Category: data

Prediction with data

Posted on by lechuck park

This image illustrates a comparison between two approaches for Prediction with Data.

Left Side: Traditional Approach (Setup First Configuration)

The traditional method consists of:

  • Condition: 3D environment and object locations
  • Rules: Complex physics laws
  • Input: 1+ cases
  • Output: 1+ prediction results

This approach relies on pre-established rules and physical laws to make predictions.

Right Side: Modern AI/Machine Learning Approach

The modern method follows these steps:

  1. Huge Data: Massive datasets represented in binary code
  2. Machine Learning: Pattern learning from data
  3. AI Model: Trained artificial intelligence model
  4. Real-Time High Resolution Data: High-quality data streaming in real-time
  5. Prediction Anomaly: Final predictions and anomaly detection

Key Differences

The most significant difference is highlighted by the question “Believe first ??” at the bottom. This represents a fundamental philosophical difference: the traditional approach starts by “believing” in predefined rules, while the AI approach learns patterns from data to make predictions.

Additionally, the AI approach features “Longtime Learning Verification,” indicating continuous model improvement through ongoing learning and validation processes.

The diagram effectively contrasts rule-based prediction systems with data-driven machine learning approaches, showing the evolution from deterministic, physics-based models to adaptive, learning-based AI systems.

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Human & Data with AI

Posted on by lechuck park

Data Accumulation Perspective

History → Internet: All knowledge and information accumulated throughout human history is digitized through the internet and converted into AI training data. This consists of multimodal data including text, images, audio, and other formats.

Foundation Model: Large language models (LLMs) and multimodal models are pre-trained based on this vast accumulated data. Examples include GPT, BERT, CLIP, and similar architectures.

Human to AI: Applying Human Cognitive Patterns to AI

1. Chain of Thoughts

  • Implementation of human logical reasoning processes in the Reasoning stage
  • Mimicking human cognitive patterns that break down complex problems into step-by-step solutions
  • Replicating the human approach of “think → analyze → conclude” in AI systems

2. Mixture of Experts

  • AI implementation of human expert collaboration systems utilized in the Experts domain
  • Architecting the way human specialists collaborate on complex problems into model structures
  • Applying the human method of synthesizing multiple expert opinions for problem-solving into AI

3. Retrieval-Augmented Generation (RAG)

  • Implementing the human process of searching existing knowledge → generating new responses into AI systems
  • Systematizing the human approach of “reference material search → comprehensive judgment”

Personal/Enterprise/Sovereign Data Utilization

1. Personal Level

  • Utilizing individual documents, history, preferences, and private data in RAG systems
  • Providing personalized AI assistants and customized services

2. Enterprise Level

  • Integrating organizational internal documents, processes, and business data into RAG systems
  • Implementing enterprise-specific AI solutions and workflow automation

3. Sovereign Level

  • Connecting national or regional strategic data to RAG systems
  • Optimizing national security, policy decisions, and public services

Overall Significance: This architecture represents a Human-Centric AI system that transplants human cognitive abilities and thinking patterns into AI while utilizing multi-layered data from personal to national levels to evolve general-purpose AI (Foundation Models) into intelligent systems specialized for each level. It goes beyond simple data processing to implement human thinking methodologies themselves into next-generation AI systems.

With Claude

Small makes BIG

Posted on by lechuck park

The image shows how even a small error or delay in GPU-based large-scale parallel AI processing can cause major output failures and energy waste, highlighting the critical importance of data quality—especially accuracy and precision—in AI systems.

Machine Changes

Posted on by lechuck park

This image titled “Machine Changes” visually illustrates the evolution of technology and machinery across different eras.

The diagram progresses from left to right with arrows showing the developmental stages:

Stage 1 (Left): Manual Labor Era

  • Tool icons (wrench, spanner)
  • Hand icon
  • Worker icon Representing basic manual work using simple tools.

Stage 2: Mechanization Era

  • Manufacturing equipment and machinery
  • Power-driven machines Depicting the industrial revolution period with mechanized production.

Stage 3 (Blue section): Automation and Computer Era

  • Power supply systems
  • CPU/processor chips
  • Computer systems
  • Programming code Representing automation through electronics and computer technology.

Stage 4 (Purple section): AI and Smart Technology Era

  • Robots
  • GPU processors
  • Artificial brain/AI
  • Interactive interfaces Representing modern smart technology integrated with artificial intelligence and robotics.

Additional Insight: The transition from the CPU era to the GPU era marks a fundamental shift in what drives technological capability. In the CPU era, program logic was the critical factor – the sophistication of algorithms and code determined system performance. However, in the GPU era, training data has become paramount – the quality, quantity, and diversity of data used to train AI models now determines the intelligence and effectiveness of these systems. This represents a shift from logic-driven computation to data-driven learning.

Overall, this infographic captures humanity’s technological evolution: Manual Labor → Mechanization → Automation → AI/Robotics, highlighting how the foundation of technological advancement has evolved from human skill to mechanical power to programmed logic to data-driven intelligence.

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Monitoring is from changes

Posted on by lechuck park

Change-Based Monitoring System Analysis

This diagram illustrates a systematic framework for “Monitoring is from changes.” The approach demonstrates a hierarchical structure that begins with simple, certain methods and progresses toward increasingly complex analytical techniques.

Flow of Major Analysis Stages:

  1. One Change Detection:
    • The most fundamental level, identifying simple fluctuations such as numerical changes (5→7).
    • This stage focuses on capturing immediate and clear variations.
  2. Trend Analysis:
    • Recognizes data patterns over time.
    • Moves beyond single changes to understand the directionality and flow of data.
  3. Statistical Analysis:
    • Employs deeper mathematical approaches to interpret data.
    • Utilizes means, variances, correlations, and other statistical measures to derive meaning.
  4. Deep Learning:
    • The most sophisticated analysis stage, using advanced algorithms to discover hidden patterns.
    • Capable of learning complex relationships from large volumes of data.

Evolution Flow of Detection Processes:

  1. Change Detection:
    • The initial stage of detecting basic changes occurring in the system.
    • Identifies numerical variations that deviate from baseline values (e.g., 5→7).
    • Change detection serves as the starting point for the monitoring process and forms the foundation for more complex analyses.
  2. Anomaly Detection:
    • A more advanced form than change detection, identifying abnormal data points that deviate from general patterns or expected ranges.
    • Illustrated in the diagram with a warning icon, representing early signs of potential issues.
    • Utilizes statistical analysis and trend data to detect phenomena outside the normal range.
  3. Abnormal (Error) Detection:
    • The most severe level of detection, identifying actual errors or failures within the system.
    • Shown in the diagram with an X mark, signifying critical issues requiring immediate action.
    • May be classified as a failure when anomaly detection persists or exceeds thresholds.

Supporting Functions:

  • Adding New Relative Data: Continuously collecting relevant data to improve analytical accuracy.
  • Higher Resolution: Utilizing more granular data to enhance analytical precision.

This framework demonstrates a logical progression from simple and certain to gradually more complex analyses. The hierarchical structure of the detection process—from change detection through anomaly detection to error detection—shows how monitoring systems identify and respond to increasingly serious issues.

With Claude

Data Security

Posted on by lechuck park

The image shows a comprehensive data security diagram with three main approaches to securing data systems. Let me explain each section:

  1. Left Section – “Easy and Perfect”:
    • Features data encryption for secure storage
    • Implements the “3A” security principles: Accounting (with Auditing), Authentication, and Authorization
    • Shows server hardware protected by physical security (guard)
    • Represents a straightforward but effective security approach
  2. Middle Section – “More complex but more vulnerable??”:
    • Shows an IP network architecture with:
      • Server IP and service port restrictions
      • TCP/IP layer security
      • Access Control Lists
      • Authorized IP only policy
      • Authorized terminal restrictions
      • Personnel authorization controls
  3. Right Section – “End to End”:
    • Divides security between Private Network and Public Network
    • Includes:
      • Application layer security
      • Packet/Payload analysis
      • Access Permission First principle
      • Authorized Access Agent Tool restrictions
      • “Perfect Personnel Data/Network” security approach
      • Unspecified Access concerns (shown with question mark)

The diagram illustrates the evolution of data security approaches from simpler encryption and authentication methods to more complex network security architectures, and finally to comprehensive end-to-end security solutions. The diagram questions whether more complex systems might actually introduce more vulnerabilities, suggesting that complexity doesn’t always equal better security.

With Claude