Salam Vibe

The goal of AI in science isn’t to make researchers lazy—it’s to liberate them from the tyranny of the trivial. By automating the mechanical, we reclaim space for the magical: curiosity, insight, and discovery. As one computational biologist put it: “Let the machine count the trees. We’ll map the forest.” 1. Definition & Scope Core Idea : The phrase “we don’t need to be right to write” challenges the traditional view that scientific writing must emerge fully formed from deep, error-free reasoning. Instead, it reframes writing as a generative, iterative, and exploratory tool —one that can be delegated, scaffolded, or accelerated by artificial intelligence (AI), particularly large language models (LLMs), to free human researchers for higher-order cognitive work. Key Terms : “Donkey-work” : Repetitive, time-consuming, low-cognitive-load tasks (e.g., formatting references, drafting boilerplate text, transcribing notes, organizing datasets). Large Language Models (LLMs) : AI systems (e...

I asked Notebook LM to review and check the recently published document for any inconsistencies, issues, or information gaps, and here is what it came back with: The sources provide extensive detail regarding the context, methodology, and challenges encountered during the preparation of the "National Program for Renewable Energies and Energy Efficiency (2025–2040)," which allows for a comprehensive assessment of the issues, inconsistencies, and missing information. Issues, Inconsistencies, and Missing Information in the Document The sources reveal several critical issues, technical inconsistencies, and information gaps related to the energy sector's current status and the structure of the proposed program: 1. Fundamental Sectoral Issues and Challenges The program was initiated in response to numerous severe national challenges facing the energy sector: Excessive Reliance on Fossil Fuels The electricity generation system relies almost entirely on fossil fuels, leadi...