Singularity Automata @singularityauto
AI, robots, and automata Singularity Joined November 2014-
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The Chinchilla scaling paper by Hoffmann et al. has been highly influential in the language modeling community. We tried to replicate a key part of their work and discovered discrepancies. Here's what we found. (1/9)
Our computer vision textbook is released! Foundations of Computer Vision with Antonio Torralba and Bill Freeman mitpress.mit.edu/9780262048972/… It’s been in the works for >10 years. Covers everything from linear filters and camera optics to diffusion models and radiance fields. 1/4
In praise of RDF RDF is a data model, a knowledge representation system, a web standard & a data exchange format Used to build #knowledgegraphs & #LLM-based applications @semihsalihoglu describes its virtues, vices, history & applications #AI #GraphDB buff.ly/49DJWus
Can AI web agents 💻 hop around websites to complete complex user tasks? We present 🌠MMInA, a multihop multimodal Internet agent benchmark, with 1050 challenging human-written web browsing tasks. arXiv preprint: arxiv.org/abs/2404.09992 Project page: mmina.cliangyu.com
CS159: LLMs for reasoning lecture slides from Caltech are really good. Link: sites.google.com/view/cs-159-20… Thank you for making them public @yisongyue and @acbuller
Introducing 𝐀𝐋𝐎𝐇𝐀 𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐞𝐝 🌋 - Pushing the boundaries of dexterity with low-cost robots and AI. @GoogleDeepMind Finally got to share some videos after a few months. Robots are fully autonomous filmed in one continuous shot. Enjoy!
🚨BREAKING: The @Stanford Institute for Human-Centered AI publishes its Artificial Intelligence Index Report 2024, one of the most authoritative sources for data and insights on AI. Below are its top 10 takeaways: 1. AI beats humans on some tasks, but not on all; 2. Industry…
Along with Core, we have published a technical report detailing the training, architecture, data, and evaluation for the Reka models. publications.reka.ai/reka-core-tech…
Reducing Hallucination in Structured Outputs via RAG Nice paper by researchers at ServiceNow where they discuss how to deploy an efficient RAG system for structured output tasks. The RAG system combines a small language model with a very small retriever. It shows that RAG can…
🤏 Why do small Language Models underperform? We prove empirically and theoretically that the LM head on top of language models can limit performance through the softmax bottleneck phenomenon, especially when the hidden dimension <1000. 📄Paper: arxiv.org/pdf/2404.07647… (1/10)
Planning is a key agentic AI design pattern in which we use a large language model (LLM) to autonomously decide on what sequence of steps to execute to accomplish a larger task. For example, if we ask an agent to do online research on a given topic, we might use an LLM to break…
Highly amusing update, ~18 hours later: llm.c is now down to 26.2ms/iteration, exactly matching PyTorch (tf32 forward pass). We discovered a bug where we incorrectly called cuBLAS in fp32 mathmode 🤦♂️. And ademeure contributed a more optimized softmax kernel for very long rows…
Highly amusing update, ~18 hours later: llm.c is now down to 26.2ms/iteration, exactly matching PyTorch (tf32 forward pass). We discovered a bug where we incorrectly called cuBLAS in fp32 mathmode 🤦♂️. And ademeure contributed a more optimized softmax kernel for very long rows… https://t.co/Orz0KUznrP
How do LLMs scale to million token context window? Ring Attention is a nice trick to parallelize long sequence across devices and rotate them in a ring with zero overhead scaling. In our new blog, we cover the tricks behind this magic. It looks like this (1/5🧵)
From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples Several LLMs (e.g., GPT-4) perform on par w/ supervised methods like Random Forest on regression repo:github.com/robertvacarean… abs: arxiv.org/abs/2404.07544
Very interesting papers @ZeyuanAllenZhu . This trick is very interesting. I recall hearing evidence OpenAI does label training data with source/provenance (the LLM sometimes spits out those memorized labels). Can't remember where/who I learnt this from
Very interesting papers @ZeyuanAllenZhu . This trick is very interesting. I recall hearing evidence OpenAI does label training data with source/provenance (the LLM sometimes spits out those memorized labels). Can't remember where/who I learnt this from
When I first saw Tree of Thoughts, I asked myself: If language models can reason better by searching, why don't they do it themselves during Chain of Thought? Some possible answers (and a new paper): 🧵
Very cool project -- a Gemma model fine-tuned to turn text into mind map summaries in dotfile format: kaggle.com/models/toshik/…
# explaining llm.c in layman terms Training Large Language Models (LLMs), like ChatGPT, involves a large amount of code and complexity. For example, a typical LLM training project might use the PyTorch deep learning library. PyTorch is quite complex because it implements a very…
And here's the technical report, if you want to learn more about the models and what they can do: storage.googleapis.com/deepmind-media…
We introduce LLM2Vec, a simple approach to transform any decoder-only LLM into a text encoder. We achieve SOTA performance on MTEB in the unsupervised and supervised category (among the models trained only on publicly available data). 🧵1/N Paper: arxiv.org/abs/2404.05961
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