Universitat Internacional de Catalunya

AI in Advertising

AI in Advertising
3
15504
4
First semester
op
Main language of instruction: Catalan

Other languages of instruction: English, Spanish

Teaching staff


Dr. BUHIGAS CARDÓ, Pere - pbuhigas@uic.es

pbuhigas@uic.es

Introduction

The "Introduction to Artificial Intelligence" course offers a practical and multidisciplinary exploration of this field, aimed at students with no prior technical knowledge. With a focus on gaining a 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 transversal 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 concrete applications in various fields, and the ethical challenges it poses.

By the end of the academic program, participants will be equipped to anticipate the changes that AI technologies will bring to their environment, as well as understand how to react appropriately, capitalizing on these technologies for their benefit. The course will also address the regulatory and ethical challenges surrounding AI technologies, enabling participants to assess their relevance in the context of their respective fields.

Objectives

  • Provide the foundation for comprehensive training on artificial intelligence and its role in digital transformation.
  • Promote 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 (LLM).
  • Provide training to develop skills in content processing, information verification, and ethics in AI usage.
  • Offer training that enables students to analyze the ethical and social challenges associated with technology.
  • Equip students with skills that provide 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 challenge posed by AI.

Skills

  • Handle information and automate processes to achieve greater efficiency and productivity in data usage 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 situation.

Competence

  • Apply AI knowledge in specific professional environments.

  • Research and evaluate new AI technologies.

Syllabus

  1. 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

  1. 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 (LLM)

    • Text generators; GPT4 and Bard and their applications

    • Multimodal AIs; Google Gemini

    • Fundamentals of Transformers and neural networks

    • Biases and operational limits in AI

    • Effective prompt creation and its applications

  • Applications of AI in Daily Life

    • AI personal 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

  1. PROCESSING AND CONTENT CREATION

  • Text and visual content processing

    • Text processing, translation, and subtitling tools

    • 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 immersion

    • Avatar creation and usage

    • Bots for interaction on social media

    • Introduction to the metaverse and its applications

  1. VERIFICATION AND ETHICS IN AI

  • Information and content verification and fact-checking

    • Verification toolkit and its features

    • Strategies for traceability and reverse search of digital information

    • Methodologies for source validation and combating misinformation

  • Legal and Regulatory Framework of AI

    • European framework on AI: Privacy, confidentiality, data protection

    • Digital rights and authorship. 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

    • Key ethical and social issues in 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

  1. AI IN SPECIFIC CONTEXTS

  • Characteristics of students' field of study

  • Practical applications of AI in specific professional environments

  • Deontology and professional ethics related to AI

  1. TRENDS AND FUTURE OF AI

  • Analysis of trends and future forecasting of AI

  • Resources for updating AI knowledge and emerging technologies

  1. FINAL PROJECT

  • Development of a project applying AI concepts in the student’s field of study, with emphasis on ethics and professional responsibility.

Evaluation systems and criteria

In person



REPORT (10%) (to be done in class) – The report has a predefined format in the template and must be neatly presented. It will be designed to support the “assigned group” with:

a) the main ideas on the proposed topic,
b) recent viewpoints and controversies,
c) published articles that help focus the topic,
d) links to useful resources to include in the article.

Each team will upload it to the drive at the end of the class. Each of the weekly group reports will be graded by the teacher. Reports not submitted will receive a score of zero points and will average with this score. The total will account for 10% of the final grade. Late submissions after the end of class will not be accepted.

ARTICLE-SUMMARY (20%) (to be done at home) – The weekly article will be approximately 800 words long. It should be enriched with the contributions of each group’s reports deposited on the course drive.

It will include a headline and a photograph generated by AI. Calibri font size 12 should be used for the text, justified, and font size 16 bold for the headline, centered. It will be uploaded to the corresponding folder of the topic in the drive no later than the Sunday following the class. Late submissions will not be graded. This will account for 20% of the final grade.

PRESENTATION (10%) – Each week, an “assigned group” will present their article in class. They will use a maximum of 3 slides to 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 will be submitted, synthesizing the content of everything developed in the course and serving as the foundation to prepare for the final test. This is worth 20% of the final grade.

CONTROLS (40%) – Two controls will be conducted during the course, combining multiple-choice and short-answer questions. One will be in the middle of the course content, and the other at the end of the subject, which will be considered the final exam. The content of these assessment tests will be based on class explanations and the articles created throughout the course. They will consist of closed questions with three possible answers or brief development questions. Incorrect answers in the test will subtract 0.25 points. The midterm test will account for 20% of the final grade, and the final exam will account for another 20% of the final grade.

SECOND AND SUBSEQUENT EXAMS. A multiple-choice exam will be administered, and a document summarizing the content of the course will be submitted. The document must be approximately 5,000 words long.

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.

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