Tag: language models
-
Hacker News: Large Language Models Are Changing Collective Intelligence Forever
Source URL: https://www.cmu.edu/tepper/news/stories/2024/september/collective-intelligence-and-llms.html Source: Hacker News Title: Large Language Models Are Changing Collective Intelligence Forever Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The paper explores how Large Language Models (LLMs) influence collective intelligence in various settings, enhancing collaboration and decision-making while also posing risks like potential misinformation. It emphasizes the need for responsible…
-
Hacker News: Scalable watermarking for identifying large language model outputs
Source URL: https://www.nature.com/articles/s41586-024-08025-4 Source: Hacker News Title: Scalable watermarking for identifying large language model outputs Feedly Summary: Comments AI Summary and Description: Yes Summary: This article presents an innovative approach to watermarking large language model (LLM) outputs, providing a scalable solution for identifying AI-generated content. This is particularly relevant for those concerned with AI security…
-
Slashdot: Leaked Training Shows Doctors In New York’s Biggest Hospital System Using AI
Source URL: https://science.slashdot.org/story/24/11/03/2145204/leaked-training-shows-doctors-in-new-yorks-biggest-hospital-system-using-ai?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Leaked Training Shows Doctors In New York’s Biggest Hospital System Using AI Feedly Summary: AI Summary and Description: Yes Summary: The text discusses Northwell Health’s launch of an AI tool called AI Hub, which utilizes large language models (LLMs) for various healthcare-related tasks, including patient data management and clinical…
-
Hacker News: gptel: a simple LLM client for Emacs
Source URL: https://github.com/karthink/gptel Source: Hacker News Title: gptel: a simple LLM client for Emacs Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text describes “gptel,” a client for interacting with Large Language Models (LLMs) in Emacs. It allows users to engage with different LLMs seamlessly within the Emacs environment, supporting features like contextual…
-
Hacker News: Zed – The Editor for What’s Next
Source URL: https://zed.dev/ Source: Hacker News Title: Zed – The Editor for What’s Next Feedly Summary: Comments AI Summary and Description: Yes Summary: The text highlights a software tool designed to enhance productivity through intelligent code generation and collaboration, particularly leveraging large language models (LLMs). This innovation can be crucial for professionals in the realms…
-
Slashdot: AI Bug Bounty Program Finds 34 Flaws in Open-Source Tools
Source URL: https://it.slashdot.org/story/24/11/03/0123205/ai-bug-bounty-program-finds-34-flaws-in-open-source-tools?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: AI Bug Bounty Program Finds 34 Flaws in Open-Source Tools Feedly Summary: AI Summary and Description: Yes Summary: The report highlights the identification of numerous vulnerabilities in open-source AI and ML tools, particularly through Protect AI’s bug bounty program. It emphasizes the critical nature of security in AI development,…
-
Hacker News: GitHub Spark lets you build web apps in plain English
Source URL: https://techcrunch.com/2024/10/29/github-spark-lets-you-build-web-apps-in-plain-english/ Source: Hacker News Title: GitHub Spark lets you build web apps in plain English Feedly Summary: Comments AI Summary and Description: Yes Summary: GitHub’s introduction of Spark marks a significant advancement in AI-driven software development, enabling users to create web applications using natural language inputs. This tool provides a new layer of…
-
Hacker News: SmolLM2
Source URL: https://simonwillison.net/2024/Nov/2/smollm2/ Source: Hacker News Title: SmolLM2 Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces SmolLM2, a new family of compact language models from Hugging Face, designed for lightweight on-device operations. The models, which range from 135M to 1.7B parameters, were trained on 11 trillion tokens across diverse datasets, showcasing…