Researchers developed RoboPAIR, an algorithm designed to jailbreak robots controlled by large language models (LLMs), demonstrating a high success rate across multiple robotic systems.
A guide on how to create and use the bubble mode in Emacs for selecting and manipulating code regions, including expanding and shifting regions, and how to integrate it with LLM queries.
This Splunk Lantern blog post highlights new articles on instrumenting LLMs with Splunk, leveraging Kubernetes for Splunk, and using Splunk Asset and Risk Intelligence.
Exploring physical interface design for LLMs, with projects like AIncense and TinyChat Computer, empowering users through tangible experiences.
The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
This article introduces a practical agent-engineering framework for the development of AI agents, focusing on the key ideas and precepts within the large language model (LLM) context.
The author tests the new GPT-4o AI from OpenAI on a standard set of coding tests and finds that it delivers good results, but with one surprising issue.
Learn how to create a low-cost, AI-powered personal assistant using Raspberry Pi and open-source software. The assistant can answer questions, play music, and control smart home devices.
OpenAI, the artificial intelligence research laboratory, has launched ChatGPT-4, an upgraded version of its popular chatbot. ChatGPT-4 is reportedly more powerful, private, and able to handle longer conversations than its predecessor. The chatbot uses a larger model and improved training techniques, allowing it to generate more nuanced and detailed responses. OpenAI also introduced a new feature called Instruct-1, a more precise way to guide the chatbot's responses, and a new interface for easier interaction with the AI.
This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
