Image created by Editor with Midjourney
Welcome to this week’s edition of “This Week in AI” on KDnuggets. This curated weekly post aims to keep you abreast of the most compelling developments in the rapidly advancing world of artificial intelligence. From groundbreaking headlines that shape our understanding of AI’s role in society to thought-provoking articles, insightful learning resources, and spotlighted research pushing the boundaries of our knowledge, this post provides a comprehensive overview of AI’s current landscape. This weekly update is designed to keep you updated and informed in this ever-evolving field.
The “Headlines” section discusses the top news and developments from the past week in the field of artificial intelligence. The information ranges from governmental AI policies to technological advancements and corporate innovations in AI.
The open source Project Jupyter team has released Jupyter AI, a new extension that brings generative AI capabilities directly into Jupyter notebooks and the JupyterLab IDE. Jupyter AI lets users leverage large language models via chat interactions and magic commands to explain code, generate new code and content, answer questions about local files, and more. It was built with responsible AI in mind, allowing control over model selection and tracking of AI-generated output. Jupyter AI supports providers like Anthropic, AWS, Cohere, and OpenAI. It aims to make AI accessible in an ethical way to enhance the Jupyter notebook experience.
Stack Overflow announced OverflowAI, their integration of AI capabilities into their public Q&A platform, Stack Overflow for Teams, and new products like IDE extensions. Features include semantic search to find more relevant results, ingesting enterprise knowledge to bootstrap internal Q&A faster, a Slack chatbot accessing Stack Overflow content, and a VS Code extension surfacing answers in developers’ workflows. They aim to leverage their community’s 58M+ questions while ensuring trust via attribution and transparency around AI-generated content. The goal is to use AI responsibly to enhance developers’ efficiency by connecting them with solutions in context.
Over the past week, several small updates were rolled out to enhance the ChatGPT experience. These updates included the introduction of prompt examples to help users begin chats, suggested replies for deeper engagement, and preferences for using GPT-4 by default for Plus users. Additional features such as multi-file uploads in the Code Interpreter beta for Plus users, a new stay-logged-in function, and a suite of keyboard shortcuts were also introduced to improve usability.
The “Articles” section presents an array of thought-provoking pieces on artificial intelligence. Each article dives deep into a specific topic, offering readers insights into various aspects of AI, including new techniques, revolutionary approaches, and ground-breaking tools.
The author experimented with ChatGPT prompts to create an AI-powered cover letter generator web application called Tally.Work in just 3 days, using Bubble.io for the frontend and the OpenAI API for generating text. It takes a user’s resume and job description as inputs and outputs a customized cover letter. The goal was to build an app with a large potential user base. Though AI-generated text isn’t perfect yet, it can help create a useful first draft. The author believes AI will eliminate many tedious tasks like cover letters, and hopes this project helps lead to more interesting AI apps in the future. Overall it shows how quickly someone can use no-code tools and AI APIs to build and launch an app idea.
The article discusses three main challenges in deploying generative AI models like GPT-3 and Stable Diffusion in production: their massive size leading to high compute costs, biases that can propagate harmful stereotypes, and inconsistent output quality requiring tuning. Solutions include model compression, training on unbiased data, post-processing filters, prompt engineering, and model fine-tuning. Overall it outlines how companies must carefully address these issues to successfully leverage generative models while avoiding potential downsides.
The “Tools” section lists useful apps and scripts created by the community for those who want to get busy with practical AI applications. Here you will find a range of tool types, from large comprehensive code bases to small niche scripts. Note that tools are shared without endorsement, and with no guarantee of any sort. Do your own homework on any software prior to installation and use!
This repository demonstrates using AI to brainstorm and refine story ideas collaboratively with a human. Rather than replacing the human, the AI acts as a creative partner, suggesting ideas and doing research. At each step, the human can accept, reject, or modify the AI’s suggestions. One of the main challenges in writing is coming up with ideas. This project aims to help writers overcome writer’s block by providing a creative partner to bounce ideas off of.
🛠️ Gdańsk AI
Gdańsk AI is a full stack AI voice chatbot (speech-to-text, LLM, text-to-speech) with integrations to Auth0, OpenAI, Google Cloud API and Stripe – Web App, API and AI
The “Research Spotlight” section highlights significant research in the realm of AI. The section includes breakthrough studies, exploring new theories, and discussing potential implications and future directions in the field of AI.
The paper introduces ToolLLM, a framework to enhance the tool-using abilities of open-source large language models. It constructs a dataset called ToolBench containing instructions involving 16,000 real-world APIs across 49 categories. ToolBench is automatically generated using ChatGPT with minimal human involvement. To improve reasoning, the authors propose a depth-first search decision tree method that allows models to evaluate multiple reasoning traces. They also develop an automatic evaluator ToolEval to efficiently assess tool-use capabilities. By fine-tuning LLaMA on ToolBench, they obtain ToolLLaMA which demonstrates strong performance on ToolEval, including generalizing to unseen APIs. Overall, ToolLLM provides a way to unlock sophisticated tool use in open-source LLMs.
This paper introduces MetaGPT, a framework to improve large language model collaboration on complex tasks. It incorporates real-world standardized operating procedures into prompts to guide multi-agent coordination. Roles like ProductManager and Architect produce structured outputs matching industry conventions. A shared environment and memory enable knowledge sharing. On software tasks, MetaGPT generated more code, documents, and higher success rates than AutoGPT and AgentVerse, showing its ability to decompose problems across specialized agents. The standardized workflows and outputs aim to reduce incoherence in conversations. Overall, MetaGPT demonstrates a way to capture human expertise in agents to tackle intricate real-world problems.