Universitat Internacional de Catalunya
AI in Audiovisual Communication
Other languages of instruction: English, Spanish,
Teaching staff
pbuhigas@uic.es
Introduction
The course "Introduction to Artificial Intelligence" offers a practical and multidisciplinary exploration of this field, aimed at students without prior technical knowledge. With a focus on deep understanding of AI and the ability to communicate with experts, the course covers the history, opportunities, and challenges of AI, as well as its applications in digital transformation and data science. The cross-disciplinary nature of this course makes it suitable for undergraduate students from any field of study. The course is designed to provide both the most innovative tools and a general understanding of the basic principles of AI, its specific applications in various fields, and the ethical challenges it entails.
By the end of the academic program, students will be equipped to anticipate the upcoming changes that AI technologies will bring to their environment and will understand how to respond appropriately, leveraging these technologies to their advantage. Additionally, the course will address the regulatory and ethical challenges surrounding AI technologies, enabling participants to assess their relevance within the context of their respective fields.
Pre-course requirements
No prior specific knowledge is required to take the course.
Objectives
- Provide the foundation for a comprehensive education in artificial intelligence and its role in digital transformation.
- Foster the development of skills to work in data science and computational thinking, including programming concepts.
- Instruct students to explore search engines, Machine Learning, and Large Language Models (LLMs).
- Provide training for the development of skills in content processing, information verification, and ethics in the use of AI.
- Offer education that enables students to analyze the ethical and social challenges associated with technology.
- Equip students with skills that offer a significant advantage in the job market.
Learning outcomes of the subject
At the end of the course, students will be able to:
Knowledge
- Discriminate reliable sources of information about AI.
- Be aware of the social challenges posed by AI.
Skill
- Manage information and automate processes to achieve greater efficiency and productivity in the use of data and tasks.
- Identify and evaluate the ethics and legality in the use of Artificial Intelligence and other technologies.
- Use Generative AI tools.
- Determine the best information verification tools for each circumstance.
Competence
- Apply AI knowledge in specific professional environments.
- Research and evaluate new AI technologies.
Syllabus
- INTRODUCTION TO ARTIFICIAL INTELLIGENCE
- Knowledge of AI. Definitions and paradigms
- Historical evolution, opportunities, and recent developments
- Development of Generative AI
- Digital Transformation, Data Science, and Computational Thinking
- Statistics and programming to understand AI
- Leading institutions and companies in AI development
- INFORMATION GENERATION
- Search engines
- Utilities of Google and Bing
- Strategies for information search and analysis
- Machine Learning and Language Models
- Machine Learning (ML) and Large Language Models (LLMs)
- Text generators; GPT-4 and Bard and their applications
- Multimodal AIs; Google Gemini
- Fundamentals of Transformers and neural networks
- Biases and operational limits in AI
- Effective creation of Prompts and their applications
- AI applications in Everyday Life
- Personal AI assistants: Specialization, popularity, and monetization
- Reliable information sources. Search and databases
- Social Media Ecosystem and its interaction with AI
- Conversational chatbots and oral information collection: Speech to Text programs
- CONTENT PROCESSING AND CREATION
- Processing of text and visual content
- Text processing tools, translation, and subtitling
- Automatic creation of graphics, images, and videos with prompts
- Visual art
- Autonomous creation of presentations with slides
- Interactivity and audio and voice synthesis
- Oral language: Text to Speech and its applications
- Conversational assistants, voice synthesis, and cloning
- Audio management and music creation with AI
- Virtual reality and immersivity
- Creation and use of avatars
- Bots for social media interaction
- Introduction to the metaverse and its applications
- VERIFICATION AND ETHICS IN AI
- Verification of information and content and fact-checking
- Toolset for verification and its characteristics
- Strategies for traceability and reverse search of digital information
- Methodologies for source validation and combating misinformation
- Legal and Regulatory Framework of AI
- The European framework on AI: privacy, intimacy, data protection
- Digital and copyright rights. Legal and ethical considerations
- Regulations on liability attribution in autonomous systems
- Ethical and social challenges of New Technologies
- The 3 levels of ethics in the technological society
- Ethical and social keys of the technological society
- Analysis of specific challenges and dilemmas
- Responsible and Sustainable Use of AI
- Recommendations for the ethical introduction and management of AI applications in professional and personal environments
- Sustainability and ethics of development: how AI can contribute to or harm a sustainable future
- AI Literacy: education and training for ethical and critical understanding of technology
Artificial Intelligence and Society
- Social and cultural impact of AI: effects on equity, inclusion, and social cohesion
- Contemporary ethical challenges: algorithmic discrimination, biases, and social justice
- Debate on the future of AI: long-term impact scenarios on society and humanity
- AI IN SPECIFIC CONTEXTS
- Characteristics of the students' field of knowledge
- Practical applications of AI in specific professional environments
- Deontology and professional ethics related to AI
- TRENDS AND FUTURE OF AI
- Analysis of trends and future prospects of AI
- Resources for updating knowledge on AI and emerging technologies
- FINAL PROJECT
- Development of a project that applies AI concepts in the student's field of study, with an emphasis on ethics and professional responsibility.
Evaluation systems and criteria
In person
REPORT (10%) (to be done in class) - The report must follow a pre-established format in the provided template and should be presented in an organized manner. It is intended to support the "assigned group" with:
a) the main ideas on the proposed topic, b) viewpoints and recent controversies, c) published articles that help focus the topic, d) links to useful resources to include in the article.
Each team will upload their report to the drive at the end of the class. Each weekly group report will be assessed by the professor. Reports not submitted will be given a zero score and will be averaged into this figure. This component will account for 10% of the final grade. Late submissions will not be accepted after the class has ended.
ARTICLE SUMMARY (20%) (to be done at home) - The weekly article should be approximately 800 words long. It must be enriched with contributions from the reports of each group uploaded to the course drive.
It should include a headline and an AI-generated photograph. Use Calibri font size 12 for the text, justified, and font size 16 bold for the headline, centered. The article must be uploaded to the corresponding folder in the drive no later than the Sunday following the class. Late submissions will not be evaluated. This component is worth 20% of the final grade.
PRESENTATION (10%) - Each week, an “assigned group” will present their article in class. They should use a maximum of 3 slides that provide an easy and visual summary of the topic. The value of this presentation is 10% of the final grade.
SYNTHESIS OF ARTICLES (20%) - A single document summarizing the content developed throughout the course must be submitted. This will serve as a basis for preparing the final test. It accounts for 20% of the final grade.
EXAMS (40%) - Two exams will be conducted during the course, combining multiple-choice questions and short-answer questions. One will be halfway through the syllabus and the other at the end of the course, which will serve as the final exam. The content for these evaluations will be drawn from class explanations and the articles developed throughout the course. They will consist of closed questions with three possible answers or short development questions. Mistakes in the test will deduct 0.25 points. The midterm exam will be worth 20% of the final grade, and the final exam will also be worth 20% of the final grade.
Bibliography and resources
Torres, J. (2023). La intel· ligència artificial explicada als humans. Plataforma.
Degli-Esposti, S. (2023). La ética de la inteligencia artificial. Los Libros de La Catarata.
Carretero, A. V. (2023). El último periodista. La inteligencia artificial toma el relevo. Marcombo.