Home > Videos | 🤖🧠💻 Andrej Karpathy

🙋💻❓ How I use LLMs

🤖 AI Summary

• ChatGPT 🥇, deployed in 2022, is the Original Gangster incumbent, being the most popular and feature-rich model [01:13].
• The LLM ecosystem 🌳 is rich, featuring big Tech offerings like Google’s Gemini, Meta, and Microsoft’s Copilot, alongside startups like Anthropic’s Claude and xAI’s Grok [01:30].
• Model leaderboards 📊, such as Chatbot Arena and the Seal Leaderboard, can be used to track model strengths and current performance across a variety of tasks [02:08].
• The basic interaction involves giving the model a text prompt ✍️ and receiving text back, which is effective for creative tasks like generating haikus [02:53].
• The chat window functions as the model’s working memory 🧠, and a buildup of tokens in the window can become distracting, decrease accuracy, and is expensive [16:45].
• Always start a new thread 💡 when previous context is no longer useful to prevent distraction and mitigate token cost issues [16:45].
• A larger model 🐘 has superior World Knowledge, answers complex questions, provides better writing, and is more creative [02:08:05].
• The latest frontier is reinforcement learning 📈 to improve accuracy in problems involving math, code, and reasoning [02:08:32].
• Models are rapidly gaining tools 🛠️ such as internet search for fresh knowledge and a Python interpreter for generating figures or plots (Advanced Data Analysis) [02:08:58].
• Multimodality 📸 is maturing, allowing models to handle text, audio, images, and video as native input and output (Omni models) [02:09:39].
• Quality of life ✨ features like file uploads, memory, instructions, and GPTs enhance the overall user experience [02:10:18].
• Pricing for flagship models like GPT-4o involves usage limits, such as 80 messages every 3 hours for the $20 per month Plus subscription [19:32].

🤔 Evaluation

• The video 🎥 offers an exceptionally practical and high-quality perspective on LLM use, drawing on the experience of a field leader.
• The speaker’s 🗣️ use of metaphors, like the LLM being a zip file of the internet and the chat history acting as working memory, is widely accepted and scientifically sound in the AI community.
• The claim that reinforcement learning is a key frontier for improving reasoning, math, and code is supported by extensive research into techniques like Reinforcement Learning from Human Feedback (RLHF), as detailed in papers like Training a Helpful and Harmless Assistant (Anthropic, 2021).
• Overall ✅, the video is highly reliable and provides an unbiased technical and practical overview of the current LLM landscape.

Topics to Explore for a Better Understanding

• The Nature of Hallucination 👻: Explore why LLMs generate false but plausible-sounding information, which is a key limitation of the next-token prediction mechanism, even in larger models.
• Attention and Context Window Limits 📏: Investigate the computational cost of the attention mechanism, the core concept introduced in the paper Attention Is All You Need (Google Brain, 2017), which explains why long chat histories are expensive and distracting.
• Model Persona Training 🎭: Research the specific post-training processes, such as Supervised Fine-Tuning (SFT) and RLHF, that transform the internet-trained zip file into a conversational assistant with a defined persona [02:08:05].

❓ Frequently Asked Questions (FAQ)

🛑 Q: Why are the conversational limits of chatbots like GPT-4o restricted, even for paid users?

💸 A: Conversational limits 🛑 are necessary because large language models, especially flagship models like GPT-4o, require a substantial amount of computational resources for each interaction. The models are inherently expensive 💰 to run due to their massive size and complexity, so capping messages helps manage server load, resource consumption, and cost.

🧠 Q: How does the size of a Large Language Model affect its performance?

📏 A: Model size 🧠 directly influences capability and performance. Larger models 🐘 typically contain more World Knowledge, effectively handle complex queries, produce higher-quality writing, and exhibit greater creativity. Smaller models 🤏 are prone to more errors, including generating false information, a behavior known as hallucination [02:08:05].

🎨 Q: What is the significance of multimodality in modern large language models?

🖼️ A: Multimodality 🎨 is a crucial feature signifying a model’s capacity to process and generate information across various formats, including text, audio, images, and video [02:09:39]. This capability evolves models past simple text conversations, allowing them to perceive and interact with the world in a more integrated, human-like way.

📚 Book Recommendations

↔️ Similar

• 🤖💻 Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond

• 🧠💻🤖 Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This serves as the definitive textbook on the fundamental concepts of deep learning, providing the theoretical foundation for how large language models are structured and trained.
• The Algorithmic Foundations of Reinforcement Learning by Csaba Szepesvári: This work provides a rigorous and deep dive into the mathematical techniques behind reinforcement learning, the advanced training method mentioned for improving an LLM’s reasoning and accuracy.

🆚 Contrasting

• The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do by Erik J. Larson: This book offers an opposing viewpoint by challenging the claims of strong general AI, arguing that current machine learning is fundamentally limited to narrow tasks and differs from human intelligence.
• 🧠🧠🧠🧠 A Thousand Brains: A New Theory of Intelligence by Jeff Hawkins: This presents an alternative, biological theory of intelligence—the Thousand Brains Theory—which offers a distinct architectural model compared to the transformer-based mechanisms of modern LLMs.

🎨 Creatively Related

• 🤔🐇🐢 Thinking, Fast and Slow by Daniel Kahneman: This book explores human cognition’s two systems (fast/intuitive and slow/deliberate), which can be analogized to an LLM’s quick token-prediction versus its slower, deliberate Chain-of-Thought reasoning using its context window working memory.
• 📱🧠 The Shallows: What the Internet Is Doing to Our Brains by Nicholas Carr: This work examines how new information technologies, like the internet that forms the basis of the LLM’s training data (the zip file), are changing human attention and knowledge consumption, providing a macro-cultural lens on the subject.