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Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 3,092 posts.
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Why We Think
Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute (Graves et al. 2016, Ling, et al. 2017, Cobbe et al. 2021) and Chain-of-thought (CoT) (Wei et al. 2022, Nye et al. 2021), have led to significant improvements in model performance, while raising many…
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Multiple cameras and motion detection on the Raspberry Pi
Learn how to access *multiple cameras* (including USB webcams and the Raspberry Pi camera module) using your Raspberry Pi, Python, and OpenCV.
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A Long Peek into Reinforcement Learning
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The OnlyFans Economy of American AI
“The dreamers look skyward with longing. The Internet hums with its usual promise — you will find your people, your myth, your wonder, and maybe your transformation.”
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Notes on AI Bias
Machine learning is the new centre of tech, and like all big new things there are issues. ‘AI bias’ is much-discussed right now: machine learning finds patterns but sometimes it finds the wrong one, and it can be hard to tell.
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Long Read: Lessons from Building Semantic Search for GitHub and Why I Failed
Made in Notion, the connected workspace where AI agents and teams organize docs, projects and knowledge together.
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Trying Kolmogorov-Arnold Networks in Practice
There's been a fair bit of buzz about Kolmogorov-Arnold networks online lately. Some research papers were posted around claiming that they offer better accuracy or faster training compared to traditional neural networks/MLPs for the same parameter count.
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Dissecting Reinforcement Learning
Explaining the basic ideas behind reinforcement learning. In particular, Markov Decision Process, Bellman equation, Value iteration and Policy Iteration algorithms, policy iteration through linear algebra methods. It includes full working code written in Python.
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MCP is a fad
Model Context Protocol has taken off as the standardized platform for AI integrations, but this popularity will be short-lived.
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Let's Talk about ChatGPT with Code Interpreter and Microsoft Copilot
Let's talk about ChatGPT with Code Interpreter & Microsoft Copilot
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George Eliot's reflections on AGI from 1879
Forgotten musings on the economics of AI, an intelligence explosion, and machine consciousness
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Early Days of AI
Rather then view LLMs, Transformers, and diffusion models as part of a continuum with past "AI", it is worth thinking of this as an entirely new era and discontinuity from the past
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Knowledge Graphs in RAG: Hype vs. Ragas Analysis
A deep dive on Microsoft's GraphRAG paper found questionable metrics with vaguely defined lift, so I analyzed knowledge graphs in RAG overall using Neo4j vs FAISS
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Learning with Not Enough Data: Semi-Supervised Learning
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AI vending machine was tricked into giving away everything
Anthropic installed an AI-powered vending machine in the WSJ office. The LLM, named Claudius, was responsible for autonomously purchasing inventory from wholesa
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The Prospect of an AI Winter
Texts on this and that.
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The Doom Justifies the Valuation
I’ve been in Berkeley for the last 2 weeks. I haven’t really been back here for a while, and it’s worse than you can believe. This is a cult of atheistic hedonists needing AI doom to be true to justify their life choices. Or acceleration. Or something. They need to make impact. I mean, narcissism of small differences…
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Why I chose OpenAI over academia
Part 2 of a blog post about my time on the computer science job market in 2021-2022, in which I discuss my personal decision-making process between offers.
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LLMs don't do formal reasoning
Important new study from Apple
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The Future of Compute: Nvidia's Crown Is Slipping
Demand consolidation, changing compute mix, custom silicon, and distributed training will hurt NVIDIA's pole position.
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Sprinkling self-doubt on ChatGPT
I replaced my ChatGPT personalization settings with this prompt a few weeks ago and promptly forgot about it: Be extraordinarily skeptical of your own…
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Some notes on the Stable Diffusion safety filter
How deep learning models are moderated
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