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The most popular blog posts about ai & machine learning on Hacker News. 789 posts.
Following up on the work from our previous blog post, we are releasing today an arXiv paper titled “Giraffe: Adventures in Expanding Context Lengths in LLMs”. Giraffe is a new family of models that are...
We are thrilled to introduce Argilla Feedback, completely open-source and designed to enhance the performance, safety and reliability of Large Language Models.
Nvidia has gone from the valley to the mountain-top in less than a year, thanks to ChatGPT and the frenzy it inspired; whether or not there is a cliff depends on developing new kinds of demand that…
A new feature that generates natural language answers to search queries using Wikipedia.
Chris is cooking up some examples for us on how to use AI inside Postgres. We have a simple demo of using pgvector and Open AI to grab relationships and query for other similar items.
Coral.ai TPUs are AI accelerators used for tasks like machine vision and audio processing. Raspberry Pis are often integrated into small robotics and IoT products—or used to analyze live video feeds with Frigate. Until today, nobody I know of has been able to get a PCI Express Coral TPU working on the Raspberry Pi…
I’ve noticed that AI twitter is talking a lot about evolution lately, and for good reason. Comparing AI development to an evolutionary system lets
Google Cloud assumes responsibility for potential legal risks of using our generative AI, offering indemnities for training data and generated output.
How we are now leveraging embeddings and vector databases to generate prompts for ChatGPT.
Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra. - run-house/kubetorch
SemiAnalysis published something of a bombshell leaked document this morning: Google “We Have No Moat, And Neither Does OpenAI”. The source of the document is vague: The text below is …
Just 14% of all U.S. adults say they have used ChatGPT for entertainment, to learn something new, or for their work.
Computers, Machine Learning and non-machine learning
The latest updates on the Llama ecosystem coming out of Meta Connect 2023
A new paper describes how Google’s Cloud TPU v4 outperforms TPU v3 by 2.1x on a per-chip basis, and improves performance/Watt by 2.7x.
Reading level: No programming knowledge required, but trying ChatGPT first is advised.
You can use AI tools like Claude to help you write full-length content. By going paragraph-by-paragraph, you can direct the AI while seemingly maintaining your own voice and ideas. However, despite my attempts to use AI with writing, I’ve found that it’s harder to pull off than I thought. I can get close, but due to…
(This is a guest post by Bhavik Mehta) On March 16, 2023, a paper by Campos, Griffiths, Morris, and Sahasrabudhe appeared on the arXiv, announcing an exponential improvement to the upper bound on R…
More details on the science of training large autoregressive language models.
While most of the attention regarding artificial intelligence and copyright has been centred in the United States, it’s useful to remember that one of the main pieces of litigation is taking …
Will OpenAI’s Q* change the world?
How to separate the wheat from the chaff when GPT-powered startups are popping up everywhere
In 2020, we initiated the Meta Training and Inference Accelerator (MTIA) family of chips to support our evolving AI workloads, starting with an inference accelerator ASIC for deep learning recommendation models (DLRMs).
Vector search isn't just for AI embeddings. Use HNSW for geo-spatial queries, stock covariance, chess positions, text tokens, and multi-modal recommendations with custom metrics.
In this blog, we discuss continuous batching, a critical systems-level optimization that improves both throughput and latency under load for LLMs.
AutoGPT decided to drop the support for vector databases: Pinecone, Milvus, Redis, Weaviate - all gone. What has happened to the original idea of efficiently storing agent's memories?
We used Multislice Training to run the world’s largest LLM distributed training job on a compute cluster of 50,944 Cloud TPU v5e chips.
