Machine Intelligence - 2026

A chronological archive of Machine Intelligence reading list posts.

2024

January 2024

πŸ”— The Frame Problem

πŸ”— Meta releases prompt engineering guide to help beginners and pros prompt like experts

πŸ”— Sean Moriarty - The Future of Large Language Models is Elixir

πŸ”— Stanford’s mobile ALOHA robot learns from humans to cook, clean, do laundry

πŸ”— RAG Using Unstructured Data & Role of Knowledge Graphs (part 2/2)

πŸ”— Mathematical Introduction to Deep Learning (Arxiv, 2310.20360)

πŸ”— Direct Preference Optimisation: Your Language Model is Secretly a Reward Model

πŸ”— Self-Consuming Generative Models Go MAD

πŸ”— Centaurs and Cyborgs on the Jagged Frontier

πŸ”— AI or Ain't: Eliza

πŸ”— Coca-Cola’s accidentally terrifying Christmas card AI image generator

πŸ”— AI Won’t Kill Our Jobs, It Will Kill Our Job Descriptionsβ€”and Leave Us Better Off

πŸ”— GenAI could make KYC effectively useless

πŸ”— crewAI

πŸ”— Embeddable AI with Nitro

πŸ”— Ten Noteworthy AI Research Papers of 2023

πŸ”— Pushing ChatGPT's Structured Data Support To Its Limits

πŸ”— OpenAI Cookbook

πŸ”— Machine Learning Video Library

πŸ”— The Random Transformer: Understand how transformers work by demystifying all the math behind them

πŸ”— State-of-the-art Code Generation with AlphaCodium – From Prompt Engineering to Flow Engineering

πŸ”— Machine Learning Engineering Open Book

πŸ”— How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

πŸ”— Torvalds Speaks: Impact of Artificial Intelligence on Programming

πŸ”— Andrei Kovalev's Midlibrary

πŸ”— The 6 Types of Conversations with Generative AI

πŸ”— The chaos inside OpenAI – Sam Altman, Elon Musk, and existential risk explained | Karen Hao

πŸ”— Attacks on machine learning models

πŸ”— State of AI & predictions for 2024

πŸ”— DocLLM: A layout-aware generative language model for multimodal document understanding

πŸ”— The Impact of Reasoning Step Length on Large Language Models

πŸ”— Seven Failure Points When Engineering a Retrieval Augmented Generation System


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