How does AI learn?

AI can do amazing things, from recognizing faces to predicting the weather. One of its most important skills is learning from data to improve over time.

Which AI field specializes in teaching computers to learn from data, recognize patterns, and make predictions - without needing to be explicitly programmed for every task?

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Understanding Artificial Intelligence

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Understanding Artificial Intelligence

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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?

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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: Algorithms and AI systems demystified Exercise 2: Inside a pizza AI system Exercise 3: Unmasking a hotel booking AI system Exercise 4: Acquiring data Exercise 5: The AI and Internet-of-Things (IoT) symbiosis Exercise 6: Structured or unstructured?Exercise 7: Learning from data Exercise 8: Training and classifying with penguins Exercise 9: The unsupervised intruder Exercise 10: Deep Learning is here to stay Exercise 11: Interacting with the Environment Exercise 12: Learning from customer reviews Exercise 13: Robots, vision and natural language mix-up Exercise 14: The True-False challenge of things AI can do

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