Artificial Intelligence (AI)

Imagine teaching a child to recognize dogs. You don’t hand them a biology textbook—you point to a golden retriever and say “dog,” then point to a poodle and say “dog.” After enough examples, the child starts identifying breeds they have never seen before. Artificial intelligence works on a similar principle: instead of writing every rule by hand, you build systems that learn patterns from data and apply those patterns to new situations [3].

What Is Artificial Intelligence?

Artificial intelligence (AI) is technology that enables computers to simulate human capabilities—learning, comprehension, problem-solving, decision-making, and increasingly, autonomy [3]. Unlike traditional software, where a programmer writes explicit instructions for every scenario, AI systems are designed to process data, identify patterns, and make predictions or decisions on their own [3].

The term was coined by John McCarthy in 1955, when he, Marvin Minsky, Nathaniel Rochester, and Claude Shannon submitted a proposal to the Rockefeller Foundation for a summer research project at Dartmouth College. Their core conjecture: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” [1]. That project, held in the summer of 1956, is widely regarded as the founding event of AI as a field [1].

The Building Blocks: AI’s Key Subfields

AI is not a single technology—it is a broad field with several specialized branches that work together [5].

Machine learning (ML) is the foundation. It involves training algorithms on data so they can make predictions or decisions without being explicitly programmed for every task [3]. A spam filter that learns to catch junk email, or a recommendation engine that learns your taste in music—those are machine learning at work.

Deep learning is a subset of ML that uses multilayered neural networks—loosely modeled on the human brain’s structure—to handle highly complex tasks like image recognition and language generation [3]. Deep learning powers most of today’s headline AI applications, from large language models to self-driving car perception systems.

Other key branches include natural language processing (NLP), which lets machines understand and generate human language; computer vision, which enables systems to interpret images and video; and robotics, which combines AI with physical actuators to interact with the real world [5].

Narrow AI vs. General AI

Almost all AI in use today is narrow AI (sometimes called “weak AI”). These systems are designed to excel at a specific task—playing chess, translating text, detecting fraud—but they cannot transfer that skill to an unrelated domain [4]. Your voice assistant can set a timer and answer trivia, but it cannot plan a dinner party or debug a software program.

Artificial General Intelligence (AGI) is a hypothetical system with human-level (or beyond) ability to learn, reason, and apply knowledge across any task or domain [4]. No known AI system currently approaches AGI. It remains a theoretical goal, and researchers disagree on whether—and when—it might be achieved [3][4].

A Brief History

The question of whether machines can think is older than computers themselves. In 1950, Alan Turing published “Computing Machinery and Intelligence” in the journal Mind, proposing what became known as the Turing test: if a machine could convince a human judge it was human through text conversation, Turing argued, that was a reasonable operational test for machine intelligence [2].

Five years later, McCarthy’s Dartmouth proposal [1] gave the field its name and its founding charter. The decades since have produced landmark milestones: IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997; DeepMind’s AlphaGo beating world Go champion Lee Sedol in 2016; and the 2022 emergence of large language models (LLMs) like ChatGPT, which brought generative AI into mainstream use [3].

Each of these milestones expanded what people believed machines could do—but the underlying question Turing asked in 1950 has not changed [2].

AI in the Agentic Economy

The most significant recent shift in AI is the move from passive tools to autonomous agents. An AI agent is a program that can perceive its environment, make decisions, and take actions toward a goal without step-by-step human instruction [3]. Instead of just answering questions, agents can book flights, negotiate contracts, manage supply chains, and coordinate with other agents.

This is the foundation of the agentic economy—a world where software acts as a participant rather than a tool. Understanding AI at the definitional level matters because every concept in this library—from machine learning to large language models to AI agents—sits underneath the AI umbrella [5].

Common Questions

Is AI the same as machine learning?
No. AI is the broad field; machine learning is a specific approach within it. ML is the most popular way to build AI systems today, but AI also includes techniques like rule-based expert systems and symbolic reasoning that don’t involve learning from data [3][5].

Can AI actually “think”?
Current AI systems produce outputs that look like reasoning, creativity, or understanding, but they do not possess consciousness or subjective experience the way humans do. They identify statistical patterns in data—very effectively, but not the same way a human mind works [2][3].

How many companies use AI?
Adoption is accelerating. In 2025, nearly 20% of EU enterprises with 10 or more employees used at least one AI technology, up from about 13.5% in 2024. Among large enterprises, adoption reached 55% [6].

What is the difference between narrow AI and AGI?
Narrow AI handles specific tasks—voice recognition, image classification, game-playing. AGI would handle any intellectual task a human can. All deployed AI today is narrow AI. AGI remains theoretical [3][4].

Sources

[1] McCarthy, J., Minsky, M., Rochester, N., Shannon, C. (1955, August 31). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf

[2] Turing, A. (1950, October). Computing Machinery and Intelligence. Mind, Vol. 59, No. 236, pp. 433-460. https://courses.cs.umbc.edu/471/papers/turing.pdf

[3] Stryker, C., Kavlakoglu, E. (2024, August 9). What is artificial intelligence (AI)? IBM Think. https://www.ibm.com/think/topics/artificial-intelligence

[4] What is AGI (Artificial General Intelligence)? (2024). Stanford HAI. https://hai.stanford.edu/ai-definitions/what-is-agi-artificial-general-intelligence

[5] Russell, S. J., Norvig, P. (2020). Artificial Intelligence: A Modern Approach. 4th Edition, Prentice Hall. https://www.pearson.com/en-us/subject-catalog/p/artificial-intelligence-a-modern-approach/P200000003195

[6] Use of artificial intelligence in enterprises. (2025). Eurostat. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises