This is by far the most comprehensive video on current trends in the AI ethics/regulation/policy/strategy/yada-yada ecosystem. "if you want to regulate it, you should be able to produce or build it"
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Thanks so much for sharing. You have explained the AI concept and the basic principles in such an easy way, the language you are using is also straightforward and catchy at a breakneck speed, really enjoy learning all these, thanks!
Children do need to observe the world constantly for several years to learn what a cat is. Every observation gives new correlations and contrasts. They receive the visual, the sounds, the smells, the touch, etc from observing the world through their senses constantly -- this is a huge amount of data. Still, they are much better at incorporate this information effectively than any AI model is close to
Gotta get AGI on the job. Ironically a company or three is probably going to scrape the data from this video in the next few years lol. Good. Dude has collected data. So it's LLMs scrapng data scraped by LLMs. Dude is stoked, it's refreshing to see. I got 25 or 30 years before the clock really starts ticking, if I'm lucky. So we'll see. Hopefully they'll be able to fix some sh*t on the way, so I don't end up like my parents' generation now, where so many people are sick or dead through no fault of their own. (And many entirely through faults of their own.)
For me he is absolutely right, we do not see changes as they happen just with the perspective of time. Book transform world as we interpret the word of civilisation, human knowledge and transfer, internet set another paradigm, the burgeoning of AI, chatGPT, and Quantum Computing, will set a new paradigm, and in the midst of time we’ll see how those standards will be inside us, creating new patterns of behaviour, affecting human natural neural networks and vice-versa. There’s always a kind of aprehension anytime we change technology. But just by the way we cross the line, we can not see in the previous way.
I'm super interested in the field, but this guy on the left seems overly eager, even flippant, in the face of real dangers. The asteroid analogy was almost ironic. I hope OpenAI, Google, etc. are more serious about their safety, preparedness, and interpretability endeavors than this interaction might suggest.
🎯 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.
Mass-information for online free ~ manufacturing consent is a call to action. Thank you I am binge watching the series tonight. One more to listen too. Its been fun.
Great conference! I came here to learn from scratch about AI, as I want to see how to join it with research in Psychology. Everything you said makes a lot of sense to me, since it somehow resembles human learning. I was wondering if AI research has been informed by the literature on human learning from Psychology (the conditioning paradigm, all types of reinforcement, reinforcement schedules, etc.). For example, self-supervised learning from the beginning made sense to me with what we know about how human learning works. So babies don't come into the world as blank slates. They have some basic reflexes and knowledge that allow them, with the guidance of the environment, to survive in the world effectively. I can't wait to see the next class, because I have some doubts about how possible it is for an AI to become sentient (develop consciouss), or what if a Super Intelligence develops. The last question I have is if chaos is the nature of the world, then what ideas do you have about research in human behavior? Is it possible to accept chaos even when the aim of all science is to explain, predict and control, in this case human cognition and behavior? In this line, the approach of your start up (identify trends through behavioral data), seems to be positioned as a gold standard, since research in psychology tends to rely heavily on self-reporting (usually the one that came after behaviorism). and this methodology has many limitations.
Thanks for sharing these videos. This is helping me to develop and intuitive understanding about LLMs and their unique characteristics. It is very interesting insight why "winner takes all" is so much magnified with LLMs.
Great question! My take: I think DNA is the result of delayed feedback -- a specimen either reproduces successfully or not, but this is only known in hindsight. DNA needs to create vehicles (specimen) that are robust and balances exploration and exploitation -- for example, many specimen are more adverse to risk than they appreciate opportunity. Unsupervised learning is more about getting an understanding of how the world is structured, without necessarily making value assessments. For DNA, survival and reproduction is good -- but unsupervised learning does not have the same sense of goals or desirables, it just summarizes and embeds.
@@Red_Blue_Green Thanks for the great lectures! I signed up last year, but I still don't have the slides. It would be great to obtain it. Thanks again!
