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.
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
- 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?
- How can we build machines that behave responsibly?
DAY 3
Autonomous drones and human-robot teaming
- What’s involved in designing an end-to-end autonomous robot for a specific task?
- How can we integrate autonomous systems into human-robot teams?
- 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