2020) OpenAI releases natural language processing model GPT-3, which is able to produce text modeled after the way people speak and write. 
  • (2021) The European Union Parliament proposes a regulatory framework that aims to ensure that AI systems deployed within the EU are “safe, transparent, traceable, non-discriminatory and environmentally friendly.”
  • (2021) OpenAI builds on GPT-3 to develop DALL-E, which is able to create images from text prompts.
  • (2022) The National Institute of Standards and Technology releases the first draft of its AI Risk Management Framework, voluntary U.S. guidance “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”
  • (2022) DeepMind unveils Gato, an AI system trained to perform hundreds of tasks, including playing Atari, captioning images and using a robotic arm to stack blocks.
  • (2022) The White House introduces an AI Bill of Rights outlining principles for the responsible development and use of AI.
  • (2022) OpenAI launches ChatGPT, a chatbot powered by a large language model that gains more than 100 million users in just a few months.
  • (2023) Microsoft launches an AI-powered version of Bing, its search engine, built on the same technology that powers ChatGPT.
  • (2023) Google announces Bard, a competing conversational AI.
  • (2023) OpenAI Launches GPT-4, its most sophisticated language model yet.
  • (2023) The Biden-Harris administration issues The Executive Order on Safe, Secure and Trustworthy AI, calling for safety testing, labeling of AI-generated content and increased efforts to create international standards for the development and use of AI. The order also stresses the importance of ensuring that artificial intelligence is not used to circumvent privacy protections, exacerbate discrimination or violate civil rights or the rights of consumers.
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