In 2019, UNESCO's World Commission on the Ethics of Scientific Knowledge and Technology (COMEST) defined artificial intelligence as a field that involves machines capable of imitating certain functionalities of human intelligence, including characteristics such as perception, learning, reasoning, problem solving, linguistic interaction and even the production of creative works.

Colloquially, the phrase "artificial intelligence" is applied when a machine imitates the "cognitive" functions that humans associate as human competencies; for example: "perceive", "reason", "learn", and "problem solve".[10] Andreas Kaplan and Michael Haenlein") define artificial intelligence as "the ability of a system to correctly interpret external data, and thus learn and employ that knowledge to achieve concrete tasks and goals through flexible adaptation."[11] As machines become increasingly capable, technology that was once thought to require intelligence is removed from the definition. Marvin Minsky, one of the creators of AI, spoke of the term artificial intelligence as a suitcase word (“suitcase word”) because a variety of elements can be placed in it.[12]​[13]​.

For example, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a common technology.[14]​ Technological advances still classified as artificial intelligence are autonomous driving systems or those capable of playing chess or Go.[15]​.

Artificial intelligence is a new way of solving problems, which includes expert systems, the management and control of robots and processors, which attempts to integrate knowledge into such systems; in other words, an intelligent system capable of writing its own program. An expert system defined as a programming structure capable of storing and using knowledge about a certain area that translates into its learning capacity.[16]​ Similarly, AI can be considered as the ability of machines to use algorithms, learn from data and use what is learned in making decisions just as a human being would do.[17]​.

According to Takeyas (2007), AI is a branch of computer science responsible for studying computing models capable of carrying out activities typical of human beings based on two of their primary characteristics: reasoning and behavior.[18]​.

In 1956, John McCarthy "John McCarthy (scientist)") coined the term "artificial intelligence," and defined it as "the science and ingenuity of making intelligent machines, especially intelligent computer programs."[19]

Grau-Luque contrasts different definitions from various sources and authors, highlighting that they differ depending on "in which specific field they are used."[20] This leads the author to define "artificial intelligence" as "systems that carry out tasks considered intelligent", and then associate concepts such as "learning" and "reasoning" with machine learning as a subdiscipline of artificial intelligence.

There are also different types of perceptions and actions, which can be obtained and produced, respectively, by physical sensors and mechanical sensors in machines, electrical or optical pulses in computers, as well as by bit inputs and outputs of software and its hardware environment.

Several examples are in the area of ​​system control, automatic planning, the ability to respond to diagnostics and consumer inquiries, handwriting recognition, speech recognition and pattern recognition. AI systems are currently part of the routine in fields such as economics, medicine, engineering, transportation, communications and the military, and have been used in a wide variety of computer programs, strategy games, such as computer chess, and other video games.

Likewise, artificial intelligence is being developed on the digital platform more and more, evolving and creating new tools, such as the labor platform that has existed since 2023 called SIVIUM, a tool through which a person automatically applies to all job offers on all job portals, without having to review each job offer that is presented and send their CV one by one.[21]​.

Stuart J. Russell and Peter Norvig differentiate several types of artificial intelligence:[22]​.

Generative artificial intelligence is a type of artificial intelligence system capable of generating text, images or other media in response to text commands known as "prompts".[27]​ A prompt is the instruction, question or set of instructions for the artificial intelligence to perform the specific task and provide the required response. The quality of the prompt directly influences the quality of the response.[28]​.

Generative AI models learn the patterns and structure of their input training data, and then generate new data that has similar characteristics.

Notable generative AI systems include ChatGPT (and its Microsoft Copilot variant), a chatbot created by OpenAI using its foundational GPT-3 and GPT-4 large language models (LLM); Gemini "Gemini (chatbot)") (previously called Bard), a chatbot created by Google using the Gemini "Gemini (language model)" language model); and Claude "Claude (chatbot)"), a chatbot created by Anthropic using the models of the same name.[29] Other generative AI models include AI art systems such as Stable Diffusion, Midjourney, and DALL-E, which allow the creation of images. Today, generative AI can create text, code, images, video, music, voices and sound effects.

Strong Artificial Intelligence (IGA) is a hypothetical type of artificial intelligence that equals or exceeds average human intelligence. If it became a reality, an IGA could learn to perform any intellectual task that humans or animals can perform. Alternatively, the IGA has been defined as an autonomous system that exceeds human capabilities in most economically valuable tasks.

Some argue that it could be possible in years or decades; others, that it could take a century or more; and a minority believes that it may never be achieved. There is debate about the exact definition of IGA and whether modern large language models (LLMs), such as GPT-4, are early but incomplete forms of IGA.

Explainable artificial intelligence refers to methods and techniques in the application of artificial intelligence technology by which humans are able to understand the decisions and predictions made by artificial intelligence.

Friendly artificial intelligence is a strong, hypothetical AI that can have a positive rather than a negative effect on humanity. 'Friendly' is used in this context as technical terminology and chooses agents that are safe and useful, not necessarily those that are 'friendly' in the colloquial sense. The concept is invoked primarily in the context of discussions of artificial recursive self-improving agents that rapidly explode into intelligence, with the argument that this hypothetical technology could have a long, fast and difficult task of controlling the impact on human society.

Multimodal artificial intelligence is a type of artificial intelligence that can process and integrate data from different modalities, such as text, images, audio and video, to obtain a more complete and contextualized understanding of a situation. Multimodal artificial intelligence is inspired by the way humans use multiple senses (Sense (perception)) to perceive and interact with the world, and offers a more natural and intuitive way to communicate with technology.

Quantum artificial intelligence is an interdisciplinary field that focuses on building quantum algorithms to improve computational tasks within AI, including subfields such as machine learning. There is evidence that shows a possible quantum quadratic advantage in fundamental AI operations.

Contenido

La IA se divide en dos escuelas de pensamiento:.

Conventional artificial intelligence

It is also known as symbolic-deductive AI. It is based on the formal and statistical analysis of human behavior when faced with different problems:

Computational artificial intelligence

Computational intelligence (also known as subsymbolic-inductive AI) involves interactive development or learning (for example, interactive modifications of parameters in connection systems). Learning is done based on empirical data.

Computational intelligence has a dual purpose. On the one hand, its scientific objective is to understand the principles that enable intelligent behavior (whether in natural or artificial systems) and, on the other, its technological objective is to specify the methods to design intelligent systems.[32]​.

In 2024, significant advances were made in several areas of AI:.

Given the possibility of creating machines equipped with intelligence, it became important to worry about the ethical issue of machines to try to guarantee that no harm is caused to human beings, other living beings and even to the machines themselves according to some currents of thought.[48] This is how a broad field of studies known as the ethics of artificial intelligence emerged, relatively recently emerging and which is generally divided into two branches, roboethics, responsible for studying the actions of human beings towards robots, and machine ethics, responsible for the study of behavior of robots towards human beings.

The accelerated technological and scientific development of artificial intelligence that has occurred in the century also has a significant impact on other fields. In the world economy, during the second industrial revolution, a phenomenon known as technological unemployment was experienced, which refers to when industrial automation of large-scale production processes replaces human labor. A similar phenomenon could occur with artificial intelligence, especially in the processes in which human intelligence intervenes, as illustrated in the story How They Had Fun! by Isaac Asimov, in which its author glimpses some of the effects that the interaction of intelligent machines specialized in children's pedagogy, instead of human teachers, would have on children in school. This same writer designed what are now known as the three laws of robotics, which appeared for the first time in the story Círculo viciousioso "Círculo viciousioso (story)") (Runaround) of 1942, where he established the following:.

Other more recent science fiction works also explore some ethical and philosophical questions regarding Strong Artificial Intelligence, such as the films I, Robot or A.I. Artificial Intelligence, in which topics such as self-awareness or the origin of an emerging consciousness of intelligent robots or computer systems are discussed, or whether they could be considered subjects of law due to their almost human characteristics related to sentience, such as being able to feel pain and emotions or to what extent they would obey the objective of their programming, and if not, whether they could exercise free will. The latter is the central theme of the famous Terminator saga "Terminator (franchise)"), in which machines overcome humanity and decide to annihilate it, a story that, according to several specialists, could not be limited to science fiction and could be a real possibility in a posthuman society that depended completely on technology and machines.[50][51]​.

Law[53]​ plays a fundamental role in the use and development of AI. Laws establish rules and norms of behavior to ensure social well-being and protect individual rights, and can help us obtain the benefits of this technology while minimizing its risks, which are significant. At the moment there are no legal regulations that directly regulate AI. But on April 21, 2021, the European Commission has presented a proposal for a European Regulation for the harmonized regulation of artificial intelligence (AI) in the EU. Its exact title is Proposal for a Regulation of the European Parliament and of the Council establishing harmonized rules on artificial intelligence – Artificial Intelligence Law – and amending other Union legislative acts.

In March 2023, hundreds of entrepreneurs such as Elon Musk, Steve Wozniak (co-founder of Apple) or the presidents of numerous technology companies; Intellectuals such as Yuval Noah Harari and hundreds of academics and researchers specialized in artificial intelligence signed an open letter warning of the danger of the lack of regulation of AI, putting the spotlight on OpenAI, the company that developed ChatGPT. They asked for a pause of at least 6 months for their most powerful experiments, until the world achieves an international consensus so that these systems "are more accurate, secure, interpretable, transparent, robust, neutral, reliable and loyal."[54]​.

Two months later, in May, 350 executives from the main AI development companies, academics and expert researchers signed a new manifesto warning that unregulated advanced AI represents a danger of extinction for humanity: "Mitigating the risk of extinction of AI should be a global priority along with other risks on a social scale such as pandemics and nuclear war." Google DeepMind with 38 executives, researchers or university professors related to the company, and representatives of smaller developers such as Anthropic, Stability AI or Inflection AI.[57]​.

Some authors have begun to explore the possibility of recognizing advanced artificial intelligences not only as tools, but as potential limited legal subjects, capable of acquiring rights and assuming tax burdens in relation to the digital or intangible goods they produce.[58] This proposal finds inspiration in figures already recognized by law, such as fictitious legal entities, which, without being human, have been endowed with the capacity to act in legal traffic. In this context, it has been proposed that certain complex automata could establish their own assets, assume civil responsibilities for the effects of their autonomous decisions, and even pay taxes, as a way of responding for their economic impact and redistributing the benefits they generate in increasingly digitalized societies.

This perspective does not imply an equation between human beings and artificial intelligences, but rather a functional legal architecture that would allow the consequences derived from the autonomous production of goods and services by AI systems to be channeled. It is proposed, for example, that income generated by creative bots or autonomous vehicles can be allocated to guarantee or tax funds, thus alleviating pressures on human economies and avoiding gray areas in legal liability. This vision contributes to the debate on technological singularity, proposing regulatory responses from private and fiscal law.[58]​.

Reasoning and problem solving

Early researchers developed algorithms that mimicked the step-by-step reasoning that humans use when solving puzzles or making logical deductions.[59] By the late 1981-1990s, artificial intelligence research had developed methods for dealing with uncertain or incomplete information, employing concepts of probability and economics.[60]

These algorithms proved insufficient to solve large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew.[61] In this way, it was concluded that humans rarely use the step-by-step deduction that early artificial intelligence research followed; instead, they solve most of their problems using quick, intuitive judgments.[62]​.

Knowledge representation

Knowledge representation[63]​ and knowledge engineering[64]​ are fundamental to classical artificial intelligence research. Some "expert systems" attempt to compile the knowledge that experts possess in a specific field. Additionally, other projects attempt to assemble the "common sense knowledge" known to the average person into a database containing extensive knowledge about the world.

Among the topics that a common sense knowledge base would contain are: objects, properties, categories and relationships between objects,[65]​ situations, events, states and time[66]​ causes and effects;[67]​ and knowledge about knowledge (what we know about what other people know)[68]​ among others.

Planning

Another goal of artificial intelligence is to be able to set goals and ultimately achieve them.[69]​ To do this, they need a way to visualize the future, a representation of the state of the world, and be able to make predictions about how their actions will change it, in order to make decisions that maximize the utility (or "value") of the available options.[70]​.

In classical planning problems, the agent can assume that it is the only system acting in the world, which allows it to be sure of the consequences of its actions.[71] However, if the agent is not the only actor, then it is required that it can reason under uncertainty. This requires an agent that can not only evaluate its environment and make predictions, but also evaluate its predictions and adapt based on its evaluation.[72]​ Multi-agent planning uses the cooperation and competition of many systems to achieve a given goal. Emergent behavior "Emergence (philosophy)") like this is used by evolutionary algorithms and swarm intelligence.[73]​.

Learning

Machine learning is a fundamental concept of artificial intelligence research since the beginning of studies in this field; It consists of the research of computer algorithms that improve automatically through experience.[74]​.

Unsupervised learning is the ability to find patterns in an input stream, without requiring a human to label the inputs first. Supervised learning includes classification and numerical regression, which requires a human to first label the input data. Classification is used to determine which category something belongs to and occurs after a program looks at several examples of input from various categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[74] Both classifiers and regression learners attempt to learn an unknown function; For example, a spam classifier can be seen as learning a function that assigns the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess students by computational complexity, sample complexity (how much data is required), or by other notions of optimization "Optimization (mathematical)").[75]​.

The world is constantly evolving, and tools like ChatGPT are at the center of this transformation. While many people see ChatGPT as an opportunity to improve their business or personal experience, there are those who are skeptical about its implementation.[76]​.

Natural language processing

Natural language processing[77]​ allows machines to read and understand human language. A sufficiently effective natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as news texts. Some simple applications of natural language processing include information retrieval, text mining, question answering, and machine translation.[78] Many approaches use word frequencies to construct syntactic representations of text. “Keyword detection” search strategies are popular and scalable, but suboptimal; A search query for “dog” can only match documents containing the literal word “dog” and miss a document with the word “poodle.” Statistical language processing approaches can combine all of these strategies, as well as others, and often achieve acceptable accuracy at the page or paragraph level. Beyond the processing of semantics, the ultimate goal of this is to incorporate a complete understanding of common sense reasoning.[79]​ In 2019, deep learning architectures based on transformers "Transformer (machine learning model)") could generate coherent text.[80]​.

Perception

Machine perception[81]​ is the ability to use input from sensors (such as visible or infrared cameras, microphones, wireless signals, and lidar, sonar, radar, and touch sensors) to understand aspects of the world. Applications include speech recognition,[82]​ facial recognition, and object recognition.[83]​ Computer vision is the ability to analyze visual information, which is often ambiguous; A giant fifty-meter-tall pedestrian far away can produce the same pixels as a normal-sized pedestrian nearby, requiring artificial intelligence to judge the relative probability and reasonableness of different interpretations, for example, using its "object model" to evaluate that fifty-meter-tall pedestrians do not exist.[84]

The great importance of AI lies in the fact that it has a wide range of applications, from automating tedious tasks to creating advanced healthcare and disease diagnosis systems, detecting fraud and optimizing business processes.[85]​ In many cases, AI can do things that humans cannot do, such as processing data in large quantities and locating patterns and interrelationships between them that would be difficult or impossible to detect otherwise.

This tool helps automate repetitive learning and discovery through data, performs frequent computerized tasks reliably, however, it needs human intervention for system configuration. It analyzes deeper data and adds intelligence as it cannot be sold as an individual application, so it is an added value to the products. It has high precision through deep neural networks; For example, in medicine, AI can be used to detect cancer with MRIs (magnetic resonance imaging). It adapts through progressive learning algorithms, finds structure and regularities in the data so that the algorithm becomes a classifier or predictor. And, finally, artificial intelligence makes the most of data.

Furthermore, one of the main reasons why AI is important is because it can automate repetitive and monotonous tasks, freeing up time and resources for people to focus on more creative and valuable tasks. For example, AI can help businesses automate back office tasks, such as accounting and invoice processing, which can reduce costs and improve efficiency. Similarly, AI can help workers perform more complex and creative tasks, such as design and strategic planning.

Another reason AI is important is because it can help businesses make informed and accurate decisions. Likewise, AI can process large amounts of data and provide valuable information for business decision-making, which can help companies identify business opportunities, predict market trends, and improve financial market efficiency.[86]​ Additionally, AI can help workers make informed decisions in real time, such as in healthcare, where AI can help doctors identify diseases and personalize treatment.

AI is also important in the field of cybersecurity. AI can help detect and prevent threats, from cyber attacks to detecting suspicious behavior. AI can analyze large amounts of data in real time and detect patterns and anomalies that could indicate a security threat. Additionally, AI can learn from behavioral patterns and improve its ability to detect future threats.[87]​ In the field of cybersecurity, AI can help protect systems and networks from computer virus attacks and malware infiltration.

Another area where AI is important is in knowledge discovery. AI can discover patterns and relationships in data that humans could not detect, which can lead to new ideas and breakthroughs in various fields. For example, AI can help researchers identify new treatments for diseases, or help scientists analyze data from sensors and satellites to better understand global warming.

Artificial intelligence (AI) offers multiple advantages to society, many of which are already beginning to become evident. One of the main benefited areas is education, since it allows classes to be more dynamic and understandable, facilitating learning and promoting more autonomous thinking in students.[88]​.

Despite its benefits, AI also has some disadvantages. One of the main concerns is the loss of jobs, as some professions have been affected, such as graphic designers, financial analysts and mathematicians, whose jobs may be replaced by automated systems.[88]​.

Sophia

In March 2016, the comment that the humanoid robot named Sophia "Sophia (robot)") from the company Hanson Robotics became popular during her presentation when her creator, David Hanson, asked her if she was willing to destroy humanity, to which the robot replied: "Okay, I'm going to destroy humanity." Subsequently, Sophía gained recognition and global media attention due to her almost human behaviors, being interviewed on many occasions by different media and holding conversations with famous and recognized personalities. In 2017, Sophia obtained Saudi citizenship, thus becoming the first robot to be recognized as a citizen by a country, which raised controversy over whether robots should be granted the same rights and obligations as if they were subjects of law.[89]​.

Alice and Bob

At the end of July 2017, several international media reported that the artificial intelligence research laboratory at the Georgia Institute of Technology, together with the Facebook Artificial Intelligence Research Group (FAIR), now Meta, had to turn off two chatbot-type artificial intelligences called Bob and Alice, since they had developed their own language more efficient than English, a language in which they had been trained to learn to negotiate, finally developing a type of incomprehensible communication that deviated from grammatical rules. of natural language and that favored the use of abbreviations. The language created by these AIs showed characteristics of corrupted English and repetitive patterns, especially of pronouns and determiners.[90]​.

This unexpected event was seen with panic in the media, as it was claimed that the chatbots had supposedly left human control and had developed the ability to communicate with each other. However, this was also subsequently denied, as it was argued that Facebook did not actually turn off the artificial intelligences, but simply paused them and changed the parameters of the chatbots, discarding the experiment in the end for not having any practical or useful interest within AI research.[91]​.

America

At the beginning of 2022, at the Consumer Electronics Show (CES) that took place in Las Vegas, the robot developed by Engineered Arts named Ameca "Ameca (robot)") caused doubt and fear among spectators during its exhibition mainly due to the resemblance of its face to that of a human being, the company expressed that the development of this humanoid robot was still in process and until September of the same year the robot was still not capable of walking or having any interaction with people.[92]​ On the other hand, in September 2023 the company once again exposed Ameca to the public by showing the robot in videos where it can be seen in front of a mirror making 25 human expressions,[93]​ as well as drawing a cat as it already had arms and legs that gave it mobility and, in the same way, using irony in conversations with people and even declaring that she made a joke when questioned about her ability to dream like a human being a robot by saying "I dreamed." with dinosaurs fighting a war against aliens on Mars»[94]​ he denied this moments later, explaining how the AI implemented in his system allowed him to create scenarios about events of humanity and was learning about them while it was turned off; These events impacted society about the resemblance that this humanoid robot was having with the human being and about the technological advance that is allowing this robot to be increasingly closer to living among people as another member of the community.

fake nudes

The use of free AI applications to transform photographs of people into fake nudes is causing problems that affect minors. The case hit the media in September 2023 when several photographs of girls and young people (between 11 and 17 years old) appeared in Almendralejo (Badajoz, Spain) that had been modified using artificial intelligence to appear naked. The images were obtained from the Instagram profiles and the WhatsApp application of at least 20 local girls. The photographs of naked girls had later circulated through WhatsApp and a video had been created from them that had also circulated among minors. The authors of this transformation were also minors and classmates from school or institute. The Spanish Data Protection Agency opened an investigation and contacted the Almendralejo City Council and the Extremadura Regional Government informing them that the removal of any image circulating on the Internet on the agency's priority channel could be requested.[95]​.

Uno de los mayores críticos de la denominación de estos procesos informáticos con el término de inteligencia artificial es Jaron Lanier. Para ello, objeta la idea de que esta sea realmente inteligente y de que podríamos estar en competencia con un ente artificial. «Esta idea de superar la capacidad humana es ridícula porque está hecha de habilidades humanas».[96]​.

Las principales críticas a la inteligencia artificial tienen que ver con su capacidad de imitar por completo a un ser humano.[97]​ Sin embargo, hay expertos[98]​en el tema que indican que ningún humano individual tiene capacidad para resolver todo tipo de problemas, y autores como Howard Gardner han teorizado sobre la solución.

En los humanos, la capacidad de resolver problemas tiene dos aspectos: los aspectos innatos y los aspectos aprendidos. Los aspectos innatos permiten, por ejemplo, almacenar y recuperar información en la memoria "Memoria (proceso)"), mientras que en los aspectos aprendidos reside el saber resolver un problema matemático mediante el algoritmo adecuado. Del mismo modo que un humano debe disponer de herramientas que le permitan solucionar ciertos problemas, los sistemas artificiales deben ser programados para que puedan llegar a resolverlos.

Muchas personas consideran que la prueba de Turing ha sido superada, citando conversaciones en que al dialogar con un programa de inteligencia artificial para chat no saben que hablan con un programa. Sin embargo, esta situación no es equivalente a una prueba de Turing, que requiere que el participante se encuentre sobre aviso de la posibilidad de hablar con una máquina.

Otros experimentos mentales como la habitación china, de John Searle, han mostrado cómo una máquina podría simular pensamiento sin realmente poseerlo, pasando la prueba de Turing sin siquiera entender lo que hace, tan solo reaccionando de una forma concreta a determinados estímulos (en el sentido más amplio de la palabra). Esto demostraría que la máquina en realidad no está pensando, ya que actuar de acuerdo con un programa preestablecido sería suficiente. Si para Turing el hecho de engañar a un ser humano que intenta evitar que le engañen es muestra de una mente inteligente, Searle considera posible lograr dicho efecto mediante reglas definidas a priori.

Uno de los mayores problemas en sistemas de inteligencia artificial es la comunicación con el usuario. Este obstáculo es debido a la ambigüedad del lenguaje, y se remonta a los inicios de los primeros sistemas operativos informáticos. La capacidad de los humanos para comunicarse entre sí implica el conocimiento del lenguaje que utiliza el interlocutor. Para que un humano pueda comunicarse con un sistema inteligente hay dos opciones: o bien que el humano aprenda el lenguaje del sistema como si aprendiese a hablar cualquier otro idioma distinto al nativo, o bien que el sistema tenga la capacidad de interpretar el mensaje del usuario en la lengua que el usuario utiliza. También puede haber desperfectos en las instalaciones de los mismos.

Un humano, durante toda su vida, aprende el vocabulario de su lengua nativa o materna, siendo capaz de interpretar los mensajes (a pesar de la polisemia de las palabras) y utilizando el contexto para resolver ambigüedades. Sin embargo, debe conocer los distintos significados para poder interpretar, y es por esto que lenguajes especializados y técnicos son conocidos solamente por expertos en las respectivas disciplinas. Un sistema de inteligencia artificial se enfrenta con el mismo problema, la polisemia del lenguaje humano, su sintaxis poco estructurada y los dialectos entre grupos.

Los desarrollos en inteligencia artificial son mayores en los campos disciplinares en los que existe mayor consenso entre especialistas. Un sistema experto es más probable que sea programado en física o en medicina que en sociología o en psicología. Esto se debe al problema del consenso entre especialistas en la definición de los conceptos involucrados y en los procedimientos y técnicas a utilizar. Por ejemplo, en física hay acuerdo sobre el concepto de velocidad y cómo calcularla. Sin embargo, en psicología se discuten los conceptos, la etiología, la psicopatología, y cómo proceder ante cierto diagnóstico. Esto dificulta la creación de sistemas inteligentes porque siempre habrá desacuerdo sobre la forma en que debería actuar el sistema para diferentes situaciones. A pesar de esto, hay grandes avances en el diseño de sistemas expertos para el diagnóstico y toma de decisiones en el ámbito médico y psiquiátrico (Adaraga Morales, Zaccagnini Sancho, 1994).

Al desarrollar un robot con inteligencia artificial se debe tener cuidado con la autonomía,[99]​ hay que tener en cuenta el no vincular el hecho de que el robot tenga interacciones con seres humanos a su grado de autonomía. Si la relación de los humanos con el robot es de tipo maestro esclavo, y el papel de los humanos es dar órdenes y el del robot obedecerlas, entonces sí cabe hablar de una limitación de la autonomía del robot. Pero si la interacción de los humanos con el robot es de igual a igual, entonces su presencia no tiene por qué estar asociada a restricciones para que el robot pueda tomar sus propias decisiones.[100]​.

Con el desarrollo de la tecnología de inteligencia artificial, muchas compañías de software como el aprendizaje profundo y el procesamiento del lenguaje natural han comenzado a producirse y la cantidad de películas sobre inteligencia artificial ha aumentado.

Stephen Hawking advirtió sobre los peligros de la inteligencia artificial y lo consideró una amenaza para la supervivencia de la humanidad.[101]​.

Privacy and copyright issues

Machine learning algorithms require large amounts of data. The techniques used to acquire this data raise concerns about privacy and surveillance issues. Technology companies collect a large amount of data from their users, including internet activity, geolocation data, video and audio.[102]​ For example, to build voice recognition algorithms, Amazon, among others, has recorded millions of private conversations and allowed temporary workers to listen to them to transcribe some of them.[103]​ Opinions on this widespread surveillance range from those who see it as a necessary evil to those for whom it is unethical and constitutes a violation of the right to information. privacy.[104]​ AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while obtaining data, such as data aggregation, de-identification "De-identification (privacy)"), and differential privacy.[105]​.

Since 2016, some privacy experts, such as Cynthia Dwork, began to view privacy from the perspective of fairness: "Fairness (machine learning)"): Brian Christian wrote that experts have shifted "from the question of 'what do they know' to the question of 'what are they doing with it'?".[106]

Generative AI is often trained with unauthorized copyrighted works, including domains such as images or computer code; the output is then used under a fair use justification. Experts disagree on the validity of this justification during a legal process, as it could depend on the purpose and character of the use of the copyrighted work and the effect on the potential market of the copyrighted work.[107]​ In 2023, writers such as John Grisham and Jonathan Franzen sued AI companies for using their works to train generative AI.[108][109]​ In 2024, 200 artists wrote an open letter. that requested "to stop the assault on human creativity."[110]​.

La normativa tiene como objetivo regular y reglamentar el uso de la IA en el entorno educativo, específicamente en el aula. La IA ha experimentado un rápido desarrollo y se ha convertido en una herramienta potencialmente beneficiosa para mejorar la enseñanza y el aprendizaje. No obstante, su implementación plantea desafíos éticos, de privacidad y equidad que deben ser abordados de manera efectiva. Esta normativa se establece en respuesta a la necesidad de garantizar que la IA se utilice de manera ética, responsable y equitativa en el ámbito educativo.

Los objetivos de esta normativa son:.

Esta normativa se aplica a todas las instituciones educativas y docentes que utilizan la IA en el aula, así como a los proveedores de tecnología educativa que ofrecen soluciones basadas en IA.

Organizaciones como UNESCO Ethics AI (2020), UNESCO Education & AI (2021), Beijin Consensus, OCDE (2021), Comisión Europea (2019), European Parliament Report AI Education (2021), UNICEF (2021) y Foro Económico Mundial (2019) han mostrado preocupación por implementar lineamientos sobre la ética y la IA en el entorno educativo.[111]​ En 2024, la Universidad Nacional de Costa rica elaboró y publicó[112]​ la Declaración de Heredia:") principios sobre el uso de inteligencia artificial en la edición científica"), referida a la publicación científica, en la que propone una serie de consideraciones para el uso responsable de la inteligencia artificial (IA), se alienta a transparentar el uso de la IA para un ejercicio claro, trazable y reproducible del conocimiento y se llama la atención sobre los retos que supone la incorporación de la IA a la edición científica en cuanto a la diversidad de opciones, el evitar la propagación de sesgos y desinformación, y el respeto a la propiedad intelectual.

El uso de la IA en el entorno educativo debe regirse por los siguientes principios éticos y valores:.

Risks of AI in the educational environment

Just as it has many benefits, we also encounter different risks to which education is exposed with its use.

Consideration of Diversity and Inclusion

Special attention must be paid to student diversity and ensuring that AI is accessible and beneficial to all, regardless of ethnicity, gender, disability or sexual orientation. AI solutions must be designed with accessibility and inclusion in mind.

This regulation is based on academic research, recommendations from educational organizations and established best practices in the use of AI in education. Institutions are encouraged to stay up to date with relevant scientific literature and guidelines.

Although AI can be a powerful tool in the classroom, it should not replace creativity, originality, and human judgment in the educational process. AI must be used in a complementary way to enrich the educational experience.

This regulation is presented as a general framework that must be adapted and expanded by educational institutions according to their specific needs and contexts. It must be communicated effectively to all those involved in the educational process and reviewed periodically to ensure its validity.

This regulation aims to ensure that AI is used ethically and responsibly in the classroom, promoting the benefit of students and the advancement of education. Its compliance is essential to achieve a successful implementation of AI in the educational environment.

Regarding the nature of learning, AI can be subdivided into two conceptually distinct fields:.

When talking about intellectual property attributed to creations of artificial intelligence, a strong debate arises around whether a machine can have copyrights. According to the World Intellectual Property Organization (WIPO), any creation of the mind can be part of intellectual property, but it does not specify whether the mind must be human or can be a machine, leaving artificial creativity uncertain.

Different legislations have begun to emerge around the world in order to manage artificial intelligence, both its use and creation. Policymakers and members of the government have begun to think about this technology, emphasizing its risk and complex challenges. Looking at work created by a machine, the laws question the possibility of granting intellectual property to a machine, opening a discussion regarding legislation related to AI.

On February 5, 2020, the United States Copyright Office and WIPO attended a symposium to take a deep look at how the creative community is using artificial intelligence (AI) to create original work. The relationships between artificial intelligence and copyright were discussed, what level of involvement is sufficient for the resulting work to be valid for copyright protection; the challenges and considerations of using copyrighted inputs to train a machine; and the future of artificial intelligence and its copyright policies.[117]​[118]​.

The Director General of WIPO, Francis Gurry, expressed concern about the lack of attention to intellectual property rights, as people often direct their interest towards issues of cybersecurity, privacy and data integrity when talking about artificial intelligence. Likewise, Gurry questioned whether the growth and sustainability of AI technology would lead us to develop two systems for managing copyright – one for human creations and another for machine creations.[119]

There is still a lack of clarity in the understanding around artificial intelligence. Technological developments are advancing at a rapid pace, increasing their complexity in policies, legalities and ethical problems that deserve global attention. Before finding a way to work with copyright, it is necessary to understand it correctly, since it is not yet known how to judge the originality of a work that is born from a composition of a series of fragments of other works.

The allocation of copyright around artificial intelligence has not yet been regulated due to a lack of knowledge and definitions. There is still uncertainty about whether, and to what extent, artificial intelligence is capable of producing content autonomously and without any humans involved, something that could influence whether its results can be protected by copyright.

The general copyright system must still adapt to the digital context of artificial intelligence, as they are focused on human creativity. Copyright is not designed to handle any policy problems related to the creation and use of intellectual property, and it can be harmful to overstretch copyright to solve peripheral problems, given that:

"Using copyright to govern artificial intelligence is unwise and contradictory to copyright's primary function of providing an enabling space for creativity to flourish."[120]

The conversation about intellectual property will have to continue to ensure that innovation is protected, but also has room to flourish.

in literature

Below is some work that has artificial intelligence as its central motif.

in the cinema

Artificial intelligence is increasingly present in society, the evolution of technology is a reality and with it, the production of films on this topic. It should be noted that there have been audiovisual pieces about artificial intelligence for a long time, whether including characters or showing a moral and ethical background. Below is a list of some of the major films that deal with this topic:.

In 2019, UNESCO's World Commission on the Ethics of Scientific Knowledge and Technology (COMEST) defined artificial intelligence as a field that involves machines capable of imitating certain functionalities of human intelligence, including characteristics such as perception, learning, reasoning, problem solving, linguistic interaction and even the production of creative works.

Colloquially, the phrase "artificial intelligence" is applied when a machine imitates the "cognitive" functions that humans associate as human competencies; for example: "perceive", "reason", "learn", and "problem solve".[10] Andreas Kaplan and Michael Haenlein") define artificial intelligence as "the ability of a system to correctly interpret external data, and thus learn and employ that knowledge to achieve concrete tasks and goals through flexible adaptation."[11] As machines become increasingly capable, technology that was once thought to require intelligence is removed from the definition. Marvin Minsky, one of the creators of AI, spoke of the term artificial intelligence as a suitcase word (“suitcase word”) because a variety of elements can be placed in it.[12]​[13]​.

For example, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a common technology.[14]​ Technological advances still classified as artificial intelligence are autonomous driving systems or those capable of playing chess or Go.[15]​.

Artificial intelligence is a new way of solving problems, which includes expert systems, the management and control of robots and processors, which attempts to integrate knowledge into such systems; in other words, an intelligent system capable of writing its own program. An expert system defined as a programming structure capable of storing and using knowledge about a certain area that translates into its learning capacity.[16]​ Similarly, AI can be considered as the ability of machines to use algorithms, learn from data and use what is learned in making decisions just as a human being would do.[17]​.

According to Takeyas (2007), AI is a branch of computer science responsible for studying computing models capable of carrying out activities typical of human beings based on two of their primary characteristics: reasoning and behavior.[18]​.

In 1956, John McCarthy "John McCarthy (scientist)") coined the term "artificial intelligence," and defined it as "the science and ingenuity of making intelligent machines, especially intelligent computer programs."[19]

Grau-Luque contrasts different definitions from various sources and authors, highlighting that they differ depending on "in which specific field they are used."[20] This leads the author to define "artificial intelligence" as "systems that carry out tasks considered intelligent", and then associate concepts such as "learning" and "reasoning" with machine learning as a subdiscipline of artificial intelligence.

There are also different types of perceptions and actions, which can be obtained and produced, respectively, by physical sensors and mechanical sensors in machines, electrical or optical pulses in computers, as well as by bit inputs and outputs of software and its hardware environment.

Several examples are in the area of ​​system control, automatic planning, the ability to respond to diagnostics and consumer inquiries, handwriting recognition, speech recognition and pattern recognition. AI systems are currently part of the routine in fields such as economics, medicine, engineering, transportation, communications and the military, and have been used in a wide variety of computer programs, strategy games, such as computer chess, and other video games.

Likewise, artificial intelligence is being developed on the digital platform more and more, evolving and creating new tools, such as the labor platform that has existed since 2023 called SIVIUM, a tool through which a person automatically applies to all job offers on all job portals, without having to review each job offer that is presented and send their CV one by one.[21]​.

Stuart J. Russell and Peter Norvig differentiate several types of artificial intelligence:[22]​.

Generative artificial intelligence is a type of artificial intelligence system capable of generating text, images or other media in response to text commands known as "prompts".[27]​ A prompt is the instruction, question or set of instructions for the artificial intelligence to perform the specific task and provide the required response. The quality of the prompt directly influences the quality of the response.[28]​.

Generative AI models learn the patterns and structure of their input training data, and then generate new data that has similar characteristics.

Notable generative AI systems include ChatGPT (and its Microsoft Copilot variant), a chatbot created by OpenAI using its foundational GPT-3 and GPT-4 large language models (LLM); Gemini "Gemini (chatbot)") (previously called Bard), a chatbot created by Google using the Gemini "Gemini (language model)" language model); and Claude "Claude (chatbot)"), a chatbot created by Anthropic using the models of the same name.[29] Other generative AI models include AI art systems such as Stable Diffusion, Midjourney, and DALL-E, which allow the creation of images. Today, generative AI can create text, code, images, video, music, voices and sound effects.

Strong Artificial Intelligence (IGA) is a hypothetical type of artificial intelligence that equals or exceeds average human intelligence. If it became a reality, an IGA could learn to perform any intellectual task that humans or animals can perform. Alternatively, the IGA has been defined as an autonomous system that exceeds human capabilities in most economically valuable tasks.

Some argue that it could be possible in years or decades; others, that it could take a century or more; and a minority believes that it may never be achieved. There is debate about the exact definition of IGA and whether modern large language models (LLMs), such as GPT-4, are early but incomplete forms of IGA.

Explainable artificial intelligence refers to methods and techniques in the application of artificial intelligence technology by which humans are able to understand the decisions and predictions made by artificial intelligence.

Friendly artificial intelligence is a strong, hypothetical AI that can have a positive rather than a negative effect on humanity. 'Friendly' is used in this context as technical terminology and chooses agents that are safe and useful, not necessarily those that are 'friendly' in the colloquial sense. The concept is invoked primarily in the context of discussions of artificial recursive self-improving agents that rapidly explode into intelligence, with the argument that this hypothetical technology could have a long, fast and difficult task of controlling the impact on human society.

Multimodal artificial intelligence is a type of artificial intelligence that can process and integrate data from different modalities, such as text, images, audio and video, to obtain a more complete and contextualized understanding of a situation. Multimodal artificial intelligence is inspired by the way humans use multiple senses (Sense (perception)) to perceive and interact with the world, and offers a more natural and intuitive way to communicate with technology.

Quantum artificial intelligence is an interdisciplinary field that focuses on building quantum algorithms to improve computational tasks within AI, including subfields such as machine learning. There is evidence that shows a possible quantum quadratic advantage in fundamental AI operations.

Contenido

La IA se divide en dos escuelas de pensamiento:.

Conventional artificial intelligence

It is also known as symbolic-deductive AI. It is based on the formal and statistical analysis of human behavior when faced with different problems:

Computational artificial intelligence

Computational intelligence (also known as subsymbolic-inductive AI) involves interactive development or learning (for example, interactive modifications of parameters in connection systems). Learning is done based on empirical data.

Computational intelligence has a dual purpose. On the one hand, its scientific objective is to understand the principles that enable intelligent behavior (whether in natural or artificial systems) and, on the other, its technological objective is to specify the methods to design intelligent systems.[32]​.

In 2024, significant advances were made in several areas of AI:.

Given the possibility of creating machines equipped with intelligence, it became important to worry about the ethical issue of machines to try to guarantee that no harm is caused to human beings, other living beings and even to the machines themselves according to some currents of thought.[48] This is how a broad field of studies known as the ethics of artificial intelligence emerged, relatively recently emerging and which is generally divided into two branches, roboethics, responsible for studying the actions of human beings towards robots, and machine ethics, responsible for the study of behavior of robots towards human beings.

The accelerated technological and scientific development of artificial intelligence that has occurred in the century also has a significant impact on other fields. In the world economy, during the second industrial revolution, a phenomenon known as technological unemployment was experienced, which refers to when industrial automation of large-scale production processes replaces human labor. A similar phenomenon could occur with artificial intelligence, especially in the processes in which human intelligence intervenes, as illustrated in the story How They Had Fun! by Isaac Asimov, in which its author glimpses some of the effects that the interaction of intelligent machines specialized in children's pedagogy, instead of human teachers, would have on children in school. This same writer designed what are now known as the three laws of robotics, which appeared for the first time in the story Círculo viciousioso "Círculo viciousioso (story)") (Runaround) of 1942, where he established the following:.

Other more recent science fiction works also explore some ethical and philosophical questions regarding Strong Artificial Intelligence, such as the films I, Robot or A.I. Artificial Intelligence, in which topics such as self-awareness or the origin of an emerging consciousness of intelligent robots or computer systems are discussed, or whether they could be considered subjects of law due to their almost human characteristics related to sentience, such as being able to feel pain and emotions or to what extent they would obey the objective of their programming, and if not, whether they could exercise free will. The latter is the central theme of the famous Terminator saga "Terminator (franchise)"), in which machines overcome humanity and decide to annihilate it, a story that, according to several specialists, could not be limited to science fiction and could be a real possibility in a posthuman society that depended completely on technology and machines.[50][51]​.

Law[53]​ plays a fundamental role in the use and development of AI. Laws establish rules and norms of behavior to ensure social well-being and protect individual rights, and can help us obtain the benefits of this technology while minimizing its risks, which are significant. At the moment there are no legal regulations that directly regulate AI. But on April 21, 2021, the European Commission has presented a proposal for a European Regulation for the harmonized regulation of artificial intelligence (AI) in the EU. Its exact title is Proposal for a Regulation of the European Parliament and of the Council establishing harmonized rules on artificial intelligence – Artificial Intelligence Law – and amending other Union legislative acts.

In March 2023, hundreds of entrepreneurs such as Elon Musk, Steve Wozniak (co-founder of Apple) or the presidents of numerous technology companies; Intellectuals such as Yuval Noah Harari and hundreds of academics and researchers specialized in artificial intelligence signed an open letter warning of the danger of the lack of regulation of AI, putting the spotlight on OpenAI, the company that developed ChatGPT. They asked for a pause of at least 6 months for their most powerful experiments, until the world achieves an international consensus so that these systems "are more accurate, secure, interpretable, transparent, robust, neutral, reliable and loyal."[54]​.

Two months later, in May, 350 executives from the main AI development companies, academics and expert researchers signed a new manifesto warning that unregulated advanced AI represents a danger of extinction for humanity: "Mitigating the risk of extinction of AI should be a global priority along with other risks on a social scale such as pandemics and nuclear war." Google DeepMind with 38 executives, researchers or university professors related to the company, and representatives of smaller developers such as Anthropic, Stability AI or Inflection AI.[57]​.

Some authors have begun to explore the possibility of recognizing advanced artificial intelligences not only as tools, but as potential limited legal subjects, capable of acquiring rights and assuming tax burdens in relation to the digital or intangible goods they produce.[58] This proposal finds inspiration in figures already recognized by law, such as fictitious legal entities, which, without being human, have been endowed with the capacity to act in legal traffic. In this context, it has been proposed that certain complex automata could establish their own assets, assume civil responsibilities for the effects of their autonomous decisions, and even pay taxes, as a way of responding for their economic impact and redistributing the benefits they generate in increasingly digitalized societies.

This perspective does not imply an equation between human beings and artificial intelligences, but rather a functional legal architecture that would allow the consequences derived from the autonomous production of goods and services by AI systems to be channeled. It is proposed, for example, that income generated by creative bots or autonomous vehicles can be allocated to guarantee or tax funds, thus alleviating pressures on human economies and avoiding gray areas in legal liability. This vision contributes to the debate on technological singularity, proposing regulatory responses from private and fiscal law.[58]​.

Reasoning and problem solving

Early researchers developed algorithms that mimicked the step-by-step reasoning that humans use when solving puzzles or making logical deductions.[59] By the late 1981-1990s, artificial intelligence research had developed methods for dealing with uncertain or incomplete information, employing concepts of probability and economics.[60]

These algorithms proved insufficient to solve large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew.[61] In this way, it was concluded that humans rarely use the step-by-step deduction that early artificial intelligence research followed; instead, they solve most of their problems using quick, intuitive judgments.[62]​.

Knowledge representation

Knowledge representation[63]​ and knowledge engineering[64]​ are fundamental to classical artificial intelligence research. Some "expert systems" attempt to compile the knowledge that experts possess in a specific field. Additionally, other projects attempt to assemble the "common sense knowledge" known to the average person into a database containing extensive knowledge about the world.

Among the topics that a common sense knowledge base would contain are: objects, properties, categories and relationships between objects,[65]​ situations, events, states and time[66]​ causes and effects;[67]​ and knowledge about knowledge (what we know about what other people know)[68]​ among others.

Planning

Another goal of artificial intelligence is to be able to set goals and ultimately achieve them.[69]​ To do this, they need a way to visualize the future, a representation of the state of the world, and be able to make predictions about how their actions will change it, in order to make decisions that maximize the utility (or "value") of the available options.[70]​.

In classical planning problems, the agent can assume that it is the only system acting in the world, which allows it to be sure of the consequences of its actions.[71] However, if the agent is not the only actor, then it is required that it can reason under uncertainty. This requires an agent that can not only evaluate its environment and make predictions, but also evaluate its predictions and adapt based on its evaluation.[72]​ Multi-agent planning uses the cooperation and competition of many systems to achieve a given goal. Emergent behavior "Emergence (philosophy)") like this is used by evolutionary algorithms and swarm intelligence.[73]​.

Learning

Machine learning is a fundamental concept of artificial intelligence research since the beginning of studies in this field; It consists of the research of computer algorithms that improve automatically through experience.[74]​.

Unsupervised learning is the ability to find patterns in an input stream, without requiring a human to label the inputs first. Supervised learning includes classification and numerical regression, which requires a human to first label the input data. Classification is used to determine which category something belongs to and occurs after a program looks at several examples of input from various categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[74] Both classifiers and regression learners attempt to learn an unknown function; For example, a spam classifier can be seen as learning a function that assigns the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess students by computational complexity, sample complexity (how much data is required), or by other notions of optimization "Optimization (mathematical)").[75]​.

The world is constantly evolving, and tools like ChatGPT are at the center of this transformation. While many people see ChatGPT as an opportunity to improve their business or personal experience, there are those who are skeptical about its implementation.[76]​.

Natural language processing

Natural language processing[77]​ allows machines to read and understand human language. A sufficiently effective natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as news texts. Some simple applications of natural language processing include information retrieval, text mining, question answering, and machine translation.[78] Many approaches use word frequencies to construct syntactic representations of text. “Keyword detection” search strategies are popular and scalable, but suboptimal; A search query for “dog” can only match documents containing the literal word “dog” and miss a document with the word “poodle.” Statistical language processing approaches can combine all of these strategies, as well as others, and often achieve acceptable accuracy at the page or paragraph level. Beyond the processing of semantics, the ultimate goal of this is to incorporate a complete understanding of common sense reasoning.[79]​ In 2019, deep learning architectures based on transformers "Transformer (machine learning model)") could generate coherent text.[80]​.

Perception

Machine perception[81]​ is the ability to use input from sensors (such as visible or infrared cameras, microphones, wireless signals, and lidar, sonar, radar, and touch sensors) to understand aspects of the world. Applications include speech recognition,[82]​ facial recognition, and object recognition.[83]​ Computer vision is the ability to analyze visual information, which is often ambiguous; A giant fifty-meter-tall pedestrian far away can produce the same pixels as a normal-sized pedestrian nearby, requiring artificial intelligence to judge the relative probability and reasonableness of different interpretations, for example, using its "object model" to evaluate that fifty-meter-tall pedestrians do not exist.[84]

The great importance of AI lies in the fact that it has a wide range of applications, from automating tedious tasks to creating advanced healthcare and disease diagnosis systems, detecting fraud and optimizing business processes.[85]​ In many cases, AI can do things that humans cannot do, such as processing data in large quantities and locating patterns and interrelationships between them that would be difficult or impossible to detect otherwise.

This tool helps automate repetitive learning and discovery through data, performs frequent computerized tasks reliably, however, it needs human intervention for system configuration. It analyzes deeper data and adds intelligence as it cannot be sold as an individual application, so it is an added value to the products. It has high precision through deep neural networks; For example, in medicine, AI can be used to detect cancer with MRIs (magnetic resonance imaging). It adapts through progressive learning algorithms, finds structure and regularities in the data so that the algorithm becomes a classifier or predictor. And, finally, artificial intelligence makes the most of data.

Furthermore, one of the main reasons why AI is important is because it can automate repetitive and monotonous tasks, freeing up time and resources for people to focus on more creative and valuable tasks. For example, AI can help businesses automate back office tasks, such as accounting and invoice processing, which can reduce costs and improve efficiency. Similarly, AI can help workers perform more complex and creative tasks, such as design and strategic planning.

Another reason AI is important is because it can help businesses make informed and accurate decisions. Likewise, AI can process large amounts of data and provide valuable information for business decision-making, which can help companies identify business opportunities, predict market trends, and improve financial market efficiency.[86]​ Additionally, AI can help workers make informed decisions in real time, such as in healthcare, where AI can help doctors identify diseases and personalize treatment.

AI is also important in the field of cybersecurity. AI can help detect and prevent threats, from cyber attacks to detecting suspicious behavior. AI can analyze large amounts of data in real time and detect patterns and anomalies that could indicate a security threat. Additionally, AI can learn from behavioral patterns and improve its ability to detect future threats.[87]​ In the field of cybersecurity, AI can help protect systems and networks from computer virus attacks and malware infiltration.

Another area where AI is important is in knowledge discovery. AI can discover patterns and relationships in data that humans could not detect, which can lead to new ideas and breakthroughs in various fields. For example, AI can help researchers identify new treatments for diseases, or help scientists analyze data from sensors and satellites to better understand global warming.

Artificial intelligence (AI) offers multiple advantages to society, many of which are already beginning to become evident. One of the main benefited areas is education, since it allows classes to be more dynamic and understandable, facilitating learning and promoting more autonomous thinking in students.[88]​.

Despite its benefits, AI also has some disadvantages. One of the main concerns is the loss of jobs, as some professions have been affected, such as graphic designers, financial analysts and mathematicians, whose jobs may be replaced by automated systems.[88]​.

Sophia

In March 2016, the comment that the humanoid robot named Sophia "Sophia (robot)") from the company Hanson Robotics became popular during her presentation when her creator, David Hanson, asked her if she was willing to destroy humanity, to which the robot replied: "Okay, I'm going to destroy humanity." Subsequently, Sophía gained recognition and global media attention due to her almost human behaviors, being interviewed on many occasions by different media and holding conversations with famous and recognized personalities. In 2017, Sophia obtained Saudi citizenship, thus becoming the first robot to be recognized as a citizen by a country, which raised controversy over whether robots should be granted the same rights and obligations as if they were subjects of law.[89]​.

Alice and Bob

At the end of July 2017, several international media reported that the artificial intelligence research laboratory at the Georgia Institute of Technology, together with the Facebook Artificial Intelligence Research Group (FAIR), now Meta, had to turn off two chatbot-type artificial intelligences called Bob and Alice, since they had developed their own language more efficient than English, a language in which they had been trained to learn to negotiate, finally developing a type of incomprehensible communication that deviated from grammatical rules. of natural language and that favored the use of abbreviations. The language created by these AIs showed characteristics of corrupted English and repetitive patterns, especially of pronouns and determiners.[90]​.

This unexpected event was seen with panic in the media, as it was claimed that the chatbots had supposedly left human control and had developed the ability to communicate with each other. However, this was also subsequently denied, as it was argued that Facebook did not actually turn off the artificial intelligences, but simply paused them and changed the parameters of the chatbots, discarding the experiment in the end for not having any practical or useful interest within AI research.[91]​.

America

At the beginning of 2022, at the Consumer Electronics Show (CES) that took place in Las Vegas, the robot developed by Engineered Arts named Ameca "Ameca (robot)") caused doubt and fear among spectators during its exhibition mainly due to the resemblance of its face to that of a human being, the company expressed that the development of this humanoid robot was still in process and until September of the same year the robot was still not capable of walking or having any interaction with people.[92]​ On the other hand, in September 2023 the company once again exposed Ameca to the public by showing the robot in videos where it can be seen in front of a mirror making 25 human expressions,[93]​ as well as drawing a cat as it already had arms and legs that gave it mobility and, in the same way, using irony in conversations with people and even declaring that she made a joke when questioned about her ability to dream like a human being a robot by saying "I dreamed." with dinosaurs fighting a war against aliens on Mars»[94]​ he denied this moments later, explaining how the AI implemented in his system allowed him to create scenarios about events of humanity and was learning about them while it was turned off; These events impacted society about the resemblance that this humanoid robot was having with the human being and about the technological advance that is allowing this robot to be increasingly closer to living among people as another member of the community.

fake nudes

The use of free AI applications to transform photographs of people into fake nudes is causing problems that affect minors. The case hit the media in September 2023 when several photographs of girls and young people (between 11 and 17 years old) appeared in Almendralejo (Badajoz, Spain) that had been modified using artificial intelligence to appear naked. The images were obtained from the Instagram profiles and the WhatsApp application of at least 20 local girls. The photographs of naked girls had later circulated through WhatsApp and a video had been created from them that had also circulated among minors. The authors of this transformation were also minors and classmates from school or institute. The Spanish Data Protection Agency opened an investigation and contacted the Almendralejo City Council and the Extremadura Regional Government informing them that the removal of any image circulating on the Internet on the agency's priority channel could be requested.[95]​.

Uno de los mayores críticos de la denominación de estos procesos informáticos con el término de inteligencia artificial es Jaron Lanier. Para ello, objeta la idea de que esta sea realmente inteligente y de que podríamos estar en competencia con un ente artificial. «Esta idea de superar la capacidad humana es ridícula porque está hecha de habilidades humanas».[96]​.

Las principales críticas a la inteligencia artificial tienen que ver con su capacidad de imitar por completo a un ser humano.[97]​ Sin embargo, hay expertos[98]​en el tema que indican que ningún humano individual tiene capacidad para resolver todo tipo de problemas, y autores como Howard Gardner han teorizado sobre la solución.

En los humanos, la capacidad de resolver problemas tiene dos aspectos: los aspectos innatos y los aspectos aprendidos. Los aspectos innatos permiten, por ejemplo, almacenar y recuperar información en la memoria "Memoria (proceso)"), mientras que en los aspectos aprendidos reside el saber resolver un problema matemático mediante el algoritmo adecuado. Del mismo modo que un humano debe disponer de herramientas que le permitan solucionar ciertos problemas, los sistemas artificiales deben ser programados para que puedan llegar a resolverlos.

Muchas personas consideran que la prueba de Turing ha sido superada, citando conversaciones en que al dialogar con un programa de inteligencia artificial para chat no saben que hablan con un programa. Sin embargo, esta situación no es equivalente a una prueba de Turing, que requiere que el participante se encuentre sobre aviso de la posibilidad de hablar con una máquina.

Otros experimentos mentales como la habitación china, de John Searle, han mostrado cómo una máquina podría simular pensamiento sin realmente poseerlo, pasando la prueba de Turing sin siquiera entender lo que hace, tan solo reaccionando de una forma concreta a determinados estímulos (en el sentido más amplio de la palabra). Esto demostraría que la máquina en realidad no está pensando, ya que actuar de acuerdo con un programa preestablecido sería suficiente. Si para Turing el hecho de engañar a un ser humano que intenta evitar que le engañen es muestra de una mente inteligente, Searle considera posible lograr dicho efecto mediante reglas definidas a priori.

Uno de los mayores problemas en sistemas de inteligencia artificial es la comunicación con el usuario. Este obstáculo es debido a la ambigüedad del lenguaje, y se remonta a los inicios de los primeros sistemas operativos informáticos. La capacidad de los humanos para comunicarse entre sí implica el conocimiento del lenguaje que utiliza el interlocutor. Para que un humano pueda comunicarse con un sistema inteligente hay dos opciones: o bien que el humano aprenda el lenguaje del sistema como si aprendiese a hablar cualquier otro idioma distinto al nativo, o bien que el sistema tenga la capacidad de interpretar el mensaje del usuario en la lengua que el usuario utiliza. También puede haber desperfectos en las instalaciones de los mismos.

Un humano, durante toda su vida, aprende el vocabulario de su lengua nativa o materna, siendo capaz de interpretar los mensajes (a pesar de la polisemia de las palabras) y utilizando el contexto para resolver ambigüedades. Sin embargo, debe conocer los distintos significados para poder interpretar, y es por esto que lenguajes especializados y técnicos son conocidos solamente por expertos en las respectivas disciplinas. Un sistema de inteligencia artificial se enfrenta con el mismo problema, la polisemia del lenguaje humano, su sintaxis poco estructurada y los dialectos entre grupos.

Los desarrollos en inteligencia artificial son mayores en los campos disciplinares en los que existe mayor consenso entre especialistas. Un sistema experto es más probable que sea programado en física o en medicina que en sociología o en psicología. Esto se debe al problema del consenso entre especialistas en la definición de los conceptos involucrados y en los procedimientos y técnicas a utilizar. Por ejemplo, en física hay acuerdo sobre el concepto de velocidad y cómo calcularla. Sin embargo, en psicología se discuten los conceptos, la etiología, la psicopatología, y cómo proceder ante cierto diagnóstico. Esto dificulta la creación de sistemas inteligentes porque siempre habrá desacuerdo sobre la forma en que debería actuar el sistema para diferentes situaciones. A pesar de esto, hay grandes avances en el diseño de sistemas expertos para el diagnóstico y toma de decisiones en el ámbito médico y psiquiátrico (Adaraga Morales, Zaccagnini Sancho, 1994).

Al desarrollar un robot con inteligencia artificial se debe tener cuidado con la autonomía,[99]​ hay que tener en cuenta el no vincular el hecho de que el robot tenga interacciones con seres humanos a su grado de autonomía. Si la relación de los humanos con el robot es de tipo maestro esclavo, y el papel de los humanos es dar órdenes y el del robot obedecerlas, entonces sí cabe hablar de una limitación de la autonomía del robot. Pero si la interacción de los humanos con el robot es de igual a igual, entonces su presencia no tiene por qué estar asociada a restricciones para que el robot pueda tomar sus propias decisiones.[100]​.

Con el desarrollo de la tecnología de inteligencia artificial, muchas compañías de software como el aprendizaje profundo y el procesamiento del lenguaje natural han comenzado a producirse y la cantidad de películas sobre inteligencia artificial ha aumentado.

Stephen Hawking advirtió sobre los peligros de la inteligencia artificial y lo consideró una amenaza para la supervivencia de la humanidad.[101]​.

Privacy and copyright issues

Machine learning algorithms require large amounts of data. The techniques used to acquire this data raise concerns about privacy and surveillance issues. Technology companies collect a large amount of data from their users, including internet activity, geolocation data, video and audio.[102]​ For example, to build voice recognition algorithms, Amazon, among others, has recorded millions of private conversations and allowed temporary workers to listen to them to transcribe some of them.[103]​ Opinions on this widespread surveillance range from those who see it as a necessary evil to those for whom it is unethical and constitutes a violation of the right to information. privacy.[104]​ AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while obtaining data, such as data aggregation, de-identification "De-identification (privacy)"), and differential privacy.[105]​.

Since 2016, some privacy experts, such as Cynthia Dwork, began to view privacy from the perspective of fairness: "Fairness (machine learning)"): Brian Christian wrote that experts have shifted "from the question of 'what do they know' to the question of 'what are they doing with it'?".[106]

Generative AI is often trained with unauthorized copyrighted works, including domains such as images or computer code; the output is then used under a fair use justification. Experts disagree on the validity of this justification during a legal process, as it could depend on the purpose and character of the use of the copyrighted work and the effect on the potential market of the copyrighted work.[107]​ In 2023, writers such as John Grisham and Jonathan Franzen sued AI companies for using their works to train generative AI.[108][109]​ In 2024, 200 artists wrote an open letter. that requested "to stop the assault on human creativity."[110]​.

La normativa tiene como objetivo regular y reglamentar el uso de la IA en el entorno educativo, específicamente en el aula. La IA ha experimentado un rápido desarrollo y se ha convertido en una herramienta potencialmente beneficiosa para mejorar la enseñanza y el aprendizaje. No obstante, su implementación plantea desafíos éticos, de privacidad y equidad que deben ser abordados de manera efectiva. Esta normativa se establece en respuesta a la necesidad de garantizar que la IA se utilice de manera ética, responsable y equitativa en el ámbito educativo.

Los objetivos de esta normativa son:.

Esta normativa se aplica a todas las instituciones educativas y docentes que utilizan la IA en el aula, así como a los proveedores de tecnología educativa que ofrecen soluciones basadas en IA.

Organizaciones como UNESCO Ethics AI (2020), UNESCO Education & AI (2021), Beijin Consensus, OCDE (2021), Comisión Europea (2019), European Parliament Report AI Education (2021), UNICEF (2021) y Foro Económico Mundial (2019) han mostrado preocupación por implementar lineamientos sobre la ética y la IA en el entorno educativo.[111]​ En 2024, la Universidad Nacional de Costa rica elaboró y publicó[112]​ la Declaración de Heredia:") principios sobre el uso de inteligencia artificial en la edición científica"), referida a la publicación científica, en la que propone una serie de consideraciones para el uso responsable de la inteligencia artificial (IA), se alienta a transparentar el uso de la IA para un ejercicio claro, trazable y reproducible del conocimiento y se llama la atención sobre los retos que supone la incorporación de la IA a la edición científica en cuanto a la diversidad de opciones, el evitar la propagación de sesgos y desinformación, y el respeto a la propiedad intelectual.

El uso de la IA en el entorno educativo debe regirse por los siguientes principios éticos y valores:.

Risks of AI in the educational environment

Just as it has many benefits, we also encounter different risks to which education is exposed with its use.

Consideration of Diversity and Inclusion

Special attention must be paid to student diversity and ensuring that AI is accessible and beneficial to all, regardless of ethnicity, gender, disability or sexual orientation. AI solutions must be designed with accessibility and inclusion in mind.

This regulation is based on academic research, recommendations from educational organizations and established best practices in the use of AI in education. Institutions are encouraged to stay up to date with relevant scientific literature and guidelines.

Although AI can be a powerful tool in the classroom, it should not replace creativity, originality, and human judgment in the educational process. AI must be used in a complementary way to enrich the educational experience.

This regulation is presented as a general framework that must be adapted and expanded by educational institutions according to their specific needs and contexts. It must be communicated effectively to all those involved in the educational process and reviewed periodically to ensure its validity.

This regulation aims to ensure that AI is used ethically and responsibly in the classroom, promoting the benefit of students and the advancement of education. Its compliance is essential to achieve a successful implementation of AI in the educational environment.

Regarding the nature of learning, AI can be subdivided into two conceptually distinct fields:.

When talking about intellectual property attributed to creations of artificial intelligence, a strong debate arises around whether a machine can have copyrights. According to the World Intellectual Property Organization (WIPO), any creation of the mind can be part of intellectual property, but it does not specify whether the mind must be human or can be a machine, leaving artificial creativity uncertain.

Different legislations have begun to emerge around the world in order to manage artificial intelligence, both its use and creation. Policymakers and members of the government have begun to think about this technology, emphasizing its risk and complex challenges. Looking at work created by a machine, the laws question the possibility of granting intellectual property to a machine, opening a discussion regarding legislation related to AI.

On February 5, 2020, the United States Copyright Office and WIPO attended a symposium to take a deep look at how the creative community is using artificial intelligence (AI) to create original work. The relationships between artificial intelligence and copyright were discussed, what level of involvement is sufficient for the resulting work to be valid for copyright protection; the challenges and considerations of using copyrighted inputs to train a machine; and the future of artificial intelligence and its copyright policies.[117]​[118]​.

The Director General of WIPO, Francis Gurry, expressed concern about the lack of attention to intellectual property rights, as people often direct their interest towards issues of cybersecurity, privacy and data integrity when talking about artificial intelligence. Likewise, Gurry questioned whether the growth and sustainability of AI technology would lead us to develop two systems for managing copyright – one for human creations and another for machine creations.[119]

There is still a lack of clarity in the understanding around artificial intelligence. Technological developments are advancing at a rapid pace, increasing their complexity in policies, legalities and ethical problems that deserve global attention. Before finding a way to work with copyright, it is necessary to understand it correctly, since it is not yet known how to judge the originality of a work that is born from a composition of a series of fragments of other works.

The allocation of copyright around artificial intelligence has not yet been regulated due to a lack of knowledge and definitions. There is still uncertainty about whether, and to what extent, artificial intelligence is capable of producing content autonomously and without any humans involved, something that could influence whether its results can be protected by copyright.

The general copyright system must still adapt to the digital context of artificial intelligence, as they are focused on human creativity. Copyright is not designed to handle any policy problems related to the creation and use of intellectual property, and it can be harmful to overstretch copyright to solve peripheral problems, given that:

"Using copyright to govern artificial intelligence is unwise and contradictory to copyright's primary function of providing an enabling space for creativity to flourish."[120]

The conversation about intellectual property will have to continue to ensure that innovation is protected, but also has room to flourish.

in literature

Below is some work that has artificial intelligence as its central motif.

in the cinema

Artificial intelligence is increasingly present in society, the evolution of technology is a reality and with it, the production of films on this topic. It should be noted that there have been audiovisual pieces about artificial intelligence for a long time, whether including characters or showing a moral and ethical background. Below is a list of some of the major films that deal with this topic:.