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Kence Anderson
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/9chpqh/designing
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.
Read more
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.
Read more
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
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
Save this course
Save Designing Autonomous AI to your list so you can find it easily later:
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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
Show steps
Reinforce your knowledge of reinforcement learning before the course begins to ease your understanding of autonomous AI brain design.
Browse courses on
Reinforcement Learning
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Practice decomposing complex tasks into smaller skills to strengthen your ability to design effective autonomous AI brains.
Browse courses on
Problem Decomposition
Show steps
-
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
Expand to see all activities and additional details
Show all eight activities
Brain Design Pattern Practice
Show steps
Practice applying brain design patterns to enhance your proficiency in designing robust and efficient autonomous AI systems.
Show steps
-
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
Show steps
Participate in group discussions to exchange ideas, learn from others' experiences, and refine your approach to extracting skills from subject matter experts.
Show steps
-
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
Show steps
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.
Show steps
-
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
Show steps
Contribute to a collective resource by creating a compilation of brain design patterns, enhancing your understanding and fostering collaboration within the AI community.
Show steps
-
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
Show steps
Apply the concepts you've learned to a real-world project that leverages AI to address a pressing social or environmental challenge.
Browse courses on
AI for Good
Show steps
-
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.
Review reinforcement learning
Show steps
Reinforce your knowledge of reinforcement learning before the course begins to ease your understanding of autonomous AI brain design.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Practice decomposing complex tasks into smaller skills to strengthen your ability to design effective autonomous AI brains.
Browse courses on
Problem Decomposition
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all eight activities
Brain Design Pattern Practice
Show steps
Practice applying brain design patterns to enhance your proficiency in designing robust and efficient autonomous AI systems.
Show steps
Show steps
Show steps
- 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
Show steps
Participate in group discussions to exchange ideas, learn from others' experiences, and refine your approach to extracting skills from subject matter experts.
Show steps
Show steps
Show steps
- 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
Show steps
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.
Show steps
Show steps
Show steps
- 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
Show steps
Contribute to a collective resource by creating a compilation of brain design patterns, enhancing your understanding and fostering collaboration within the AI community.
Show steps
Show steps
Show steps
- 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
Show steps
Apply the concepts you've learned to a real-world project that leverages AI to address a pressing social or environmental challenge.
Browse courses on
AI for Good
Show steps
Show steps
Show steps
- 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
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.
Data Scientist
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.
Artificial Intelligence Engineer
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.
Computer Vision 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.
Robotics 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.
Natural Language Processing 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.
Bioinformatics 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.
Operations Research Analyst
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.
Financial 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.
Quantitative Analyst
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.
Network Administrator
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.
System Administrator
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.
Database Administrator
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.
Software Engineer
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.
Data Architect
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.
For more career information including salaries, visit:
OpenCourser.com/course/9chpqh/designing
Reading list
We've selected 13 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
Designing Autonomous AI.
Save
Comprehensive guide to deep learning, covering the latest research and techniques. It provides a solid foundation in deep learning concepts and algorithms, and valuable reference for anyone working in the field. This book is commonly used as a textbook at academic institutions.
Classic introduction to reinforcement learning, covering the fundamental concepts and algorithms. It valuable resource for anyone interested in learning about reinforcement learning, and is commonly used as a textbook at academic institutions.
More advanced treatment of reinforcement learning, covering topics such as function approximation, policy gradient methods, and deep reinforcement learning. It valuable resource for anyone interested in learning about advanced reinforcement learning techniques.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including supervised learning, unsupervised learning, and Bayesian inference. It valuable resource for anyone interested in learning about the latest advances in machine learning.
Provides a practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, model training, and model evaluation. It valuable resource for anyone interested in learning how to apply machine learning to real-world problems.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and speech synthesis. It valuable resource for anyone interested in learning about the latest advances in speech and language processing.
Provides a practical introduction to deep reinforcement learning, covering topics such as Q-learning, policy gradient methods, and actor-critic methods. It valuable resource for anyone interested in learning how to apply deep reinforcement learning to real-world problems.
Save
Provides a comprehensive overview of computer vision, covering topics such as image formation, feature detection, and object recognition. It valuable resource for anyone interested in learning about the latest advances in computer vision.
Provides a practical introduction to natural language processing using Python. It covers a wide range of topics, including text preprocessing, text classification, and text generation. It valuable resource for anyone interested in learning how to apply natural language processing to real-world problems.
Save
Provides a comprehensive overview of autonomous agents and multi-agent systems, covering topics such as agent architectures, communication, and coordination. It valuable resource for anyone interested in learning about autonomous agents, and is commonly used as a textbook at academic institutions.
Provides a comprehensive overview of probabilistic robotics, covering topics such as state estimation, motion planning, and mapping. It valuable resource for anyone interested in learning about the latest advances in probabilistic robotics.
Provides a practical introduction to machine learning, covering topics such as data cleaning, feature engineering, and model training. It valuable resource for anyone interested in learning how to apply machine learning to real-world problems.
Save
Provides a comprehensive overview of planning algorithms, covering topics such as search, sampling, and optimization. It valuable resource for anyone interested in learning about the latest advances in planning algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9chpqh/designing
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Effort
4 weeks of study, 2 hours/week
Level
Introductory
Via
Coursera
Institution
University of Washington
Instructor
Kence Anderson
Language
English
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This course belongs to a
collection
called
Autonomous AI for Industry. It's comprised of three courses:
- Machine Teaching for Autonomous AI
- Designing Autonomous AI (viewing)
- Building Autonomous AI
Good to know
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
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