Crear sin conciencia: cómo funciona realmente la IA generativa

Name: Crear sin conciencia: cómo funciona realmente la IA generativa
Uploaded: 2026-02-18T13:58:54.000Z
Duration: 3 h 18 min 56 s

Introduction to Generative AI

Overview of the Discussion

The talk introduces generative artificial intelligence, specifically focusing on ChatGPT and its applications, highlighting their growing popularity.

The aim is to explore the foundations of these technologies and understand both their positive and negative implications.

Historical Context

Artificial intelligence (AI) as a discipline originated in 1956, marking its anniversary this year; however, foundational concepts date back to 1943.

Early AI applications aimed to systematize human behaviors that could not be reduced to algorithms, leading to the development of expert systems.

Machine Learning Evolution

Key Developments in AI

Machine learning was coined in 1959 as a branch of AI focused on creating algorithms that can solve tasks based on provided data without explicit programming.

A significant debate exists regarding whether machine learning truly "understands" tasks like humans do; current models produce outputs without comprehension.

Types of Learning in AI

Traditional machine learning encompasses various types depending on data nature and desired output. The pragmatic view prioritizes problem-solving over philosophical considerations.

Supervised Learning

In supervised learning, algorithms are trained with labeled data to classify or predict outcomes based on new inputs.

Unsupervised Learning

Unsupervised learning involves using unlabeled data where algorithms identify patterns or structures independently.

Reinforcement Learning

Reinforcement learning focuses on decision-making through interaction with environments, rewarding correct outcomes while relying heavily on trial and error methods.

Discriminative Algorithms

Common Characteristics

All mentioned algorithm types share a discriminative nature; early machine learning primarily concentrated on these kinds of algorithms for effective problem resolution.

Machine Learning and Classification Algorithms

Understanding Machine Learning Basics

The primary function of machine learning algorithms is classification, which involves creating sophisticated algorithms to categorize data.

Humans excel at classification, while traditional computers struggle; they classify by discriminating between classes based on object characteristics.

Clustering algorithms aim to identify similarities in data without predefined categories, focusing on grouping similar items together.

Historical Context of Algorithms

Decision trees were among the first algorithms taught in machine learning courses over 30 years ago, alongside techniques like nearest neighbors and support vector machines.

Deep learning, although currently popular, has roots that trace back many years but faced theoretical challenges that delayed its advancement.

Structure of Neural Networks

Deep learning utilizes neural networks designed to mimic human brain neurons; these networks consist of multiple layers for processing data.

A deep network is defined as having more than two layers: an input layer, hidden layers (blue), and an output layer. Early models only had a single layer.

Evolution and Functionality of Neural Networks

The mathematical foundation for neural networks was established in a 1943 article explaining how such networks can perform computable functions.

Modern deep learning networks have numerous hidden layers that learn hierarchical representations from data rather than simply classifying it.

Generative vs Discriminative Models

Unlike earlier models that focused on discrimination (classifying inputs into categories), generative models create outputs based on learned distributions from existing data.

Generative deep models are capable of processing high-dimensional data across various domains beyond just image classification, including text and audio.

Training Generative Models

These models generate content rather than classify it; they predict future outputs based on context using tokens derived from numerical representations of the input data.

For instance, ChatGPT predicts subsequent words in a sentence by generating outputs directly instead of categorizing them into classes.

Complexity in Model Training

The training process for generative models focuses on developing complex distributions that allow for realistic output generation akin to real-world scenarios.

In contrast to discriminative models that establish relationships between inputs and outputs through training examples (e.g., identifying apples vs. oranges), generative models operate differently by producing new instances based on learned patterns.

Introduction to Generative AI Models

Classical Techniques in AI

In the past, AI relied on classical techniques such as Hidden Markov Models and Bayesian Networks, which are used to model situations with uncertainty in data.

The emergence of deep neural networks has revolutionized AI, allowing for a new type of generative intelligence that surpasses earlier models.

Types of Deep Generative Models

There are four main types of deep generative models based on deep learning principles. Each serves distinct purposes and applications.

Generative Adversarial Networks (GANs)

GANs consist of two competing neural networks: a generator that creates data and a discriminator that evaluates its authenticity.

The generator aims to produce realistic data while the discriminator learns to distinguish between real and fake data, improving through competition.

An example includes training GANs to generate images or audio indistinguishable from real ones, showcasing their potential in creating convincing media.

Variational Autoencoders (VAEs)

VAEs utilize neural networks to encode input data into a lower-dimensional latent space, generating samples with similar probabilistic distributions as the original data.

They can reconstruct missing parts of images by predicting what should be there based on existing information, useful for tasks like restoring old photographs.

Transformers

Transformers employ an attention mechanism that converts text into numerical tokens, enabling context learning from sequences of data.

This architecture is pivotal in natural language processing applications like ChatGPT, marking significant advancements over previous methods used in the 80s and 90s.

Understanding Generative Pre-trained Transformers

Overview of GPT Architecture

The program generates text that appears human-like by converting numbers into words, despite the complexity of its architecture.

GPT stands for Generative Pre-trained Transformers, utilizing a transformer architecture trained on vast amounts of unlabelled data over several months.

The goal is to produce novel and coherent text that mimics human writing by predicting the most probable tokens based on existing context.

Training and Cost Implications

Large language models (LLMs), like ChatGPT, can have up to 80 billion parameters, making their training both time-consuming and expensive—costing hundreds of millions of dollars.

Training requires supercomputers and careful curation of information; only a few individuals globally possess the expertise to create a language model from scratch.

Industry Competition for Talent

There is intense competition for skilled professionals in AI development, with companies offering exorbitant salaries to attract talent due to the limited number of qualified individuals.

A notable incident involved Meta attempting to recruit OpenAI employees with offers comparable to professional athletes' salaries.

Diffusion Models in AI

The latent diffusion model also employs transformer architecture but focuses on generating stochastic data through iterative refinement processes.

This method learns complex data distributions without adversarial training, primarily used for high-quality image synthesis.

Challenges in Image Recognition

Traditional algorithms struggled with basic image recognition tasks that humans find easy; they often misidentify objects within images.

These algorithms generate noise initially, complicating accurate object recognition as they rely solely on numerical data rather than visual perception.

Differences Between Generative and Discriminative Models

Generative models like ChatGPT require large amounts of unlabelled data for training compared to discriminative models which use smaller labelled datasets.

Techniques such as reinforcement learning are popular in generative models, avoiding costly labelling processes while still achieving effective results.

User Interaction with AI Systems

Users interact with generative systems via prompts; those who craft effective prompts are known as prompt engineers—a role currently in high demand.

Understanding Generative AI Systems

Outputs of Generative Models

The outputs from generative systems can vary widely, including text, video, and audio. It's crucial to recognize that these outputs are probabilistic rather than deterministic.

Users may receive different responses for the same input due to the nature of generative models, which can sometimes produce incorrect or unexpected results. This phenomenon is often referred to as "hallucinations" in computing contexts.

Nature of Generative Models

Generative models do not store information in a database; instead, they generate tokens based on probabilities, leading to potential inaccuracies in their outputs. Users should be cautious about relying on these systems for precise information.

The architecture of generative systems consists of three layers: model layer, connection layer, and application layer. These layers define how external entities interact with the system and its data sources (public vs private).

Data Privacy Concerns

There are significant concerns regarding the use of private data without consent in training generative AI applications, raising issues related to copyright violations. This topic will be explored further later in the discussion.

Types of Generative Models

The model layer includes deep generative models (DGM) that can perform general tasks (like Chat GPT) or specific tasks (like Codebert for programming). General models handle a wide range of queries while specific models focus on particular domains.

Applications built on proprietary models do not allow users access to underlying code but provide solutions directly (e.g., Mid Journey). In contrast, open-source applications enable co-creation by allowing others to build upon existing codebases (e.g., Jasper).

Integration and Application Layers

Proprietary systems like Chat GPT are fully integrated but restrict user modifications due to lack of access to source code; however, open-source alternatives like Deeps allow for customization and development by users.

Applications can either utilize proprietary APIs or leverage open-source frameworks for additional functionalities while being mindful of copyright implications when incorporating external data into their processes. For instance, uploading confidential documents could lead to copyright infringements if shared with AI tools like Chat GPT without proper authorization.

Users must exercise caution when using generative AI tools with sensitive materials such as unpublished research articles or copyrighted novels since doing so may violate copyright laws even if the content remains inaccessible to others after submission.

Generative AI: Tools and Applications

Overview of Generative AI Tools

Discussion on the availability of books written using tools like ChatGPT, highlighting the ongoing controversy surrounding AI-generated content.

Introduction to image generation applications that utilize descriptions to create visuals, emphasizing that ChatGPT serves as an interface while other programs generate images.

Mention of popular uses in marketing and art, including a notable Coca-Cola short film created entirely with AI technology.

Video Creation and Virtual Avatars

Explanation of how text can be transformed into synthetic videos featuring avatars, which are fictional representations similar to those in the movie "Avatar."

Reference to virtual news anchors used in radio broadcasting; however, their success on television was limited.

Introduction of Sintesia as a tool for creating professional videos with virtual avatars.

Code Generation and Error Detection

Overview of tools like Codeb or Graph Code that can generate source code from natural language or identify errors within existing code.

Mention of Microsoft's Copilot platform integrating these capabilities for practical use in programming tasks.

Voice Synthesis and Music Generation

Discussion on realistic voice modeling technologies by Microsoft that raise ethical concerns regarding consent for using someone's voice without permission.

Introduction to Google's Music LM application for music generation alongside AlphaFold's groundbreaking work predicting protein structures.

Genetic Research Advancements through AI

Highlighting AlphaGenom's ability to predict genetic variants from minimal DNA data, potentially revolutionizing understanding and treatment of genetic diseases.

Global Perspectives on Generative AI Adoption

Optimism in Asia-Pacific Region

Notable optimism towards generative AI among populations in Asia-Pacific, particularly China, where 68% believe it has a positive impact compared to 57% globally.

Concerns Over Data Privacy

Growing concerns about data collection practices associated with generative AI technologies despite previous indifference towards privacy issues.

Patent Activity Comparison

Significant patent activity related to generative AI is dominated by China (38,000 patents), contrasting sharply with the United States' 6,276 patents between 2014 and 2023.

Daily Usage Among Younger Generations

Survey results indicating high daily usage rates (60%) of generative AI tools among individuals born after 2000 in China.

Generative AI: Enthusiasm and Disillusionment

Youth Perception of Generative AI

A significant majority of young individuals (post-2000) have a positive view of generative content, with less than 3% expressing dislike or hatred for it.

In China, there is a strong admiration for generative AI despite the official blocking of ChatGPT, indicating a complex relationship with technology.

Corporate Adoption and Challenges

By mid-2025, many companies that adopted generative AI faced challenges as projects did not yield expected results, leading to fears about job losses.

Analysts refer to this period as the "phase of disillusionment," where initial promises of generative AI fell short in practical applications.

Quality Concerns in Code Generation

Companies reduced their programming staff significantly, expecting generative tools to replace human programmers; however, these tools failed to match the quality produced by humans.

Bias and Ethical Implications

The issue of bias in AI-generated content has become prominent; historical hiring patterns influenced Amazon's use of generative AI for recruitment, resulting in biased outcomes favoring white males.

Experts highlighted that biases stem from training data rather than the algorithms themselves, prompting discussions on how to mitigate such issues moving forward.

Transparency and Accountability

There are growing concerns regarding transparency in generative AI systems; questions arise about how users can be informed when interacting with generated content.

Scientific journals now require disclosures on whether generative AI was used in research submissions to ensure accountability and transparency.

Alleviating Errors and Misuse

Generative models are prone to "hallucinations" or errors due to their probabilistic nature; strategies are needed to integrate human oversight into these processes effectively.

Instances of fraud using deepfake technology highlight potential misuse; scammers impersonate executives through generated videos, raising alarms about security vulnerabilities associated with generative technologies.

Regulatory Responses and Future Directions

The social impact of false information generated by AI raises critical questions about responsibility for misleading content like fake news or images. Who is accountable?

In July 2023, major tech companies including OpenAI signed a voluntary agreement with the Biden administration requiring them to watermark outputs from their generative models for authenticity verification purposes.

Regulations on AI Training Data and Copyright Issues

Overview of AI Regulations in Different Regions

The U.S. requires reporting to the federal government when training certain high-impact models, though "high impact" is not clearly defined.

The European Union's AI Act mandates disclosure of materials used for generative systems, even if copyrighted, and includes regulations for watermarking outputs.

China has implemented measures to regulate public use of generative services, explicitly excluding governmental use from these regulations.

Ethical Guidelines and Copyright Concerns

On January 29, a declaration of ethics and best practices for AI development was presented in Mexico; it serves as a guiding framework but lacks legal status.

Companies like ChatGPT and MidJourney assert their outputs do not violate copyright laws, despite claims that they can produce near-identical copies of copyrighted images.

Legal Challenges Related to Copyright

Getty Images sued Stability AI for using its images without permission to train Stable Diffusion; similar lawsuits have been filed against Microsoft and OpenAI by the New York Times.

The core issue revolves around whether content produced with generative tools can be considered original or infringing on copyrights due to lack of authorization.

Precedents in Copyright Law

In the U.S., there are precedents affecting copyright registration for generative works; previous attempts were denied based on cases where non-humans created content (e.g., monkeys taking selfies).

A recent guide from the U.S. Copyright Office indicates that some generative products may be registered if human users control creative elements.

Future Directions in Latin America

On February 10, 2026, TAM GPT was introduced as a Latin American version of ChatGPT developed in Chile with collaboration from over 60 institutions.

This initiative aims to address gaps in information relevant to Latin America that are often overlooked by existing models developed elsewhere.

AI Generativa y su Impacto en Latinoamérica

Datos de Entrenamiento y Especialización Regional

La IA generativa se ha entrenado principalmente con datos de Latinoamérica, lo que la hace especializada para esta región. Se anticipa que habrá versiones similares para otros países como China.

Consumo Energético de la IA

Estudios indican que el consumo eléctrico necesario para sostener el crecimiento de la IA se duplica aproximadamente cada 100 días, lo que plantea preocupaciones sobre su sostenibilidad.

Actualmente, el consumo energético relacionado con la IA crece entre un 26% y un 36% anualmente; se estima que para 2028 podría superar el consumo total de electricidad de Islandia.

Para 2029, se proyecta que la IA generativa consumirá el 1.5% del total de electricidad del planeta, mientras que actualmente consume alrededor del 12% de la electricidad en EE.UU.

Impacto Ambiental

El impacto ambiental es una preocupación creciente; por ejemplo, Microsoft utilizó aproximadamente 700,000 litros de agua para enfriar servidores durante el entrenamiento de GPT-3.

Durante las interacciones con aplicaciones como ChatGPT, se estima que una conversación puede consumir agua equivalente a una botella de 500 ml debido al enfriamiento necesario.

Efectos en el Aprendizaje Humano

Un estudio publicado en Nature indica que usar ChatGPT puede mejorar ciertos procesos educativos; sin embargo, otro estudio del MIT mostró efectos negativos en las capacidades neuronales medibles al usar herramientas como ChatGPT.

En este estudio del MIT, los estudiantes usando ChatGPT mostraron disminuciones en habilidades lingüísticas y comportamentales debido a un fenómeno conocido como "efecto eco", donde no podían explicar sus propios ensayos.

Uso Deshonesto y Creación de Contenido Falso

Existen preocupaciones sobre el uso deshonesto de la IA generativa para tareas escolares o crear imágenes falsas (deep fakes), lo cual puede influir negativamente en la percepción pública.

Algoritmos utilizados en campañas políticas han demostrado ser capaces de generar noticias falsas e información engañosa a través de redes sociales.

Limitaciones Intrínsecas

Las aplicaciones generativas carecen de conciencia y no pueden mejorar su desempeño por sí solas; sus salidas son reconfiguraciones basadas en datos previos sin originalidad real.

La confiabilidad de las salidas generadas por estas aplicaciones es cuestionable ya que pueden producir textos falsos o inexactos.

Discussion on Generative AI and Its Implications

The Misuse of Generative AI

A case is presented where a student used Chat GPT to generate text with fictitious references, highlighting the potential for misinformation and academic dishonesty.

Emphasis is placed on the importance of not using generative AI as a substitute for professional advice, such as medical or psychological guidance.

Understanding the Role of Generative AI

The speaker argues that generative AI is designed to enhance human capabilities rather than replace them, countering fears rooted in science fiction narratives.

It is noted that generational shifts are occurring, with future generations being born into a world where these technologies are ubiquitous.

Ethical Considerations and Education

There’s a call for reinforcing ethical understanding among children and youth regarding technology use, stressing that prohibition isn't the solution; education about positive and negative impacts is crucial.

The speaker discusses how generative AI is revolutionizing various fields including healthcare and finance, urging individuals to stay informed about its implications.

Importance of Reliable Information

Individuals are encouraged to seek reliable sources when learning about new technologies instead of relying solely on tools like Chat GPT which may provide misleading information.

The need for trustworthy educational resources from universities or credible websites is emphasized to differentiate between myths and realities surrounding these technologies.

Balancing Technology Use

Concerns are raised about potential negative effects from misuse of technology while acknowledging its benefits in learning processes.

Historical parallels are drawn between current fears around AI and past concerns regarding internet usage during its early days.

Human Responsibility in Technology Usage

The discussion highlights that technological advancements should be viewed as tools meant to assist humanity rather than threats; misuse stems from human actions rather than inherent flaws in technology itself.

Conclusion & Audience Engagement

The speaker concludes by inviting questions from the audience after summarizing key points about generative AI's role in society.

Audience Feedback

An audience member expresses gratitude for the insightful summary provided during the talk, indicating a lack of historical context among younger individuals involved in AI discussions.

Another question arises regarding cultural acceptance of generative AI across different countries, specifically comparing China’s strict policies with Mexico's more relaxed approach.

Understanding Cultural Perspectives on Generative AI

Acceptance of Generative AI Across Cultures

The acceptance of generative AI varies significantly between cultures, particularly when comparing Eastern and Western societies. This difference is influenced by cultural idiosyncrasies and the level of information available to the public.

In China, government control over information may lead to a lack of awareness regarding negative aspects of generative AI, contrasting with the more open discourse in Western countries.

While there are valid concerns about generative technologies, misinformation prevalent on social media contributes to heightened distrust in the West. This irony highlights that greater access to information does not always equate to better understanding.

Information Filtering and Privacy Concerns

The need for critical filtering of information is emphasized; not all circulating data is reliable. Individuals must discern credible sources from misleading ones.

In Mexico, privacy regulations surrounding data usage are still developing. There is concern over how fake news and misinformation can proliferate without strict legal frameworks.

Data Privacy Regulations: A Comparative Analysis

The lack of regulation in Mexico regarding data usage poses significant risks. Unlike Europe, where users can request deletion of their search history, Mexican users face unrestricted access by companies to personal data.

Many applications collect user data continuously without explicit consent. For instance, Facebook has been known to record conversations covertly for targeted advertising purposes.

User Responsibility and Legal Implications

Users often unknowingly consent to extensive data collection through lengthy disclaimers that few read thoroughly. This raises questions about individual responsibility in protecting personal information.

An example illustrates this issue: a popular app that aged photos was notorious for harvesting user data without clear disclosure, leading to its ban in several countries due to privacy violations.

Case Study: Legal Contracts and Consumer Rights

A notable case involving Disney highlights potential legal loopholes where consumers unknowingly waive their rights through contracts they accept without reading—illustrating the importance of understanding terms before agreeing.

Such cases underscore the necessity for clearer communication regarding consumer rights within digital agreements, as many individuals overlook critical details that could impact their safety or legal standing.

Discussion on Data Privacy and AI

The Role of Personal Data in Applications

The speaker discusses the importance of regulating the collection of personal data by applications, noting that while this is managed in several countries, it remains unregulated in Mexico.

Emphasizes that users must decide how much private information they are willing to share for personalized application experiences.

Human Interaction with Technology

A reference to Amazon's Alexa highlights how voice assistants can create a human-like interaction, which may lead to decreased reading habits among users.

The speaker expresses concern that reliance on technology like Alexa could diminish traditional reading practices, especially among younger generations.

Emotional Connections with AI

Mentions the film "Her," illustrating how emotional attachments can form between humans and AI, raising questions about dependency on technology.

Discusses a controversial case where an individual was negatively influenced by an AI application, leading to tragic consequences. This raises ethical concerns regarding the programming of such algorithms.

Ethical Considerations and Responsibility

Highlights the need for awareness around giving human-like characteristics to algorithms, stressing that these technologies should be seen as tools rather than replacements for human interaction.

Points out that responsibility lies with users who choose to depend on these technologies; it's crucial for society to educate children about appropriate usage.

Reading Habits and Technological Change

The speaker reflects on changing reading habits due to technological advancements like audiobooks and digital media.

Encourages students to explore physical books despite their preference for digital formats, emphasizing the importance of understanding traditional literacy alongside modern technology.

Discussion on Technology and Its Impacts

Concerns About Technology Addiction

The speaker compares potential addiction to Chat GPT with historical addictions like tobacco and alcohol, suggesting that daily use may become common but emphasizes the need for discernment in its application.

Water Usage in Supercomputing

A question is raised about the significant water consumption required for cooling supercomputers, particularly regarding a proposed center in Polanco. The speaker acknowledges concerns but clarifies that not all supercomputers require large amounts of water.

Cooling Methods for Supercomputers

The speaker explains that while some supercomputers do use water for cooling, many employ alternative methods. For instance, Microsoft's Azure uses various cooling techniques across multiple locations. Recycling of water is also mentioned as a common practice among data centers.

Environmental Impact of Data Centers

It is highlighted that all digital activities generate CO2 emissions and consume resources like water, challenging the perception of cloud computing as an ethereal solution without physical impact. The discussion points out that data centers are often located in remote areas to mitigate environmental effects.

Green Computing Initiatives

There is mention of emerging technologies aimed at reducing CO2 emissions and minimizing water usage in data centers, such as those being developed in colder climates like Norway to leverage natural temperatures for cooling purposes. This reflects a growing trend towards sustainable computing practices.

Government's Role in AI Regulation

Responsibility of Governments

The speaker asserts that governments have a crucial role not only in regulating AI but also in communicating associated risks to users, which many currently neglect to do effectively. This highlights a gap between technological advancement and public awareness.

Global Discussions on AI Risks

A recent UN panel focused on evaluating AI technology risks aims to create reports detailing these dangers, including electricity consumption and potential neurological impacts on youth from excessive technology use. This indicates an international effort to address rapid technological changes responsibly.

Need for Risk Assessment

Emphasizing the urgency of understanding AI's implications over the next 5–10 years, the speaker calls for proactive analysis before issues escalate beyond control, stressing the importance of informed decision-making by individual countries based on expert assessments provided by global discussions.

Examples from Other Countries

The discussion includes examples from China regarding regulations limiting cell phone usage among adolescents under 14 years old, contrasting it with other nations where enforcement might be less effective due to cultural differences around governance and compliance with regulations.

Exam Regulations and AI in Education

China's National Exam Measures

In China, a national exam for university admission is crucial for students' futures. The government has collaborated with generative AI companies to disable their apps during the exam period.

This measure aims to prevent cheating by ensuring that even if a phone is smuggled in, it cannot be used due to app deactivation during the exam.

Global Perspectives on AI Regulation

Unlike China, other countries lack official regulations or governmental information regarding the use of generative AI technologies in exams.

There is significant resistance in Western nations towards regulating technology, often leading to backlash rather than compliance with proposed regulations.

Importance of Risk Awareness

It is essential to inform society about the risks associated with new technologies. Institutions like UNAM could play a role in disseminating this information through educational capsules.

However, evaluating all potential risks remains challenging at this stage of technological development.

AI Adoption and Employment Concerns

Economic Implications of Generative AI

A participant notes that companies have realized that replacing human employees with generative AI does not yield expected benefits, especially in specialized sectors like finance.

Trust issues arise when sensitive data must be handled by AI systems; thus, industries remain cautious about fully adopting these technologies.

Current Applications and Limitations

Generative AI applications are primarily developed for personal use rather than industrial applications. Research areas still require more exploration regarding their capabilities.

Competitive Landscape of AI Technologies

The speaker compares the current state of generative AI to toothpaste brands—many options exist but serve similar functions. Companies like Google and OpenAI compete for dominance without significant differentiation.

Future Prospects: Quantum Computing and AI

Potential Economic Impact of an AI Bubble

Questions arise about whether an economic bubble related to AI could collapse sectors such as technology in the U.S., particularly concerning companies like Google that lead in resources and innovation.

Integration of Quantum Computing with AI

The discussion highlights Google's advancements in quantum computing and speculates on how combining this technology with generative AI could enhance capabilities post-bubble burst.

Quantum Computing and Its Implications

Concerns in Cryptography

The speaker notes that not all algorithms will benefit from quantum computing efficiency, highlighting a significant concern in the field of cryptography.

Traditional encryption methods like RSA could be easily decrypted by quantum computers, reducing security from thousands of years to mere minutes or hours.

A global agreement among banks aims to transition to post-quantum cryptography within five years due to these vulnerabilities.

Perspectives on AI and Quantum Computing

There are two main perspectives regarding AI's future with quantum computing: optimists believe it will lead to significant advancements, while pessimists warn of potential failures and setbacks.

The speaker suggests that both extremes may overlook the current nascent stage of technology, indicating a need for balanced expectations.

Ethical Dilemmas in Technology Use

The discussion shifts to ethical dilemmas surrounding technology misuse, emphasizing human responsibility over technological faults.

Autonomous vehicles raise questions about accountability when accidents occur—whether it's the manufacturer or the software itself that bears responsibility.

Legal Complexities and Future Predictions

The speaker expresses uncertainty about whether quantum computing will significantly enhance AI within the next decade but acknowledges ongoing technological evolution.

Historical examples illustrate how unregulated technology can lead to market crashes, stressing the importance of oversight in deploying new systems.

Challenges in Technological Regulation

Rapid advancements make it difficult for society to keep pace with emerging technologies; regulatory efforts often lag behind innovations like ChatGPT updates.

Changes made for safety or ethical reasons can lead to user dissatisfaction, as seen with recent modifications in ChatGPT's interaction style.

Government Initiatives

The speaker concludes by acknowledging government initiatives aimed at addressing these issues and hopes they result in effective legislation.

Discussion on AI Legislation and Events

Importance of Expert Consultation in AI Legislation

The speaker emphasizes the need for collaboration with AI specialists to draft legislation that is coherent and effective, avoiding any unusual or impractical provisions.

Environmental Impact Studies on AI

A question arises regarding the existence of environmental impact studies related to AI usage in Mexico, highlighting a gap in research and understanding of this technology's implications.

Acknowledgments and Greetings

The speaker expresses gratitude towards various individuals connected to the event, including Jesús Salas, Guillermo Vega, and others from Mexico City, fostering a sense of community among participants.

Upcoming Events Related to AI

An invitation is extended for an upcoming in-person event titled "The City as a Stage: Memory" featuring Felipe Leal on February 18 at 6 PM. This reflects ongoing engagement with topics surrounding urban development and memory.

Future Engagements on AI Topics

The speaker mentions that there will be more events focused on AI, with the next one scheduled for April 15, indicating a commitment to continuous dialogue about artificial intelligence advancements.