Professional Practice in Artificial Intelligence

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Credits
3
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
Mail
The main objective of the PPAI seminar is to build the capacity of young professionals to investigate, identify, demonstrate and promote Artificial Intelligence (Al) techniques for industrial applications.

This seminar will explore these questions about the ethics of artificial intelligence and a number of other questions, including:

What ethical principles should AI researchers follow?
Are there restrictions on the ethical use of AI?
What is the best way to design AI that aligns with human values?
Is it possible or desirable to build moral principles into AI systems?
When AI systems cause benefits or harm, who is morally responsible?
Are AI systems themselves potential objects of moral concern?
What moral framework and value system is best used to assess the impact of AI?


The objective is to promote cross-fertilising Al techniques with other scientific fields today applied in industry.

The seminar programme will include invited seminars and conferences, writing papers workshops, complementary training, visits.

Teachers

Person in charge

  • Ulises Cortés García ( )

Weekly hours

Theory
2
Problems
0
Laboratory
0
Guided learning
0.08
Autonomous learning
4

Competences

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.

Technical Competences of each Specialization

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP4 - Capability to design, write and report about computer science projects in the specific area of ??Artificial Intelligence.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
  • CEP7 - Capability to respect the legal rules and deontology in professional practice.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Transversal Competences

Entrepreneurship and innovation

  • CT1 - Capability to know and understand a business organization and the science that defines its activity; capability to understand the labor rules and the relations between planning, industrial and commercial strategies, quality and profit.

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Objectives

  1. To determine the AI technologies, tools, architectures, and algorithms that would be most suitable for the Industrial applications.
    Related competences: CG1, CEP3, CT1, CT6,
  2. To be able to develop a set of criteria for AI applications development, and evaluate each of the identified applications in terms of this criteria
    Related competences: CG1, CEP4, CEP5,
  3. To make short- and long-term ethical recommendations for the industrial AI applications development and work in a multidisciplinary team
    Related competences: CG1, CEP7, CEP8, CT3, CT6,

Contents

  1. Industrial Applications of Artificial Intelligence
    This part of seminar is a compendium of Artificial Intelligence applications naturally taking full advantage of the research potential of professors at Universitat de Barcelona, Universitat Politècnica de Catalunya and Universitat Rovira i Virgili and the experience of their members in numerous R&D projects undertaken in recent years.
  2. Face to face with the Artificial Intelligence Industrial Applications
    Al is being used extensively in the business world. Its applications cross a wide spectrum. For example, Al is being applied in management and administration, science, engineering, manufacturing, financial and legal areas, military and space endeavors, medicine, and diagnostics.
    Senior managers in many companies use Al-based strategic planning systems to assist in functions like competitive analysis, technology deployment, and resource allocation. They also use programs to assist in equipment configuration design, product distribution, regulatory-compliance advisement, and personnel assessment. Al is contributing heavily to management's organization, planning, and controlling operations, and will continue to do so with more frequency as programs are refined. AI is also influential in science and engineering.

    In this part of the seminar students will be face to face with successful industrials that have been using AI techniques in their businesses
  3. Introduction
    Methodological issues and discussion about the general calendar.

Activities

Activity Evaluation act


Conferences with CEO's from AI industries

A cycle of conferences with CEOs will be organized so students get to know successful industrial histories that used Artificial Intelligence as their basis for success.
  • Theory: In each of the 4 sessions of this activity there it will be an invited guest that will be giving a talk about his business idea using Artificial Intelligence techniques
  • Autonomous learning: To study the available materials
Objectives: 1 2 3
Contents:
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
16h

Examples of Industrial Applications of Artificial Intelligence from R&D

The Artificial Intelligence applications developed for science and engineering are used to organize and manipulate the ever-increasing amounts of information available to scientists and engineers. The Artificial Intelligence is used in complex processes and it is the increased use of robotics in business. In this part of the seminar we will study edge applications of Artificial Intelligence born as R&D results. Most of the examples will come from European Union funded research.
  • Theory: Each one of the study cases will have a material that will provided in advance. The student has to read it to be able to discuss its contents. Also, the student has to prepare a paper of each one of them. The students will be asked to choice one among the study cases and prepare a more detailed paper.
  • Autonomous learning: To read the provided materials and prepare the discussion
Objectives: 1 2 3
Contents:
Theory
15h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
44h

Introduction

The student will learn the objectives of this seminar. He will receive the materials and learn the calendar of activities.
  • Theory: Introduction

Contents:
Theory
1.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

There it will be invited speakers from the industry --at least 4-- and up to 7 case studies of Artificial Intelligence Industrial applications.
The format will be a seminar with direct participation and reporting tasks.

Evaluation methodology

We will be asking all participants - through an evaluation questionnaire - how far they believed the seminar met the objectives.

Participants will be asked to provide papers evaluation each of the 7 seven relevant applications. There it will be a single note for 6 of the papers and another one for a paper selected by the student

Bibliography

Basic:

Complementary:

  • 21st Century Innovations [post] - Meieran, E, National Academy of engineering , 2007.

Previous capacities

We require the student to be knowledgeable on:

* Processes for developing large and complex software systems
* Roles and technologies to help you control such projects
* Research-level issues in areas such as software engineering, information systems, simulation modelling, digital media and games, network computing and artificial intelligence.