Being “Confidently Wrong” is holding AI back
The failure mode that stalls “AI for data” efforts or "AI on my APIs" efforts isn’t psychedelic hallucination—it’s confident inaccuracy: plausible answers that are wrong in subtle and costly ways.
Every blog post that took off on Hacker News, ranked by the votes of its best submission. 6,334 posts.
The failure mode that stalls “AI for data” efforts or "AI on my APIs" efforts isn’t psychedelic hallucination—it’s confident inaccuracy: plausible answers that are wrong in subtle and costly ways.
On making use of large thinking models.
The work Devin has been doing and how your team can start using Devin today.
This is a memo I published internally to my team at Monarch. I’m sharing it more publicly in case it helps other software engineering teams that are managing the crazy times we’re exper…
When I first started writing software, developers had to implement most of the algorithms they used; yes, hardware vendors provided libraries, but the culture was one of self-reliance (except for maths functions, which were technical and complicated).
Judea Pearl and Dana Mackenzie sent me a copy of their new book, “The book of why: The new science of cause and effect.”
When code execution really is all you need. Exploring what happens when an AI agent can only write and run code.
Constrained decoding seems like the greatest thing since sliced bread, but it often forces models to prioritize output conformance over output quality.
A professor at Stanford once said, If you really want to impress your friends and confound your enemies, you can invoke tensor products… People run in terror from the $ \otimes$ symbol. He was explaining some aspects of multidimensional Fourier transforms, but this comment is only half in jest; people get confused by…
RAG poisoning attack walkthrough: an attacker injects fabricated documents into a ChromaDB knowledge base and the LLM reports false financials as fact. Fully reproducible, 100% local, no GPU required.
Lossless compression of a target model's KV cache by up to 4×, using a cheaper predictor model to drive an arithmetic coder.
Inference techniques either move a deployment along the latency–throughput frontier or push the entire frontier out, creating more efficiency to allocate.
Large language models are valuable research assistants, especially when they refuse to follow instructions.
Supabase recently contributed to the OpenAI Retrieval Plugin repo with a Postgres and a Supabase implementation to help developers build ChatGPT plugins using pgvector.
Creating animations for robotic characters is very challenging due to the constraints imposed by their physical nature. In particular, the combination of fast motions and unavoidable structural deformations leads to mechanical oscillations that negatively affect their performances. Our goal is to automatically…
Run AI models locally in Ruby! 💻 Use Ollama to download & run open-source models like Mistral. Integrate them into your Ruby apps using HTTP requests. Great for data security & privacy.
Debugging with intelligence via probabilistic inference Xu et al., ICSE’18 Xu et al. have built a automated debugger that can take a single failing test execution, and with minimal interactio…
Design matters more than ever in consumer AI, as long as you have money to play.
Isn't there somebody you forgot to ask?
New connectors let Claude work inside Adobe Creative Cloud, Blender, Autodesk Fusion, SketchUp, and music production tools to support creative workflows.
Or, how well can GPT-4 simulate an acid trip in 1963?
OverviewArtificial Intelligence is already impacting higher education and the legal profession, and the pace of change appears only to be accelerating. It is thus critical for us to use this moment to carefully reflect on how legal education should adapt. This memo describes the approach we are taking to legal…
How can we use familiar design patterns to constrain the unwieldy state space of large language models?