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The field of artificial intelligence (AI) is advancing at a rapid pace, making it challenging for both experts and beginners to keep up with the latest developments. To help with this, we have compiled a curated list of resources that have had a significant impact on the field in recent years. We call it the AI Canon, which includes papers, blog posts, courses, and guides that can enhance your understanding of modern AI.

We begin with an introduction to transformer and latent diffusion models, which are driving the current AI wave. These resources provide a gentle introduction to these concepts, allowing you to grasp the fundamental ideas behind them. Understanding transformers and latent diffusion models is vital in comprehending the latest advancements in AI.

Next, we delve into technical learning resources and practical guides for building with large language models (LLMs). Andrej Karpathy, a respected figure in the field of AI, explains how AI represents a powerful new way to program computers. His insights from 2017 have proven to be incredibly insightful and continue to shape our understanding of the AI market.

The State of GPT, also written by Karpathy, offers an approachable explanation of how ChatGPT and GPT models function, how to utilize them effectively, and what future research and development might entail. Additionally, computer scientist and entrepreneur Stephen Wolfram provides a detailed explanation of modern AI models, outlining their progression from early neural networks to present-day LLMs and ChatGPT.

To gain a better understanding of transformers, Dale Markowitzs post offers a concise answer to the question of what an LLM is and how it operates. Although the post primarily focuses on GPT-3, the information is transferable to newer models. Similarly, Chris McCormicks explanation of how Stable Diffusion works provides valuable intuition around text-to-image models, specifically in the field of computer vision.

To familiarize yourself with the key terms and technologies in modern AI, we recommend consulting the AI glossary by a16z. This resource offers definitions of commonly used terminology in the field, ensuring you stay informed and up-to-date with the latest advancements.

For those seeking a more in-depth understanding of machine learning and AI fundamentals, there are university-level courses available. Stanfords CS229: Introduction to Machine Learning with Andrew Ng covers the basics of machine learning, while CS224N: NLP with Deep Learning with Chris Manning focuses on natural language processing (NLP) and the initial generation of LLMs.

Furthermore, we have compiled a selection of resources that explain how LLMs work, catering to a diverse audience. Stanfords online seminar, CS25: Transformers United, provides an in-depth exploration of transformers, and CS324: Large Language Models offers insights into both technical and non-technical aspects of LLMs.

In terms of reference and commentary, we recommend exploring Yann LeCuns talk on predictive learning from NIPS 2016, emphasizing the significance of unsupervised learning as a crucial aspect of large-scale AI models. Andrej Karpathys talk on AI for full-self driving at Tesla provides valuable insights into the challenges associated with long-tailed problems in the field of AI. Additionally, Gwerns post on the scaling hypothesis elucidates the concept that increasing data and compute can enhance accuracy in LLMs.

For a comprehensive overview of current LLMs, including their development timeline, size, training strategies, and more, we suggest referring to a survey of large language models. Spark of artificial general intelligence: Early experiments with GPT-4 offers preliminary analysis from Microsoft Research on the capabilities of the most advanced LLM, GPT-4. Familiarizing yourself with AI agents, such as Auto-GPT, is also critical, as they represent a new era of automation and creativity.

As LLMs become increasingly central to AI applications, we have gathered resources to aid in understanding the application stack. Although formal education on this topic is still limited, we recommend exploring resources such as building a GitHub support bot with GPT3, LangChain, and Python. This early explanation of the modern LLM app stack kickstarted widespread adoption and experimentation of new AI applications. Additionally, Chip Huyens discussion on building LLM applications for production addresses key challenges and recommends suitable use cases.

To enhance your prompt engineering skills when utilizing LLMs, we suggest referring to the Prompt Engineering Guide, which provides comprehensive guidance and specific examples for popular models. Brexs prompt engineering guide offers a lighter, more conversational approach to this topic.

In conclusion, the AI Canon is a curated collection of resources designed to broaden your knowledge of modern AI. By exploring these materials, you can stay informed about the latest advancements and deepen your understanding of this rapidly evolving field.

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The AI Canon: A Curated List of Resources to Get Smarter About ... - Fagen wasanni