Arthur C. Clarke famously said, “Any sufficiently advanced technology is indistinguishable from magic.” Perhaps the first time you interacted with Generative AI, it felt just like that—magic. Suddenly, we have a technology that can speak our languages, understand our requests, and produce entirely novel outputs. AI can write poetry, draw otherworldly images, code software, and even surprise us with original jokes or musical compositions. It creates, and in its act of creation, it often inspires wonder. But AI is not magic. It’s math and science, a culmination of decades of research and development. And while AI is poised to touch every aspect of our lives, how it changes the world is up to us—all of us.
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Welcome to the AI Academy. My name is Darío Gil. I’m an electrical engineer and computer scientist by trade, and I currently serve as the head of IBM Research and a senior vice president at IBM. In this series, we aim to demystify AI. We’ll explore how we arrived at this moment, how generative AI functions, and the ways it will transform business and society. So, let’s start at the beginning.
Speculation about the possibility of thinking machines began in the late 1800s, but the concept truly took root with Alan Turing’s seminal paper in 1950. Often referred to as the father of AI, Turing theorized that we could create computers capable of playing chess, surpassing human players, and even processing natural language. He believed machines could eventually think—a notion that set the stage for many of the milestones in AI that we’ve achieved, including IBM’s Deep Blue chess-playing computer, Jeopardy-winning AI systems, and advanced debating technologies.
Turing’s ideas were just the beginning. In 1956, six years after Turing’s paper, the Dartmouth Workshop marked the “Big Bang” of AI. A small group of academics and senior scientists from Bell Labs and IBM convened for an extended summer workshop, laying the foundation for AI as a distinct field of research. This workshop coined the term “artificial intelligence” and outlined the challenges we’ve been working to solve for decades, such as neural networks, self-directed learning, and creativity—all topics still relevant today.
To put this in perspective, 1956 was also the year the invention of the transistor was awarded the Nobel Prize. Today, we have over 100 billion transistors on a single GPU, interconnected to provide the computational power needed for generative AI functions. The theories, techniques, and ideas of AI have evolved alongside hardware advancements, resulting in dramatic reductions in compute and storage costs, making generative AI a practical reality.
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However, it’s crucial to note that AI’s success is not just about powerful hardware and clever algorithms. The third, and arguably the most important ingredient, particularly for your business, is data. You can’t talk about generative AI without discussing data. The triad of AI consists of model architecture, compute power, and data.
Large Language Models (LLMs) are a prime example of this triad in action. At their core, LLMs represent a new way of encoding language into a high-dimensional space with a vast number of parameters, achieved through training on massive quantities of text. Historically, computing has always been about finding new ways to represent data and extract value from it. From rows and columns in databases to graphs mapping relationships between data points, each new representation has unlocked new capabilities.
Now, with LLMs, data is represented in neural networks that simulate abstract brain cell connections—layers upon layers of connections with billions or even trillions of parameters. This allows AI to predict relationships and sequences with such precision that it can generate entirely new content, whether it be text, images, or sounds. This generative capability is what defines modern AI: the ability to analyze data, discover patterns, and create something that didn’t exist before.
Before the current era of AI, deep learning represented a significant milestone. It involved training massive neural networks on annotated data, a process known as supervised learning. However, this approach was time-consuming and costly, typically requiring large institutions to undertake the work for specific tasks. Around 2017, a new method called self-supervised learning emerged, powered by an architecture known as transformers. This method allowed AI to train on large amounts of unlabeled data, making the process more scalable and efficient.
Self-supervised learning enables the creation of foundation models—large-scale neural networks trained on vast amounts of data, which can then be adapted for a wide range of specific tasks. This means businesses can now take a large pre-trained model, integrate their own industry-specific data, and fine-tune the model to excel at their unique challenges, all while deploying it more quickly and efficiently.
Generative AI’s ability to handle language has sparked the question: What is a language? From the signals of industrial equipment to user clicks on a website, from software code to chemical diagrams, nearly everything can be viewed as a language that AI can decipher. This realization means that AI can stretch across every aspect of a business—from HR processes and customer service to cybersecurity and application modernization.
Today’s AI models may not yet constitute true general intelligence, but some can pass the Turing test, blurring the lines between human and machine capabilities. As we stand on the brink of an AI-driven future, opinions are divided. Some see it as the dawn of a utopian age, while others fear a dystopian outcome. As a scientist, I believe both perspectives have merit. We’ve asked similar questions at every major innovation milestone, from the Industrial Revolution to the advent of the internet.
AI’s potential extends far beyond the digital realm. Imagine what AI could do for the pace of discovery in fields like medicine, energy, and climate science. The impact of AI will depend on how we choose to approach it. As AI continues to evolve, it’s critical that we engage with it actively, shaping its development and integration into society.
As AI becomes an integral part of our lives, here are four key pieces of advice for businesses and individuals alike:
1. Protect Your Data: Your data, and the representations of that data, will be your competitive advantage. Ensure that you control and protect it.
2. Embrace Transparency and Trust: Understand and explain the decisions or recommendations made by AI systems. Transparency is crucial for trust.
3. Implement AI Ethically: Ensure that your AI models are trained on high-quality, legally accessed data. Control for bias and avoid toxic elements like hate speech.
4. Take Control of Your AI Journey: Don’t be a passive user of AI. Empower yourself with the knowledge and tools needed to control how AI is integrated into your business.
Generative AI has rapidly gone from a fascinating novelty to a business imperative. The future of AI is not about a few models doing everything for everyone. It’s about a multimodal approach that is democratized and transparent, allowing everyone to have a voice in shaping its impact on society.
