AI Starter Reading Kit
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AI Starter Reading Kit

Written by
Arjun Ram
Date published
November 7, 2023

While machine learning has been around for a while (my first startup 14 years ago was based on ML & natural language processing), this wave of AI (transformers & generative AI) feels like a tsunami. Its is a culmination of multiple waves listed below that are crashing at the same time:

  • Natural Language as an interface (Voice to Text and Text To Speech)
  • Generate human like content & code (Generative AI using existing patterns)
  • Reasoning Capabilities (Able to break down into smaller issues)
  • Hardware (GPUs)

Given how fast this space is moving, it is natural to feel daunted. So if you are looking for starter kit to get started, here are my recommendations. Feel free to tweet me if I am missing something.

šŸ‘“šŸ½Ā Messiahā€™s Corner

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Marc provides an optiistic view, must read!
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Marc goes toe to toe with Ben Thompson
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No better thought leader than Elon who takes a very long view
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Folks that truly have the pulse of the ecosystem - Nat Friedman & Daniel Gross

šŸ’ŖšŸ¼The Enablers

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The company that started this rush - OpenAI
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The Juggernaut that powers it all
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Facebook learned from their mobile fiasco & enabled the opensource ecosystem

šŸ’°Gamblerā€™s Paradise

šŸ˜¬Ā 101

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Into to Large Language Models
[1hr Talk] Intro to Large Language Models

This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm. As of November 2023 (this field moves fast!). Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that's my hotel room during the thanksgiving break. - Slides as PDF: https://drive.google.com/file/d/1pxx_ZI7O-Nwl7ZLNk5hI3WzAsTLwvNU7/view?usp=share_link (42MB) - Slides. as Keynote: https://drive.google.com/file/d/1FPUpFMiCkMRKPFjhi9MAhby68MHVqe8u/view?usp=share_link (140MB) Few things I wish I said (I'll add items here as they come up): - The dreams and hallucinations do not get fixed with finetuning. Finetuning just "directs" the dreams into "helpful assistant dreams". Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the "working memory" of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, I'd always recommend double-checking the math/code the LLM did. - How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code "above" that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the ā€œsystem messageā€). - You might also enjoy my 2015 blog post "Unreasonable Effectiveness of Recurrent Neural Networks". The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015/05/21/rnn-effectiveness/ - What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/llama2.c/blob/master/run.c Chapters: Part 1: LLMs 00:00:00 Intro: Large Language Model (LLM) talk 00:00:20 LLM Inference 00:04:17 LLM Training 00:08:58 LLM dreams 00:11:22 How do they work? 00:14:14 Finetuning into an Assistant 00:17:52 Summary so far 00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard Part 2: Future of LLMs 00:25:43 LLM Scaling Laws 00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E) 00:33:32 Multimodality (Vision, Audio) 00:35:00 Thinking, System 1/2 00:38:02 Self-improvement, LLM AlphaGo 00:40:45 LLM Customization, GPTs store 00:42:15 LLM OS Part 3: LLM Security 00:45:43 LLM Security Intro 00:46:14 Jailbreaks 00:51:30 Prompt Injection 00:56:23 Data poisoning 00:58:37 LLM Security conclusions End 00:59:23 Outro

www.youtube.com

[1hr Talk] Intro to Large Language Models
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Large Language Models (LLMs) explained
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Reinforcement Learning from Human Feedback (RHLF) explained
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Various terms explained

šŸ—ļøBuilderā€™s Toolkit

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Sarah Guo Lays out a template for strategy

šŸ‘·šŸ»ā€ā™€ļøProduct Managerā€™s Corner

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šŸ¤”Ā Things to ponder

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Impact on energy consumption
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Impact on water resources needed for cooling
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Impact of Generative AI on education
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Data licensing issues that power LLMs
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Human reinforced learning with International labor

šŸ“šReading Corner

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If you want to understand the foundations of ML from an economic perspective.
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Historic context of ML & a compelling case about the opportunity

šŸŒ±Ā Staying upto date

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This space moves at a lightening pace, my Twitter List to keep upto date