fr
en
Explore the state of the art in artificial intelligence and innovation
To contribute to an AI project, you need to distinguish genuine capabilities from hype. Connect data, models and use cases to understand choices and limitations. Develop a framework that makes discussions with technical teams more precise and decisions better supported.
- Duration
- 2 days 14 hours
- Code
- IA029FR Code
Presentation
The rapid acceleration of cognitive technologies is redefining innovation standards. Understanding deep learning and generative models is a strategic necessity for companies that do not want to fall behind. This 2-day course provides comprehensive immersion in current technological breakthroughs, helping you distinguish passing trends from genuine drivers of transformation.
The programme combines theory and practice to demystify complex architectures such as transformers and diffusion models. Through practical work on accessible platforms, experience the power of large language models (LLMs) and image generation while analysing their inner workings. This pragmatic approach helps technical and business teams adopt the tools.
Beyond technology, the course addresses the critical challenges of ethical governance and digital sovereignty. Leave with a clear view of use cases in healthcare, finance and industry, and the insights needed to anticipate innovation such as embedded and collaborative AI and inform future decisions.
Objectives
By the end of this AI state-of-the-art course, you will be able to:
- analyse the theoretical foundations and recent developments in artificial intelligence;
- identify high-performing model architectures, including transformers and diffusion models;
- identify practical AI opportunities in sectors such as healthcare and finance;
- experiment with pretrained models through web interfaces and open-source libraries;
- assess ethical, regulatory and societal impacts to deploy responsible AI.
Program
Module 1: Tracing AI's development and key concepts
- Distinguishing the historical symbolic approach from the modern connectionist approach.
- Comparing definitions of weak, specialised AI and strong, general AI.
- Historical overview of major milestones and AI winters.
Module 2: Understanding neural network architectures
- How deep neural networks (DNNs) work, including CNNs for images and RNNs for sequential data.
- The revolution in attention mechanisms and transformer architecture.
- Principles of diffusion models for visual generation, including Stable Diffusion and DALL·E.
Module 3: Exploring generative and multimodal AI
- The rise of large language models such as GPT, LLaMA and Mistral.
- Cross-media generation: creating images, sound and video.
- Multimodal interaction combining text, audio and visuals for rich applications.
Hands-on exercises
- Use a GPT model through Hugging Face to understand its parameters.
- Generate original images using Stable Diffusion through a web interface.
Module 4: Analysing sector-specific use cases
- HR applications: automated CV screening and conversational recruitment assistants.
- Healthcare innovation: computer-assisted diagnosis and medical imaging analysis.
- Financial transformation: credit scoring and real-time fraud detection.
- Industry 4.0: predictive maintenance and computer-vision quality control.
Module 5: Integrating ethical and regulatory considerations
- Detecting and managing algorithmic bias and discrimination risks.
- GDPR compliance and personal data protection requirements.
- Transparency and explainability requirements for automated decisions (XAI).
Module 6: Anticipating future trends
- Deploying embedded AI through edge computing for greater responsiveness and privacy.
- The emergence of collaborative AI to augment human capabilities.
- Geopolitical challenges of technological sovereignty and digital independence.
Hands-on exercises
- Build a specialised business chatbot using an open-source LLM.
- Critically audit an AI use case from ethical and societal impact perspectives.
Audience
This course is intended for technical and business innovation professionals, including:
- innovation project managers integrating technology components to create business value;
- digital transformation consultants supporting companies' AI adoption strategies;
- developers and software engineers developing deep learning architecture skills;
- business managers in HR, marketing and other functions seeking to understand AI's operational impacts;
- IT recruiters assessing candidates' technical skills in a tight labour market.
Prerequisites
The following prerequisites apply:
- Professional experience: familiarity with IT environments or digital projects is recommended to benefit fully from technical workshops.
- Basic knowledge:
- general IT literacy and understanding of basic data science concepts;
- Python proficiency is a useful advantage but is not mandatory.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
- Case study
Course highlights
- 360° perspective: gain a comprehensive overview from theoretical foundations to practical sector applications.
- Immersive workshops: use leading models such as GPT and Stable Diffusion in four accessible practical sessions.
- Ethical grounding: develop responsible, compliant AI through critical analysis of current risks and regulations.
- Innovation focus: anticipate the future by exploring emerging trends such as embedded and generative AI.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Brand names and logos mentioned in this course description, such as Stable Diffusion, Hugging Face and GPT, belong to their respective owners. Their use for educational purposes does not constitute a commitment or partnership.