Universitat Internacional de Catalunya
AI in Advertising
Other languages of instruction: English, Spanish,
Teaching staff
To meet with the professor, you must schedule an appointment by sending an email to 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
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Discriminate reliable sources of information about AI.
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Be aware of the social challenge posed by AI.
Skills
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Handle information and automate processes to achieve greater efficiency and productivity in data usage and tasks.
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Identify and evaluate the ethics and legality in the use of Artificial Intelligence and other technologies.
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Use Generative AI tools.
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Determine the best information verification tools for each situation.
Competence
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Apply AI knowledge in specific professional environments.
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Research and evaluate new AI technologies.
Syllabus
Knowledge of AI: Definitions and Paradigms
Historical Evolution, Opportunities and Recent Developments
Development of Generative AI
Digital Transformation, Data Science and Computational Thinking
Leading Institutions and Companies in AI Development
2. INFORMATION GENERATIONStrategies for Information Search and Analysis
Machine Learning and Language Models
Machine Learning (ML) and Large Language Models (LLMs)
Text Generators.
Multimodal AI.
Fundamentals of Transformers and Neural Networks
Biases and Operational Limitations in AI
Effective Prompt Creation and Its Applications
Applications of AI in Everyday Life
Personal AI Assistants: Specialization, Popularity and Monetization
Reliable Information Sources. Search and Databases
The Social Media Ecosystem and Its Interaction with AI
Conversational Chatbots and Oral Information Collection: Speech-to-Text Programs
3. CONTENT PROCESSING AND CREATIONText and Visual Content Processing
Text Processing, Translation and Subtitling Tools
Automatic Creation of Graphics, Images and Videos with Prompts
Visual Art
Autonomous Creation of Slide Presentations
Interactivity and Audio and Voice Synthesis
Spoken Language: Text-to-Speech and Its Applications
Conversational Assistants, Voice Synthesis and Cloning
Audio Management and AI Music Creation
Virtual Reality and Immersion
Creation and Use of Avatars
Bots for Interaction on Social Media
Introduction to the Metaverse and Its Applications
4. AI VERIFICATION AND ETHICSInformation and Content Verification and Fact-Checking
Verification Toolbox and Its Features
Strategies for Traceability and Reverse Search of Digital Information
Methodologies for Source Validation and Combating Disinformation
Legal and Regulatory Framework for AI
European AI Framework: Privacy, Confidentiality and Data Protection
Digital and Copyright. Legal and Ethical Considerations
Regulations on the Attribution of Responsibility in Autonomous Systems
Ethical and Social Challenges of New Technologies
The 3 Levels of Ethics in a Technological Society
Ethical and Social Keys to a 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 an 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 for Society and Humanity
5. AI IN SPECIFIC CONTEXTSCharacteristics of the Field of Knowledge.
Practical Applications of AI in Advertising, Marketing and Public Relations.
Professional Ethics and Deontology Related to AI
6. AI TRENDS AND THE FUTURE OF AIAnalysis of AI Trends and Future Foresight
Resources for Keeping Knowledge Up to Date in AI and Emerging Technologies.
Teaching and learning activities
In person
Theory will be combined with practical exercises, differentiating application cases according to areas of knowledge.
1. Theoretical Classes:
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Lectures to introduce the theoretical foundations of artificial intelligence, digital transformation, and associated ethics.
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Detailed analysis of practical cases illustrating the application of AI in various sectors and its impact on society.
2. Practical Sessions:
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Practical exercises to foster the development of skills in data science and computational thinking.
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Working with Generative AI tools and practical applications for problem-solving.
3. Study of Ethical Cases:
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Detailed analysis of ethical cases related to the use of artificial intelligence.
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Debates and class discussions to encourage reflection on the ethical and social aspects of AI.
4. Applied Projects:
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Development of projects that apply the knowledge acquired to simulated professional situations.
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Integration of information verification tools and reliable source analysis into project implementation.
6. Continuous Assessment:
- Periodic assessment of progress through assignments, projects, class participation, and examinations evaluating the application of the concepts learned to specific cases.
7. Online Resources:
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Use of online platforms to access additional resources, case studies, and supplementary materials.
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Encouragement of self-directed learning and research to explore specific topics in greater depth.
Evaluation systems and criteria
In person
WEEKLY REPORTS (30%). The weekly report should be approximately 800 words long. It must include: a) the main ideas related to the proposed topic; b) recent points of view and controversies; c) published articles that help to contextualize and focus on the topic; d) links to useful resources to incorporate into the article. It should be enriched with the discussions held in class and the team’s reflections on the topics covered each week.
It must include a headline and an AI-generated photograph. The text should use Calibri, size 12, justified, and the headline should be in bold, size 16, and centered. The report must be uploaded to the corresponding topic folder in the shared class Drive no later than midnight on the day before the next class. Late submissions will not be assessed.
PRESENTATION (10%) – Each week, an “assigned group” will present its report in class. The presentation should be based on a maximum of 5 slides that provide an easy-to-understand and visual summary of the topic.
ARTICLE SYNTHESIS (10%) – Each group will submit a single document summarizing the content covered throughout the course. This document will serve as a basis for preparing the second test.
TESTS (50%) – Two tests will be conducted during the course, combining multiple-choice questions and short-answer questions. One will take place halfway through the syllabus and the other at the end of the course. The content of these assessment tests will be based on the explanations given in class and the reports prepared throughout the course. They will consist of multiple-choice questions with three possible answers or short-answer questions. Incorrect answers in the multiple-choice section will result in a deduction of 0.25 points. The first midterm test will account for 20% of the final course grade, while the second will account for another 30%. A minimum grade of 4 is required in order to average the test results with the rest of the grades.
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.