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Manjesh Gupta

The European Union Artificial Intelligence Act came into force on 02 August 2024 to govern the use of AI in the European market.

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The European Union Artificial Intelligence Act came into force on 02 August 2024 to govern the use of AI in the European market.

With the recent advances in Machine Learning & Artificial Intelligence and the introduction of generative AI like GPT-4 and DALL·E, government and regulatory bodies around the world are showing tremendous interest in strengthening existing regulations or introducing new ones. The EU AI act was first proposed in April 2021. On 14 June On 9th December 2023 MEPs reached a political deal with the Council on the Act. On 6th March, 2024 the regulation was endorsed by MEPs with 523 votes in favor, 46 against and 49 abstentions.

What this course is - A thorough chapter wise walkthrough of the EU AI Act to help you grasp its tenets.

What this course is not - Analysis or implementation of the Act. At this point, the information in the Act is at a very high level and the European Commission and the AI Office will release further information like common standards, templates, clarified definitions with example and other such information closer to the time when this Act becomes applicable.

The proposed penalties for non-compliance are as below:

  • 35 million EUR or 7% of worldwide revenue in past financial year (whichever is higher) for non-compliance with prohibited AI practices in Article 5

  • 15 million EUR or 3% of worldwide revenue in past financial year (whichever is higher) for non-compliance with any other regulation in the Act (other than Article 5)

  • 7.5 million EUR or 1% of worldwide revenue in past financial year (whichever is higher) for supplying incorrect, incomplete or misleading information to notified bodies or national authorities

  • 15 million EUR or 3% of worldwide revenue in past financial year (whichever is higher) for general purpose AI models when the Commission finds that the provider intentionally or negligently:

What is covered in this course?

Section 1

  • Course Introduction

  • Introduction and Purpose of the Act

Section 2

  • Chapter 1: General Provisions (Scope & Definitions)

  • Chapter 2: Prohibited AI practices

Section 3

  • Chapter 3 Section 1: Classification of AI systems as High Risk

  • Chapter 3 Section 2: Requirements for High Risk AI systems

  • Chapter 3 Section 3: Obligations of providers and deployers of High-Risk AI systems and other parties

  • Chapter 3 Section 4: Notifying authorities and notified bodies

  • Chapter 3 Section 5: Standards, Conformity Assessment, Certificates, Registration

Section 4

  • Chapter 4: Transparency Obligations for Providers and Deployers of certain AI Systems

  • Chapter 5: General Purpose AI Models

    • Section 1: Classification rules

    • Section 2: Obligations for providers of general-purpose AI models

    • Section 3: Obligations for providers of general-purpose AI models with systemic risk

  • Chapter 6: Measures in Support of Innovation

Section 5

  • Chapter 7: Governance

    • Section 1: Governance at Union level

    • Section 2: National competent authorities

  • Chapter 8: EU Database for High-Risk AI Systems

  • Chapter 9: Post-market Monitoring, Information Sharing, Market Surveillance

    • Section 1: Post-market monitoring

    • Section 2: Sharing of information on serious incidents

    • Section 3: Enforcement

    • Section 4: Remedies

    • Section 5: Supervision, investigation, enforcement and monitoring in respect of providers of general-purpose AI models

Section 6

  • Chapter 10: Codes of Conduct and Guidelines

  • Chapter 11: Delegation of Power and Committee Procedure

  • Chapter 12: Penalties

  • Chapter 13: Final Provisions

Enroll now to develop a thorough understanding of European Union Artificial intelligence Act and the regulatory requirements for taking AI systems to market in the EU.

Disclaimer: This course is only intended to serve as training material to aid in understanding of the EU AI Act and not as a legal interpretation of the Act. The instructor cannot be held responsible for any interpretation, judgement or view expressed in the course.

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What's inside

Learning objectives

  • Introduction and purpose of the new ai regulation
  • Understand all the chapters of eu artificial intelligence act including prohibited ai practices and high risk ai systems
  • Governance requirements for high risk ai systems
  • Conformity assessment procedure for key high risk ai systems
  • Transparency requirements and general purpose ai models

Syllabus

Introduction
Course Introduction
Introduction and Purpose of the Act
Chapters 1: General Provisions, 2: Prohibited AI practices
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Covers Introduction & Purpose, General Provisions, Prohibited AI Practices

Covers High Risk AI Systems

Covers Chapter 4, 5 & 6

Covers Chapter 7, 8 and 9

Covers Chapters 10, 11, 12 and 13

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Offers a thorough walkthrough of each chapter of the EU Artificial Intelligence Act to aid in understanding its tenets
Covers the requirements for taking AI systems to market in the EU and the penalties for non-compliance
Teaches learners about the classification of AI systems as high-risk and the obligations of providers and deployers of high-risk AI systems
Provides clear guidance on the transparency requirements for providers and deployers of certain AI systems

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Reviews summary

Comprehensive eu ai act legislative overview

According to learners, this course offers a clear and concise walkthrough of the European Union Artificial Intelligence Act, providing a comprehensive understanding of its legislative tenets. Students consistently praise its ability to break down complex legal concepts into digestible chapters, making it accessible even without prior legal knowledge. It serves as an excellent foundation for professionals needing to grasp the regulations, focusing directly on the text of the Act rather than practical implementation, which aligns with its stated scope. Many find it crucial for navigating compliance.
Explains the Act's text, not practical application or examples.
"While it doesn't go into practical implementation, it sets a solid basis for further study."
"I would have liked more real-world examples, but for a direct explanation of the legislation, it's very effective."
"The course treats the Act as a legal document, explaining each part without over-interpreting, which helped me immensely."
"I found it to be a good high-level overview for a direct explanation of the legislation."
Establishes a solid basis for understanding EU AI regulations.
"Recommended if you want to understand the legislative aspect of it."
"It provides an excellent foundation for anyone needing to grasp the regulations."
"This course is a superb introduction to the EU AI Act... it sets a solid basis for further study."
"A must-do course for anyone involved with AI development or deployment in the EU."
"I gained a much better understanding of the different risk classifications and compliance requirements."
Provides a thorough, chapter-by-chapter understanding of the Act.
"The course content is very comprehensive and breaks down the EU AI Act into digestible chapters."
"This course delivered exactly what it promised: a chapter-wise walkthrough of the EU AI Act. No fluff, just facts."
"The depth of coverage for each chapter is impressive. I appreciate the focus on the actual text of the Act."
"It covers the entirety of the EU AI Act in a systematic way. Perfect for professionals who need to get up to speed quickly..."
"It's truly a 'walkthrough' of the Act, which is exactly what I needed. It's a legal document, and the course treats it as such..."
Explains complex legal topics simply, avoiding jargon.
"Clearly explained the different sections and requirements."
"I found it avoids getting bogged down in legal jargon and provides a good overview for anyone needing to grasp the regulations."
"The instructor did a great job explaining complex topics in a simplified manner for me."
"I found the explanations concise and well-structured, helping me consolidate my understanding."
"The instructor's explanations were clear and easy to follow, helping me gain a better understanding."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in European Union Artificial Intelligence Act with these activities:
Analyze AI case studies and proposed AI systems
Deepens understanding of the practical implications and challenges associated with developing and deploying AI systems in various contexts.
Show steps
  • Select real-world case studies or proposed AI systems that illustrate different types and applications of AI.
  • Analyze the systems based on the criteria and requirements outlined in the EU AI Act, identifying potential risks and benefits.
  • Consider the ethical, legal, and societal implications of the systems and propose recommendations for responsible development and use.
Participate in a discussion group on the EU AI Act
Provides an opportunity to engage with peers, exchange ideas, and gain diverse perspectives on the EU AI Act.
Show steps
  • Join or start a discussion group focused on the EU AI Act.
  • Actively participate in discussions by sharing insights, asking questions, and responding to others.
  • Listen attentively to different viewpoints and consider how they may challenge or complement your own understanding.
Participate in a hackathon or competition related to AI ethics and regulation
Provides a challenging and practical environment to apply your understanding of the EU AI Act and ethical considerations in AI development.
Show steps
  • Identify and register for a hackathon or competition focused on AI ethics and regulation.
  • Form a team or work individually to develop a solution that addresses the competition's challenge.
  • Ensure that your solution complies with the provisions of the EU AI Act and demonstrates a strong understanding of ethical principles.
  • Present your solution to a panel of judges and receive feedback on its effectiveness and potential impact.
Show all three activities

Career center

Learners who complete European Union Artificial Intelligence Act will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in European Union Artificial Intelligence Act.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
Directly addresses prompt engineering, a key skill for effectively interacting with models like GPT-4, as highlighted in several course titles. It provides strategies and techniques for crafting effective prompts to achieve desired outputs from generative AI models. Essential reading for anyone looking to maximize the utility of GPT-4. Suitable for all audience levels.
This type of guide focuses specifically on prompt engineering for ChatGPT and GPT-4, directly addressing a key aspect of the provided course topics. It would offer practical techniques and examples for effectively interacting with these models. Highly relevant for anyone looking to immediately improve their ability to use GPT-4. Suitable for all audience levels.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
Offers a practical introduction to working with large language models, including GPT-4 and ChatGPT. It covers strategies and best practices for using these models effectively, which aligns directly with the practical aspects highlighted in the course descriptions like prompt engineering and building applications. Suitable for a broad audience, including professionals and students.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
This short, accessible book by a prominent scientist provides an intuitive explanation of how models like ChatGPT (and by extension, GPT-4) work. It connects the underlying technology to fundamental concepts in computation and language. This is an excellent resource for gaining a conceptual understanding without deep technical jargon. Suitable for all audience levels.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Offers a broad exploration of Transformers, including their application in both NLP and computer vision, and specifically mentions GPT-4 and other related models. It provides practical guidance and covers generative AI concepts. This good resource for understanding the versatility of the Transformer architecture and its use in state-of-the-art models. Suitable for students and professionals.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
This foundational text applies economic principles to the analysis of various areas of law, including regulation. It is crucial for understanding the economic rationale and consequences of regulatory policies. is widely used in law and economics courses at the graduate level and key reference for professionals.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Demystifies practical transformer model implementation through the Hugging Face ecosystem, demonstrating how to fine-tune, optimize, and deploy attention architectures across diverse natural language processing and understanding tasks.
This comprehensive work provides an in-depth understanding of the intersection of finance, technology, and regulation in the FinTech world. It covers various FinTech technologies and offers a unique perspective on the interplay between these elements. is an ideal reference for graduate students and professionals in finance, technology, and law.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Provides a hands-on approach to understanding and working with large language models. It covers concepts related to language understanding and generation, offering practical examples. Given the focus on building AI apps with GPT-4 in the course descriptions, this book would be a valuable resource for practical implementation. Suitable for students and professionals with some programming background.
Takes a hands-on approach to building an LLM from the ground up, without relying on high-level libraries. This provides a deep understanding of the internal workings of these models, which is invaluable for those who want to move beyond simply using GPT-4 and understand its architecture and training. Suitable for advanced students and professionals with a strong programming background.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
This textbook provides a rigorous economic analysis of regulation and antitrust policy. It standard text in economics programs and is essential for a deep understanding of the economic effects of regulation. is suitable for advanced undergraduate and graduate economics students.
Offers a practical, hands-on approach to generative AI using Python and TensorFlow. It covers various generative models and their implementation, including image and text generation. This book is particularly useful for those who want to gain practical experience in building generative models relevant to DALL-E.
This practical guide explores the regulatory landscape specifically for FinTech companies. It addresses key issues such as financial crime, cybersecurity, and data privacy in the context of financial technology. is highly relevant for professionals and graduate students working in the FinTech sector.
Prompt engineering crucial skill for effectively using models like DALL-E. focuses on the techniques and strategies for crafting effective prompts to guide generative AI models to produce desired outputs. It is highly relevant for practical application of DALL-E.
Focuses on the practical aspects of building applications using foundation models, which include large language models like GPT-4. It covers the engineering challenges and considerations involved in taking these models from research to production. Highly relevant for those interested in the application development side of GPT-4. Suitable for advanced students and professionals.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Introduces core computational linguistics concepts and practical tokenization, tagging, and syntactic parsing workflows through clear Python examples, establishing strong fundamentals for language analysis.
Examines the challenges and future directions of regulation in a rapidly changing world, focusing on governing risk. It provides insights into contemporary regulatory issues and approaches. This valuable resource for graduate students, researchers, and professionals dealing with emerging regulatory challenges.
Authored by a former Supreme Court Justice, this book offers a deep dive into the complexities of government regulation and proposals for reform. It provides a wide perspective on the economic, legal, administrative, and political implications of regulation. This classic text suitable for advanced undergraduate and graduate students, as well as professionals.
This type of book provides a comprehensive overview of foundation models in NLP, which include large pre-trained language models like the predecessors and contemporaries of GPT-4. It delves into their architecture, capabilities, and applications. This valuable resource for gaining a deeper, more academic understanding of the models underlying GPT-4. Suitable for graduate students and researchers familiar with basic NLP.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment.
Synthesizes the five foundational tribes of machine learning into a unified conceptual framework, illuminating how symbolists, connectionists, evolutionaries, Bayesians, and analogizers approach intelligence.
Combines interactive Jupyter notebooks with rigorous mathematical explanations to teach machine learning practitioners how modern neural architectures are designed, implemented, and trained.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
Explores the relationship between regulation and governance in shaping economic outcomes. It delves into the theoretical and empirical aspects of how economies are governed through regulatory means. This relevant text for graduate students and researchers in political economy and public policy.
Provides a thought-provoking exploration of the future of generative AI, discussing its potential benefits and risks. It is written by Gary Marcus, a leading researcher in the field.
Provides a concise introduction to the fundamental concepts and theoretical frameworks of regulation. It is an excellent starting point for those new to the topic, offering a clear overview of its historical evolution and current practices. This useful resource for high school and early undergraduate students.
Focuses on applying deep learning to computer vision tasks, covering topics such as image classification, object detection, and generative adversarial networks (GANs). It provides practical guidance and is relevant for understanding the image generation aspect of DALL-E.

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