Artificial Intelligence
An introductory MIT course on foundation models and generative AI, covering how models like ChatGPT and Stable Diffusion work, plus applications in biology, autonomy, and ethics.
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Now playing: MIT 6.S087: Foundation Models & Generative AI. INTRODUCTION
This learning path follows MIT's 2024 introductory course on foundation models and generative AI, taught by Rickard Brüel Gabrielsson. The course starts with the basics of how these models work, then moves through large language models, image generation, and the surrounding ecosystem. It includes a guest lecture on applications in biology, a session on autonomy, a discussion of ethics, and a closing panel. The material is lecture-based and assumes no deep prior background, though some comfort with technical concepts will help. Videos are kept in their original order.
Beginners who want a structured free introduction before deeper practice.
Lesson 1: MIT 6.S087: Foundation Models & Generative AI. INTRODUCTION
This opening lecture introduces the course and the field of foundation models and generative AI. It sets up the main topics, including ChatGPT, Stable Diffusion, DALL-E, and the underlying machine learning ideas. The session gives context for why these models matter and what the rest of the course will cover.
Open on YouTubeLesson 2: MIT 6.S087: Foundation Models & Generative AI. HOW IT WORKS
This lecture explains the mechanics behind foundation models. It covers core ideas such as neural networks, supervised learning, and how these models are trained on large datasets. The goal is to give a working understanding of what happens inside systems like ChatGPT and image generators.
Open on YouTubeLesson 3: MIT 6.S087: Foundation Models & Generative AI. CHAT-GPT & LLMs
This session focuses on ChatGPT and large language models. It covers how LLMs are structured, how they generate text, and what makes them effective for conversation and other tasks. The lecture connects these models to the broader family of foundation models.
Open on YouTubeLesson 4: MIT 6.S087: Foundation Models & Generative AI. IMAGE GENERATION
This lecture covers image generation with models such as Stable Diffusion and DALL-E. It introduces diffusion and related techniques used to create images from text prompts. The session explains the main steps these models take to produce an image.
Open on YouTubeLesson 5: MIT 6.S087: Foundation Models & Generative AI. ECOSYSTEM
This lecture looks at the ecosystem around foundation models. It covers the tools, platforms, and practices that have grown up around these models. The session gives a practical view of how people and organizations use them.
Open on YouTubeLesson 6: MIT 6.S087: Foundation Models & Generative AI. BIOLOGY
This is a guest lecture by Professor Manolis Kellis on applications of AI in biology. It covers topics such as regulatory genomics, single-cell analysis, and the impact of genetic variation. The lecture shows how foundation model ideas connect to biological data and medicine.
Open on YouTubeLesson 7: MIT 6.S087: Foundation Models & Generative AI. AUTONOMY
This lecture examines autonomy and how foundation models relate to autonomous systems. It discusses what autonomy means in this context and where these models fit in. The session connects generative AI to real-world decision-making systems.
Open on YouTubeLesson 8: MIT 6.S087: Foundation Models & Generative AI. ETHICS
This lecture addresses ethical questions raised by generative AI and foundation models. It covers concerns such as bias, misuse, and societal impact. The session encourages thinking about how these systems should be developed and used.
Open on YouTubeLesson 9: MIT 6.S087: Foundation Models & Generative AI. PANEL
This closing panel brings together multiple speakers to discuss the course topics and the future of foundation models. It offers a range of perspectives on where the field is heading. The session wraps up the course with open discussion.
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