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AI Fundamentals
This "AI Fundamentals" Master Class offers a structured immersion into the world of Artificial Intelligence. Far from the hype, this training aims to provide a clear and pragmatic understanding of current technologies, from historical concepts to the recent revolutions of Generative AI (GenAI). Through concrete examples and situational exercises, participants will learn to distinguish the different types of AI, identify value drivers for the business (notably via the "Augmented Collaborator" concept), and master the crucial challenges of responsibility (AI Act, ethics, bias). Finally, the course integrates an essential dimension of digital sobriety, analyzing the environmental impact of AI to encourage sustainable innovation.
What you will learn
- Master the fundamentals and vocabulary of AI (Machine Learning, Deep Learning, GenAI, LLM, Prompt, RAG) and understand the structure of the current ecosystem.
- Identify the potential of AI in business by distinguishing traditional use cases from new generative opportunities, and understanding the strategic role of a GenAI platform.
- Adopt a responsible posture towards ethical and regulatory risks by understanding the requirements of the European AI Act and the mechanisms of algorithmic bias.
- Integrate digital sobriety issues into the use of technologies by knowing the environmental impact of the AI life cycle.
Prerequisites
- No prior technical skills are required (neither in development nor in data science). A curiosity for digital issues and innovation is recommended.
- A laptop with an internet connection for demonstrations and access to online resources.
- Access to internal collaboration tools (Teams/Google Meet) if the session is held remotely.
Target audience
- All employees, regardless of their profession or hierarchical level (technical, functional, support, managers)
Training Program
6 modules to master the fundamentals
Topics covered
- →Welcome and presentation of objectives.
- →Round table: expectations and sharing of representations of AI (myths vs. reality).
Topics covered
- →History and definitions: From Alan Turing to current models. Precise distinctions between Artificial Intelligence, Machine Learning (supervised/unsupervised/reinforcement), Deep Learning, and Generative AI.
- →Learning mechanisms: Understand the functioning of a neural network and the training process (weights, bias).
- →Generative AI decrypted: Functioning of LLMs (Large Language Models), definition of Prompt, RAG (Retrieval-Augmented Generation), and key parameters (Tokens, Temperature).
- →Market overview: Mapping of actors, distinction between "Hyperscalers" (Microsoft Azure, Google Cloud, AWS) and "Pure Players" (OpenAI, Mistral, Anthropic).
Topics covered
- →Data as a foundation: The importance of data quality and volume (Big Data) to feed the models.
- →"Traditional" vs. "Generative" use cases: Traditional AI: augmented OCR, predictive maintenance, scoring, anomaly detection. Generative AI: text, image, code generation, and virtual assistance.
- →Towards the Augmented Enterprise: Distinction between Assistant (reactive) and Agent (autonomous). The concept of the "Augmented Collaborator": opportunities for efficiency and limitations.
- →Scaling up: Introduction to the architecture of an enterprise GenAI Platform to secure and industrialize uses.
Topics covered
- →Ethical Framework: The 4 pillars of AI ethics according to UNESCO and the different types of ethics (computer, digital, usage).
- →Algorithmic Biases: Understanding representation biases (gender, origin) and cognitive biases. Analysis of their consequences (discrimination, legal risks) through concrete examples.
- →European Regulation (AI Act): Deciphering the regulation, approach by risk level (Prohibited, High, Specific, Minimal), and compliance schedule.
- →Practical case study: "LazyTech Lab". Compliance analysis of a fictitious high-risk system. Application of the 10 compliance steps (risk management, data governance, human supervision).
Topics covered
- →Hardware life cycle: Impact of rare earth extraction and equipment manufacturing.
- →Energy consumption: Reality of Data Center consumption (electricity, water for cooling) and the carbon footprint of model training.
- →Best practices: Levers of digital sobriety (choice of adapted models, eco-design) and AI in service of the environment (energy optimization, waste sorting).
Topics covered
- →Summary of takeaways: "Intelligence lies in the prompt and in the critical analysis of the response".
- →Question / Answer Session.
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote