OpenAI has recently shared insights into how they are evolving their organizational structure to better foster innovation and ensure safety in AI development. This move is aimed at enhancing collaboration, transparency, and efficiency as they continue to push the boundaries of artificial intelligence. By refining their structure, OpenAI is working to address the challenges of scaling AI responsibly while delivering impactful solutions for the future. Som say its OpenAI backtracking from seeking to be a "For-Profit" company .. whats your take on this ? Learn more about their approach here: https://openai.com/index/evolving-our-structure Explore more about OpenAI: https://openai.com #OpenAI #ArtificialIntelligence #AIInnovation #TechDevelopment #AIResearch #ResponsibleAI #FutureOfAI #MachineLearning #AICommunity #AIProgress/

China's own Open Source AI development Community (dare l say HugginFace 😆) featuring all the latest Chinese AI (perhaps more) with a Comprehensive open-sourced framework to assist model development and building of AI applications https://modelscope.cn/home #modelscope #hugginface #LLMs #Qwen #Deepseek #Wan

Build reliable AI applications Prompt quality plays a significant role in how successful a model's responses are for a given task. The Anthropic Console already streamlines AI development with tools to write, evaluate, and optimize prompts more efficiently. Write prompts: Use the Workbench to structure your prompts effectively, incorporate examples, and integrate external tools, in an interactive environment for testing API calls. Automatically generate prompts: Describe what you want to achieve, and Claude will use prompt engineering techniques such as chain-of-thought reasoning to create an effective, precise, and reliable prompt. Evaluate model responses: Evaluate your prompts against real-world scenarios with automatic test case generation and side-by-side output comparison. Easily run test suites, grade response quality, and make data-driven decisions about which prompts to deploy. Improve prompts: Automatically refine prompts using advanced prompt engineering techniques. This is ideal for adapting prompts that were originally written for other AI models, as well as for optimizing hand-written prompts. https://www.anthropic.com/

https://www.vccafe.com/2024/09/26/ai-agents-the-evolution-of-rpa/?amp This article discusses the AI Agent Frameworks for 2024. The 2024 AI Agent Ecosystem framework provides a useful categorisation of the emerging layers in AI development: Ecosystem Layer: Foundational models (like OpenAI’s GPT) and enterprise tools form the bedrock. Application Layer: Thousands of companies will build agent apps, leveraging multimodal capabilities and no-code tools. Management Layer: Critical functions include managing AI agents, securing permissions, and enabling payments. Data Layer: AI agents will rely on both exclusive private data and public datasets.