Generative AI Handbook: A Roadmap for Learning Resources

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2024-06-07 01:00:05

This document aims to serve as a handbook for learning the key concepts underlying modern artificial intelligence systems. Given the speed of recent development in AI, there really isn’t a good textbook-style source for getting up-to-speed on the latest-and-greatest innovations in LLMs or other generative models, yet there is an abundance of great explainer resources (blog posts, videos, etc.) for these topics scattered across the internet. My goal is to organize the “best” of these resources into a textbook-style presentation, which can serve as a roadmap for filling in the prerequisites towards individual AI-related learning goals. My hope is that this will be a “living document”, to be updated as new innovations and paradigms inevitably emerge, and ideally also a document that can benefit from community input and contribution. This guide is aimed at those with a technical background of some kind, who are interested in diving into AI either out of curiosity or for a potential career. I’ll assume that you have some experience with coding and high-school level math, but otherwise will provide pointers for filling in any other prerequisites. Please let me know if there’s anything you think should be added!

As of June 2024, it’s been about 18 months since ChatGPT was released by OpenAI and the world started talking a lot more about artificial intelligence. Much has happened since: tech giants like Meta and Google have released large language models of their own, newer organizations like Mistral and Anthropic have proven to be serious contenders as well, innumerable startups have begun building on top of their APIs, everyone is scrambling for powerful Nvidia GPUs, papers appear on ArXiv at a breakneck pace, demos circulate of physical robots and artificial programmers powered by LLMs, and it seems like chatbots are finding their way into all aspects of online life (to varying degrees of success). In parallel to the LLM race, there’s been rapid development in image generation via diffusion models; DALL-E and Midjourney are displaying increasingly impressive results that often stump humans on social media, and with the progress from Sora, Runway, and Pika, it seems like high-quality video generation is right around the corner as well. There are ongoing debates about when “AGI” will arrive, what “AGI” even means, the merits of open vs. closed models, value alignment, superintelligence, existential risk, fake news, and the future of the economy. Many are concerned about jobs being lost to automation, or excited about the progress that automation might drive. And the world keeps moving: chips get faster, data centers get bigger, models get smarter, contexts get longer, abilities are augmented with tools and vision, and it’s not totally clear where this is all headed. If you’re following “AI news” in 2024, it can often feel like there’s some kind of big new breakthrough happening on a near-daily basis. It’s a lot to keep up with, especially if you’re just tuning in.

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