What Is Artificial Intelligence… Really? And Why Is AI Everywhere Now?

Milestones in AI History That Shaped the Future

As of 2025, AI has been around for about 70 years. Long before it became the buzzword of today, AI goes as far back as the 1950s. It's not a brand-new technology, but rather an old idea that has slowly matured, waiting for the right moment to take off. And that moment is now. In this article, we explore a timeline of breakthroughs that turned artificial intelligence into a force for the future.

Neural Networks, Machine Learning and Deep Learning | Brief History of AI

What is Artificial Intelligence (AI)? 

The term Artificial Intelligence (AI) was coined in a workshop held at the Dartmouth Summer Research Project, in 1956. It is known to be the first event on AI as a field. Since then, academic discourse and practical needs have redefined the concept of AI.  

Today, global organizations such as the World Economic Forum describe AI as 

“Systems that act by sensing, interpreting data, learning, reasoning and deciding the best course of action”. 

and OECD characterizes AI as

“a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments”. 

Why does AI Matter Now? 

While AI has been around for decades and there are many AI algorithms, two main types of AI systems exist in business. 

• rule-based systems, which follow human-defined instructions 

• machine learning (ML) systems, which learn patterns directly from data. 

At its core, ML is a branch of AI that intersects computer science, mathematics and statistics. The focus of ML is on developing algorithms that allow computers to learn from data and enhance their performance without being programmed by experts. These systems improve over time and can even adapt after deployment, which is why ML is behind many of the smart tools we use today.

At the core of modern AI applications, a special type of ML called “deep learning” is used for complex problems. For instance, computer vision, speech recognition or natural language processing. Their levels of autonomy and adaptiveness can vary post-deployment, depending on their AI systems. In that sense, some AI systems can continue to develop and improve during their operational phase (i.e. during usage). 

Despite ML enabling the development of AI systems such that they can surpass human performance in certain tasks, its learning capabilities also brings risks, such as bias in data or vulnerability to adversarial attacks. 

AI becomes more accessible and embedded in everyday tools so it is crucial to not only understand how these systems work but also ensure they’re used responsibly and fairly. As such, with this rapid adoption comes responsibility: AI systems are only as good as the data they learn from, and flaws or biases in that data can lead to unintended consequences. 

A Brief History of AI 

Brief AI Timeline | Image source: World Economic Forum

The timeline of AI spans decades of breakthroughs, setbacks and reinvention. Born from the fusion of logic, mathematics, and computing, early AI systems in the 1960s were rule-based.

Core Characteristics of Rule-Based Systems: 

• Rules are manually programmed: Logic must be explicitly defined by developers and manually programmed. 

• Struggles with complexity or ambiguity: Not well-suited for handling uncertain, dynamic, or nuanced scenarios. 

• Dependent on human expertise for setup and updates: Requires ongoing input from subject-matter experts to build and refine rules. 

They aimed to increase processing power and data, with several experiments such as mimicking human intelligence, but that fell short. This disillusionment led to the first “AI winter” in the 1970s, followed by dwindling interest and funding.

A resurgence in the 1980s focused on expert systems that tackled narrow tasks using predefined rules, sparking some commercial success before limitations triggered a second AI winter, where interest died down in late 80s.

1990s

A major shift occurred in the 1990s with the rise of ML, which emphasized the “bottom-up approach”, whereby “intelligence” was acquired through learning rather than being preprogrammed. Landmark moments, like IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997, highlighted AI’s growing capabilities. The 2000s and 2010s marked a new era driven by exponential computing power, internet-scale data, and deep learning. 

At Present

AI is integral to nearly every industry. But as AI systems grow more powerful and surpass human performance in more tasks, their inner workings often become more opaque. That’s why transparency, fairness and data quality remain critical to responsible AI adoption. 

Understanding Why AI Technologies Have Become So Popular Lately 

As mentioned, AI has been around for decades. Yet, this widespread adoption of AI in the recent years is the result of powerful outcomes coming together. More specifically, the unprecedented growth in data, advanced algorithms and accessible technology make AI attainable and indispensable. 

International Data Corporation (IDC) predicts that the volume of data “created, captured, copied, and consumed globally” will continue to increase rapidly. The market-research firm estimates that: 

“the amount of data created over the next three years will be more than all the data created over the past 30 years, and the world will create more than three times the data over the next five years than it did in the previous five.” 

Consequently, it is not surprising that one of the major drivers behind AI’s recent surge is data. Without vast, high-quality datasets, nearly all modern AI applications would not exist as they rely on this data to learn, improve and make decisions. This flood of data growing at breakneck pace has created not only the supply for AI but also the demand. In fact, there is simply too much information for humans to process alone. Yet again, AI is increasingly needed to make sense of it all, from analyzing medical images to monitoring traffic cameras or reviewing scientific literature. For instance, this applies to medical images where many researchers are exploring AI-assisted radiology, given the growing difficulty for human radiologists to dedicate ample attention to each image.

Enhanced Processing Power

Beyond data, breakthroughs in machine learning, particularly deep learning, reinforcement learning, and transformer models (i.e. those powering GPT-3), are other enabling factors that have accelerated AI’s growth. At the same time, cloud computing and GPUs tailored for AI workloads have led to massive gains in processing power also explains AI’s recent popularity. They made it cheaper and faster than ever to train and deploy AI models, compared to a decade ago.

Accessible AI Tools

Other than the enhanced processing power and AI becoming more accessible, open-source tools such as cloud-based APIs and user-friendly development frameworks have “democratized” AI. That is, businesses of all sizes can integrate smart capabilities into their products without building everything from scratch. Even low-code and no-code platforms now offer AI-powered features.

Socio-political Factors

Several socio-political dynamics have contributed to the recent surge in AI development and government investments (such as DARPA in the U.S. and similar institutions in China) have played a role with the rapid convergence of big data, powerful hardware, advanced algorithms, and easy-to-use software. These have been the real catalyst for AI’s mainstream breakthrough. As these influential factors continue to grow, AI is likely to become even more embedded in the fabric of how we live and work. Still, it is important to keep in mind that these elements only partially explain AI’s momentum, as other key drivers have played a larger role in driving its rapid adoption throughout the past decade.