What is artificial intelligence (AI)?

Artificial intelligence (AI) is a branch of computer science that lets machines perform tasks that normally require human intelligence, like learning, reasoning, problem-solving, recognizing patterns and making decisions. Put more simply, AI is software that learns from data and uses what it learns to make predictions, decisions or new content without being explicitly programmed for each task.

Today’s AI runs everything from spam filters and recommendation engines to chatbots like ChatGPT and image generators. It draws on a range of techniques, most notably machine learning and generative AI, and it has moved from research labs into products people use every day.

Stanford computer scientist Fei-Fei Li, writing in the Stanford Emerging Technology Review, places AI in the same category as the most transformative technologies in modern history: “AI is a foundational technology that is advancing other scientific fields and, like electricity and the internet, has the potential to transform how society operates.” Adoption is now scaling across every sector, from healthcare and financial services to retail and manufacturing, and the pace is accelerating.

This page covers how AI works, the main types of AI, real-world examples, the limitations to watch for and a brief history of the field.

What is AI in simple words?

Think of AI as teaching a computer by example instead of writing step-by-step instructions. Show a system thousands of photos of cats and it learns to recognize cats on its own, not because someone told it that cats have whiskers and pointed ears, but because it has seen enough examples to figure out the pattern. AI is not “thinking” the way you or I do. It is finding patterns in data and using those patterns to make a best guess. That distinction matters: AI can get remarkably good results in narrow domains, but it does not understand anything in the human sense.

The same pattern-matching approach that lets a model recognize cats also lets it spot cancer cells in a biopsy or flag fraudulent transactions among millions of legitimate ones. The underlying mechanism, finding patterns in data, is the same even when the application looks dramatically different. It is already part of everyday tools: search engines, voice assistants, navigation apps, spam filters and the recommendations you see on streaming services.

How does AI work?

Most modern AI works by learning patterns from large amounts of data, then applying those patterns to new situations. Instead of a developer writing rules (“if email contains ’free money,’ mark as spam”), the system is shown many examples and figures out the rules itself.

The basic process looks like this:

1. Collect data. The system is fed large amounts of relevant text, images, numbers, audio or video, and gaps in that data become gaps in the model.
2. Train a model. An algorithm studies the data and tunes its internal weights and parameters until it reliably produces correct outputs, the computationally expensive step that can run for hours, days or weeks across many GPUs.
3. Test and refine. The model is evaluated on a “held-out” test set it wasn’t trained on, where catching mistakes is far cheaper than catching them in production.
4. Make predictions. Once trained, the model answers questions, classifies inputs, generates content or triggers actions on data it has never seen, the “inference” step end users actually interact with.
5. Learn and improve. Many AI systems keep improving as they are exposed to more data and feedback, including signals from how people respond to their outputs.

Modern AI training is also a question of scale: frontier models train on trillions of tokens of text, run on tens of thousands of GPUs and cost hundreds of millions of dollars to build. Most organizations don’t train models from scratch. Instead, they fine-tune existing foundation models on their own data, which is dramatically faster and cheaper while still producing models tailored to a specific task or domain.

The quality of an AI system depends heavily on the data it learns from: when training data is incomplete, biased or low-quality, AI outputs will be too. You can read more about the building blocks in our overviews of machine learning models and neural networks.

What are the 4 types of AI?

Researchers commonly group AI into four categories based on capability, a taxonomy usually attributed to Michigan State University researcher Arend Hintze, who proposed it in 2016 as a way to think about how AI might evolve. Only the first two categories exist in the real world today, while the other two remain open questions in research and philosophy.

 The taxonomy is useful because it draws a clean line between what AI can actually do now and what it can only do in theory or fiction.

Type	What it does	Status today	Example
Reactive machines	Responds to a specific input with a fixed output. Has no memory of past events, no ability to learn from experience and no model of the world beyond the immediate input.	Among the earliest AI architectures; still in use for narrow tasks today.	IBM’s Deep Blue, which defeated chess world champion Garry Kasparov in 1997, evaluated the board from scratch every turn. Simple spam filters that match keywords against a fixed list belong to the same category.
Limited memory	Learns from historical data to make predictions or decisions. Can use recent inputs to refine its outputs but does not retain a persistent long-term memory the way humans do.	Powers nearly all modern AI in production, including the most capable systems.	Self-driving cars that pull from short-term sensor history to anticipate the road ahead. ChatGPT, which holds the context of the current conversation but starts fresh in a new session. Netflix’s recommendation engine, which learns from viewing patterns over time.
Theory of mind	Would understand the emotions, intentions and beliefs of other people, the cognitive ability to model another mind. Researchers are exploring narrow versions, but no system genuinely demonstrates it.	Theoretical; active research area.	Not yet built. The closest analogues are AI tutors and customer-service bots that adapt tone based on user signals, but these are pattern matching rather than real understanding of intent.
Self-aware	Would have consciousness and a sense of self, an inner experience of being.	Theoretical. Whether this is achievable, or even definable, is contested among researchers and philosophers.	Not yet built. Often discussed in science fiction and AI safety debates, but no clear technical path currently exists.

Nearly every AI product people use today, including the most capable large language models, falls into the limited-memory category.

Narrow AI vs. general AI vs. superintelligence

All AI systems in use today are classified as narrow. The table below differentiates these current systems from the theoretical concepts of general AI and superintelligence.

Category	Definition	Status today	Example
Narrow AI (weak AI)	AI systems engineered to perform specific, domain-limited tasks, with capabilities strictly constrained by their training data and architectural design.	All AI in use today, including the most capable systems.	ChatGPT, facial recognition, Netflix recommendations, fraud detection, voice assistants.
General AI (AGI)	A system designed to learn and perform any intellectual task a human can: flexibly, across domains, without retraining.	Theoretical. Active debate over whether current trajectories will lead to AGI and on what timeline.	None.
Superintelligence	A system capable of exceeding human intelligence across every domain, including the ability to improve itself.	Theoretical and largely speculative.	None.

Whether AGI exists today depends largely on how it is defined. Advanced models can reason across domains and complete complex tasks, but their persistent errors and uneven reliability make the classification contested.

AI vs. machine learning vs. deep learning vs. generative AI

These four terms are often used interchangeably, but they mean different things, and those differences matter when teams choose tools, scope projects or evaluate vendors.

A helpful way to think about them is as nested circles: AI is the broadest category, machine learning is a subset of AI, deep learning is a subset of machine learning, and generative AI is an application of deep learning focused on creating new content. The table below breaks down what each term means and how they differ. For a deeper comparison, see our breakdown of machine learning vs. deep learning.

Term	What it is	Simple example
Artificial intelligence (AI)	The broad field of building machines that perform tasks requiring human intelligence. Encompasses both rule-based systems and learning systems.	A chatbot that answers customer questions, regardless of whether it learned from data or follows scripted logic.
Machine learning (ML)	A subset of AI in which systems learn patterns from data rather than being explicitly programmed for each rule.	A model that predicts which customers are most likely to churn next quarter by studying past behavior.
Deep learning	A subset of ML that uses multi-layered neural networks to handle complex inputs, such as images, speech and language, that earlier ML techniques struggled with.	Image recognition that identifies tumors in radiology scans.
Generative AI	A type of deep learning that creates new content (text, images, audio, video or code) rather than classifying or predicting.	ChatGPT writing an email; an image generator creating original artwork from a text prompt.

What are examples of artificial intelligence?

AI already runs quietly inside everyday tools, drafting emails, flagging fraud in milliseconds and forecasting store inventory. The trajectory from prototype to embedded product has compressed sharply, and the solutions Databricks offers span many of these categories:

Industry	AI example
Healthcare	AI that reads medical images to help radiologists detect cancer earlier; clinical decision support systems that flag potential drug interactions; agents that summarize patient charts for clinicians.
Financial services	Fraud detection systems that flag suspicious credit card transactions in real time; algorithmic trading; AI-assisted underwriting for loans and insurance.
Retail and ecommerce	Product recommendations on Amazon; personalized search results; demand forecasting that decides how much stock to hold at each warehouse.
Transportation	Self-driving features in vehicles; route optimization in navigation apps; predictive maintenance that anticipates when a vehicle will need service.
Manufacturing	Computer vision systems that spot defects on assembly lines; predictive maintenance on factory equipment; supply chain optimization.
Customer service	Chatbots and virtual agents that handle support questions; AI that routes calls to the right human agent; sentiment analysis on customer interactions.
Media and entertainment	Netflix and Spotify recommendations; generative tools for video and music production; AI-powered subtitling and translation.
Everyday consumer tech	Voice assistants (Siri, Alexa), email spam filters, smartphone face unlock, photo apps that recognize faces and places.

The breadth is the story. AI is no longer concentrated in a few technical applications; it has spread into nearly every category of work where pattern recognition or content generation creates value. The pattern of adoption tends to be the same in each: the first wave handles narrow, repetitive tasks. Later waves take on more judgment-heavy work as model capabilities mature and as organizations build the data foundations to support them.

What are the main branches of AI?

AI is an umbrella term covering several specialized fields. Each branch focuses on a different kind of task or capability, though the boundaries between them have blurred as deep learning has become the common engine underneath much of the work.

• Machine learning: Systems that learn patterns from data and improve with experience. The dominant approach within AI today and the foundation most other branches build on.
• Deep learning: A more advanced form of machine learning that uses layered neural networks to handle complex inputs like images, speech and language. The breakthrough that made the current wave of AI possible.
• Natural language processing (NLP): Understanding and generating human language. NLP powers chatbots, translation, voice assistants and sentiment analysis. See our overview of natural language processing for more.
• Computer vision: Interpreting images and video. Used in facial recognition, medical imaging, quality inspection in manufacturing and the perception systems that let self-driving cars see the road. Read more about computer vision.
• Robotics: Combining AI with physical machines to perform tasks in the real world: warehouse robots, surgical assistants, autonomous vehicles, agricultural drones. The intersection of AI software and mechanical engineering.
• Generative AI: Models that create new content rather than just analyzing existing data. Text, images, audio, video and code are all in scope. The branch that exploded into public awareness in 2022 with ChatGPT.
• Expert systems: Older rule-based AI that mimics the decision-making of a human expert in a narrow domain. Largely superseded by machine learning approaches, but still used in places where explicit, auditable rules are required.

In practice, most modern AI systems combine multiple branches. A self-driving car uses computer vision to perceive the world, machine learning to predict the behavior of nearby vehicles and robotics to actuate the controls. The branches are useful as a mental map, but the products that ship usually live across them.

A brief history of AI

AI has been a research field for more than 70 years, with major shifts in capability concentrated in the past decade. The most recent wave has moved the field from academic curiosity to everyday infrastructure.

• 1950 — Turing test proposed: Alan Turing publishes “Computing Machinery and Intelligence,” asking whether machines can think and proposing a test for machine intelligence that still anchors debate today.
• 1956 — AI is named: John McCarthy coins “artificial intelligence” at the Dartmouth Conference, formally launching the field as a discipline.
• 1960s–1970s — Early optimism and AI winters: Researchers build early rule-based systems and natural-language programs. As progress stalls against the hard problems, funding dries up. The first of two “AI winters” sets in.
• 1997 — Deep Blue beats Kasparov: IBM’s chess computer defeats world champion Garry Kasparov in a six-game match, the first time a machine beat a reigning world champion at chess and a public milestone for AI.
• 2012 — Deep learning breakthrough: A neural network called AlexNet delivers a leap in image recognition accuracy at the ImageNet competition, sparking the modern AI boom and convincing the field that deep learning was the path forward.
• 2017 — Transformers introduced: Google researchers publish “Attention Is All You Need,” introducing the transformer architecture that opens the door to today’s large language models (LLMs).
• 2022 — ChatGPT launches: Generative AI enters the mainstream when OpenAI releases ChatGPT to the public. It reaches 100 million users in two months, the fastest consumer technology adoption on record at the time.
• 2023–today — Enterprise AI scales: Organizations move from experiments to production AI and AI agents across business functions, from customer service to software development to internal operations.

What stands out about the past three years is the pace. From 2022 onward, AI capabilities have advanced faster than most experts expected, and the gap between research breakthrough and shipped product has compressed from years to months. The shape of the next decade will depend less on raw model capability and more on how organizations turn those capabilities into reliable, governed systems.

What are the limitations and risks of AI?

AI is powerful but imperfect. The following risks commonly appear in production and generally fall into three categories: technical limitations, operational challenges, and broader societal impacts.

Hallucinations and inaccurate outputs

Generative AI can produce confident-sounding answers that are factually wrong. The industry term is “hallucination.” A chatbot may invent a citation, misquote a source or fabricate facts that look plausible on the surface. It happens because large language models predict likely next words rather than retrieve verified information: the model is optimized for fluency, not truth.

In high-stakes contexts like healthcare, legal advice and financial decisions, AI outputs should be verified by a human before they are acted on. Even in lower-stakes settings, organizations increasingly pair generative models with retrieval-augmented generation systems that ground outputs in trusted source documents. Systematic evaluation also helps: testing models against benchmark question sets before deployment catches many hallucinations early, before they reach users.

Bias in training data

AI learns from data. If the data reflects human bias, such as historical hiring patterns that favored one demographic or lending decisions that disadvantaged another, the AI will reproduce and often amplify that bias. As the Stanford Emerging Technology Review notes: “Without sufficient high-quality data, AI models may generate inaccurate or biased outcomes.”

Bias is a major concern in hiring, lending and criminal justice applications, where biased outputs cause real harm. Mitigation requires careful curation of training data, ongoing evaluation against fairness metrics and the discipline to test models on populations that may have been underrepresented during training. It is not a one-time fix. Models drift as the world they operate in changes, so fairness monitoring has to be an ongoing operational practice rather than a launch-day checkpoint.

The “black box” problem

With deep learning especially, it is often hard to tell exactly why an AI made a specific decision. The model’s reasoning is distributed across millions or billions of parameters, none of which map cleanly to a human-readable explanation. That matters most in regulated industries such as banking, healthcare and insurance, where a decision must be explainable to a customer, an auditor or a court.

The field of explainable AI (XAI) has emerged in response, building tools that surface which features most influenced a given model output. Some industries go further and require simpler, “interpretable” model architectures for high-stakes use cases, accepting a small cost in raw accuracy in exchange for decisions that can be traced and defended.

Privacy and security risks

AI systems often need access to large amounts of data, raising questions about how that data is collected, stored and used. Generative AI introduces new risks of its own: deepfakes that impersonate real people, AI-generated misinformation at scale and prompt injection attacks that trick models into revealing information they shouldn’t or taking actions they weren’t authorized to perform. Privacy controls and security guardrails are part of responsible AI design, not an afterthought.

Job displacement concerns

AI is automating tasks across many industries, which raises real questions about how jobs and skills will shift. The likely pattern is change, not wholesale replacement: AI tends to alter the mix of tasks within a job rather than eliminate the role outright. Some roles will fade, new ones will emerge and many existing roles will require new skills, particularly the ability to work effectively alongside AI systems. The disruption is real, the pace is fast and the workforce implications deserve serious attention from leaders, educators and policymakers.

Governance and compliance

Organizations deploying AI need clear guardrails: who can access which models, what data those models use, how outputs are monitored and how access can be revoked when something goes wrong. The lesson from the past decade of cloud security is that controls built in from the start hold up better than controls bolted on after the fact.

The same applies to AI. Regulations are also catching up, with the EU AI Act, state-level US laws and sector-specific rules in finance and healthcare all imposing new obligations on AI deployments. The practical implication for builders is that governance can no longer be an afterthought. It has to be designed in from the data layer up. For a closer look at the discipline behind it, see our AI governance overview.

Why does AI matter for businesses?

AI is reshaping how organizations operate, compete and serve customers. Its value comes from applying trusted, governed data to real business problems, not from running disconnected experiments.

Businesses are using AI to:

• Make faster, more informed decisions
• Automate repetitive work
• Personalize customer experiences
• Deploy agents that complete multi-step tasks
• Improve forecasting and customer service

Realizing that value requires a unified platform where data is prepared, models are trained, agents are deployed and the full stack is governed end to end.

Competitive pressure is also increasing. As AI becomes standard across many sectors, leading organizations are:

• Starting with a specific business problem
• Prioritizing data quality before model sophistication
• Evaluating performance against real-world outcomes
• Building governance into the platform from the start

How Databricks supports AI

Building production AI is hard: data lives in many places, models have to be trained and evaluated and governance has to span the whole pipeline. The Databricks Platform brings data and AI together in one place, so teams can store and prepare data, train and fine-tune models, deploy AI agents and govern it all end to end. That includes Agent Bricks for building agents grounded in enterprise data and Unity Catalog for governance across data and AI assets. The platform connects to leading models from OpenAI, Anthropic, Google and Meta, alongside open source alternatives, so you can pick the right model for each task without rebuilding your stack.

More than 20,000 organizations worldwide use Databricks to build, scale and govern their AI work. The advantage of a unified platform is fewer seams: teams move from data to model to deployment without copying data between systems or losing lineage, which makes AI work faster, cheaper and easier to audit. See more in the Databricks customers directory.

Frequently asked questions

What is an example of artificial intelligence?

ChatGPT, voice assistants like Siri and Alexa, Netflix’s recommendation engine, fraud detection on credit card transactions and self-driving features in cars are all examples of AI in use today. Most of these fall into the “limited memory” category: they learn from historical data to make predictions or generate responses, but they don’t retain a persistent long-term memory the way humans do.

What are the 4 types of AI?

Reactive machines, limited memory, theory of mind and self-aware AI. The first two exist today: everything from spam filters to ChatGPT belongs to one of those categories. The latter two remain theoretical, and there is no clear technical path to either one yet.

Is AI the same as machine learning?

No. AI is the broader field of building machines that perform intelligent tasks. Machine learning is one branch of AI: systems that learn from data rather than being explicitly programmed. All machine learning is AI, but not all AI is machine learning.

What is the difference between AI and generative AI?

AI is the umbrella field that covers any system performing tasks associated with human intelligence. Generative AI is a specific type of AI, built on deep learning, that creates new content (text, images, audio, video or code) rather than classifying or predicting from existing inputs. ChatGPT and image generators are everyday examples.

What are the biggest risks of AI?

The most common risks are hallucinations (confidently wrong outputs), bias inherited from training data, the “black box” problem (decisions you can’t easily explain), privacy and security gaps, job displacement and weak governance. Mitigation comes from verification, oversight, careful data curation and built-in guardrails.

Getting started with AI

AI is no longer experimental. It is a foundational technology powering everyday products and reshaping how businesses work, and the pace of adoption is accelerating. Understanding the basics of what it is, how it works, where it fits and where it falls short is the starting point for using it well. From there, the work is to apply it to real problems, on trusted data, with the governance to scale responsibly.

See how Databricks helps organizations build and scale AI on their own data — explore the Databricks Platform.