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Simple & Pragmatic Problem Solving
#109Chapter 8: Modern Problem-Solving Techniques• Modern Problem Solving TechniquesGenerative Computational ThinkingIndividual or group with artificial intelligence assistant Mode30-90 Minutes(Fast (several minutes to several hours))

Deep Learning Technique for Idea Generation

From the book "Advanced Problem-Solving Toolbox: 115 Creative Plays" | Compiled & Edited by: Mojtaba Goudarzi, Open translation: Mehrshid Goudarzi
Executive Synopsis & Core Logic:

Applying deep neural networks and Large Language Models (LLMs) for unlimited brainstorming; Crossing human cognitive barriers by generating hundreds of innovative ideas and interdisciplinary combinations in a few seconds and refining it by the human team.

Operational Parameters & Specifications

Category
Modern Problem Solving Techniques
Dominant Thinking
Generative Computational
Participation
Individual or group with artificial intelligence assistant
Estimated TimeFast (several minutes to several hours)
30-90 Minutes
Workshop
Input Format
Problem statement, engineered prompts, creativity variables, textual/visual data
Output Format
List of divergent ideas, unexpected scenarios and textual and visual concepts
Key Application
Designing new product concepts, advertising and branding campaigns, scenario writing and game development, and exploring innovative scientific hypotheses.
Core Differentiator:

Using Generative AI as a tireless co-thinker that accesses the ocean of human knowledge and connects far-fetched concepts without human biases.

Quick Field Example:

an advertising agency for the launch of an electric car, by designing multi-stage prompts in a deep linguistic model, extracts 100 creative slogans and 4 video scenarios within 10 minutes and selects the best one.

Operational Benefits & Implementation Risks

Advantages & Value Creation:

Transcendental speed in generating a huge variety of ideas, breaking mental deadlocks and creative locks, amazing ability to combine unrelated industries.

Risks & Potential Trade-offs:

Probability of producing hallucinatory or impractical ideas in the real world, dependence of output quality on human prompt engineering skill.

Real-World Organizational & Industry Scenarios

Industrial Design and Packaging: Production of Organic Bottle Forms with Deep Learning Image Models (Diffusion Models).
Content and Media Production: Creating Non-Linear Storylines for Video Games with Advanced Language Models.
Pharmaceuticals and Biotechnology: Generating Innovative Protein Structure Hypotheses with Biological Deep Learning Models.

Strategic Rationale & Why to Apply

1The end of the Blank Page Syndrome: with one command, the system puts dozens of pristine angles in front of you.
2Access to all written human knowledge: AI can bridge medieval metallurgy to modern software architecture.
3Cheap and fast iteration: you can generate 1000 ideas, filter, change the tone and get to the gold core in half an hour.
4Impartiality to organizational judgment: The AI ​​model is not afraid to mock radical ideas and dare to take risks.

Conceptual Framework & Book Method Description

"Deep Learning Technique for Idea Generation" (Deep Learning Technique for Idea Generation) is a symbol of the leap of creativity in the age of generative artificial intelligence. This technique is based on transformer neural networks (such as GPT family and diffusion models). The process begins with structured prompt engineering (role definition, context, constraints, metaphorical triggers, and output format). The system generates a wealth of divergent ideas, and the human team, as referees and guides, refines the best sparks and transforms them into actionable solutions.

Step-by-Step Real-World Implementation Scenario

Ideation of a national advertising campaign for a natural beverage brand with deep learning
1
Step 1: Develop a Structured Engineered PromptThe Precision Prompt team wrote: 'You are a creative creative genius; Write 10 unconventional ideas for a sugar-free juice campaign that uses mythological and astronautical metaphors.
2
Step 2: Run the model and generate divergent ideas in 10 secondsthe model generated 40 ideas; including the idea of ​​'nature's rocket fuel' and 'elixir of immortality in a clear glass bottle with a silver cap'.
3
Step 3: Co-promptingThe team chose the idea of ​​'nature's rocket fuel' and asked the model to develop a 30-second stop-motion scenario and its corresponding slogan.
4
Step 4: Logical filter and matching with brand identity by humansHuman experts removed the illusory and costly elements and turned the scenario into a humorous story of an astronaut on the island of fruits.
5
Step 5: Create content and win on social mediaThe campaign was launched with the tagline 'Natural fuel for your ground missions' and the stop motion video received over 3 million views.

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Previous Play (#108)
Virtual and Augmented Reality (VR/AR) for Problem Solving
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Human-AI Collaboration Technique
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