The history of artificial intelligence (AI) can be traced back through ancient literature and philosophy, where deep thinkers imagined intelligent machines and artificial beings.
As a technical discipline, the field of AI began in the mid-20th century. In 1950, British mathematician Alan Turing proposed the idea of a machine that could simulate any human intelligence, conceiving the now-famous Turing test.
Just six years later, at the 1956 Dartmouth Conference, computer scientists John McCarthy, Marvin Minsky, and Claude Shannon coined the term artificial intelligence, laying the foundation for modern AI research and development.
In the decades that followed, AI went through cycles of optimism and disappointment—often referred to as “AI winters,” when progress floundered due to technological limitations and overhyped expectations. Still, significant strides were made in expert systems in the 1980s and machine learning techniques in the 1990s.
The 21st century proved to be a new era for AI, fueled by massive data availability, advances in computing power, and breakthroughs in neural networks. In 2012, a deep learning system built at the University of Toronto dramatically improved image recognition, sparking renewed interest in AI. Tech giants quickly adopted these methods, driving rapid developments in natural language processing, computer vision, and robotics.
Today, AI is embedded in everyday life—from virtual assistants to recommendation algorithms—and continues to evolve quickly. As it grows more powerful and integrated into society, discussions about ethics, safety, and human-AI collaboration have become more crucial than ever.
artificial intelligence (AI)
A human-made computer system that can emulate or exceed human thought and perform complex tasks of reasoning, decision-making, communication, and creation.
algorithm
A defined, sequential set of instructions that a computer program follows to solve a specific problem or complete a task. The algorithm works step by step, following code written by humans. For example, when Netflix recommends a show based on your viewing history, it is using an algorithm. Algorithms are often informed by machine learning.
machine learning
A system that can identify patterns, learn from data, and improve its performance. Machine learning can adapt through an automated process and make decisions without being explicitly programmed for every possible scenario. Large language models (LLMs) are a type of machine learning, trained on vast amounts of text to recognize patterns and respond to natural-language prompts in a contextually appropriate way.
generative AI
An artificial intelligence model that uses its training data and learned patterns to create original content, such as text, images, or music. Generative AI relies on statistical patterns and associations, not step-by-step instructions, to fulfill user prompts. Chatbots such as ChatGPT, Grok, and Claude are examples of generative AI.
agentic AI
An AI system that operates with autonomy, enabling it to make decisions, take actions, and interact with its environment to achieve specific goals. Agentic AI can initiate tasks, adapt to changing circumstances, and pursue objectives independently, not simply responding to external prompts or following instructions. Self-driving cars are an example of agentic AI.
Ali Llewellyn and Nick Skytland are futurists and technologists with experience spanning space exploration, ministry, and church planting. They work at the intersection of faith and AI and are coauthors of What Comes Next?
