Tag: human

New Era of Digitals

Posted on by lechuck park

This image presents a diagram titled “New Era of Digitals” that illustrates the evolution of computing paradigms.

Overall Structure:

The diagram shows a progression from left to right, transitioning from being “limited by Humans” to achieving “Everything by Digitals.”

Key Stages:

  1. Human Desire: The process begins with humans’ fundamental need to “wanna know it clearly,” representing our desire for understanding and knowledge.
  2. Rule-Based Era (1000s):
    • Deterministic approach
    • Using Logics and Rules
    • Automation with Specific Rules
    • Record with a human recognizable format
  3. Data-Driven Era:
    • Probabilistic approach (Not 100% But OK)
    • Massive Computing (Energy Resource)
    • Neural network-like structures represented by interconnected nodes

Core Message:

The diagram illustrates how computing has evolved from early systems that relied on human-defined explicit rules and logic to modern data-driven, probabilistic approaches. This represents the shift toward AI and machine learning, where we achieve “Not 100% But OK” results through massive computational resources rather than perfect deterministic rules.

The transition shows how we’ve moved from systems that required everything to be “human recognizable” to systems that can process and understand patterns beyond direct human comprehension, marking the current digital revolution where algorithms and data-driven approaches can handle complexity that exceeds traditional rule-based systems.

With Claude

Basic of Reasoning

Posted on by lechuck park

This diagram illustrates that human reasoning and AI reasoning share fundamentally identical structures.

Key Insights:

Common Structure Between Human and AI:

  • Human Experience (EXP) = Digitized Data: Human experiential knowledge and AI’s digital data are essentially the same information in different representations
  • Both rely on high-quality, large-scale data (Nice & Big Data) as their foundation

Shared Processing Pipeline:

  • Both human brain (intuitive thinking) and AI (systematic processing) go through the same Basic of Reasoning process
  • Information gets well-classified and structured to be easily searchable
  • Finally transformed into well-vectorized embeddings for storage

Essential Components for Reasoning:

  1. Quality Data: Whether experience or digital information, sufficient and high-quality data is crucial
  2. Structure: Systematic classification and organization of information
  3. Vectorization: Conversion into searchable and associative formats

Summary: This diagram demonstrates that effective reasoning – whether human or artificial – requires the same fundamental components: quality data and well-structured, vectorized representations. The core insight is that human experiential learning and AI data processing follow identical patterns, both culminating in structured knowledge storage that enables effective reasoning and retrieval.

Human-AI Collaborative Reasoning

Posted on by lechuck park

This image illustrates the collaborative problem-solving process between humans and AI through reasoning, emphasizing their complementary relationship rather than a simple comparison.

Key Components and Interpretation

1. AI’s Operational Flow (Upper Section)

  • Big Data → Learning → AI Model: The process by which AI builds models through learning from vast amounts of data
  • Reasoning → Inferencing → Answer: The process by which AI receives questions and generates answers through reasoning

2. Human Role (Lower Section)

  • Experience: Knowledge and information acquired through direct experience
  • Logic: A logical thinking framework built upon experience
  • Reasoning: The cognitive process that combines experience and logic

3. Critical Interaction Mechanisms

Question:

  • Human reasoning results are input to AI in the form of sophisticated questions
  • These are not simple queries, but systematic and meaningful questions based on experience and logic

Answer:

  • AI’s responses are fed back into the human reasoning process
  • Humans verify AI’s answers and integrate them into new experiences and logic for deeper reasoning

4. Core Message

The red-highlighted phrase “humans must possess a strong, experience-based logical framework” represents the diagram’s central theme:

  • To collaborate effectively with AI, humans must also possess strong logical thinking frameworks based on experience
  • The ability to provide appropriate questions and properly verify and utilize AI’s responses is essential

Conclusion

This image demonstrates that human roles do not disappear in the AI era, but rather become more crucial. Human reasoning abilities based on experience and logic play a pivotal role in AI collaboration, and through this, humans and AI can create synergy for better problem-solving. The diagram presents a collaborative model where both entities work together to achieve superior results.

The key insight is that AI advancement doesn’t replace human thinking but rather requires humans to develop stronger reasoning capabilities to maximize the potential of human-AI collaboration.

With Claude, Gemini

The Evolution of Mainstream Data in Computing

Posted on by lechuck park

This diagram illustrates the evolution of mainstream data types throughout computing history, showing how the complexity and volume of processed data has grown exponentially across different eras.

Evolution of Mainstream Data by Computing Era:

  1. Calculate (1940s-1950s) – Numerical Data: Basic mathematical computations dominated
  2. Database (1960s-1970s) – Structured Data: Tabular, organized data became central
  3. Internet (1980s-1990s) – Text/Hypertext: Web pages, emails, and text-based information
  4. Video (2000s-2010s) – Multimedia Data: Explosive growth of video, images, and audio content
  5. Machine Learning (2010s-Present) – Big Data/Pattern Data: Large-scale, multi-dimensional datasets for training
  6. Human Perceptible/Everything (Future) – Universal Cognitive Data: Digitization of all human senses, cognition, and experiences

The question marks on the right symbolize the fundamental uncertainty surrounding this final stage. Whether everything humans perceive – emotions, consciousness, intuition, creativity – can truly be fully converted into computational data remains an open question due to technical limitations, ethical concerns, and the inherent nature of human cognition.

Summary: This represents a data-centric view of computing evolution, progressing from simple numerical processing to potentially encompassing all aspects of human perception and experience, though the ultimate realization of this vision remains uncertain.

With Claude

Together is not easy

Posted on by lechuck park

This infographic titled “Together” emphasizes the critical importance of parallel processing = working together across all domains – computing, AI, and human society.

Core Concept:

The Common Thread Across All 5 Domains – ‘Parallel Processing’:

  1. Parallel Processing – Simultaneous task execution in computer systems
  2. Deep Learning – AI’s multi-layered neural networks learning in parallel
  3. Multi Processing – Collaborative work across multiple processors
  4. Co-work – Human collaboration and teamwork
  5. Social – Collective cooperation among community members

Essential Elements of Parallel Processing:

  • Sync (Synchronization) – Coordinating all components to work harmoniously
  • Share (Sharing) – Efficient distribution of resources and information
  • Optimize (Optimization) – Maximizing performance while minimizing energy consumption
  • Energy (Energy) – The inevitable cost required when working together

Reinterpreted Message: “togetherness is always difficult, but it’s something we have to do.”

This isn’t merely about the challenges of cooperation. Rather, it conveys that parallel processing (working together) in all systems requires high energy costs, but only through optimization via synchronization and sharing can we achieve true efficiency and performance.

Whether in computing systems, AI, or human society – all complex systems cannot advance without parallel cooperation among individual components. This is an unavoidable and essential process for any sophisticated system to function and evolve. The insight reveals a fundamental truth: the energy investment in “togetherness” is not just worthwhile, but absolutely necessary for progress.

With Claude

Human Extends

Posted on by lechuck park

This image is a conceptual diagram titled “Human Extend” that illustrates the cognitive extension of human capabilities and the role of AI tools.

Core Concept

“Human See” at the center represents the core of human observation and understanding abilities.

Bidirectional Extension Structure

Left: Macro Perspective

  • Represented by an orange circle
  • “A deeper understanding of the micro leads to better macro predictions”

Right: Micro Perspective

  • Represented by a blue circle
  • “A deeper understanding of the macro leads to better micro predictions”

Role of AI and Data

The upper portion shows two supporting tools:

  1. AI (by Tool): Represented by an atomic structure-like icon
  2. Data (by Data): Represented by network and database icons

Overall Meaning

This diagram visually represents the concept that human cognitive abilities can be extended through AI tools and data analysis, enabling deeper mutual understanding between microscopic details and macroscopic patterns. It illustrates the complementary relationship where understanding small details leads to better prediction of the big picture, and understanding the big picture leads to more accurate prediction of details.

The diagram suggests that AI and data serve as amplifying tools that enhance human perception, allowing for more sophisticated analysis across different scales of observation and prediction.

with Claude

Human data

Posted on by lechuck park

This updated image titled “Data?” presents a deeper philosophical perspective on data and AI.

Core Concept:

Human Perception is Limited

  • Compared to the infinite complexity of the real world, the scope that humans can perceive and define is constrained
  • The gray area labeled “Human perception is limited” visualizes this boundary of recognition

Two Dimensions of AI Application:

  1. Deterministic Data
    • Data domains that humans have already defined and structured
    • Contains clear rules and patterns that AI can process in predictable ways
    • Represents traditional AI problem-solving approaches
  2. Non-deterministic Data
    • Data from domains that humans haven’t fully defined
    • Raw data from the real world with high uncertainty and complexity
    • Areas where AI must discover and utilize patterns without prior human definitions

Key Insight: This diagram illustrates that AI’s true potential extends beyond simply solving pre-defined human problems. While AI can serve as a tool that opens new possibilities by transcending human cognitive boundaries and discovering complex patterns from the real world that we haven’t yet defined or understood, there remains a crucial human element in this process. Even as AI ventures into unexplored territories of reality beyond human-defined problem spaces, humans still play an essential role in determining how to interpret, validate, and responsibly apply these AI-discovered insights. The diagram suggests a collaborative relationship where AI expands our perceptual capabilities, but human judgment and decision-making remain fundamental in guiding how these expanded possibilities are understood and utilized.

With Claude