This course is designed to provide philosophy students with an overview of artificial intelligence (AI) issues and problems by considering them in their historical development and examining their philosophical implications.

1. Introduction to Artificial Intelligence
1.1 Definition and Basic Concepts
1.2 Brief History of AI
1.3 Main Fields of Application
1.4 Ethics and Philosophy of AI

2. The Origins and Early Development of AI
2.1 Philosophical and Mathematical Roots
2.2 AI Pioneers: Turing, McCarthy, and Others
2.3 Early Experiments and Prototypes
2.4 The Birth of AI Research Laboratories

3. AI in the Second Half of the 20th Century
3.1 The Era of Algorithms: From the 1950s to the 1970s
3.2 Expert Systems and Early Industrial Applications
3.3 The "AI Winter": Challenges and Setbacks
3.4 Renewed Interest in the 1980s and 1990s

4. The Rise of Machine Learning and Deep Learning
4.1 The Emergence of Machine Learning: Theory and Practice
4.2 The Deep Learning Revolution
4.3 Applications in Various Fields: From Medicine to Finance
4.4 Computational and Technological Challenges

5. Contemporary Issues and Challenges in AI
5.1 Ethics and Bias in Artificial Intelligence
5.2 Data Privacy and Security
5.3 Economic and Social Impact of AI
5.4 AI and the Future of Work

6. Future Prospects and Scenarios
6.1 Strong AI: Myth or Reality?
6.2 Ongoing Innovations: AI and Robotics
6.3 AI and Creativity: Art, Music, and Literature
6.4 Predictions and Possible Developments

The course does not require propaedeuticities other than the History of Cognitive Models module.

Very helpful to attend the Training Camp (info: Anya Pellegrin): https://filosofia.uniurb.it/training-camp/

Knowledge and understanding (knowledge and understanding)
By the end of the course, students should be able to understand and explain some texts belonging to the history of artificial intelligence, know and discuss some of the classical problems in artificial intelligence, and use some of the main bibliographic and information tools relevant to the field.
Ability to apply knowledge and understanding (applying knowledge and understanding).
By the end of the course, students should demonstrate ability to discuss and evaluate the main arguments and theses within the history of the mechanization of human reasoning and be able to use this knowledge in analyzing contemporary debates on the subject.
Autonomy of judgment (making judgements)
At the end of the course, students will be expected to demonstrate autonomy in making judgments regarding the main topics covered in the course. To this end, space will be given to class discussion. The ability to personally rework knowledge will also be relevant in the assessment of learning.
Communication skills (communication skills)
By the end of the course, students should be able to expound and discuss the problems studied with conceptual and linguistic precision, and to outline general frameworks that effectively and succinctly illustrate the issues addressed. To this end, careful reading and timely analysis of reference texts will be important, among other things, in addition to interactions during lectures.
Learning skills (learning abilities)
By the end of the course, students should have achieved a certain familiarity with the subject matter and method of research in the field so that they can independently acquire new knowledge by consulting the main bibliographical tools in this and related fields. 

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Exercises conducted for self-assessment of preparation level are available within the Moodle platform for blended learning.

Non-graded tests will also be conducted during the class period, aimed at ascertaining the average level of preparation and identifying those who need support.

Supporting activities will be some of the seminars within the following activities:

Training Camp (info: Anya Pellegrin): https://filosofia.uniurb.it/training-camp/

Seminars: Lectiones Commandinianae: https://sites.google.com/site/lectionescommandinianae/

Synergia Seminars: https://sites.google.com/a/uniurb.it/synergia

Teaching

Lectures and tutorials.

Teaching is delivered in a blended mode, i.e., lectures take place in the classroom and are simultaneously transmitted remotely within the Moodle platform.

Attendance

It is recommended to attend class from the beginning, actively and regularly. Given the critical-analytic nature of the problems addressed, active participation in classroom discussion will be very important. 

This course is to be considered modular together with the History of Cognitive Models course. As such, the grade for the entire course (module 1 + module 2) will be based on the arithmetic mean of the grades received in the individual modules.

Course books

Melanie Mitchell "Artificial Intelligence: A Guide for Thinking Humans." Pelican, 2020.

Altri testi e video dati dal professore (resi disponibili nella piattaforma Moodle › blended.uniurb.it ).

Assessment

Written and oral examination. You will be asked to analyze concepts, solve exercises, and demonstrate theorems.
This dual mode makes it possible to assess, in the best way, the achievement of the established formative objectives and competencies.
The final evaluation will consider the student's knowledge in terms of analysis of concepts, definitions, theorems, problems, theories, techniques, methods, scientific instruments, etc. The student's ability to use conceptual tools to solve problems and prove/analyze theorems and active participation in the classroom will also contribute to the final evaluation. Finally, the student's capacity for rigorous analysis of themes and problems, autonomy in solving problems and proving theorems, personal and autonomous reworking of knowledge, and planning will be particularly well-appreciated.

All these elements will have equal weight in the assessment. They will be distinguished on a scale of four levels (not sufficient, sufficient, good, excellent).
The final mark will be expressed in a range from 18/30 to 30/30. A sufficiently rigorous and clear exposition -using adequately specific terms- of the basic contents, concepts, methods, and the ability to solve simple exercises and prove simple theorems will be enough to obtain a sufficient evaluation and to pass the examination (18/30). The other marks will be calibrated on this basis.

The present course is considered modular, along with the Philosophy of Science course. In this sense, the evaluation for the entire course (module 1 + module 2) will be based on the arithmetic average of the grades received in the individual modules.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

Teaching is delivered in a blended mode, i.e., lectures take place in the classroom and are simultaneously transmitted remotely within the Moodle platform.

Personal study following the guidelines of the Vademecum and availing oneself as much as possible of the tutoring offered by the lecturer during office hours and via telecommunication. 

Attendance

Particularly careful application is required in order to compensate for non-attendance in class. Good learning autonomy and basic comprehension skills of philosophical texts are required. Study together with other students, whether attending or not, is recommended whenever possible.

This course is to be considered modular along with the History of Cognitive Models course. As such, the assessment for the entire course (module 1 + module 2) will be based on the arithmetic mean of the marks received in the individual modules.

Course books

Melanie Mitchell "Artificial Intelligence: A Guide for Thinking Humans." Pelican, 2020.

Other texts and videos given by the professor (made available in the Moodle platform ' blended.uniurb.it ).

Non-attending students are asked to contact the professor at the beginning of the course and in any case at least three months before the date on which they intend to take the exam.

Assessment

Written and oral examination. You will be asked to analyze concepts, solve exercises, and demonstrate theorems.
This dual mode makes it possible to assess, in the best way, the achievement of the established formative objectives and competencies.
The final evaluation will consider the student's knowledge in terms of analysis of concepts, definitions, theorems, problems, theories, techniques, methods, scientific instruments, etc. The student's ability to use conceptual tools to solve problems and prove/analyze theorems will also contribute to the final evaluation. Finally, the student's capacity for rigorous analysis of themes and problems, autonomy in solving problems and proving theorems, personal and autonomous reworking of knowledge, and planning will be particularly well-appreciated.

All these elements will have equal weight in the assessment. They will be distinguished on a scale of four levels (not sufficient, sufficient, good, excellent).
The final mark will be expressed in a range from 18/30 to 30/30. A sufficiently rigorous and clear exposition -using adequately specific terms- of the basic contents, concepts, methods, and the ability to solve simple exercises and prove simple theorems will be enough to obtain a sufficient evaluation and to pass the examination (18/30). The other marks will be calibrated on this basis.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

This course is to be considered modular together with the History of Cognitive Models course. As such, the assessment for the entire course (module 1 + module 2) will be based on the arithmetic mean of the marks received in the individual modules.