What is artificial intelligence and how does it work

Artificial intelligence (AI) has rapidly transitioned from a concept often relegated to the realms of science fiction to an integral part of our daily lives. Its prevalence is evident in headlines, conferences, product announcements, and even casual family discussions. However, what exactly does the term "artificial intelligence" encompass? This article aims to delve into the many facets of AI, offering clarity and insight into a concept that is often misunderstood and misrepresented.

The paradox of artificial intelligence lies in the fact that the more widely it is used, the harder it becomes to define. AI manifests as the backbone of virtual assistants capable of engaging in conversation, the core of self-driving cars, and the technology behind images and texts generated in mere seconds. Yet, what we call AI ranges from simple programs that automate tasks to complex neural networks that learn from vast datasets. Under this umbrella, a multitude of technologies coexist, united only by the aspiration to replicate human-like thinking, decision-making, and creativity.

Hence, it is crucial to ask: what exactly is artificial intelligence? Beyond media hype, Silicon Valley rhetoric, or cinematic dystopias, a clear understanding of AI is necessary to interpret its implications critically. When we talk about AI, we are not just discussing machines but also our relationship with them—our expectations, fears, and desires projected onto algorithms. This ambiguous definition reflects both the current state of technology and the portrait of a society seeking, within the artificial, a mirror of the human experience.

Understanding the origins and definitions of artificial intelligence

The journey of artificial intelligence did not begin in Silicon Valley or with today’s generative models. Its roots trace back to the earliest inquiries made by philosophers regarding the human mind: Can thought be reduced to rules? Is it possible to imitate intelligence through symbols? In the 20th century, these questions found fertile ground due to advancements in computing technology. In 1950, Alan Turing posed his famous “Imitation Game,” widely known as the Turing Test, as a means of determining whether a machine could behave indistinguishably from a human. Although we now know that this test does not define intelligence, it marked the beginning of a scientific exploration that continues to this day.

In 1956, researcher John McCarthy coined the term "Artificial Intelligence" during the Dartmouth Conference, bringing together pioneers such as Marvin Minsky, Claude Shannon, and Herbert Simon. The goal was clear: to create machines capable of performing tasks that traditionally required human intelligence. At that time, AI was primarily associated with symbol manipulation, formal logic, and rule-based programming to solve mathematical or planning problems. This optimistic vision was rooted in the belief that simply increasing computational power would allow intelligence to emerge.

However, as time passed, it became evident that the definition of artificial intelligence could not be so rigid. What once seemed extraordinary—such as a program solving equations, playing chess, or recognizing characters on paper—ceased to be considered AI once it became routine. This phenomenon is often referred to as the “AI effect”: when a technology operates stably, it stops being perceived as intelligent and integrates into the background of computing. Today, we use the term AI to refer to both a chatbot that converses and a system that translates languages in real-time, despite the fact that they represent very different challenges.

Key approaches to artificial intelligence

Throughout its history, artificial intelligence has followed several approaches that reflect various ways of understanding what it means to "think." One of the most influential was symbolic AI, also known as rule-based or “GOFAI” (Good Old-Fashioned Artificial Intelligence). This approach rests on the idea that human knowledge can be represented as symbols manipulated by logical rules. Expert systems from the 1970s and 1980s, used in fields like medicine or technical diagnostics, exemplified this: programs that deduced conclusions based on a database of facts and rules. However, they were limited by their rigidity; any unforeseen situation could bring the system to a halt.

In contrast, connectionist AI emerged, inspired by the structure of the brain. Instead of explicit rules, interconnected networks of nodes—artificial neurons—learn patterns from data. This approach led to artificial neural networks, which, although conceived in the 1950s, remained in the shadows until increased computational power and the availability of large datasets propelled the rise of deep learning. Its strength lies in flexibility: it does not require pre-defined rules but extracts relationships directly from experience.

A third path includes evolutionary and probabilistic techniques, mimicking biological processes like natural selection or using statistical models to handle uncertainty. Genetic algorithms, particle swarms, and Bayesian networks represent this trend, where intelligence is defined not as the search for a perfect solution but as the ability to approach the best option in a changing environment. Today, AI systems often combine elements from all these approaches: rules for consistency, neural networks for data learning, and probabilistic algorithms for managing the unpredictable. Thus, artificial intelligence emerges not as a single technology but as a mosaic of strategies interwoven to achieve a common goal.

Machine learning and deep learning: transforming AI

In recent decades, the term artificial intelligence has increasingly become associated with machine learning. Unlike traditional approaches, where programmers explicitly defined the rules the system should follow, here, the machine extracts patterns from data. The paradigm shifts: the solution is not programmed; the model is trained to discover it independently. This leap has enabled AI to tackle problems where rules would be impossible to articulate, such as voice recognition, computer vision, or trend prediction in massive datasets.

Machine learning divides into several branches. Supervised learning trains the system with labeled data—for instance, thousands of images marked as “cat” or “dog”—to learn how to classify new examples. Unsupervised learning, in contrast, seeks hidden patterns in unlabeled data, clustering similarities or reducing dimensions. Lastly, reinforcement learning draws inspiration from behavioral psychology: the system tests various actions and receives rewards or penalties based on the outcome, fine-tuning its behavior over time. Each of these modalities has opened doors to various fields, from content recommendation to gaming and robotics.

Within this landscape, deep learning has triggered a genuine revolution. Based on deep neural networks with multiple processing layers, it has enabled spectacular advances in areas such as machine translation, autonomous driving, or natural language generation. Its ability to handle massive datasets and detect complex relationships has made it the driving force behind contemporary AI technologies. However, it has also introduced new challenges: models with millions or even billions of parameters require enormous computational resources and energy, raising concerns about sustainability and equitable access to technology.

Perhaps the most intriguing aspect of deep learning is not just what it achieves but how it does so. Unlike rule-based systems, these networks do not provide clear explanations for their decisions: they are genuine “black boxes” that produce precise results but are difficult to interpret. This phenomenon, known as the interpretability problem, remains one of the field's significant challenges. Understanding how and why a neural network reaches a conclusion is crucial not only for improving its performance but also for ensuring its ethical and reliable use in sensitive areas such as medicine, justice, or finance.

Generative artificial intelligence: a new frontier

One of the most talked-about concepts in recent discussions about artificial intelligence has been generative AI. Unlike systems designed to classify data or detect patterns, these models can create new content: writing coherent texts, composing music, producing realistic images, generating synthetic voices, or even designing software code. They do not merely respond to inquiries; they invent, simulate, and expand the realm of what is possible, becoming one of the most visible and disruptive manifestations of modern AI.

The technological foundation of this revolution lies in large-scale language models (LLM), built on transformer architectures. Trained with massive datasets, these models learn statistical relationships between words, images, or sounds and can reproduce them as fluid and surprisingly natural outputs. Alongside them, image generation systems like DALL·E, Stable Diffusion, or MidJourney can create illustrations, fictitious photographs, or conceptual designs from simple natural language descriptions. Meanwhile, multimodal models expand these capabilities by combining text, image, audio, and video in a single environment.

The applications are as diverse as they are controversial. In creative fields, generative AI has become an ally for designers, musicians, or writers, offering drafts, visual inspiration, or instant melodies. In professional settings, it assists in software programming, summarizes lengthy documents, or generates tailored educational materials. However, the same technology that enables an artist to experiment with new ideas also facilitates the creation of deepfakes, large-scale misinformation, or unauthorized reproductions of copyrighted works. The boundary between tool and risk has never been so blurred.

This double-edged sword compels us to rethink what it means to create in the age of AI. On one hand, generative models democratize access to creative resources that previously required years of training or specialized equipment. On the other hand, they raise uncomfortable questions about authorship, intellectual property, and the authenticity of what we consume. Generative artificial intelligence serves as a laboratory in motion: it promises an explosion of assisted creativity but also demands a profound debate about its limits, regulation, and cultural impact.

Current uses of artificial intelligence

While generative artificial intelligence captures much media attention, it is by no means the only relevant application. AI has infiltrated multiple layers of our daily lives, often so invisibly that we barely recognize it. Recommendation algorithms that determine what we see on video platforms or what we listen to on music services, the systems that filter spam email, and virtual assistants on our mobile devices are everyday examples of technologies processing and learning from data without it registering as extraordinary.

In the healthcare sector, AI is becoming a crucial ally for medical diagnoses. Models trained with radiological images can detect tumors with precision comparable to or even exceeding that of human specialists. Other algorithms analyze genomic data to personalize treatments or anticipate risks. In pharmacology, AI is also employed to accelerate the search for new molecules, reducing the time and cost of drug development. Medicine, traditionally slow in its validation processes, finds here a tool that promises to transform its pace of innovation.

Productive sectors have not been left behind. Industries use AI to optimize supply chains, predict machinery failures using sensors and predictive analysis, or automate quality control processes. In finance, algorithms detect fraud in real-time and calculate investment risks based on a sea of data impossible to process manually. Even transportation is being revolutionized by autonomous driving systems, which combine computer vision, deep learning, and state-of-the-art sensors to navigate without human intervention.

What’s interesting is that all these applications share a characteristic: they function as a transversal layer that integrates into existing technologies. They do not completely replace systems but enhance them, offering speed, efficiency, or precision where traditional methods fall short. Thus, AI becomes less an isolated product and more an invisible infrastructure that redefines how entire sectors operate. The reality is that, both in our personal routines and in industrial dynamics, we have been coexisting with artificial intelligences long before we realized it.

Myths and misunderstandings about artificial intelligence

Discussing artificial intelligence often evokes images of conscious robots capable of feeling and deciding like a human being. This imagery, fueled by decades of literature and cinema, often confuses technological reality with fiction. The AI we use today lacks consciousness or free will: they are statistical systems that learn to mimic data patterns. Their apparent "creativity" or "personality" results from large-scale mathematical calculations, not from a mind that thinks independently. Confusing coherent responses with autonomous thinking is one of the most widespread errors.

Another common misunderstanding relates to the difference between weak AI (Narrow AI) and strong AI (General AI). The former encompasses current applications designed for specific tasks: recognizing images, translating languages, recommending products. The latter, still hypothetical, refers to a general intelligence comparable to human intelligence, capable of transferring knowledge across domains and adapting to new contexts. Many headlines speak of the impending arrival of this general AI as if it were imminent, when in fact we are far from achieving it. What exists are highly capable systems in their domain but unable to operate outside the confines for which they have been trained.

The final myth pertains to the “AI effect”: every time a technology normalizes, it ceases to be perceived as artificial intelligence. In the 1980s, optical character recognition seemed an extraordinary advancement; today, it is an integrated feature in any office application. The same happened with chess: when Deep Blue defeated Kasparov in 1997, it was heralded as a milestone in AI, but today we consider game engines as commonplace tools. This trend reminds us that the concept of AI is fluid: what we define as intelligent today may be regarded as simple automation tomorrow.

Challenges and debates surrounding artificial intelligence

The expansion of artificial intelligence presents challenges that transcend purely technological concerns. One of the most urgent issues is bias in data: if systems learn from incomplete or discriminatory information, they reproduce and amplify those same biases. This results in hiring algorithms that disadvantage certain profiles, facial recognition systems with lower accuracy for specific ethnic groups, or language models that replicate stereotypes. The quality of data is not just a technical issue; it is a matter of social justice.

Accompanying this is the challenge of privacy and the use of personal information. Most current models require massive amounts of data to train, raising questions about how data is collected, who controls it, and for what purposes it is used. From medical histories to social media interactions, everything can become raw material for training algorithms. The line between technological benefit and invasion of privacy is increasingly blurred, necessitating a clear framework for digital rights.

The environmental impact is another debate that has gained significant traction in recent years. Training a state-of-the-art model can require weeks of computation on hundreds of GPUs, with enormous energy consumption and a significant carbon footprint. In a climate crisis context, the sustainability of AI becomes a point of contention: how can we justify the environmental cost of these technologies against their benefits? Initiatives to optimize models, reuse parameters, or adopt more efficient architectures are beginning to pave the way, but they remain insufficient.

Lastly, the matter of regulatory frameworks emerges. The European Union is moving forward with the approval of the AI Act, which seeks to establish clear rules for the development and use of high-risk systems. The United States opts for a more fragmented approach, while China combines heavy public investment with strict information control. The underlying debate is universal: how to balance innovation with the protection of citizens? Finding that balance will be key to determining not only the technological direction but also the social trust in artificial intelligence.

The future of artificial intelligence

Discussing the future of artificial intelligence is to venture into a landscape filled with promises and uncertainties. One of the most solid predictions is that AI will become a ubiquitous co-pilot, present in virtually all digital tasks: from drafting a report to planning a trip or managing personal finances. It will not completely replace human intervention but will always be available to facilitate processes, automate routines, and expand our cognitive capabilities. The user of the future will not only use applications but will interact with a transversal assistant integrated into every device and service.

Another horizon for development lies in the advancements in multimodal AI, capable of understanding and generating information in different formats simultaneously: text, image, audio, and video. The evolution of generative models points to assistants that can receive spoken instructions, process an image, and return a response in video or interactive graphics. This leap could transform sectors such as education, where content would adapt dynamically to each student, or medicine, with visual diagnoses explained in accessible natural language.

The most speculative debate revolves around the concept of general artificial intelligence (AGI), an AI capable of transferring knowledge across domains and autonomously facing new problems. Some researchers believe we are decades away from achieving it, while others argue that we may never replicate the breadth of human intelligence. What is certain is that today we are far from that scenario: current models are powerful but remain limited to the specific tasks for which they have been trained. Nonetheless, the mere possibility of AGI fuels both utopian expectations and dystopian fears.

Perhaps the most critical aspect of discussing the future is not so much predicting what will happen but asking ourselves what we want to happen. Artificial intelligence does not advance in a vacuum: it reflects investment decisions, political priorities, and social values. Will we use it to broaden access to knowledge, reduce inequalities, and tackle global challenges? Or will it become a tool for concentration of power and control? The future of AI is unwritten and depends as much on technology as on how we choose to integrate it into our lives.

After exploring the history, approaches, myths, and possibilities of artificial intelligence, I am left with a sense that attempting to define it is also an exercise in introspection. Observing how we strive to replicate the human in the artificial reveals our limitations, ambitions, and our understanding of what it means to think. AI is not just a technological mirror; it is also a cultural reflection of what we believe intelligence to be.

It is fascinating to witness how something that began as a marginal academic field has transformed into a phenomenon that shapes daily life. At the same time, I am concerned about the speed at which we adopt these tools without pausing to consider their implications. Behind every algorithm are human choices: what data is used, what objectives are prioritized, and what risks are assumed. Artificial intelligence is not neutral, and neither will our relationship with it be.

Perhaps that is why I prefer to view AI not as a definitive answer but as an open question. What will we do with this capacity to create systems that learn, predict, and generate? Will it be a tool for emancipation or a mechanism of control? The answer, to a large extent, will depend on us. And perhaps the true value of artificial intelligence lies therein: in compelling us to decide what kind of future we wish to build alongside it.

GTA VI delayed to October 2026 release date

Meta Ray-Ban Display: la revolución de las gafas con pantalla y control neuronal

Meta aims to transform the metaverse into an AI-powered Roblox