Artificial Intelligence

What Hacker News says

1. maqpJan 2026
   Top 5 will never cover the field. Here's my top 10

   * Brookshear and Brylow - Computer Science - An Overview

   * Forta - Teach yourself SQL in 10 minutes

   * Stallings - Computer Organization and Architecture

   * Stallings - Operating Systems - Internals and Design Principles

   * CLRS

   * Kurose, Ross - Computer Networking - A Top Down Approach

   * Sipser - Introduction to The Theory of Computation

   * Stallings, Brown - Computer Security - Principles and Practice

   * Aumasson - Serious Cryptography

   * Russell, Norvig - Artificial Intelligence - A Modern Approach

   And even this fails to cover programming languages. Python is the lingua franca of the field. Most past recommended books are getting outdated, but perhaps Matthes' Python Crash Course 3rd edition.

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2. antegamisouJul 2025
   Understandably I'm becoming a bit dogmatic but I'll say it again, AIMA/PRML/ESL are still the best reference textbooks for foundational AI/ML and will be for a long time.

   AIMA is Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig

   PRML is Pattern Recognition and Machine Learning by Christopher Bishop.

   ESL is Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman.

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3. kevindammJul 2025
   My first suggestion would be Norvig's text "Artificial Intelligence Programming" (note: not Russel and Norvig's "Modern Approach" but the "Case Studies" one with the teal and orange cover), start with chapter 3 for a straightforward description of the core set of routines (more than a minimal impl but not everything you'd see described in CL) and choose a few from any of the other chapters as a demo program that you can use as a first target for your implementation. Yeah it's GOFAI and might feel dated but it worked well for me to get a stronger grasp of Lisp implementations.

   This won't hold your hand through implementing Lisp within another language like C or Rust, but it does show the representation of Lisp within Lisp (and a few chapters on implementing a logic programming language in Lisp as well). Most of the task of transpiling into another language reduces to how you represent functions and function results, and the extent to which your compiler reduces specific built-ins. There's a range of implementation here that you can find covered in PL research papers, but you may be better off looking through compiler books and articles for that.

   This book also has a couple chapters on efficiency concerns for implementations of Lisp, chapters 9 and 10, which look at both dev-side and compiler-side approaches to more efficient Lisps.

   https://www.goodreads.com/book/show/15534706

   Despite the focus on GOFAI, I think the lessons apply to good Lisp programs in general from a practitioner's PoV. If you want a more abstract vantage then I'd say the classic "Structure and Interpretation of Computer Programs" by Abelson & Sussman should be mentioned. It includes a lot of diagrams of CONS structures to keep things focused but it doesn't have the kind of full case study applications of the Norvig text.

   https://www.goodreads.com/book/show/43713

   It's funny how all the good books on Lisp implementations assume that first you start with a Lisp. I wonder if this goes back to there being Lisp machines on which the language was already deeply situated.

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4. owl_visionJul 2025
   perceptrons, memory-in-a-box, back-propagation (even though it's computation expensive), Long-term-short-term memory, Support Vector Machines and other Neural Networks.

   In the book "Artificial Intelligence A Modern Approach" by Stuart Russell and Peter Norviq, you may find more gems.

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5. mindcrimeNov 2024
   Lots of good suggestions here already. I'd start by adding one quick note though. "AI" is more than just LLM's. Sure, the "current, trendy, fashionable" thing is all LLM's, but the field as a whole is still much larger. I'd encourage you to not myopically focus on LLM's to exclusion. Depending on your existing background knowledge, there's a lot to be said for going out and getting a copy of Artificial Intelligence: A Modern Approach and reading through it. Likewise for something like Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow.

   Beyond that: there are some decent sub-reddits for keeping up with AI happenings, a lot of good Youtube channels (although a lot of the ones that talk about the "current, trendy" AI stuff tend to be a bit tabloid'ish), and even a couple of Facebook groups. You can also find good signal by choosing the right people to follow on Twitter/LinkedIn/Mastodon/Bluesky/etc.

   https://www.reddit.com/r/artificial/

   https://reddit.com/r/machineLearning/

   https://www.reddit.com/r/LLM/

   https://www.reddit.com/r/agi

   https://www.reddit.com/r/ollama/

   https://www.youtube.com/@matthew_berman

   https://www.youtube.com/@TheAiGrid

   https://www.youtube.com/@WesRoth

   https://www.youtube.com/@DaveShap

   https://www.youtube.com/c/MachineLearningStreetTalk

   https://www.youtube.com/@twimlai

   https://www.youtube.com/@YannicKilcher

   And you can always go straight to "the source" and follow pre-prints showing up in arXiv.

   https://arxiv.org/corr

   For tools to make it easier to track new releases, arXiv supports subscriptions to daily digest emails, and also has RSS feeds.

   https://info.arxiv.org/help/subscribe.html

   https://info.arxiv.org/help/rss.html

   There are also some bots in the Fediverse that push out links to new arXiv papers.

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6. dr_dshivJun 2024
   Well, I believe LLMs are general purpose AI. And, for the record, so does Peter Norvig. He coauthored the most popular textbook on Artificial Intelligence, so this belief can be considered a somewhat mainstream perspective. His article, “Artificial General Intelligence Is Already Here“ [1] is really worth reading.

   https://www.noemamag.com/artificial-general-intelligence-is-...

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