2701
Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 3,092 posts.
2702
2703
Hacking Hearthstone with machine learning – Defcon talk wrap-up
A wrap-up of my Defcon talk about hacking Hearthstone with machine learning including slides and a video.
2704
Scaffolded LLMs as natural language computers
Recently, LLM-based agents have been all the rage – with projects like AutoGPT showing how easy it is to wrap an LLM in a simple agentic loop and prompt it to achieve real-world tasks. More generally, we can think about the class of ‘scaffolded’ 1 LLM systems – which wrap...
2705
2706
Introduction to genetic algorithms (example in Javascript)
In this post, we'll learn the basics of one of the most interesting machine learning algorithms, the genetic algorithm. This article is part of a series.
2707
2708
2709
2710
Teaching an AI to play a simple game using Q-learning
In this post I will walk you through how to teach a computer to master a simple video game using the q-learning reinforcement learning algorithm. We will implement the algorithm from scratch in Ruby without the use of external gems. To enable us to illustrate the inner workings of the algorithm we will be teaching […]
2711
LLMs are a 400-year-long confidence trick
In 1623 the German Wilhelm Schickard produced the first known designs for a mechanical calculator. Twenty years later Blaise Pascal produced a machine of an improved design, aiming to help with the large amount of tedious arithmetic required in his role as a tax collector. The interest in mechanical calculation showed…
2712
Why is GPT-3 15.77x more expensive for certain languages?
How large language models process multilingual data differently
2713
When online commenters detect my art as AI
Here is a collection of screenshots (obfuscated) of dozens and dozens of online comments from many platforms (Reddit, YouTube, Instagram, Facebook) containing accusations or confusion that my artworks and comics are AI-generated. Despite creating every piece by hand, despite sharing timelapses, the comments keep…
2714
Computing Inside an AI
Since the launch of ChatGPT, explorations in two directions kicked into high gear.
2715
2716
2717
How people talk about marijuana on Reddit: a natural language analysis
In honor of today being April 20th, I thought it would be interesting to do some NLP on Reddit comments about marijuana (shout out to…
2718
Deterministic Quoting: Making LLMs safer for healthcare
LLMs have the potential to revolutionise our field of healthcare, but the fear and reality of hallucinations prevent adoption in most applications.
2719
Gwern’s AI-Generated Poetry
Gwern has answered my prayers and taught GPT-2 poetry. GPT-2 is the language processing system that OpenAI announced a few weeks ago. They are keeping the full version secret, but have released a s…
2720
2721
Latent Space as a New Medium
Lately I’ve been asking myself: what might artificial intelligence be good for besides answering questions and writing code?
2722
Context Engineering for Agents
Patterns for managing agent context.
2723
Embeddings, vectors, and arithmetic
An embedding is the representation of a piece of text like a word, sentence, or paragraph. Traditionally this value is in the form of a mathematical vector - a point in space. You can think of it as analogous to coordinates on a map that just happens to have many, many dimensions.
2724
Language Models, Word2Vec, and Efficient Softmax Approximations
The Word2Vec model has become a standard method for representing words as dense vectors. This is typically done as a preprocessing step, after which the learned vectors are fed into a discriminative model (typically an RNN) to generate predictions such as movie review sentiment, do machine translation, or even…
2725
2726
Small offline large language model – TinyChatEngine from MIT
BLUF : TinyChatEngine provides an off-line open-source large language model (LLM) that has been reduced in size compared to Llama2. This po...
2727
Notes on Weight Initialization for Deep Neural Networks
Neural networks involve long sequence of multiplications, usually between a matrix and a vector, say \(a*x\). The result of this sequence of multiplications will either have a huge magnitude or be reduced to 0. We can divide \(a\) by a number ( scaling_factor ) to scale down its magnitude to the right level. Proper…
2728
2729
2730
