一些常聽的 Podcast 節目和培養英文聽力的方法

一些常聽的 Podcast 節目 (*)，適合坐車、(讓眼睛) 休息、睡前、或運動時聽

• Acquired (new): 
• How ARM Became The World’s Default Chip Architecture (with ARM CEO Rene Haas)
• The Complete History & Strategy of Microsoft: Vol. I, Vol. II 
• Renaissance Technologies: The best-performing investment firm of all time. A book. The hosts even talk about the hidden Markov process!
• TSMC Founder Morris Chang
• American Public Media: Marketplace
• Asianometry (new): Global Semiconductor Issues, Semiconductor "Course", TSMC Analysis
  

中原隨筆 (My days at CYCU)

在台南住了20幾年，因為種種因素，來到中原大學任教。懷念台南的好天氣、上下班不太塞車、和美食。秉持共好的精神，紀錄我在中原的所見所聞。

• 商業分析實驗室 (Business Analytics Laboratory)：指引 (Guides)，論文報告 (Group meeting)
• 翻轉教室 (Flipped classroom)
• Feel stressed (壓力山大) about your graduate studies?，快樂和健康地活著，壓力管理
• 工業系必修 (應用和產業知識)：微積分 (計算螺絲的體積)，機率與統計，電路和電子學，線性代數，作業研究 (含最佳化)，學習數學的四個層次，首頁右側的標籤有更多的資訊
• 作業研究：學科競賽，期末報告，到高中的介紹 (提問)，最差和最佳的應用場景，進一步的資訊
• Some books and information on machine learning and AI
• AI 時代的數學基礎：微積分，機率與統計，線性代數，作業研究 (含最佳化)。
• 學習：學習動力與方向 (1, 2)，態度與方法，聽不懂演講時怎麼辦，上台報告的方法與建議，專題和論文的製作與報告 (tips for the final project and your thesis)，國科會大專學生研究計畫，如何準備研究所，預備研究生 (4 + 1) (包含資料科學相關研究所和職涯準備)
• Some information and papers related to the seminar
• 培養思辨能力
• 為什麼工 (資) 管的課程看起來很雜？
• 機器學習和作業研究的奇妙結合  (最後一頁：天賦或努力)  
• 李芳齡譯，心態致勝：全新成功心理學 (Carol S. Dweck, Mindset: The New Psychology of Success)

Guides for students in Business Analytics Laboratory (商業分析實驗室學生指引)

Knowledge to master for a better foundation (and future)

Tools and general: 

• Antonio Torralba, Phillip Isola, and William Freeman, Foundations of Computer Vision, The MIT Press, 2024. (On Research, Writing and Speaking) (new)
•  Dr Takeo Kanade, Think Like an Amateur, Do As an Expert (new)
• AI in education: Geoffrey Hinton’s and Yann LeCun’s vision of the future, The Buzz Business, 2023. 
• The role of AI in education extends to fostering creativity and critical thinking.
• Another significant impact of AI in education is its potential to democratize access to quality learning.

Reinforcement learning is enough to reach general AI

David Silver, Satinder Singh, Doina Precup, and Richard S. Sutton, Reward is enough, Artificial Intelligence, Volume 299, October 2021, 103535.

In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.

Summarized AI information from IEEE Spectrum

Eliza Strickland, 12 Graphs That Explain the State of AI in 2025 Stanford’s AI Index tracks performance, investment, public opinion, and more, IEEE Spectrum, 07 Apr 2025

• Stanford HAI, The 2025 AI Index Report

Eliza Strickland, The Top 10 AI Stories of 2024, IEEE Spectrum, 31 Dec 2024.

Yearly summarization of Google AI research coauthored by Jeff Dean and more

Jeff Dean (google scholar)