AI Applied to Industry: How to implement it effectively ?

• Introduction and objectives
  • Roundtable Discussion and Participant Expectations
  • Presentation of the training objectives

• Introduction to Artificial Intelligence
  • Definition of AI: What is AI and how does it work?
  • Differentiation between AI, machine learning and deep learning.
  • Examples of AI in everyday life (voice assistants, movie recommendations, etc.)

• AI through history
  • A brief history of AI: from Alan Turing to recent innovations.
  • Evolution of algorithms and technologies

• Data at the heart of AI
  • What is data? Types of data used by AI.
  • Importance of data quality and quantity.
  • Data processing architecture

• Workshop: AI in the workplace – Identifying Potential Applications in Participants’ Companies.

• Practical applications of AI in different sectors
  • AI in healthcare, industry and the service sector.
  • Use cases in everyday life.

• AI R&D processes
  • Different Stages of an AI Project
  • Key challenges
  • Good practices
  • Prerequisites and critical considerations
  • Individual Work: Drafting an initial reflection on a use case in your company/industry.

• AI Myths and Realities
  • Will AI Replace Humans? Myths vs. Reality of Automation.
  • Current limitations of AI: understanding, decision-making, and biases.
  • Discussion: Common concerns about AI (job loss, surveillance, etc.).

• Ethical challenges of AI
  • Bias and Discrimination in Algorithms: How and Why?
  • Privacy and protection of personal data.
  • Regulations and legislations (GDPR, AI laws).
  • The importance of developing responsible and transparent AI.

• Workshop: Ethical Dilemma Case Study in AI

• Conclusion
  • Key points of the training
  • Open discussion: how to apply this knowledge in your professional environment?
  • Resources for further study, training evaluation.

None. For remote training, a computer with a webcam, microphone, speakers, and an Internet connection is required.

Strategic decision-makers, project managers, engineers and technicians involved in the development and design of industrial products.

AI & Data Engineer

Presentations (projected and printed), practical case studies, video materials, etc.

Pre- and post-course assessments, quizzes, etc.

5 working days before the course start date (if financed by OPCO)

A training certificate complying with the provisions of Article L. 6353-1 paragraph 2 is issued to the trainee.