TGTGInsighttelegram intelligenceLIVE / telegram public index
← GitHub Trends

TGINSIGHT SIMILAR POSTS

Find similar content

Source channel @githubtrending · Post #15549 · Mar 8

#python#ai_automation#api#audio_overview#claude#cli_tool#flashcards#google_notebooklm#notebooklm#notebooklm_api#notebookln#podcast_generator#python#python_api#quiz_generator#sdk#skills#study_tools notebooklm-py is a free Python tool and CLI for full access to Google NotebookLM's features, like creating notebooks, adding sources (URLs, PDFs, YouTube), chatting, deep research, and generating podcasts, videos, quizzes, slides, mind maps in formats like MP3, MP4, JSON. It offers extras the web lacks, such as batch downloads, editable PPTX, and mind map data. You benefit by automating research, content creation, and exports programmatically for faster prototypes, pipelines, or AI agents—saving time on manual UI work. https://github.com/teng-lin/notebooklm-py

Results

1 similar post found

Search: #oecdinsights

当前筛选 #oecdinsights清除筛选
AI & Law

@ai_and_law · Post #75 · 08/04/2023, 07:04 AM

The Complexity of Regulating Foundation Models in the AI Act Hello, AI & Law community! Kai Zenner, the Head of Office and Digital Policy Adviser at the Office of MEP Axel Voss, shared his opinion on the OECD website about regulating foundation models in the AI Act. 🔹 The Existing Gap: The proposed AI Act by the European Commission, created before foundation models gained prominence in AI, doesn't explicitly cover these versatile models. Their potential for diverse, unforeseen purposes makes it tricky to fit them into the current product safety approach. The Act's use case approach, limiting AI systems to specific risk classes, is too inflexible for the latest foundation models that can handle various tasks. This creates a regulatory gap that needs to be addressed. 🔹 Positive Progress: The European Parliament has taken a proactive step to tackle this issue by introducing Article 28b, which adds a regulatory layer specifically for foundation models. This article outlines nine essential obligations for developers, including identifying risks, testing, evaluation, and thorough documentation. These measures aim to strike a balance between ensuring safety and fostering innovation in the AI landscape. 🔹 Targeted Approach: A crucial consideration is to avoid putting too much burden on smaller providers while still effectively regulating foundation models. Zenner proposes adopting a systemic approach, targeting only a small number of highly capable and relevant foundation models under the AI Act. This strategy could be similar to how Very Large Online Platforms are designated under the Digital Services Act, ensuring a balanced and efficient regulatory framework. #AIRegulation#FoundationModels#AIAct#AIInnovation#AICommunity#TechLaw#OECDInsights