Thank you Rickard for sharing these MIT lectures! This is amazing and free knowledge. Can't wait to watch all the lectures as they come out.

@Red_Blue_Green

2 ай бұрын

Thank you!!

Thank you VERY MUCH for posting this!!! Love it.

@Red_Blue_Green

3 ай бұрын

Thank you so much!! :D

Great - easy to undestand :)

Loved the video, thank you so much for sharing this. Will you be posting future lectures on this topic as well?

@Red_Blue_Green

3 ай бұрын

Thank you! Yes! The whole course will be released here, week by week. Stay tuned!

🎯 Key Takeaways for quick navigation: 00:00 *🎓 Introduction to Future of AI Foundation Models and Generative AI* - Introduction to the lecture series on Foundation Models & Generative AI at MIT. - Explanation of the purpose: understanding the current AI landscape, the underlying changes, and diving deep into various subjects beyond surface-level. - Overview of previous year's topics and excitement around them, leading into current advancements and the trajectory of the course. 02:14 *🌐 Recent Developments in AI* - Discussion on recent advancements and hype surrounding AI. - Mention of increased investments, valuation of companies, regulatory actions, and industry drama. - Exploration of questions about artificial general intelligence (AGI) and its current state. 04:05 *🧠 Instructor's Background and Course Schedule* - Introduction to the instructor's background and expertise in AI. - Overview of the course schedule, including topics to be covered in upcoming lectures. - Mention of guest speakers and the thematic focus of each lecture. 06:35 *📚 Core Concepts Covered in the Course* - Explanation of core concepts: neural networks, supervised learning, unsupervised learning, reinforcement learning, generative AI, foundation models, and self-supervised learning. - Emphasis on providing intuitive understanding with examples from various domains. - Objective to distinguish between hype and foundational aspects of AI. 08:13 *🧩 Understanding AI Through Human Learning Processes* - Analogizing human learning with AI learning processes. - Examination of different influences on human learning: parents, genetics, academia, and environmental interactions. - Comparison of human learning models to AI learning paradigms: supervised learning, reinforcement learning, and self-supervised learning. 11:25 *🧠 Relational Understanding and Generative AI Models* - Explanation of how relational understanding contributes to learning in both humans and AI models. - Illustration of how generative AI models comprehend concepts through relational context. - Example of generating images to demonstrate contextual understanding in generative AI. 15:58 *🤔 Philosophical Perspectives on AI Evolution* - Exploration of philosophical perspectives influencing AI development: learning vs. designing, chaos vs. order, and bottom-up vs. top-down approaches. - Examination of historical influences, including ancient Greek philosophical ideas. - Consideration of the limitations of top-down, ordered thinking in understanding complex systems like AI. 22:07 *🔄 Bottom-Up Perspective and Adaptation in Chaotic Systems* - Argument for embracing a bottom-up perspective and adaptation in dealing with chaotic systems. - Recognition of human adaptability, intuition, and flexibility in navigating chaotic environments. - Critique of the over-reliance on top-down, ordered thinking in understanding complex phenomena. 23:15 *🧠 Understanding Chaos and the Brain* - Chaos is inherent in the world, and the brain serves as a tool to navigate and learn within this chaos. - Artificial neural networks attempt to replicate the brain's flexibility and adaptability. - Supervised learning, while structured, faces limitations due to scalability and the inability to label all aspects of the world accurately. 32:34 *🎓 Self-Supervised Learning and Its Applications* - Self-supervised learning relies on learning from data without human experts, making it scalable and applicable to a wide range of tasks. - Predicting the future based on past data and positive pair contrastive learning are examples of self-supervised learning algorithms. - Applications include understanding DNA sequences for protein structure prediction and analyzing consumer behavior in retail for targeted recommendations. 45:23 *💡 Understanding Self-Supervised Learning* - Self-supervised learning is the foundation of training models like CHP, enabling them to learn from unlabeled data. - Foundation models and generative AI are the output of self-supervised learning algorithms. - While the technical terminology can vary, the essence lies in leveraging self-supervised learning for training advanced AI models.

Really great resource with Andrew Karpathy's video's

Great lecture. May I ask how to get access to the slides? Thanks.

Great lecture! Can I ask which room and time this is held? I'm at MIT. thx!

@Red_Blue_Green

3 ай бұрын

Thanks!! Tuesdays and Thursdays 2:00-3:15pm in E25-111