Le vrai et le faux de l'IA

Les systèmes d'IA et les algorithmes peuvent contribuer à la réalisation d'un nombre croissant de tâches, dont la plupart sont fortement basées sur des données. Gardez toujours à l'esprit que de nombreuses applications pratiques impliquent l'utilisation combinée de plusieurs techniques et tâches vues dans ce chapitre.

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<cours>Comprendre l'intelligence artificielle</cours>

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Get started with your journey into AI! Artificial Intelligence has rapidly shifted from a purely scientific subject to an umbrella field whose applications and uses are countless as of today, be it in business, industry, or simply as part of our daily lives. You will learn what is AI, what types of problems can be solved using AI, and what are its current limitations. You will also gain a broad understanding of the subareas within AI and its related disciplines.

Exercise 1: What is Artificial Intelligence?Exercise 2: The big question Exercise 3: Narrow or General?Exercise 4: What can AI do?Exercise 5: AI at work Exercise 6: AI in action Exercise 7: Areas and related disciplines of AI Exercise 8: How does AI learn?Exercise 9: Listen and learn

What are the core functions of AI systems and the possibilities each of its areas has to offer? Where does the data that fuels AI systems come from, and how do techniques like machine learning make use of data to solve a variety of problems? This chapter answers these questions and more, unveiling how different forms of AI can acquire data, learn from the data, and interact with its surroundings- be it the physical or digital environment.

Exercise 1: Démystification des algorithmes et systèmes d'IA Exercise 2: À l'intérieur d'un système d'IA pour pizzas Exercise 3: Démasquage d’un système d'IA de réservation d'hôtels Exercise 4: Acquisition de données Exercise 5: Symbiose entre l'IA et l'internet des objets (IoT)Exercise 6: Structurées ou non structurées Exercise 7: Apprentissage à partir des données Exercise 8: Formation et classification avec les manchots Exercise 9: Intrus non supervisé Exercise 10: Le deep learning est là pour durer Exercise 11: Interaction avec l'environnement Exercise 12: Apprentissage à partir des commentaires des clients Exercise 13: Robots, vision et confusion du langage naturel Exercise 14: Le vrai et le faux de l'IA

Exercice actuel

Get ready to plunge into the exciting world of AI in business and enterprise! This chapter uncovers the fundamental concepts, guidelines, and best practices to build or become an AI-driven organization. Whether it is for equipping your products, services, and projects with a touch of intelligence, or revolutionizing operational processes within your organization, by the end of this chapter, you will understand the essential elements to embrace this technology: from culture to data and infrastructure, to building teams of talented AI professionals. You'll also learn about challenges that may appear along the way and examples of success stories from big companies that learned to make AI a central aspect of their brand.

Exercise 1: Establishing an AI culture Exercise 2: Four ingredients to AI-driven organizations Exercise 3: Home, secured home Exercise 4: Data strategy, resources, and people Exercise 5: Infrastructure dilemmas Exercise 6: The "zen" of MLOps Exercise 7: Team building!Exercise 8: Is your deployed AI system successful?Exercise 9: Performance stories Exercise 10: An academic Proof-of-Concept (PoC)Exercise 11: Challenges and success stories Exercise 12: Ways to foster an AI culture Exercise 13: Paola and the fashion project

As our thrilling AI journey comes to an end, we delve into the profound connection between AI and its impact on humanity and society. We will explore subjects like AI democratization and responsible AI, encompassing ethics, interpretability, fairness, and safety – all crucial aspects for cultivating AI systems that positively influence our lives. Lastly, we cast our gaze towards the horizon, outlining the exciting prospects and challenges AI presents in shaping a sustainable future.

Exercise 1: Democratizing Artificial Intelligence Exercise 2: Best practices for AI democratization Exercise 3: Opening the Open Data doors Exercise 4: Explainability and interpretability Exercise 5: A tree inside the white-box Exercise 6: Explaining university admissions Exercise 7: Unboxing the SHAP Exercise 8: Social challenges: ethics, fairness and privacy Exercise 9: Data sharing for AI progress Exercise 10: Biased or unbiased behavior?Exercise 11: The biased vehicle Exercise 12: Social challenges: the future of AI Exercise 13: The thousand faces of sustainable AI Exercise 14: Paola and the human side of AI Exercise 15: One journey ends, another begins