At its re:Invent conference, Amazon’s AWS cloud arm today announced the launch of SageMaker HyperPod, a new purpose-built service for training and fine-tuning large language models. SageMaker HyperPod is now generally available. Amazon has long bet on SageMaker, its service for building, training and deploying machine learning models, as the backbone of its machine learning […]
Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual errors, so wary readers may want to know if generative AI tools have been used to ghostwrite news articles or other sources before trusting them.
Exploring the architecture of OpenAI’s Generative Pre-trained Transformers.
Will be the transformer the model leading us to artificial general intelligence? Or will be replaced?
Mozilla’s innovation group and Justine Tunney just released llamafile, and I think it's now the single best way to get started running Large Language Models (think your own local copy of ChatGPT) on your own computer.
This post is the second part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance features alongside practical examples to see how far we can push PyTorch native performance. In part one, we showed how to accelerate Segment Anything over 8x using only pure, native PyTorch. In this blog we’ll focus on LLM optimization.
Rodney Brooks, co-founder of iRobot, kicks off an MIT symposium on the promise and potential pitfalls of increasingly powerful AI tools like ChatGPT.
Level up your agent to win more difficult games!