The term Artificial Intelligence (AI) was coined at a defense conference in the 1950s. It referred to any computer system that can perform tasks that typically require human intelligence. These tasks include learning, problem-solving, understanding language, and recognizing patterns.
Early AI (1950s-1980s)
Early AI began with simple rule-based programs that followed strict "if-then" logic. Like a basic calculator, these systems could only do exactly what they were programmed to do.
Machine Learning Evolution (1959-2000s)
In 1959, IBM programmer Arthur Samuel proposed the term machine learning to describe a branch of computer science that uses statistical techniques to allow computers to learn from data without being explicitly programmed. In AI, this allows a system to improve its performance through experience. Instead of being explicitly programmed for every scenario, these systems learn from data and can recognize and predict patterns.
In the 1990s, machine learning became the focus of AI research.
Neural Networks Emerge (1990s-2010s)
In the 1990s, scientists began to train computer systems as modeled by the human brain, with interconnected "neurons" that process information in layers. Early neural networks could handle simple tasks like basic image recognition, and more advanced research allowed scientists to train AI systems in more complex, wide-ranging tasks.
Deep Learning Breakthrough (2010s-Present)
More powerful computers and larger datasets led to deeper neural networks with many layers. This enabled much more sophisticated learning and better performance on complex tasks. Systems like ChatGPT and Claude use advanced neural networks called "transformers" that can process language in ways that weren't possible before based on deep learning breakthroughs.
Natural Language Processing brings AI to the Masses (2010s-Present)
Advances in deep learning paved the way to Natural Language Processing (NLP), which opened the door to the mass use of AI. Natural Language Processing is a branch of artificial intelligence that focuses on the interaction between computers and human language. It involves the ability of a computer program to understand, interpret, manipulate, and generate human language in a valuable way. NLP combines computational linguistics (the rule-based modeling of human language) with statistical data, machine learning, and deep learning models.
Currently… (2020s)
Modern AI combines massive amounts of training data and computing power with natural language to bring AI systems into everyone’s pockets. It allowed for the development of Generative AI, which refers to artificial intelligence systems that use deep learning to create new content, such as text, images, or audio.
TLDR
Recent breakthroughs in AI have been driven by increased computing power, larger datasets, and improved algorithms. This has paved the way for accessible language processing, cloud computing that makes AI services widely available, and user-friendly interfaces that make AI tools easier to use.
ChatGPT represented a leap forward in AI's ability to engage in natural conversation and assist with various tasks. Its user-friendly interface made advanced AI accessible to the general public for the first time, and the quality and versatility of its responses surprised many people, leading to widespread media coverage and adoption.
DeepSeek is a Chinese company that develops open-source Large Language Models (LLMs). DeepSeek's R1 reasoning model was released in January 2025, and the company became globally famous when the output could rival OpenAI’s ChatGPT technology at a considerably lower cost and energy output. It could do the same for less.
DeepSeek's popularity caused U.S. tech stocks to drop, and wiped billions of dollars off Nvidia's market value. Nvidia is a U.S.-based leader in AI that produces graphic processing units (GPUs) essential for the development of AI systems. DeepSeek’s model reportedly achieves a similar high performance with less computational power, potentially impacting the demand for Nvidia’s GPUs and stronghold on the market.
An AI reasoning model is specialized to handle logical thinking, analysis, and problem-solving tasks. Think of it like the difference between a calculator (basic AI) and a mathematical proof solver (reasoning AI). While standard AI models like ChatGPT and Claude are trained to generate responses based on patterns in their training data, reasoning models are specifically designed to break down problems step-by-step and apply logical rules. Both approaches are valuable, but they solve problems in fundamentally different ways.
