Learning AI: From GenAI to AGI and ASI

Welcome to our curated collection of resources for diving deep into the world of Artificial Intelligence. Whether you're a budding AI enthusiast, a seasoned developer, or a curious mind interested in the future of intelligence, this section is your launchpad. We've compiled a list of essential materials to guide you through the fascinating concepts of Generative AI (GenAI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).

For Building GenAI Models:

• Andrej Karpathy's "Let's build GPT from scratch": A detailed tutorial on building a GPT model from scratch using Python and PyTorch. The video covers everything from the basics of language models to the intricacies of the Transformer architecture, tokenization, and implementation of core components like self-attention.
• Fast.ai's Practical Deep Learning Course: A free course for those with some coding experience who want to learn how to apply deep learning to practical problems. The course covers computer vision, natural language processing, tabular data, and collaborative filtering.
• Hugging Face's NLP Course: This course covers a wide range of NLP topics, including sentiment analysis, embeddings, semantic search, text summarization, and neural machine translation. You'll get hands-on experience using the Hugging Face library.
• "Attention Is All You Need" paper (Transformers): The landmark 2017 research paper that introduced the Transformer architecture, which is the foundation for most modern Large Language Models (LLMs).
• OpenAI's GPT-3 Paper: This paper introduces the Generative Pretrained Transformer 3 (GPT-3) model, one of the most powerful language models to date, with 175 billion parameters. It reviews the technical advancements and applications of GPT-3.

For Understanding AGI:

• DeepMind's "Reward is Enough" paper: This paper hypothesizes that an agent that maximizes reward in a complex environment will develop intelligence as a by-product, suggesting that reward maximization is a key path to AGI.
• "Sparks of AGI" Microsoft Research on GPT-4: This paper argues that GPT-4 exhibits a more general intelligence than previous AI models and can be seen as an early version of an AGI system.
• Max Tegmark's "Life 3.0" (book on AGI futures): This book discusses the societal implications of AI and explores potential futures for humanity in the age of intelligent machines.
• OpenAI's "Planning for AGI and Beyond": This blog post from OpenAI outlines their approach to building AGI, with a focus on safety, and their mission to ensure that AGI benefits all of humanity.

For Exploring ASI Concepts:

• Nick Bostrom's "Superintelligence: Paths, Dangers, Strategies": A book that explores the potential impact of superintelligent AI on humanity, focusing on the associated risks.
• Future of Humanity Institute Papers: A collection of research papers from the Future of Humanity Institute on a wide range of topics related to AI, existential risk, and the future of humanity.
• Eliezer Yudkowsky's "Artificial Intelligence as a Positive and Negative Factor in Global Risk": This paper analyzes the potential risks and benefits of AI, including the philosophical and technical challenges of creating a friendly AI.
• Stuart Russell's "Human Compatible: AI and the Problem of Control": This book argues that the standard model of AI research is misguided and proposes a new model based on the idea of creating AI that is provably beneficial to humans.

YouTube Channels for Continuous Learning:

• Two Minute Papers: This channel explains the latest AI research in a simple and accessible way, with short videos that are easy to digest.
• Yannic Kilcher: This channel features deep technical reviews of machine learning research papers, as well as videos on programming and the broader impact of AI.
• Lex Fridman Podcast: This podcast features long-form interviews with leaders in the AI community, covering a wide range of topics from the technical to the philosophical.