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.

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LMM Operation

Posted on by lechuck park

LLM Operations System Analysis

This diagram illustrates the architecture of an LLM Operations (LLMOps) system, demonstrating how Large Language Models are deployed and operated in industrial settings.

Key Components and Data Flow

1. Data Input Sources (3 Categories)

  • Facility: Digitized sensor data that gets detected and generates alert/event logs
  • Manual: Equipment manuals and technical documentation
  • Experience: Operational manuals including SOP/MOP/EOP (Standard/Maintenance/Emergency Operating Procedures)

2. Central Processing System

  • RAG (Retrieval-Augmented Generation): A central hub that integrates and processes all incoming data
  • Facility data is visualized through metrics and charts for monitoring purposes

3. LLM Operations

  • The central LLM synthesizes all information to provide intelligent operational support
  • Interactive interface enables user communication and queries

4. Final Output and Control

  • Dashboard for data visualization and monitoring
  • AI chatbot for real-time operational assistance
  • Operator Control: The bottom section shows checkmark (✓) and X-mark (✗) buttons along with an operator icon, indicating that final decision-making authority remains with human operators

System Characteristics

This system represents a smart factory solution that integrates AI into traditional industrial operations, providing comprehensive management from real-time data monitoring to operational manual utilization.

The key principle is that while AI provides comprehensive analysis and recommendations, the final operational decisions and approvals still rest with human operators. This is clearly represented through the operator icon and approval/rejection buttons at the bottom of the diagram.

This demonstrates a realistic and desirable AI operational model that emphasizes safety, accountability, and the importance of human judgment in unpredictable situations.

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3 Key on the AI era

Posted on by lechuck park

This diagram illustrates the 3 Core Technological Components of AI World and their surrounding challenges.

AI World’s 3 Core Technological Components

Central AI World Components:

  1. AI infra (AI Infrastructure) – The foundational technology that powers AI systems
  2. AI Model – Core algorithms and model technologies represented by neural networks
  3. AI Agent – Intelligent systems that perform actual tasks and operations

Surrounding 3 Key Challenges

1. Data – Left Area

Data management as the raw material for AI technology:

  • Data: Raw data collection
  • Verified: Validated and quality-controlled data
  • Easy to AI: Data preprocessed and optimized for AI processing

2. Optimization – Bottom Area

Performance enhancement of AI technology:

  • Optimization: System optimization
  • Fit to data: Data fitting and adaptation
  • Energy cost: Efficiency and resource management

3. Verification – Right Area

Ensuring reliability and trustworthiness of AI technology:

  • Verification: Technology validation process
  • Right?: Accuracy assessment
  • Humanism: Alignment with human-centered values

This diagram demonstrates how the three core technological elements – AI Infrastructure, AI Model, and AI Agent – form the center of AI World, while interacting with the three fundamental challenges of Data, Optimization, and Verification to create a comprehensive AI ecosystem.

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network issue in a GPU workload

Posted on by lechuck park

This diagram illustrates network bottleneck issues in large-scale AI/ML systems.

Key Components:

Left side:

  • Big Data and AI Model/Workload connected to the system via network

Center:

  • Large-scale GPU cluster (multiple GPUs arranged in a grid pattern)
  • Each GPU is interconnected for distributed processing

Right side:

  • Power supply and cooling systems

Core Problem:

The network interface specifications shown at the bottom reveal bandwidth mismatches:

  • inter GPU NVLink: 600GB/s
  • inter Server Infiniband: 400Gbps
  • CPU/RAM/DISK PCIe/NVLink: (relatively lower bandwidth)

“One Issue” – System-wide Propagation:

A network bottleneck or failure at a specific point (marked with red circle) “spreads throughout the entire system” as indicated by the yellow arrows.

This diagram warns that in large-scale AI training, a single network bottleneck can have catastrophic effects on overall system performance. It visualizes how bandwidth imbalances at various levels – GPU-to-GPU communication, server-to-server communication, and storage access – can compromise the efficiency of the entire system. The cascading effect demonstrates how network issues can quickly propagate and impact the performance of distributed AI workloads across the infrastructure.

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Data Center Mgt. System Req.

Posted on by lechuck park

System Components (Top Level)

Six core components:

  • Facility: Data center physical infrastructure
  • Data List: Data management and cataloging
  • Data Converter: Data format conversion
  • Network: Network infrastructure
  • Server: Server hardware
  • Software (Database): Applications and database systems

Universal Mandatory Requirements

Fundamental requirements applied to ALL components:

  • Stability (24/7 HA): 24/7 High Availability – All systems must operate continuously without interruption
  • Performance: Optimal performance assurance – All components must meet required performance levels

Component-Specific Additional Requirements

1. Data List

  • Sampling Rate, Computing Power, HW/SW Interface

2. Data Converter

  • Data Capacity, Computing Power, Program Logic (control facilities), High Availability

3. Network

  • Private NW, Bandwidth, Architecture (L2/L3, Ring/Star), UTP/Optic, Management Include

4. Server

  • Computing Power, Storage Sizing, High Availability, External (Public Network)

5. Software/Database

  • Data Integrity, Cloud-like High Availability & Scale-out, Monitoring, Event Management, Analysis (AI)

This architecture emphasizes that stability and performance are fundamental prerequisites for data center operations, with each component having its own specific additional requirements built upon these two essential foundation requirements.

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