Category: machine learning

Learning , Reasoning, Inference

Posted on by lechuck park

This image illustrates the three core processes of AI LLMs by drawing parallels to human learning and cognitive processes.

Learning

  • Depicted as a wise elderly scholar reading books in a library
  • Represents the lifelong process of absorbing knowledge and experiences accumulated by humanity over generations
  • The bottom icons show data accumulation and knowledge storage processes
  • Meaning: Just as AI learns human language and knowledge through vast text data, humans also build knowledge throughout their lives through continuous learning and experience

Reasoning

  • Shows a character deep in thought, surrounded by mathematical formulas
  • Represents the complex mental process of confronting a problem and searching for solutions through internal contemplation
  • The bottom icons symbolize problem analysis and processing stages
  • Meaning: The human cognitive process of using learned knowledge to engage in logical thinking and analysis to solve problems

Inference

  • Features a character confidently exclaiming “THE ANSWER IS CLEAR!”
  • Expresses the confidence and decisiveness when finally finding an answer after complex thought processes
  • The bottom checkmark signifies reaching a final conclusion
  • Meaning: The human act of ultimately speaking an answer or making a behavioral decision through thought and analysis

These three stages visually demonstrate how AI processes information in a manner similar to the natural human sequence of learning → thinking → conclusion, connecting AI’s technical processes to familiar human cognitive patterns.

With Claude

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.

With Claude

The Age of Utilization

Posted on by lechuck park

This image is an infographic depicting “The Age of Utilization.”

On the left side, a gray oval contains “All knowledge of mankind” represented by various icons including letter and number blocks, books with writing tools, and a globe symbolizing the internet, illustrating the diverse forms of knowledge humanity has accumulated over time.

In the center, there’s a section labeled “Massive parallel processing” showing multiple eye icons with arrows pointing toward a GPU icon. This illustrates how vast amounts of human knowledge are efficiently processed through GPUs.

On the right side, a purple arrow-shaped area labeled “Easy to utilize” demonstrates how processed information can be used. At the top is an “EASY TO USE” icon, with “Inference” and “Learning” stages below it. This section includes Q&A icons, a vector database, and neural network structures.

The infographic comprehensively shows how humanity has entered a new era where accumulated knowledge can be processed using modern technology and easily accessed through question-and-answer formats, making all human knowledge readily available for utilization.

With Claude

Home LLM

Posted on by lechuck park

This image shows the architecture of a “Home LLM” system, illustrating an innovative change in how home appliances are used.

Key points:

  1. Evolution from Traditional Approach: While traditional electronics came as ‘product + paper manual’ packages, this new system replaces manuals with small LLM models.
  2. Home Foundation Model: Homes are equipped with a main LLM model (“Home Foundation LLM Model”) that learns from environmental data.
  3. Knowledge Exchange: Product-specific small LLM models and the home foundation model exchange data and learning outcomes with each other.
  4. User Interface: Users can easily interact through the LLM by asking questions and giving commands, making product usage much more intuitive and convenient.
  5. AI Agent Control: Additionally, AI agents automatically optimize the control of these products, increasing efficiency.

This system presents a smart home architecture that fundamentally improves the user experience of electronic products by integrating AI and LLM technologies in the home environment.

With Claude

Rule-Based vs LLM AI

Posted on by lechuck park

Rule-Based AI vs. Machine Learning: Finding the Fastest Hiking Route

Rule-Based AI

  • A single expert hiker analyzes a map, considering terrain and conditions to select the optimal route.
  • This method is efficient and requires minimal energy (a small number of lunchboxes).

Machine Learning

  • A large number of hikers explore all possible paths without prior knowledge.
  • The fastest hiker’s route is chosen as the optimal path.
  • This approach requires many attempts, consuming significantly more energy (a vast number of lunchboxes).

👉 Comparison Summary

  • Rule-Based AI: Finds the best route through analysis → Efficient, low energy consumption
  • Machine Learning: Finds the best route through trial and error → Inefficient but discovers optimal paths, high energy consumption

with ChatGPT

Rule-base AI vs ML

Posted on by lechuck park

The primary purpose of this image is to highlight the complementary nature of Rule-base AI and Machine Learning (ML), demonstrating the need to integrate these two approaches.

Rule-base AI (Top):

  • Emphasizes the importance of fundamental and ethical approaches
  • Designing strict rules based on human expertise and logical thinking
  • Providing core principles and ethical frameworks

Machine Learning AI (Bottom):

  • Highlighting scalability and innovation through data-driven learning
  • Ability to recognize complex patterns and adaptive learning
  • Potential for generating new insights and solutions

Hybrid Approach:

  • Combining the strengths of both approaches
  • Maintaining fundamental principles and ethical standards
  • Simultaneously achieving innovation and scalability through data-driven learning

The image illustrates the complementary nature of Rule-base AI and Machine Learning (ML). Rule-base AI represents precise, human-crafted logic with limited applicability, while ML offers flexibility and innovation through data-driven learning. The key message is that a hybrid approach combining the fundamental ethical principles of rule-based systems with the scalable, adaptive capabilities of machine learning can create more robust and intelligent AI solutions.

with Claude

CFD & AI/ML

Posted on by lechuck park

CFD (Computational Fluid Dynamics) – Deductive Approach [At Installation]

  • Data Characteristics
    • Configuration Data
    • Physical Information
    • Static Meta Data
  • Features
    • Complex data configuration
    • Predefined formula usage
    • Result: Fixed and limited
    • Stable from engineering perspective

AI/ML – Inductive Approach [During Operation]

  • Data Characteristics
    • Metric Data
    • IoT Sensing Data
    • Variable Data
  • Features
    • Data-driven formula generation
    • Continuous learning and verification
    • Result: Flexible but partially unexplainable
    • High real-time adaptability

Comprehensive Comparison

Harmonious integration of both approaches is key to future digital twin technologies

CFD: Precise but rigid modeling

AI/ML: Adaptive but complex modeling

The key insight here is that both CFD and AI/ML approaches have unique strengths. CFD provides a rigorous, physics-based model with predefined formulas, while AI/ML offers dynamic, adaptive learning capabilities. The future of digital twin technology likely lies in finding an optimal balance between these two methodologies, leveraging the precision of CFD with the flexibility of machine learning.

With Claude