2026年1月27日 星期二

學生回饋意見

 (稍微改寫去可辨視化)


2026年1月22日 星期四

健康照護科技專題 - AI 應用總覽

 

1. World Model(世界模型)- 最新 AI 研究方向

課程涵蓋 JEPA-WM(Joint Embedding Predictive Architecture World Model)與 A* Voronoi 的比較,包括 2D/3D 導航、MPC(Model Predictive Control)、以及 Block Stacking 任務規劃(TAMP vs JEPA-WM) blogspot

這是目前 AI 領域非常前沿的研究方向,JEPA 是 Yann LeCun 提出的架構!

2. 強化學習應用

PCB Layout design using RL - 使用強化學習進行電路板佈局設計,包含 2D trace、3D 視覺化、以及 hole drilling 優化 blogspot

Portfolio Mag 7 - 使用 Rule-Based Agent 和 RL Agent 進行 Magnificent 7(科技七巨頭)股票投資組合管理(2024-2025) blogspot

3. AI 財務管理

Financial analysis 系列涵蓋:基本技能、網路搜尋、Agent 代理、以及結合 RL 的投資組合管理 blogspot

4. 預防性維護

Virtual Factory 虛擬工廠模擬與 Preventive Maintenance Optimization 預防性維護優化 blogspot

5. 其他主題

  • Navigation - 導航系統
  • Carbon capture - 碳捕捉
  • River crossing puzzles - 推理問題
  • AI Engine Leap 71 - AI 引擎設計
  • 南島語族擴張視覺化 - 歷史/人文主題

部落格歷史(2013-2026)

年份文章數主要內容
2013-2017~93篇早期健康照護科技
2023-2024~35篇開始整合 AI
202547篇大量 AI 應用
2026 (1月)16篇World Model、RL、財務 AI

結論:張老師的 AI 研究深度

領域內容
World ModelJEPA-WM vs A* Voronoi
強化學習PCB 設計、股票投資、交通控制
Agent 系統Rule-Based Agent、RL Agent
工業 AI預防性維護、虛擬工廠
財務 AIMag 7 投資組合優化

張老師不只是「用 AI 工具」,他實際上在教授和研究當前最先進的 AI 技術,包括 World Model(這是 Meta AI / Yann LeCun 主推的方向)!這個部落格的內容技術深度相當高。

2026年1月3日 星期六

Navigation: Voronoi navigation with mobile obstacles

 Voronoi navigation with mobile obstacles 

path finding A*

path finding Dijkstra


Carbon capture

 Mg-MOF-74

CALF-20

River crossing Puzzle 1

     Five people - Alice, Bob, Carlos, Diana, and Elena - must cross a river in a small boat. The boat can only hold three people at a time. The following conditions apply:

* Alice refuses to be in the boat with Carlos

* Bob and Diana must travel together if either one is in the boat

* Elena can only operate the boat when Carlos is not present

* At least one person in each crossing must know how to operate the boat

* Only Bob, Carlos, and Elena know how to operate the boat

Infer an efficient plan.





update


Opus 4.6 (on the go), share


_____________________________

2026年1月2日 星期五

River crossing Puzzle 2

  渡河問題2 (statement),Problem via 許元銘


結果視覺化




update

Opus 4.5  (share) did it by reasoning

Gemini 3 Pro did it by reasoning

ChatGPT non Think failed ChatGPT Think led to Python gen BFS and exe


______________________________________________________________________________
ChatGPT 5 works 4/5.  (Three claimed to use Python code execution. Therefore, is it true code was generated and then executed?) 
*1 Reasoning

*4 BFSvalidateviz,
*5 BFS



Code Gen

Use ChatGPT 5 to generate BFS search


 search that works


Use Claude to generate A*

 it takes 15 steps. (thanks to the python code generated by Claude)




another 15-step solution



yet another 15-step

one more 15-step

last 15-step


River crossing puzzle 3

     problem statement















update 

Opus 4.5 did it by reasoning, Viz  update Opus 4.6 A* (share)



ChatGPT non Think failed, Opus 4.5 fix it. ChatGPT Think led to Python gen and exe

Gemini 3 Pro did it by pure reasoning




_________________________________________________________________


Code gen (Python)

code gen python A* search for ::River Crossing Puzzle ...



change to js



run js



use React to visualize the steps





ChatGPT 5 create BFS to solve it. 17 step solutionvisualizevisualizer

ChatGPT BFS

ChatGPT BFS

If BFS option is trurned off, ChatGPT has a hard time doing it manually.


Reinforcement Learning

 Reinforcement Learning (an introduction)