Seventy years of ideas, cycles, and transformations
Artificial intelligence is a field of computer science that has had a significant impact on the technological development of our society. Although it is often presented today as a recent innovation, its history spans more than seven decades and is marked by recurring cycles of enthusiasm and disillusionment. Tracing this historical trajectory is not an academic exercise, but a fundamental tool for understanding how expectations around technology are constructed and why its adoption — especially in cultural industries like journalism — depends less on technical advances than on organizational decisions, economic contexts, and professional practices that surround it.
Since its origins in the mid-1950s, AI has achieved significant breakthroughs, from programs capable of playing chess to systems that diagnose diseases, drive vehicles, produce images, text, and video, and assist with complex cognitive tasks. Approaching artificial intelligence from a historical perspective allows us to understand that current developments do not represent an isolated break, but rather the acceleration of ideas, techniques, and debates that have been brewing for decades. In journalism, this trajectory is key to contextualizing why many of the promises associated with automation, efficiency, or personalization have had predecessors, with uneven impacts depending on how they were integrated into newsrooms.
Throughout its history, AI has experienced highs and lows known as the "summers" and "winters" of this technology, where optimism and investment have alternated with long periods of skepticism and funding cuts. Alongside technological development, popular culture has played a key role in shaping the social imagination around artificial intelligence. Works such as 2001: A Space Odyssey, Blade Runner, The Matrix, Westworld, or Black Mirror, along with Isaac Asimov's I, Robot, not only anticipated technical dilemmas but also helped frame questions about power, control, responsibility, and autonomy. For decades, these narratives portrayed AI as an exceptional or threatening entity. In recent years, these questions have moved beyond fiction and entered public, regulatory, and professional debates.
At this point it is essential to distinguish between the two main types of AI. Narrow AI, also known as weak AI, refers to systems designed to perform specific tasks without possessing consciousness or genuine understanding. All current systems belong to this category, including those reasoning models launched between 2024 and 2025 that exhibit advanced planning and problem-solving capabilities. They remain specialized systems operating within bounded domains, without consciousness or genuine comprehension of the world. Strong or general AI, on the other hand, describes a theoretical scenario in which a machine could understand, learn, and reason in a way comparable to a human being. Although it remains a speculative horizon, the debate about its ethical, political, and social implications has regained centrality following recent advances.
The emergence of ChatGPT in late 2022 marked the beginning of the latest great summer, reaching a decisive inflection point that extends to 2026. This milestone did not represent only a specific technical advance, but a structural shift in the way AI became accessible, everyday, and operational for millions of people without technical training. This new cycle is explained, in large part, by the consolidation of natural language as a universal interface between humans and machines, dramatically reducing the cognitive costs associated with its use.
Between 2023 and 2026 that initial summer evolved toward selective integration, the construction of productive systems, and the definition of institutional policies. What was novelty in 2023 became infrastructure by 2026. AI stopped being solely a set of isolated tools and became an ecosystem of interconnected technologies: multimodal models that process text, image, audio, and video in an integrated manner; agents that execute tasks in sequence with variable supervision; and architectures that combine generation, information retrieval, and reasoning.
However, the contemporary landscape proposes a significant shift: artificial intelligence has ceased to be an extraordinary figure and has become a presence integrated into everyday processes, often in an omnipresent way. In this sense, AI can be understood as a new structural layer of professional practice, comparable to the role that SEO, social platforms, or audience analytics once played. This passage explains why contemporary conversations about AI are no longer limited to technological innovation, but involve regulation, copyright, technological sovereignty, business models, and social trust — especially in the field of journalism and media.
Understanding this historical trajectory allows us to approach the subsequent chapters of this guide with a gaze less dazzled by novelty and more attentive to the criteria, frameworks, and responsibilities that a sustainable integration of artificial intelligence in newsrooms demands.
AI Timeline
The history of AI is dense and dizzying. To explore it in detail, with every milestone, reference, and context, an interactive timeline was developed. From the Turing Test in 1950 to the consolidation of sovereign AI in 2026 — seventy years of ideas, people, and decisions that explain the present.
Explore the timelineDefinition of terms associated with artificial intelligence
The field of artificial intelligence has its own vocabulary — dense and in constant expansion. Building that shared language between journalists, editorial decision-makers, product teams, and technology leads is what allows moving from fascination or skepticism to concrete, well-grounded decisions.
The terms in this glossary are not organized alphabetically. They follow the order of the technological layers that make up artificial intelligence, from the most general to the most specific. Each layer depends on the previous one to exist and function.
Key terms for navigating the AI ecosystem
Artificial intelligence enables machines to imitate human cognitive functions, such as learning and problem-solving. Think of a system capable of interpreting and responding to questions in natural language, similarly to how a human would during a conversation.
It is the ability of a computer system to learn and improve its performance without being explicitly programmed to do so. In simpler terms, imagine a system that can learn to play chess on its own, analyzing millions of games and improving its strategy with each move.
Deep Learning is a type of machine learning based on artificial neural networks with multiple layers. These networks can learn complex patterns in large volumes of data, similarly to how humans identify and process information, since they are inspired by the functioning of the human brain.
A branch of AI focused on the interaction between computers and humans through natural language, enabling machines to understand, interpret, and generate human language.
Computer vision enables machines to "see" and interpret visual content the same way humans do. It is the ability of a computer system to interpret and understand images and videos.
The term used to refer to large datasets that are difficult to process with traditional tools. These enormous amounts of information can be analyzed computationally to reveal patterns, trends, and associations, especially those related to human behavior and interactions.
Algorithms are sets of instructions that tell a computer system how to perform a task. These systems analyze user behavior and preferences to suggest relevant, personalized content — similarly to how entertainment platforms operate.
The basic units into which language models divide text for processing. A token can be a full word, part of a word, or a punctuation mark. Models have token limits that determine how much information they can process or generate in a single interaction.
The maximum amount of information (measured in tokens) that an AI model can process simultaneously. It determines how much text it can "remember" or consider when generating responses.
Mathematical representations of information (text, images, audio) in the form of numerical vectors. They allow machines to "understand" semantic similarities between concepts, even when expressed with different words.
The process of training a previously trained AI model with data specific to a particular domain, organization, or style. It allows adapting general models to specific needs without having to train a complete model from scratch.
A technique that combines AI's generative capability with information retrieval from specific databases. Rather than relying solely on knowledge trained into the model, the system first retrieves relevant information from documents or databases, then generates responses based on that updated, verifiable information.
Tools that allow different software systems to communicate with each other efficiently. They function as a set of rules and protocols that one application can follow to access services and data from another application.
Generative artificial intelligence specializes in producing new content from existing digitized data. Imagine a system that can compose music, write poems, or design images based on styles learned from large datasets.
AI systems capable of processing and generating multiple types of content simultaneously: text, images, audio, and video. They can understand relationships between different modalities and produce outputs that combine several formats.
A software application designed to simulate conversations with human users over the internet. It uses artificial intelligence and natural language processing to understand user queries and provide automatic responses. Modern chatbots are evolving toward more sophisticated conversational agents capable of handling complex tasks and maintaining context across multiple interactions.
AI systems capable of acting autonomously to complete complex tasks requiring multiple steps. Unlike traditional models that respond to a single prompt, agents can plan, execute sequential actions, use tools, and adjust their behavior based on results obtained.
The automated coordination of multiple systems, tools, or AI agents to complete complex workflows. It involves designing how different technological components work together sequentially or in parallel.
Vibe coding describes a way of working with generative AI in which the user describes their objectives, intentions, or the "spirit" of what they want to achieve in natural language, without specifying precise technical instructions. The term, coined by researcher Andrej Karpathy in early 2025, captures a more intuitive and exploratory mode of interaction.
Restrictions and filters programmed into AI systems to prevent inappropriate, inaccurate, or policy-violating outputs. They function as limits that guide system behavior within acceptable parameters.
The term used to identify information generated by an AI model that appears coherent and convincing but is factually incorrect or invented. It is one of the main risks in using generative AI for journalism.
The ability to track and document the origin of AI-generated content, including which model was used, what data fed the process, which prompts were employed, and what human interventions occurred. It is fundamental to ensuring editorial transparency and accountability.
Blockchain is a distributed ledger technology that ensures the integrity and transparency of data stored across a network of computers. It functions as a digital ledger in which each block of information is linked and encrypted with the previous block, forming an immutable chain.
What is a prompt?
In the context of generative artificial intelligence, a prompt is the instruction or set of instructions given to a system to induce a specific response or generate new content. The prompt acts as a trigger, guiding the model on what type of information to produce or what task to perform.
How a prompt works step by step
Advanced practical example
By 2026, professional use of generative AI in newsrooms already transcends single prompts. More complex journalistic tasks require instruction chains where each step feeds the next. The following example illustrates how a newsroom can design a prompt system to produce a special feature on water access in Latin America.
This three-step flow illustrates the qualitative leap between using AI as a text generator and using it as part of an editorial system with explicit criteria, defined constraints, and human oversight at each stage. The journalist organizes the process, supervises it, and makes the final decisions. AI expands their processing capacity and accelerates intermediate stages; editorial judgment remains a human responsibility.
Final reflection
Mastering AI terminology is a prerequisite for participating with one's own criteria in the decisions now crossing every newsroom: what to automate and what to preserve, how to supervise generative systems, where to set editorial limits, and how to communicate to audiences the role of these technologies in content production.
The AI ecosystem will continue producing new terms, systems, and capabilities. What remains constant is the need to evaluate them from a journalistic perspective: what editorial problem do they solve, what risks do they introduce, and what principles should govern their use. With that framework, this chapter's glossary functions as a starting point for continued learning in a field that will never stop moving.