Artificial Intelligence and the Future of Universities

Dimitris Bertsimas, Artificial Intelligence and the Future of Universities, Texas A&M Institute of Data Science, 2023.

智慧農業的成功因素

林一平，智慧農業的成功因素，電子時報，2023-08-21

在台灣，農業物聯網感測設備供應商眾多，然而通訊技術和資料傳輸格式卻千差萬別，導致資料在不同系統間的流通和加值應用面臨著困難。

為了解決這一重要問題，農業部於2023年4月27日推出「智慧農業感測資料格式標準與測試規範」。透過推動資料格式的標準化，提高農業物聯網應用領域中資料串接的效率，同時也降低開發成本，推動農業物聯網的深入應用。

Tackling Climate Change with Machine Learning

David Rolnic et al., Tackling Climate Change with Machine Learning, ACM Computing Surveys, Volume 55, Issue 2, March 2023, Article No.: 42, pp 1–96, https://doi.org/10.1145/3485128

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

Industry 4.0: Opportunities and Challenges for Operations Management

Tava Lennon Olsen, Brian Tomlin (2020) Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1):113-122. (pdf)

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which include both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

科技魅癮 (數位季刊)

 科技魅癮 

《科技魅癮》每期都精選1個國際關注的科技議題，邀請1位國內資深學者擔任客座編輯，並訪談多位來自相關領域的科研菁英，以「領路人」、「談觀點」、「探研究」、「躍思考」、「看世界」、「飆研值」等6個專欄，自多元視角剖析、探討該領域在臺灣及全球的研發現況、未來發展、相關衝擊及影響，希望藉由這個平臺，促進科際交流及跨域合作、激發科研人員創意思維、拓展產學鏈結網絡，進而增進國內研發能量。

沈孟儒院長：「精準健康，用科技讓醫療深植人心」，2021-03-31

簡禎富教授：「產業維新的初衷，是為了讓臺灣社會邁向以人為本的智慧生活。」，2021-06-09

Charting a business course for reinforcement learning

Jacomo Corbo, Oliver Fleming, and Nicolas Hohn, It’s time for businesses to chart a course for reinforcement learning, McKinsey, April 1, 2021.

Broadly speaking, we see reinforcement learning delivering this value across the business, with potential applications in every business domain and industry (Exhibit 2). Some of the near-term applications for reinforcement learning fall into three categories: speeding design and product development, optimizing complex operations, and guiding customer interactions.

 Exhibit 2 some applications.

To be sure, implementing reinforcement learning is a challenging technical pursuit. A successful reinforcement learning system today requires, in simple terms, three ingredients:

1. A well-designed learning algorithm with a reward function. 
2. A learning environment.  
3. Compute power. 

Computing power is better than compute power. 

奧丁丁 (OwlTing) 的區塊鏈多元運用

張道宜，區塊鏈不只是「幣」而已：奧丁丁多元運用，奠定食品溯源、旅宿管理基礎，Cheers， 2018-09 

2014年，奧丁丁開始涉入區塊鏈領域服務，除了接受虛擬貨幣支付外，更推出全球第一個區塊鏈食品溯源系統OwlNest，將區塊鏈「不可篡改」的特性，應用在食品履歷功能上，強化消費者對產品的信心。  

2018年8月，也與台東池上青農合作，希望在稻米種植過程中導入物聯網與區塊鏈技術，使整個生產過程更透明、更自動化、人為干預更少。 

另方面，奧丁丁也將區塊鏈導入旅宿管理，推出OwlChain系統，解決讓旅宿業者頭痛不已的重複訂房難題。目前以台灣為基地，要推向日本、馬來西亞等國市場，預計在2019年達到3萬家的規模。

呂晏慈，區塊鏈賣米　解決混米痛點賣進杜拜，商業周刊第 1628 期， 2019-01-24

精準施肥、驅蟲全年無休 小農一人都搞定

黃亞琪，精準施肥、驅蟲全年無休 小農一人都搞定，今周刊，2019-01-23

玩味的是，泥土和空氣中隱藏的「詭譎迷離」，是肉眼看不見的。然而，陳健章堅信著古老智慧，也倚重著科技解決問題的威力。「智慧化還是要流汗的。」他解釋，這裡插了約一百支的感應器，從泥土下的溫度、地底鑽的蟲子，到土地上的溼度、酸鹼值，都是被蒐集的數據；空氣中飄散的粉塵、風向、紅外線等變數，也不放過。...