Democratizing Artificial Intelligence

1. Democratizing Artificial Intelligence

It's time to broaden our focus and analyze the position and implications of AI in society as a whole.

2. AI democratization

AI is already bringing a profound transformation in every aspect of our lives, part of which we still cannot fully understand. It is expanding the limits and meaning of many industries, and this paradigm shift is also happening at a societal level. How to bring the beneficial side of AI to everyone and eliminate its potentially harmful side? That's the purpose of AI democratization. Democratizing AI not only means bringing the practical use of AI systems and solutions to everyone, without the technical knowledge needed. It can also be understood as the design of AI tools that any human can effortlessly utilize to supplement many of our daily tasks requiring human intelligence. In summary, helping us make decisions and conduct processes, rather than replacing us.

3. AI literacy

AI literacy plays an important role in bringing AI democratization. It refers to the understanding individuals or organizations have about AI concepts

4. AI literacy

technologies

5. AI literacy

And their implications, both in concrete organizations and in society, the economy, and the environment. One of the aims of this course is precisely to provide you with this sense of AI literacy.

6. How AI literacy contributes to AI democratization?

Here's how AI literacy contributes to AI democratization: It empowers individuals by equipping them with the knowledge and skills to engage with AI technologies, be aware of their capabilities and limitations, and make informed decisions guided by data. It promotes an understanding of ethical considerations surrounding AI, such as fairness, privacy, and transparency, advocating for responsible AI practices. It advocates inclusivity and participation in an AI-driven society, empowering individuals from diverse backgrounds to engage in AI-related activities and participatory decision-making. Lastly, it encourages critical thinking and the ability to critically evaluate AI systems, their outputs, and their impact, making informed judgments about their use.

7. Data democratization

Data democratization is closely linked to AI democratization, and it can be understood at an organizational level and societal level. Data democratization in organizations consists in making the information that underlies data transparent and accessible to every different role in the organization, helping gain a competitive advantage in the market, optimizing activities, and adopting a proactive strategic mindset. Upskilling is key in this democratization process, training people in the responsible and effective use of data.

8. Data democratization

In society, data democratization refers to the idea of making data and information accessible to a wide range of individuals, without barriers and enabling people to access, use, and contribute to data-driven insights. Data democratization can be achieved through initiatives such as open data policies, transparent data-sharing practices, user-friendly data visualization tools, and the promotion of data literacy. The goal is to empower communities with data, fostering innovation, informed decision-making, and societal progress.

9. Let's practice!

Now that you've been exposed to the building blocks of AI democratization, it's time to reinforce your learning with a bit of practice.

This exercise is part of the course

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?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

Current Exercise

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 wine quality 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