Designing Autonomous AI from Coursera

To design an autonomous AI system, you must figure out how to distill a business challenge into its component parts.

When children learn how to hit a baseball, they don’t start with fastballs. Their coaches begin with the basics: how to grip the handle of the bat, where to put their feet and how to keep their eyes on the ball. Similarly, an autonomous AI system needs a subject matter expert (SME) to break a complex process or problem into easier tasks that give the AI important clues about how to find a solution faster.

In this course, you’ll learn how to create an autonomous AI design plan. By setting goals, identifying trainable skills, and employing those skills in goal-oriented strategies, you’ll incorporate your SME’s knowledge directly into your AI’s “brain,” the agent that powers your autonomous system. You'll learn when and how to combine various AI architecture design patterns, as well as how to design an advanced AI at the architectural level without worrying about the implementation of neural networks or machine learning algorithms.

At the end of this course, you’ll be able to:

• Interview SMEs to extract their unique knowledge about a system or process

• Combine reinforcement learning with expert rules, optimization and mathematical calculations in an AI brain

• Design an autonomous AI brain from modular components to guide the learning process for a particular task

• Validate your brain design against existing expertise and techniques for solving problems

• Produce a detailed specifications document so that someone else can build your AI brain

This course is part of a specialization called Autonomous AI Fundamentals.

What's inside

Syllabus

Defining your AI

The first step in designing an autonomous AI is defining what your AI is going to do and what the goals are. Think about it like describing a game to someone. First you explain what the object of the game is, and then you describe the rules. In this module you'll learn how to do the same for your autonomous AI use case.

Teaching Skills to your AI

Autonomous AI brains are built from skills. Skills are “units of competence for completing tasks that have sub-goals associated with them.” This week you'll learn to outline the skills you want your autonomous AI brain to learn. First, you’ll identify three different types of skills that you can build into your brains. Then, you’ll learn a strategy that will help easily extract and document skills from subject matter experts you interview. Along the way we’ll look at some design patterns that you can use as templates to start your use case brain designs.

Organizing Skills in your AI

Now that you understand how to interview a subject matter expert and lay out all the skills that you want your AI to practice, you need to organize those skills in the brain. In this module you’ll learn two organizing paradigms for skills in autonomous AI, and a three-step framework for completing this orchestration. This week you’ll see some brain design patterns for example use cases, to help you with thinking about organizing your own use case brain design.

Putting it All Together

Now it's time to put it all together. You've defined your AI, you've identified a set of skills that you want to teach your AI and you've used brain design patterns in the paradigms of orchestration to snap those skills together in the right arrangement. There's a few pitfalls to orchestration that you should be aware of and you’ll have lots of opportunity to practice creating variations on brain designs for sample problems. Make sure to share your brain designs from the lab in the forum, so we can discuss them together and learn from each other.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops both technical and non-technical skills in autonomous AI design, which are core skill sets in machine learning and data science

Taught by Kence Anderson, who is recognized for their work in autonomous AI

Examines advanced AI architecture design patterns, which are highly relevant to real-world applications

Provides hands-on experience in designing autonomous AI systems, which is beneficial for students seeking practical skills

Teaches both theoretical foundations and practical applications of autonomous AI, which provides a comprehensive understanding of the subject

Offers a modular approach to learning, which allows students to customize their learning experience based on their interests and goals

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 Designing Autonomous AI with these activities:

Review reinforcement learning

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Reinforce your knowledge of reinforcement learning before the course begins to ease your understanding of autonomous AI brain design.

Browse courses on Reinforcement Learning

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Review the fundamentals of reinforcement learning, such as value functions, reward functions, and exploration-exploitation trade-offs.
Solve simple reinforcement learning problems to practice applying the concepts.

Reinforcement Learning: An Introduction

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Supplement the concepts from the lectures and tutorials with a comprehensive review of this foundational text on reinforcement learning.

View Reinforcement Learning, second edition: An... on Amazon

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Read the book thoroughly.
Complete the exercises at the end of each chapter.
Implement some of the algorithms described in the book.

Skill Decomposition Practice

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Practice decomposing complex tasks into smaller skills to strengthen your ability to design effective autonomous AI brains.

Browse courses on Problem Decomposition

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Choose a real-world problem that you want to solve with an autonomous AI system.
Break the problem down into a hierarchy of smaller skills.
Identify the key dependencies between the skills.
Develop a plan for how the AI will learn each skill.

Five other activities

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Brain Design Pattern Practice

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Practice applying brain design patterns to enhance your proficiency in designing robust and efficient autonomous AI systems.

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Familiarize yourself with different brain design patterns.
Choose a brain design pattern that is appropriate for your AI problem.
Implement the brain design pattern in your AI system.
Evaluate the performance of your AI system and make adjustments as needed.

Skill Extraction Discussion Group

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Participate in group discussions to exchange ideas, learn from others' experiences, and refine your approach to extracting skills from subject matter experts.

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Join a discussion group with other students.
Share your experiences with interviewing subject matter experts.
Discuss different techniques for extracting skills.
Provide feedback on others' skill extraction approaches.

AI Brain Design Proposal

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Develop a detailed AI brain design proposal to solidify your understanding of the design process and demonstrate your ability to apply the concepts covered in the course.

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Define the problem that your AI system will solve.
Identify the skills that your AI system will need.
Design the architecture of your AI brain.
Develop a plan for how your AI system will learn.
Evaluate the performance of your AI system and make adjustments as needed.

AI Brain Design Pattern Library

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Contribute to a collective resource by creating a compilation of brain design patterns, enhancing your understanding and fostering collaboration within the AI community.

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Research different brain design patterns.
Create a document that describes each brain design pattern, including its benefits, drawbacks, and use cases.
Share your document with other students.

AI for Good Project

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Apply the concepts you've learned to a real-world project that leverages AI to address a pressing social or environmental challenge.

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Identify a social or environmental problem that you're passionate about.
Research existing AI solutions to the problem.
Develop a plan for how you can use AI to address the problem.
Implement your plan.
Evaluate the impact of your project.

Career center

Learners who complete Designing Autonomous AI will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer is responsible for developing and deploying machine learning models. They use their skills in computer science and applied mathematics to create models that can learn from data and make predictions. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in machine learning algorithms and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist is responsible for collecting, analyzing, and interpreting data in order to make informed decisions. They use their skills in statistics, mathematics, and computer science to extract meaningful insights from data. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and machine learning. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

An Artificial Intelligence Engineer is responsible for designing and developing AI systems. They use their skills in computer science, mathematics, and cognitive science to create systems that can think and learn like humans. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in AI principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems.

See salaries and explore the career path for Artificial Intelligence Engineer

Computer Vision Engineer

A Computer Vision Engineer is responsible for developing and implementing computer vision systems. They use their skills in computer science, mathematics, and image processing to create systems that can see and understand the world around them. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in computer vision principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to analyze images and videos and extract meaningful insights.

See salaries and explore the career path for Computer Vision Engineer

Robotics Engineer

A Robotics Engineer is responsible for designing, building, and maintaining robots. They use their skills in mechanical engineering, electrical engineering, and computer science to create robots that can perform tasks such as manufacturing, exploration, and surgery. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in robotics principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to control robots and enable them to perform complex tasks.

See salaries and explore the career path for Robotics Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer is responsible for developing and implementing natural language processing systems. They use their skills in computer science, mathematics, and linguistics to create systems that can understand and generate human language. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in natural language processing principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to process text and speech data and extract meaningful insights.

See salaries and explore the career path for Natural Language Processing Engineer

Bioinformatics Engineer

A Bioinformatics Engineer is responsible for developing and implementing bioinformatics systems. They use their skills in computer science, mathematics, and biology to create systems that can analyze and interpret biological data. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in bioinformatics principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to analyze genetic data and other biological data and extract meaningful insights.

See salaries and explore the career path for Bioinformatics Engineer

Operations Research Analyst

An Operations Research Analyst is responsible for using mathematical and analytical techniques to solve problems in business and industry. They use their skills in mathematics, statistics, and computer science to develop models and algorithms that can help organizations make better decisions. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and machine learning. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in operations research.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

A Financial Analyst is responsible for analyzing and interpreting financial data. They use their skills in mathematics, statistics, and economics to make recommendations about investments and financial decisions. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and machine learning. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in the financial industry.

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Quantitative Analyst

A Quantitative Analyst is responsible for developing and implementing mathematical models to analyze financial data. They use their skills in mathematics, statistics, and computer science to make predictions about financial markets and make recommendations about investments. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data analysis and machine learning. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in quantitative finance.

See salaries and explore the career path for Quantitative Analyst

Network Administrator

A Network Administrator is responsible for managing and maintaining computer networks. They use their skills in computer science and networking to ensure that networks are running smoothly and efficiently. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in network administration principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in network administration.

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System Administrator

A System Administrator is responsible for managing and maintaining computer systems. They use their skills in computer science and operating systems to ensure that systems are running smoothly and efficiently. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in system administration principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in system administration.

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Database Administrator

A Database Administrator is responsible for managing and maintaining databases. They use their skills in computer science and database management to ensure that databases are running smoothly and efficiently. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in database administration principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in database administration.

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Software Engineer

A Software Engineer is responsible for designing, developing, and maintaining software systems. They use their skills in computer science and mathematics to create software that meets the needs of users. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in software engineering principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in software development.

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Data Architect

A Data Architect is responsible for designing and managing data systems. They use their skills in computer science and database management to create systems that can store, process, and analyze data. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in data architecture principles and techniques. You will learn how to design and implement autonomous AI systems, which can be used to automate tasks and solve complex problems in data architecture.

See salaries and explore the career path for Data Architect