Which NLP task finds what customers think?

Lumina Audio wants to learn what customers like and dislike from thousands of product reviews. Use the workbench above to run each NLP task on a review and compare what it produces.

Select the NLP subarea best suited to finding what customers like or dislike.

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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: Does it fit the definition of AI?Exercise 3: Narrow today, general tomorrow?Exercise 4: What can AI do?Exercise 5: Which capability is the planner using?Exercise 6: Which system uses predictions and inference?Exercise 7: Areas and related disciplines of AI Exercise 8: Which discipline helps the robot see?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: What does the system produce?Exercise 3: Unmasking a hotel booking AI system Exercise 4: Acquiring data Exercise 5: Which setup is a true sense-and-act loop?Exercise 6: Structured or unstructured?Exercise 7: Learning from data Exercise 8: Training and classifying with penguins Exercise 9: Which task needs unsupervised learning?Exercise 10: Deep Learning is here to stay Exercise 11: Interacting with the Environment Exercise 12: Which NLP task finds what customers think?

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Exercise 13: Robots, vision and natural language mix-up

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: Get the organization ready Exercise 4: Data strategy, resources, and people Exercise 5: Pick the right infrastructure Exercise 6: The "zen" of MLOps Exercise 7: Team building!Exercise 8: Is your deployed AI system successful?Exercise 9: What's happening to this model?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: Trace the penguin's path Exercise 6: Read the SHAP explanations Exercise 7: Social challenges: ethics, fairness and privacy Exercise 8: Data sharing for AI progress Exercise 9: Biased or unbiased behavior?Exercise 10: The biased vehicle Exercise 11: Social challenges: the future of AI Exercise 12: The thousand faces of sustainable AI Exercise 13: Paola and the human side of AI Exercise 14: One journey ends, another begins