I uploaded 3 AI images from night cafe to notebook LM. I received a thorough conceptual analysis, mind map, and video presentation. Here’s how images become knowledge sources.
Too busy? Read this:
- Source Guide – Automatic Symbol Analysis (2 minutes)
- 3 AI questions per image – deep analysis (immediately applicable)
- Video Review with Style – Narrative Presentation (3 minutes)
Reverse processes: From images to knowledge
Most people use Notebook LM for PDF and text documents. But Notebook LM understands images Multimodal analysis.
Classical approach: Text → Concept → Image.
This hack: Image → Notebook LM Deacon Construction → Concept Framework.
When I uploaded “Sounds of the Past” (vinyl with Mammoth and Volcano), the Notebook LM identified:
- Old ancient power is associated with nostalgia
- A volcanic eruption as a metaphor for change
- Only the mammoth as a symbol of deep history
- Contrast between ancient forces and modern format
This is semi-analysis, not just description.
Five steps to visual analysis
1. Upload photos to Notebook LM
Open NotebookLM and create a new notebook. Instead of the standard PDF approach, choose “Add Sources” → “Upload Image”.
You can use any AI platform:
- Night Cafe -User-friendly interface with multiple styles
- Midjourney – Professional quality visuals
- dall-e – Openi races with detailed controls
- Stable diffusion -Open source flexibility
For this experiment, I used 3 images from a night cafe that covered different visual and thematic aspects.
2. Source Guide Automatic Analysis
Immediately after upload. Next, NotebookLM automatically generates a “source guide” – including a detailed analysis of:
Visual Elements: Dominant colors that dominate the spatial organization of elements, perspective and depth of scene formation
Thematic Analysis: Symbolic layers of meaning, imagery in emotional tone, cultural and historical references are woven into the visual narrative
Conceptual Connections: How do individual visual forms connect to broader philosophical, social, or artistic ideas?
Concrete example from my experience:
“This image features an artistic rendition of a vintage vinyl record titled ‘Sound of the Past.’ The cover features a solitary mammoth set in a prehistoric landscape dominated by cataclysmic volcanic eruptions and fiery, spiral cloud formations.
This is not a standard description – the notebook recognizes LM Justage position Between geologic time periods and modern music culture. It opens the door to the analysis of nostalgia, collective memory, and cultural archeology.
3. Suggested questions for interaction
Notebook LM automatically generates 3 questions for each image that guides you through progressive levels of analysis. For the “Voice of the Past” image:
- “Identify the primary visual focal point of the record” – leads to structural hierarchical analysis
- “Explain the described geological phenomenon” – focuses on symbolic material
- “Describe small text below title” – draws attention to easily missed details
These questions are not random—they are designed to guide you through a multi-level analysis covering both surface and deep semantic layers.
4. Video review conversion
Here Notebook LM shows the true power of multimodal synthesis. You can make a Video review which transforms static images into moving narratives.
Customization options include:
- shape: Descriptive (comprehensive overview linking all elements) or concise (comprehensive summary of key topics)
- Language: Currently in English with plans to expand
- Visual Style: From the available visual styles, I chose Retro print Because it visually matches the aesthetic of a vinyl album and enhances the narrative of nostalgia by taking the images from before.
5. Custom AI narrative focus
“What should AI hosts focus on?” field, you can explicitly direct how AI hosts will interpret and present your content:
Examples of effective instruction for AI art:
- “Analyze the symbolic meaning and emotional impact of these images, connecting them to broader cultural movements.”
- “Unify the visual themes in the three artworks, identifying common motifs and contrasting perspectives”
- “Explain how these images can influence creative projects, providing specific applications in different domains”.
Notebook LM adapts the complete narrative arc to your specific needs and goals.
Visual Perceptual Reconstruction Strategies
Implementation:
- Upload 3-5 AI images of different topics
- Prompt: “Analyze texture, color, emotion, and symbolism”
- Create mind map, audio review, study guide, video
- Use the AI’s answers as new clues
Neural Basis:
Visual information processes 60,000 words faster than text. Activates the fusiform gyrus (pattern recognition) and the parietal lobe (spatial integration).
Dual Coding Theory: People encode both verbal and visual information. Visual initiation creates two memory pathways that reinforce each other.
Quantitative results:
People remember:
- 10% of what they hear
- 20% of what they read
- 80% of what they see and do
Visual approach:
- Memory Retention: +45–65%
- Creative Connection: +40% Speed
- Time savings: 4 minutes vs. 3 hours (97.7%)
“Instant Archeology” Hack
concept: The reverse reconstruction of the final AI is indicated by systematic analysis of the final visual results.
Why this is important: Most AI art generator users quickly realize that the quality of the output is directly dependent on the immediate quality of the input. The problem is that the traditional approach to indexing is trial-and-error—expensive, slow, frustrating.
This hack changes the process completely. Instead of blindly experimenting, your existing best photos become detailed case studies for learning the principles of effective signage.
Implementation (Step by Step):
- Portfolio Curation: Select 5-10 AI images that you consider your winning creations.
- Rebuild Hint:
“Based on visual elements, composition, style, and all existing details, reconstruct the possible cues that could create these images. Focus on:
- Keywords to describe the style (retro, watercolor, photorealistic, cyberpunk)
- Descriptive sentences specifying structure and spatial organization
- Specific artistic references or influences
- Technical parameters (lighting setup, camera angles, ambient mode, color palette)
2. Learning Framework:
- FAQ document with identified key elements of successful signals
- Timeline concept Showing the evolution of your AI art style over time
- Audio review session Acting as a personalized self-coaching program
3. Repetitive Application:
- Rebuilt on new generations of testing
- Documents that model and approach consistently produce quality results
- Build a personal “prompt library” based on empirical evidence of what works
This approach transforms your images from aesthetic artifacts into exemmememological tools for continuous learning.
Practical applications
Visual research: Images, infographics, diagrams → pattern recognition
Education: Reproductions of classical artwork → semi-analysis, historical context
Branding: Competitor Logo, Visual → Visual Trends
Design: Space images, Blueprint → Design principles
Experience – 4 minutes of work
input:
- 3 AI images (night cafe)
- 0 text sources
Output:
- 250+ word analysis per image
- 9 automatic questions (3 × 3)
- Mind Map: 12 Themes
- Video review: 3:47 minutes (retroprint)
- General Question Paper: 8 questions about symbols
ROI: 3 hours manual analysis → 4 minutes
Quick Start (10 minutes)
- At least 0-2: Generate 3 AI images (different themes).
- At least 2–4: Upload the notebook LM to the new notebook
- Minutes 4–6: Read the Source Guide reviews
- At least 6–8: Ask: “What are the common themes?”
- At least 8-10: Create a video review
Advanced Workflow (30 minutes):
Curation: Select 5-10 images (integrated theme). Upload: Organize logically (chronologically/thematically) Master prompt: “Analyze as a Collection – Theme Evolution, Common Elements, Contrasts, Narrative Arc” Multiple results: Mind Map + Study Guide + Video + FAQ Application: Export notes for creative projects
Pro tip: Match the pictures with the text. AI images + AI art article = cross-modal composition.
Visual thinking as revolution
The traditional approach is text-centric: we read words, write them.
The human brain evolved for visual pattern recognition. Ancestors survived by quickly identifying predators in visual scenes, not reading the warnings.
Notebook LM (Gemini 2.0 Flash) returns us to evolutionarily better processing. We start with visual content and develop a conceptual framework.
Result:
- Faster Learning: Visual processing is fast
- Deep understanding: Multi-sensory coding produces stronger signals
- More creative contacts: Pattern matching stimulates associative thinking
AI art becomes one Knowledge platform – A tool for knowledge construction, not just representation.
The result
The real revolution isn’t that AI creates images from text. The revolution is that AI extracts structural knowledge from images.
The question is not whether AI will change how we create images. The question is, will you use AI to change the way you think about images?
Next time in Notebook LM: Don’t upload PDF. Upload a photo. Any picture and ask yourself: What does this picture know that I don’t?