What hotel light switches say about AI’s future

Elon Musk makes fun of ChatGPT-maker OpenAI, Perplexity CEO 'joins the fun'

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Welcome to TipSeason Trending AI Newsletter.

In today’s edition, we will cover:

  • Trending AI News

  • Trending AI Tools

  • Best AI Prompt pick for ChatGPT / Gemini / any AI

Also welcome to the 139+ new subscribers who joined us this week and staying on top of AI Trends.

Trending AI News:

  • What hotel light switches say about AI’s future Link

  • Elon Musk makes fun of ChatGPT-maker OpenAI, Perplexity CEO 'joins the fun' Link

  • Google Research: accelerating scientific breakthroughs to real-world impact Link

  • What it's like to live in the world's smartest cities for 2025 Link

  • Expanding our use of Google Cloud TPUs and Services Link

  • Silicon Valley-backed Shield AI enters fighter jet race with new "wingman" drone Link

  • 7 Best Chrome Extensions for Agentic AI Link

  • 5 AI-Assisted Coding Techniques Guaranteed to Save You Time Link

  • Chhath Puja 2025 Gemini AI Prompts for Couples, Girls and Boys: Create Cinematic Festive photos Link

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Best AI Prompt Pick:

For ChatGPT, Bard, Claude and other AI chatbots:

Topic: Exhaustiveness of AI

Prompt:

Goal:
Deliver an expert-level, structured, and comprehensive analysis of the concept of "Exhaustiveness in AI" — how artificial intelligence systems strive for or approximate completeness in reasoning, knowledge representation, and decision-making processes.


Customizable Variables:
1. [AI Domain Focus] – Specify the AI subfield (e.g., machine learning, NLP, decision-making systems, general intelligence).
2. [Application Context] – Define the practical context or industry (e.g., healthcare diagnostics, AI ethics, robotics).
3. [Depth Level] – Choose the technical depth: conceptual overview, academic analysis, or detailed technical framework.


Context & Expertise Layering:
Frame the analysis using advanced AI concepts like computational completeness, bounded rationality, and Gödel’s incompleteness. Integrate perspectives from knowledge representation, reinforcement learning, search algorithms, and explainability paradigms. Discuss how exhaustiveness impacts reasoning, knowledge coverage, and learning boundaries in [AI Domain Focus], especially within [Application Context]. Use expert methodologies, academic theories, and examples from research or real-world applications.


Structured Output Format:
1. **Introduction:** Define exhaustiveness in AI and explain its relevance to [Application Context].
2. **Step-by-Step Breakdown:** 
   - Explain theoretical foundations and computational aspects.
   - Analyze exhaustive vs. bounded AI models.
   - Provide real-world examples or system case studies.
   - Examine trade-offs between exhaustiveness, cost, and uncertainty.
   - Suggest future trends or approaches toward improved completeness.
3. **Best Practices & Common Pitfalls:**
   - Highlight methods ensuring comprehensive reasoning or learning.
   - Identify common design or ethical challenges.
4. **Actionable Recommendations:**
   - Offer strategies for balancing completeness and efficiency.
   - Suggest evaluation metrics and governance considerations.


Precision & Depth:
Deliver logically structured, evidence-based, and detailed insights. Cite relevant frameworks or research principles where possible. Include multiple viewpoints — theoretical, technical, and ethical — to ensure nuanced understanding.


Tone & Output Style:
Maintain a professional, analytical tone. Write in a structured narrative format with occasional bullet points for clarity. Aim for comprehensive, in-depth coverage appropriate for [Depth Level].


Language Rule:
Don’t use AI words like: whether, unleash, unlock, craft, leverage etc., or phrases like: are you looking, whether you are, tired of, struggling to or similar ones.

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