Category: Lechuck

Make Better Questions

Posted on by lechuck park

This diagram titled “Make Better Questions” illustrates a methodology for effective questioning. The key concepts are:

  1. Continuous Skepticism and Updates: Personal beliefs should be continuously updated following the principle “Always be suspicious.” This suggests that our knowledge and understanding should not remain static but should evolve constantly.
  2. Fluidity of Collective Truth: “Humans Believe (Truth)” represents collectively accepted truths, which are also subject to change and interact with personal beliefs through “Nice Update,” creating a reciprocal influence.
  3. Immutable Foundations: Some basic principles (“Immutable Rule”) provide an unchanging foundation, but flexible thinking should be developed based on these foundations.
  4. Starting with Fundamentals: “Start with fundamentals” emphasizes the importance of beginning with basic principles when approaching complex questions or problems.
  5. Collaboration with AI: By utilizing this thinking framework in conjunction with AI, we can create better questions and gain richer insights.

This diagram ultimately suggests a method for optimizing interactions with AI through constant skepticism and adherence to fundamentals while maintaining flexible thinking. It emphasizes the importance of not settling for fixed beliefs but continuously learning and evolving.

With Claude

Personal with AI

Posted on by lechuck park

This diagram illustrates a “Personal Agent” system architecture that shows how everyday life is digitized to create an AI-based personal assistant:

Left side: The user’s daily activities (coffee, computer, exercise, sleep) are represented, which serve as the source for digitization.

Center-left: Various sensors (visual, auditory, tactile, olfactory, gustatory) capture the user’s daily activities and convert them through the “Digitization” process.

Center: The “Current State (Prompting)” component stores the digitized current state data, which is provided as prompting information to the AI agent.

Upper right (pink area): Two key processes take place:

  1. “Learning”: Processing user data from an ML/LLM perspective
  2. “Logging”: Continuously collecting data to update the vector database

This section runs on a “Personal Server or Cloud,” preferably using a personalized GPU server like NVIDIA DGX Spark, or alternatively in a cloud environment.

Lower right: In the “On-Device Works” area, the “Inference” process occurs. Based on current state data, the AI agent infers guidance needed for the user, and this process is handled directly on the user’s personal device.

Center bottom: The cute robot icon represents the AI agent, which provides personalized guidance to the user through the “Agent Guide” component.

Overall, this system has a cyclical structure that digitizes the user’s daily life, learns from that data to continuously update a personalized vector database, and uses the current state as a basis for the AI agent to provide customized guidance through an inference process that runs on-device.

with Claude

Analytical vs Empirical

Posted on by lechuck park

Analytical vs Empirical Approaches

Analytical Approach

  1. Theory Driven: Based on mathematical theories and logical reasoning
  2. Programmable with Design: Implemented through explicit rules and algorithms
  3. Sequential by CPU: Tasks are processed one at a time in sequence
  4. Precise & Explainable: Results are accurate and decision-making processes are transparent

Empirical Approach

  1. Data Driven: Based on real data and observations
  2. Deep Learning with Learn: Neural networks automatically learn from data
  3. Parallel by GPU: Multiple tasks are processed simultaneously for improved efficiency
  4. Approximate & Unexplainable: Results are approximations and internal workings are difficult to explain

Summary

This diagram illustrates the key differences between traditional programming methods and modern machine learning approaches. The analytical approach follows clearly defined rules designed by humans and can precisely explain results, while the empirical approach learns patterns from data and improves efficiency through parallel processing but leaves decision-making processes as a black box.

with claude