Giving GPT "Infinite" Knowledge 121 HN points • 06 May 23 🕹 Technology Data processing Machine Learning AI Applications Real-Time Data Training Large Language Models (LLMs) with new data constantly is impractical due to the vast amount of information and privacy concerns. OpenAI's focus on improving LLMs in other ways instead of just increasing model size indicates the end of giant model era. Using tokens, embeddings, vector storage, and prompting can help provide LLMs with large amounts of data for better interpretation and understanding.
Should we worry? 16 implied HN points • 22 Dec 23 🕹 Technology AI Safety Automation Regulations Innovation AI advancements come with risks like misuse and content flooding. AI automation may lead to job displacement and increased productivity. Managing AI advancement involves differing perspectives, safety regulations, and government frameworks.
The Age of Agents 2 HN points • 24 May 23 🕹 Technology AI Software Experiment Innovation Research Advancements in large language models have enabled new possibilities through chat interfaces. Experimenting with instructing multiple agents shows potential for improved outcomes in task completion. Using a lead engineer can help review, guide, and improve outputs from engineering agents in experiments.
Gorilla LLM: Bridging APIs with User-Specified Tasks 2 HN points • 15 Jun 23 🕹 Technology AI APIs Machine Learning Software Development Artificial Intelligence Gorilla LLM is designed to connect large language models with various services and applications through APIs. LLaMA was chosen as the base model for Gorilla, which has since been fine-tuned with GPT-4, GPT-3.5, and other models. Gorilla LLM introduces novel concepts like retriever-aware training and AST sub-tree matching for more accurate inferences.
Technical Dive Into AutoGPT 2 HN points • 22 Apr 23 🕹 Technology AI Machine Learning Open Source Autonomous Agents Natural Language Processing Auto-GPT uses various techniques to make GPT autonomous in completing tasks with executable commands. Auto-GPT addresses GPT's lack of explicit memory by using external memory modules like embeddings and vector storage. Interpreting responses with fixed JSON format and executing commands allows Auto-GPT to interact with the real world and complete tasks.