The emergence of transformer architectures in 2017 triggered a breakthrough in machine …
The emergence of transformer architectures in 2017 triggered a breakthrough in machine learning that today lets anyone create computer-generated essays, stories, pictures, music, videos, and programs from high-level prompts in natural language, all without the need to code. That has stimulated fervent discussion among educators about the implications of generative AI systems for curricula and teaching methods across a broad range of subjects. It has also raised questions of how to understand both these systems and the at times overstated claims made for them. This class will introduce the foundations of generative AI technology, and participants will explore new opportunities it enables for K–12 education. It will also describe and explore how an analytical frame of mind can help make clear the core issues underlying both the successes and failures of these systems. Much of the work will be project-based, involving implementing innovative teaching and learning tools and testing these with K–12 students and teachers.
Everyone will be impacted by AI in daily life and in the …
Everyone will be impacted by AI in daily life and in the workplaces of the future. It is critical for all students to have fundamental knowledge of AI and to understand AI’s potential for good and harm. The Daily-AI program will jumpstart your readiness for AI and give you the tools you need to prepare for the AI-enabled world.
The Daily-AI workshop, designed by MIT educators and experienced facilitators, features hands-on and computer-based activities on AI concepts, ethical issues in AI, creative expression using AI, and how AI relates to your future. You will experience training and using machine learning to make predictions, investigate bias in machine learning applications, use generative adversarial networks to create novel works of art, and learn to recognize the AI you interact with daily and in the world around you.
This curriculum is currently being piloted through NSF EAGER Grant 2022502. This is a joint venture between the Personal Robots Group at the MIT Media Lab, MIT STEP Lab, and Boston College.
Contents: Unit 0: What is AI? - What is AI - Algorithms as Opinions - Ethical Matrix - Decision Trees - Investigating Bias Unit 1: Supervised Machine Learning - Introduction to Supervised Machine Learning - Neural Networks - Classifying AI vs. Generating AI Unit 2: GANs - What are GANs? - Generator vs. Discriminator - Unanticipated Consequences of Technology - AI Generated Art - What are Deepfakes? - Spread of Misinformation - Generate a Story Unit 3: AI + My Future - Environmental Impact of AI - Redesign YouTube - Careers in AI
This course examines the issues, principles, and challenges toward building relational machines …
This course examines the issues, principles, and challenges toward building relational machines through a combination of studio-style design and critique along with lecture, lively discussion of course readings, and assignments. Insights from social psychology, human-computer interaction, and design will be examined, as well as how these ideas are manifest in a broad range of applications for software agents and robots.
Innovation in expression, as realized in media, tangible objects, performance and more, …
Innovation in expression, as realized in media, tangible objects, performance and more, generates new questions and new potentials for human engagement. When and how does expression engage us deeply? Many personal stories confirm the hypothesis that once we experience deep engagement, it is a state we long for, remember, and want to repeat. This class will explore what underlying principles and innovative methods can ensure the development of higher-quality “deep engagement” products (artifacts, experiences, environments, performances, etc.) that appeal to a broad audience and that have lasting value over the long term.
This course examines the issues, principles, and challenges toward building machines that …
This course examines the issues, principles, and challenges toward building machines that cooperate with humans and with other machines. Philosophical, scientific, and theoretical insights into this subject will be covered, as well as how these ideas are manifest in both natural and artificial systems (e.g. software agents and robots).
No restrictions on your remixing, redistributing, or making derivative works. Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make derivative works.
Most restrictive license type. Prohibits most uses, sharing, and any changes.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.
