What do you know about artificial intelligence? Learn more with this library guide

Can you tell the difference between machine learning, deep learning and neural networks? We hear a lot about artificial intelligence (AI), but understanding how it works can be challenging. To help you, the Library team, in collaboration with Carles Ventura, a member of the Faculty of Computer Science, Multimedia and Telecommunications, has prepared the Theoretical framework of artificial intelligence guide, which explains the key concepts of AI, its practical applications and the ethical dilemmas it poses.

With this guide, you will be able to learn the basics of AI and answer common doubts using the sources of information selected by the library team and teaching staff. In addition to introductory content, it also contains a specific bibliography to provide a deeper dive into the most complex aspects.

What will you find in it?

• Basic concepts: this includes the meaning and evolution of artificial intelligence, the types of data on which it trains (both structured and unstructured) and the algorithmic models used to process them, such as neural networks and decision trees. The section also covers prompts, which are the instructions we give to AI systems to communicate with them or to request an action.

Recommended resources providing more information include the book Machine Learning Pocket Reference, a practical and easy-to-read guide available in the Library for the UOC community.

• Types of learning: the second section includes information on one of the branches of AI, machine learning, as well as its specific methodologies: supervised, semi-supervised, unsupervised and reinforcement learning. There is also content dedicated to deep learning, a subset of machine learning, which has made possible advances such as assisted driving.

Among the recommended resources is the book Introduction to Machine Learning with Python, also available in the Library, which combines theory with practical explanations.

• Applications: information on AI-related fields of study, such as recommender systems (which make personalized film, series and music recommendations possible on streaming platforms such as Netflix and Spotify), natural language processing (NLP), which allows interaction with virtual assistants or chatbots such as ChatGPT, and computer vision, used in image analysis, for example.
• Ethics and reliability: this section helps you explore AI's ethical challenges. It provides details of organizations, reports, self-assessment models and guides from leading tech companies on topics such as data privacy, biases in algorithms and transparency in the development of AI models.

Come on in and learn more about artificial intelligence!