For models trained using opt.impute single imputations with 50% data missing, the average out-of-sample R^2 is 0.339 in the regression tasks and the average out-of-sample accuracy is 86.1% in the classification tasks, compared to 0.315 and 84.4% for the best cross-validated benchmark method.論文中的參考文獻大都來自醫學，看了 Y. Zhuo 的資料後，才知道和 D. Bertsimas 開了家新創公司 Interpretable AI，也可以從中了解美國大學的產學運作機制，如何將最新的研究轉成商品或軟體公司。
進一步來說，創業家兄弟的AI系統，在篩選好賣又利潤高的商品（他們稱為高性價比商品）時，會透過一星期的觀察，來分析商品銷售率、退貨率，以及消費者比價（包括評價星數和文字評價）等指標，來自動調整架上的品項。而這攸關創業家兄弟營收的7,000品項選品過程，完全由AI操作，不需要人為介入。另一方面，公司在選品時，也會偏好毛利相對高的非標準品，也就是變化性多元、沒有標準規格的商品，比如居家用品和服裝等，以與綜合型電商產生差異。
此外，創業家兄弟也將AI選品系統，運用在限時特賣上。該系統分析過往商品銷售資料，找出在特定時節銷售量特別好的商品，來進行限時特賣。不過，所以，創業家兄弟發言窗口坦言，由於限時特賣會受到許多變因影響，還需人為介入。
除了將AI和大數據分析用在選品策略上，創業家兄弟也用於客製化廣告和個人化精準行銷。廖家欣指出，透過採集商品點擊數據和消費者行為資料，自家公司的資料科學團隊可用來分析購買過程，提高個人化商品推薦。而創業家兄弟的關係企業松果購物，也已運用AI來開發「猜你喜歡」功能，也就是根據消費者行為軌跡，來推薦可能感興趣的商品。
「現在我們是日本傳動零件第二大在地供應商，占有率超過日本精工（NSK）、東晟（IKO）和椿本（Tsubaki）。」別看卓永財說得一派輕鬆，要讓台灣製造敲開由日本製造掌握的技術局面，甚至後發先至，其實困難重重。
日本市場，是上銀整體營運的縮影。今年前三季，上銀營收近二二七億元、每股稅後純益（EPS）十五．五九元，雙雙超越去年全年水準；法人預估，二○一八年營收更可望寫下新高的三百億元，有機會挑戰賺進兩個股本。
此外，卓永財也因一九二一％的總股東報酬率、市值增加一二三六億新台幣等綜合績效，在九月摘下《哈佛商業評論》「二○一八台灣執行長五十強」第八名。...
即使漁民頻繁巡察蝦塭，也難以精確掌握蝦子生長進度，必須靠撈蝦檢視的方式抽樣整個蝦塭生長進度，加上台灣養殖業主高齡化，高齡漁夫冒惡劣天氣巡察也讓人驚心，中山大學海洋科學系教授洪慶章推動跨學系專案，與資訊工程系教授黃英哲合作，要讓AI應用落地，解決老漁夫痛點。
Millions of tonnes of clothes end up in landfill every year—it’s one of the fastest-growing categories of waste in the world. How can the fashion industry continue to grow while addressing the environmental need for people to buy fewer clothes?
Otto’s work stands out because it is already automating business decisions that go beyond customer management. The most important is trying to lower returns of products, which cost the firm millions of euros a year.
Its conventional data analysis showed that customers were less likely to return merchandise if it arrived within two days. Anything longer spelled trouble: a customer might spot the product in a shop for one euro less and buy it, forcing Otto to forgo the sale and eat the shipping costs.
We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water service lines, the pipes connecting each home to the water system. In the absence of accurate records, and with the high cost of determining buried pipe materials, we put forth a number of predictive and procedural tools to aid in the search and removal of lead infrastructure. Alongside these statistical and machine learning approaches, we describe our interactions with government officials in recommending homes for both inspection and replacement, with a focus on the statistical model that adapts to incoming information. Finally, in light of discussions about increased spending on infrastructure development by the federal government, we explore how our approach generalizes beyond Flint to other municipalities nationwide....
Through an analysis of about 160 AI social impact use cases, we have identified and characterized ten domains where adding AI to the solution mix could have large-scale social impact. These range across all 17 of the United Nations Sustainable Development Goals and could potentially help hundreds of millions of people worldwide. Real-life examples show AI already being applied to some degree in about one-third of these use cases, ranging from helping blind people navigate their surroundings to aiding disaster relief efforts.
TFT-LCD panel manufacturers rely on experimental design and engineering experience for process monitoring and quality control throughout the production line. To shorten production and reduce the cost of labor resources, this study proposes a three-phase data science framework embedded with several data mining and machine learning techniques, which can identify the variables affecting yield, predict the metrology result of photo spacer process, and suggest the process control in the color filter manufacturing process. An empirical study of Taiwan's leading TFT-LCD manufacturer is conducted to validate the proposed framework. The results indicate that the proposed framework effectively and quickly selects the important variables, predicts the metrology result with higher performance, and identifies the main effect and interaction effect of the selected variables for yield improvement.
