Description

• Introduction to Generative AI fundamentals
• Generative AI Use-Cases and Applications
• GenAI Modeling and Scheduling 
• Traffic Modeling and Network Accelleration protocols 

The detailed lessons are as follows. The first four sessions are lectures, with some parallel hands-on activity, while the following sessions are dedicated to tutorials and practical work (group projects).

Lectures  

These sessions are dedicated to acquiring the theoretical concepts and fundamentals of Generative AI.

• Introduction to Generative AI and foundational models (GANs, VAEs)
  • What is Generative AI? (vs. Discriminative AI)
  • Early statistical models (e.g., Markov Chains, HMMs)
  • Generative Adversarial Networks (GANs): architecture, training, strengths, and weaknesses
  • Variational Autoencoders (VAEs): architecture, training, and applications
• Transformer architectures and Large Language Models (LLMs)
  • The Transformer architecture: self-attention, QKV vectors, and multi-head attention
  • The Encoder-Decoder framework and its variants
  • Large Language Models (LLMs): pre-training and fine-tuning paradigms
  • Prompt engineering: principles and techniques
• Diffusion models and creative applications
  • The forward and reverse diffusion processes
  • Latent Diffusion Models (LDMs): concepts and efficiency
  • Applications in image and video generation (e.g., text-to-image)
  • Tools and frameworks for implementing diffusion models
• Ethical issues, challenges, and future trends
  • Ethical implications: bias, misinformation (deepfakes), and intellectual property
  • Current challenges: hallucinations, controllability, and computational cost
  • Responsible AI principles
  • Future trends: multimodal generation, efficiency, and scientific discovery
• GenAI traffic modeling and network accelleration
  • Collective Communications
  • Data-driven versus Traffic Model
  • Transport protocols (RDMA, ROCE)

Tutorials / Practical Work (Group Projects)

These sessions are dedicated to applying knowledge through group projects, including project assignment and regular follow-ups.

• Project assignment and beginning of practical work (1 lesson)
• Group project follow-up (2 lessons)
• Use-case seminars (1-3 lessons)

Finalité

The objectives of the module is to understand in detail Generative Artificial Intelligence technologies and how they can be served using networking technologies.

Compétences visées

Applied AI Skills 

Description des modalités d'évaluation

Lab/project reports, final exam.

Public

Applied AI Skills from the first year of master, in particular:

• Basics in AI, ML and distributed learning.
• Basics in ML programming