2022/2023
Artificial Intelligence in Communication
Code: 106672 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2503873 Interactive Communication | OT | 4 | 1 |
Contact
- Name:
- Francesc Xavier Ribes Guardia
- Email:
- xavier.ribes@uab.cat
Use of Languages
- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No
Prerequisites
To be able to take this subject it is necessary to have basic knowledge of the English language to face the reading of the bibliography.
Objectives and Contextualisation
Have an overview but complete of what is artificial intelligence, its possibilities and the application of these technologies in the field of communication.
1. Study, analysis and theory of artificial intelligence systems.
2. Machine learning, deep learning and data science.
3. Deep fakes.
4. Ethical principles, algorithms and biases.
5. Application of artificial intelligence systems to communicative spaces (content recommendation, device autonomy, image and video processing ...)
Competences
- Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values.
- Act within one's own area of knowledge, evaluating sex/gender-based inequalities.
- Determine and plan the technological infrastructure necessary for the creation, storage, analysis and distribution of interactive multimedia and social-networking products.
- Introduce changes in the methods and processes of the field of knowledge to provide innovative responses to the needs and demands of society.
- Manage time efficiently and plan for short-, medium- and long-term tasks.
- Promote and launch new products and services based on massive-scale mining and analysis of data from the Media.
- Search for, select and rank any type of source and document that is useful for creating messages, academic papers, presentations, etc.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
- Take account of social, economic and environmental impacts when operating within one's own area of knowledge.
Learning Outcomes
- Analyse a situation and identify its points for improvement.
- Communicate using language that is not sexist or discriminatory.
- Critically analyse the principles, values and procedures that govern the exercise of the profession.
- Cross-check information to establish its veracity, using evaluation criteria.
- Describe the infrastructure needed to store big data.
- Differentiate between the various types of existing architectures for working with big data.
- Distinguish the salient features in all types of documents within the subject.
- Evaluate the impact of problems, prejudices and discrimination that could be included in actions and projects in the short or medium term in relation to certain people or groups.
- Explain the characteristics of the infrastructure needed to recover big data.
- Explain the explicit or implicit deontological code in your area of knowledge.
- Explain the infrastructure needed to process big data.
- Extract large volumes of data from social networks and the new digital media in particular.
- Identify situations in which a change or improvement is needed.
- Identify the social, economic and environmental implications of academic and professional activities within one's own area of knowledge.
- Plan and execute academic projects in the field of big data.
- Propose new methods or well-founded alternative solutions.
- Propose projects and actions that are in accordance with the principles of ethical responsibility and respect for fundamental rights and obligations, diversity and democratic values.
- Propose projects and actions that incorporate the gender perspective.
- Propose viable projects and actions to boost social, economic and environmental benefits.
- Share experiences with the group as a path to learning, in order to work subsequently in multidisciplinary groups.
- Solve basic problems in big data.
- Submit course assignments on time, showing the individual and/or group planning involved.
- Weigh up the risks and opportunities of both one's own and other people's proposals for improvement.
Content
1. What is artificial intelligence (AI) and its characteristics
2. Data collection processes
2.1 Big Data: data generation
2.2 Sources
3. Introduction to different AI techniques
3.1 Data processing and application of algorithms
3.2 AI techniques
3.3 Results and interpretation of data for decision making
4. New horizons, as AI allows the creation of new content in communication
4.1 video creation
4.2 text creation
4.3 image creation
4.4 to creativity
Methodology
The acquisition of knowledge will be done through various methodological procedures that include different types of activities, grouped into: master classes, internships and seminars.
In the theoretical sessions, the contents of the program will be presented, thus providing the necessary elements to carry out the practical exercises in the laboratories.
As for the practices, they will be used to apply in real cases the knowledge acquired in the theoretical sessions. The seminars encourage critical reflection and debate on the analysis of real cases and models.
The detailed calendar and content of the different sessions will be presented on the day of presentation of the subject and will also be posted on the virtual campus where students can find the detailed description of the exercises and practices, as well as the various teaching materials and any information necessary for the proper follow-up of the subject. In the case of a change in teaching modality for health reasons, the teaching staff will inform of the changes that will take place in the programming of the subject and the teaching methodologies.
Note: 15 minutes of a class will be reserved, within the calendar established by the center / degree, for students to complete the surveys for evaluating the performance of teachers and evaluating the subject / module.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Laboratory practices | 12 | 0.48 | 1, 20, 12, 14, 13, 15, 23, 16, 19, 21 |
| Master classes with ICT support | 15 | 0.6 | 3, 1, 5, 6, 10, 11, 9, 14, 13, 8 |
| Seminars | 21 | 0.84 | 3, 1, 2, 4, 7, 14, 13, 23, 22, 16, 17, 18, 19, 8 |
| Type: Supervised | |||
| Theoric exam | 3 | 0.12 | 3, 5, 6, 10, 14, 21, 8 |
| Tutorials (individual or group face-to-face activity aimed at solving learning problems) | 10 | 0.4 | 1, 2, 7, 13, 22 |
| Type: Autonomous | |||
| Study: Reading and synthesis of scientific documents | 56 | 2.24 | 3, 1, 6, 14, 13, 17, 8 |
Assessment
The competencies of this subject are evaluated with different activities:
- Theoretical test (40% of the final grade)
- Group practice presentations (40% of the final grade)
- Delivery of individual works (20% of the final mark)
The final grade will be the sum of the score obtained in each of these parts.
It is essential to take the three assessment tests to pass the subject.
The weighting of the three evaluable parts will be done, even if one of them is suspended. But the weighting will not be performed if two are suspended.
The evaluation system of this subject corresponds to continuous evaluation.
OPTIONAL RECOVERY SYSTEM:
Students will be entitled to retake the subject only if they have been assessed in the set of activities. Only suspended laboratory practices and the written test may be recovered. Therefore, all activities not submitted are excluded from recovery. Seminars are not recoverable and therefore are not re-evaluable.
The maximum mark in the recovered laboratory practices will be 5 out of 10.
The mark obtained in the recovery of the written test will be the final mark of this section, regardless of whether it is better or worse than the first test taken.
Attendance: Attendance at seminar classes and laboratory practices is mandatory. The unjustified absence of the students in these sessions entails a "not presented" in the note of the seminar or specific practice, and therefore will not be recoverable.
In the event that the student performs any irregularity that may lead to a significant variation of an evaluation act, this evaluation act will be graded with 0, regardless of the disciplinary process that could be instructed. In the event, that several irregularities occur in the evaluation acts of the same subject, the final grade for this subject will be 0.
The proposed teaching methodology and assessment may be subject to change depending on the attendance restrictions imposed by the health authorities.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Delivery of individual works | 20% | 15 | 0.6 | 20, 4, 5, 7, 6, 11, 9, 12, 13, 23, 22, 16, 17, 18, 19, 21 |
| Group practice presentations | 40% | 15 | 0.6 | 1, 20, 2, 5, 6, 11, 9, 12, 13, 15, 16, 19 |
| Theoric exam | 40% | 3 | 0.12 | 3, 4, 5, 6, 10, 14, 21, 8 |
Bibliography
Alonso, Amparo y Bonillo, Vicente, Fundamentos de inteligencia artificial, Universidade da Coruña, 1998.
Escolano, Francisco [et al.]. Inteligencia Artificial: modelos, técnicas y áreas de aplicación. Madrid Thomson. 2003.
Latorre, josé Ignacio. Ética para máquinas, Ariel, 2019.
Penrose, Roger, La nueva mente del emperador, Mondadodi, 1991.
Russell, S., Norvig, P. "Inteligencia Artificial". Ed. Plaza Edición, 2004.
Ryszard S. Michalski, Jaime G. Carbonell y Tom M. Mitchell. Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann. 2014.
Software
Code-oriented text editor