Learning Machines
Training Objectives
Autonomous robots such as drones are increasingly leveraging machine learning methods to acquire new capabilities that enable them to perform tasks more flexibly and robustly. At the same time, applications of autonomous robots also have important ethical and policy considerations to consider as this exciting technology continues to expand and evolve.
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Accordingly, we consider how to design, train, and integrate autonomous robots into human-robot teams to better achieve mission objectives and reduce uncertainty especially with operatives in military and government functions.
Learning Machines Training is a team-based, hands-on learning experience created by the MIT Media Lab where participants work on a series of highly structured coding projects to build and interact with their own machine learning models applied to physical robots and virtual agents.
By the end of this exciting three-day course, participants will have:
Set Learning Machine Strategies.
Lead Learning Machine Tasks.
Asked Important Questions Around Autonomous Systems.
Received a Learning Machines Certificate of Completion.
Key Learning Objectives
DAY 1
Robot perception: Learning to see
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- What are the challenges and opportunities in machine vision and its use in robots?
​- How do robots learn to interpret what they see and hear?
- How can we build machine perception responsibly?
DAY 2
Robot behavior: Learning to act
- How do robots learn from experience how to do tasks?
- What are the challenges and opportunities in the use of autonomous robots?
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- How can we build machines that behave responsibly?
DAY 3
Autonomous drones and human-robot teaming
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​- What’s involved in designing an end-to-end autonomous robot for a specific task?​
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- How can we integrate autonomous systems into human-robot teams?
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- What are ethical considerations of human-robot teaming?
- How are autonomous agents relevant to my organization?
FAQS
The 12 Learning Milestones Covered In This Training
Seeing
Machines
Supervised Machine Learning
Conversational AI
Bias and Privacy
Reinforcement Learning
Human-in-the-Loop Training of Robots
Deep Reinforcement Learning
Ethics and Policy Responsibility
Human-Robot Teaming
Emotion and Human-Robot Teaming
Acquisition of AI
Organizational and Culture Change
This three-day workshop isn’t just a course, it’s an immersive learning experience!
We hope you not only leave this course with deeper technical, operational, and strategic insights as to the challenges and opportunities of responsible autonomous robotic systems -- but also we hope you have a ton of fun and make new friends. Be warned! There will be a friendly team-based competition to bring it all together. So get ready to be in it to win it!
Professor Cynthia Breazeal
Lead Instructor and Principal Investigator
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The description says this is an experimental course. What does that mean?At MIT, we believe the best way to learn about technology and their broader implications is through designing them first hand. This is a custom designed course for the Lead-Drive cohort of the MIT-USAF AI Education Research Pilot. We will be exploring how leaders and managers learn through team-based, project-based learning in this course. This is the first time the course will be offered, so we anticipate learning and iterating the course, the learning activities, and the technologies from participant feedback.
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Do I need any prior coding experience to participate in this course?No prior coding experience is necessary. Our team has developed a number of tools for novice coders to support hands-on learning experience.
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Do I need to bring any equipment, like my own laptop?"No. We will provide all the equipment needed for the course.
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Can I get a tour of the MIT Media Lab?Yes! We are delighted to offer a tour of the Media Lab on Day 2 of the course.
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Must I sign the media release form to participate?No, it is totally up to you. The teaching staff will be asking your permission to video you during the course. These videos help MIT researchers analyze team-based learning and participant engagement with the course activities and topics. We might also interview some of the participants to better understand their experience with this style of project-based, team-based learning. Videos we make may also be used in presentations to describe the course or we may use video snippets on the course website to help future participants better understand what they will experience by taking this course. The lectures will be video recorded and will be made available to participants after the course.