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2 September 2024
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2 September 2024

Introduction to the Conference

Welcome and Announcements

  • The School of Language Sciences welcomes attendees to the first activity of the conference series.
  • Important announcements include a prohibition on eating in the arts center due to upcoming graduations and restrictions on cellphone use during presentations.

Context of Discussion

  • A report from the World Economic Forum (2018) predicts that automation will eliminate 25 million jobs by 2025 but create 133 million new roles, highlighting a mismatch between displaced workers and new job requirements.
  • Emphasis is placed on learning from history while analyzing current contexts, including regional and institutional challenges related to unemployment.

Panelist Introductions

Overview of Panelists

  • Irina Sancho Chavarría: Computer engineer with extensive experience in software engineering for public institutions; currently coordinates a master's program in computing.
  • Sa Calderón Ramírez: Expert in digital signal processing and machine learning; involved in biomedical image analysis initiatives.
  • Juan Luis Crespo Mar: Senior member of various professional organizations; has published extensively on systems engineering and automation.
  • Mauricio Arroyo Herrera: Experienced in technology project management; focuses on sustainable cities as part of his doctoral research.

Discussion Topic: Digital Transformation

Key Concepts

  • The panel will discuss digital transformation as an overarching concept, setting the stage for deeper dives into artificial intelligence and data visualization.

Transformation Digital y su Impacto en la Sociedad

Cambios en la Producción y Consumo

  • La transformación digital está modificando fundamentalmente cómo vivimos, trabajamos y nos relacionamos como sociedad.
  • Se prevé que los negocios se centren más en producir lo que el cliente realmente desea, eliminando técnicas de mercadeo tradicionales.
  • Los clientes podrán personalizar productos a través de proveedores virtuales, utilizando tecnología avanzada para crear un "gemelo virtual" que refleje sus necesidades.

Innovaciones Tecnológicas

  • La producción se adaptará a las especificaciones del cliente, permitiendo una fabricación bajo demanda mediante impresión 3D y automatización con robots.
  • Cuatro áreas clave están convergiendo: biología (Bio), cognición (Cognos), nanotecnología (Nano) e información (Info), creando nuevas posibilidades en la economía digital.

Digitalización de Datos Biológicos

  • La digitalización permite almacenar datos biológicos como ADN en formatos manejables, abriendo la puerta al diseño de seres humanos.
  • Los datos se convertirán en insumos fundamentales para el trabajo económico, transformando cómo se genera valor.

Tendencias Futuras

  • Garner menciona una red digital inteligente como tendencia general; esto incluye vehículos autónomos que aumentarán su capacidad operativa significativamente para 2021.
  • Se espera que el 10% de los nuevos vehículos sean autónomos, comparado con solo el 1% en 2017.

Colaboración entre Dispositivos Autónomos

  • Los dispositivos autónomos no solo funcionarán individualmente sino también colaborarán entre sí formando "enjambres", mejorando su eficiencia operativa.
  • El Internet de las Cosas (IoT) evolucionará hacia dispositivos inteligentes capaces de generar grandes volúmenes de datos y colaborar entre ellos.

Analítica Aumentada

  • La analítica aumentada permitirá a ciudadanos acceder a herramientas avanzadas como Watson para análisis de datos sin necesidad de ser expertos científicos.

Understanding the Impact of Artificial Intelligence

The Role of AI in Hypothesis Testing and Precision Agriculture

  • The integration of artificial intelligence (AI) allows for testing 22,000 hypotheses in just five seconds, significantly enhancing fields like precision agriculture.
  • AI-driven development will foster interdisciplinary collaboration among scientists to create mathematical models that address business challenges.

Immersive Digital Experiences and Human-Machine Interaction

  • Future advancements may enable the digitalization of human senses, transforming interactions between humans and machines or algorithms.
  • This evolution raises concerns about digital ethics and privacy due to the increasing number of connections facilitated by IoT devices.

Ethical Considerations in a Digitally Connected World

  • The proliferation of IoT devices can track personal behaviors and thoughts, leading to significant ethical implications regarding privacy.

Education's Role in Shaping Citizens

  • Emphasizes the importance of education in forming well-rounded citizens rather than merely training professionals with technical skills.

Defining Intelligence and Artificial Intelligence

  • Questions arise about what constitutes intelligence and how it relates to artificial intelligence; it's crucial to differentiate between current capabilities and future possibilities.
  • Acknowledges misconceptions surrounding AI, clarifying that discussions should not revolve around dystopian scenarios but focus on understanding true intelligence.

Exploring the Concept of Intelligence

  • Discusses various definitions of intelligence from different perspectives, emphasizing problem-solving abilities and experiences as key components.

Albert Einstein as an Icon of Intelligence

Understanding Intelligence Through Examples

The Case of a Female Football Player

  • The speaker discusses the behavior of a female player from FC Barcelona, emphasizing that her actions can be understood as intelligent without needing complex mathematical or physical derivations.

Defining Intelligence in Dynamic Environments

  • Intelligence is described as the ability to react and adapt to changing environments in real-time, challenging the notion that such behavior cannot be classified as intelligent.

Engineering Challenges in Replicating Human Behavior

  • The speaker poses a challenge to engineers: create an artificial solution that can react similarly to unpredictable actions of other players, highlighting the complexity of human intelligence.

Recognition and Information Processing

  • A question about recognizing a person through different life stages illustrates the complex cognitive processes involved in associating sensory information with prior knowledge, exemplified by identifying actor Jack Nicholson.

Collective Behavior in Nature: Ants as an Example

  • The discussion shifts to ants, which efficiently gather food without prior planning or communication. This showcases an innate form of intelligence based on collective behavior rather than individual cognition.

Language Comprehension Beyond Definitions

  • Understanding language requires more than just knowing vocabulary and grammar; it involves deeper cognitive abilities that allow individuals to grasp meaning intuitively.

Problem-Solving Methodologies

  • Emphasizing the importance of methodologies for solving objective problems rather than subjective questions, illustrating how some issues require complex solutions beyond analytical methods.

Flexibility and Autonomy in Problem Solving

  • Effective problem-solving necessitates flexibility and autonomy while managing uncertainty within engineering contexts, particularly when discussing artificial intelligence technologies.

Characteristics of Artificial Technologies

  • The speaker stresses that discussions around artificial intelligence do not need to focus solely on robots or ethical consciousness but should consider broader technological characteristics relevant for future developments.

Introduction to Group Parma's Work

Introduction to the Research Group

Overview of the Group's Purpose

  • The group is relatively new, having been active for 2-3 years, and consists of various professors and students from undergraduate to postgraduate levels collaborating on multiple projects.
  • The main objective is to stimulate research and reflection on artificial intelligence (AI) and machine learning topics, positioning the institution as an active contributor in proposing solutions.

Collaborative Efforts

  • Approximately 12 professors from different disciplines are involved in the group, including notable members like Mauricio, Juan Luis, and Liliana.
  • The approach aims to tackle complex problems by creating intelligent systems that learn automatically through a transdisciplinary lens.

Key Areas of Focus

National Context and Importance

  • Three significant lines of research have been identified:
  • Machine learning applied to biodiversity protection.
  • Precision agriculture technology development.
  • Precision medicine initiatives.

Biodiversity Protection

  • Costa Rica hosts about 5-6% of global biodiversity within its territory; thus, addressing climate change challenges is crucial for survival.

Agriculture Technology Development

  • Given Costa Rica's strong agricultural sector, there’s a need for developing smart technologies for more precise farming practices known as precision agriculture.

Applications in Precision Agriculture

Recent Projects

  • The public health system in Costa Rica presents opportunities for impactful research in precision medicine due to its exemplary status compared to other Latin American countries.

Example Project: Chemical Property Estimation

  • A project with Earth University focused on estimating soil chemical properties using multispectral images captured by drones. This method allows quicker decision-making regarding fertilization schemes and crop productivity assessments.

Efficiency Improvements

  • Traditional methods involve labor-intensive soil sampling; however, this drone-based approach enables real-time data collection for better agricultural decisions.

Technological Innovations

Plant Identification Application

  • Collaboration with Professor Eric Mata led to the development of an application called "Plannet," which identifies plant species from images. This tool aids both professionals and laypeople in recognizing various plant species efficiently.

Understanding Machine Learning

Core Concept Explanation

  • Machine learning involves constructing mathematical models based on datasets. For instance, distinguishing between fish types using image analysis relies on parameters such as width and skin clarity.

Mathematical Modeling Process

Understanding Machine Learning and Its Applications

Key Concepts in Machine Learning

  • The model parameters, specifically values m and b, are crucial for adjusting a line to minimize classification error, effectively separating different sample types (e.g., róbalo and salmón).
  • Optimization techniques, such as derivatives, are essential for finding optimal parameters in machine learning models.

Medical Image Analysis Projects

  • A current project aims to segment cells over time to help microbiologists understand tissue reactions to specific chemotherapy treatments, addressing the complexity of cancer as a group of diseases.
  • Studying individual dynamics of cancerous tissues can significantly impact therapy formulation success rates.

Practical Applications in Medicine

  • Another application involves automatically estimating bone age from X-ray images, aiding endocrinologists and pediatricians who typically perform this task manually.
  • This automated system enhances efficiency by providing quick assessments that inform growth disorder evaluations.

The Future of Technology in Healthcare

  • There is immense potential for technology convergence to automate processes within healthcare settings, improving efficiency for professionals like pediatricians.
  • Automating tasks such as bone age estimation could drastically reduce the time spent on manual assessments from half a day to mere seconds.

The Role of Artificial Intelligence in Human Interaction

Perspectives on AI Development

  • Discussion focuses on the current state and future directions of artificial intelligence and its interaction with humans.

Adapting Technology for Users

  • The importance of designing technology that aligns with human needs is emphasized; historically, users have had to adapt to technological constraints.

Evolution of User Experience

  • Over time, advancements like ergonomic designs have improved user experiences with technology.
  • The ongoing question remains whether technology is designed for humans or if humans must adapt their behaviors around existing technologies.

Historical Context of Technological Advancements

  • Since the 1980s, there has been a shift towards more user-friendly designs in technology development.

The Impact of Artificial Intelligence on User Experience

The Amplification of Human Capabilities

  • The advent of artificial intelligence (AI) amplifies human capabilities, transforming user experiences and emphasizing a shift towards more personalized technology centered around individuals.

Personalization Through Data

  • Technology development is increasingly driven by known information about users, leading to enhanced personalization based on previous experiences during online activities like shopping or social media interactions.

Machine Learning and Deep Learning Advances

  • Current advancements in machine learning and deep learning enable sophisticated personalization techniques, allowing for tailored content delivery based on user behavior across different platforms.

Evolution of Recognition Technologies

  • Historical challenges in voice recognition highlight the evolution of technology; past failures due to environmental noise contrast sharply with today's reliable systems that can understand diverse accents and commands.

Future Expectations in Human-Computer Interaction

  • The focus is shifting towards improving human-computer interaction through natural language processing, accommodating various accents and dialects as AI becomes more integrated into daily life.

The Role of Intelligent Agents in Daily Tasks

Automation of Routine Tasks

  • Intelligent agents are expected to automate routine tasks such as scheduling appointments, significantly reducing the burden on users by managing these tasks autonomously.

Smart Home Integration

  • Future smart home technologies may include refrigerators that track inventory and automatically order groceries via drones, showcasing how once-impossible ideas are becoming reality through technological advancement.

Skills for the Future Workforce

  • As AI evolves, there will be an increased demand for skills related to programming and emotional intelligence, indicating a shift in educational focus toward understanding human perception alongside technical knowledge.

Technological Revolution: Employment Concerns

Changing Nature of Employment

  • Acknowledgment that significant changes in employment landscapes are imminent due to technological advancements; those who deny this change are misleading others about future job markets.

Conclusion: Embracing Change

Impact of Technological Revolution on Employment

The Nature of Change in Employment

  • Acknowledges that a technological revolution is underway, similar to past revolutions, but with heightened awareness of its implications.
  • Discusses the potential disappearance of many jobs while new roles may emerge, particularly in technology and care sectors.

Automation and Job Creation

  • Highlights the necessity for skilled individuals to design, program, implement, sell, and maintain AI systems and robots.
  • Points out that modern tools like Google Maps have replaced traditional navigation methods without displacing workers directly.

Communication Skills in the Digital Age

  • Emphasizes the importance of communication skills when interacting with machines as we do with people.

Historical Context: Industrial Revolutions

  • Introduces perspectives on how digital transformation differs from previous industrial revolutions; it could lead to reduced work hours rather than job loss.
  • Raises concerns about power dynamics where machine control might be concentrated among a few individuals.

Future Workforce Considerations

  • Suggests a possible scenario where significant portions of the population may become unemployed due to automation advancements.

Implications for Current Workers

  • Questions what will happen to taxi drivers when autonomous vehicles become mainstream and discusses their precarious employment status.
  • Notes historical patterns where job losses during industrial changes necessitated political responses for workforce management.

Reflection on Work Structures

Discussion on the Fourth Industrial Revolution

Perspectives on Retirement Age and Technological Impact

  • The speaker expresses skepticism about increasing the retirement age, arguing that advancements in technology allow for greater productivity with less human effort.
  • Emphasizes the need for political and organizational responses to adapt to these technological changes, suggesting a return to philosophical discussions in academia.

Transportation Disruption Examples

  • References Juan Luis's point about education fostering better citizenship, linking it to transportation examples which illustrate societal shifts.
  • Discusses potential future scenarios where traditional driving licenses may become obsolete due to innovations like Uber, highlighting disruptions caused by the Fourth Industrial Revolution.

Future Work Dynamics

  • Suggests that job structures will change significantly; fixed career paths may no longer exist as people will likely experience multiple career shifts throughout their lives.
  • Speculates on urban living arrangements evolving, potentially reducing commuting needs and allowing for more flexible work hours.

Call for Reflection and Adaptation

  • Urges listeners to remain open-minded and reflective about impending changes in work-life balance and community engagement.

Questions About Defining the Fourth Industrial Revolution

  • Invites questions from the audience regarding milestones marking the onset of the Fourth Industrial Revolution.

Identifying Key Milestones of Change

  • Asks panelists what specific event or development they believe signifies the definitive start of this new industrial era.
  • One panelist argues that significant technological capabilities have already marked this transition, similar to how steam engines signified earlier revolutions.

Current Technological Capabilities

  • Highlights that modern computing power allows us to tackle complex problems involving uncertainty—an essential characteristic of this revolution.

Evolution of Computing Technology

  • Discusses how computing has evolved beyond large machines; now individuals can access powerful computational resources through personal devices connected globally.

Recognizing Consolidated Changes

The Role of Data in the Fourth Industrial Revolution

Importance of Connected Devices

  • Discussion on how connected devices, like smart chairs, will generate data and provide warnings about incorrect usage. This exemplifies the integration of technology into daily life as a hallmark of the fourth industrial revolution.

Challenges in AI Development

  • Inquiry into the challenges faced by those looking to develop applications in artificial intelligence (AI), particularly regarding high-demand technologies such as web and mobile applications.

Cost and Business Models in AI

  • Explanation of how costs for developing applications are typically based on time estimates for project completion, contrasting this with the complexities involved in training neural networks.

Training Neural Networks

  • Emphasis on the necessity to train neural networks using various algorithms (e.g., gradient descent, Newton's method), highlighting uncertainty around iteration counts needed to minimize errors.

Data Requirements for Model Training

  • Insight into deep learning's reliance on large datasets for effective model training. The discussion points out that having sufficient computational power can facilitate algorithm training but emphasizes patience is required during this process.

Complexities in Medical Data Collection

Challenges in Medical Environments

  • Acknowledgment that generating datasets for medical AI tools is complex due to high costs associated with professionals' time (e.g., radiologists).

Resistance from Medical Professionals

  • Mention of resistance from medical communities towards data generation for AI tools, stemming from fears of job replacement among professionals like radiologists.

Conclusion and Call to Action