It’s official that I had ended my very pleasant and amazing 1-year sabbatical leave in IBM TJ Watson Research Center, New York, as the Visiting Scientist. I did learn a lot from the Watson’s AI developments dedicated to enterprise solutions and feel proud to contribute for Watson’s future developments.

I am so grateful to work with Dr. John Smith, Dr. Benoit Huet, and Dr. Dhiraj Joshi for the first AI-enabled movie trailer for “Morgan”. It’s our surprise that the impacts are so huge — the team are also recognized with 2016 IBM Research Image Award for the technology, industry, and media impacts. We also have the opportunity to meet with CEO and President, Ginni Rometty for our prominent performance. The project had shown the AI capability for numerous promising applications in the media industry. The work is also accepted for oral presentation [1] in the Brand New Idea (BNI) track in ACM Multimedia 2017, a top ACM conference, for its technical breakthrough.

It also my honor that the following research work — contextual understanding and prediction — can be treated as an emerging direction for IBM cognitive computing and included as the final candidates for IBM Global Technology Outlook (GTO), i.e., the coming 5-year direction. It is a huge recognition as a visiting scientist for the research department.

It’s my huge honor to work with the best team members in Computer Vision and Multimedia Department, IBM Watson: Matt HillRogerio FerisMichele MerlerChung-Ching LinNoel Codella, and many others. Our family are also grateful for the very warm friendships and care from C Eric Wu and IHsin Chung.

Every people and happening in IBM TJ Watson gave us a amazing year — 2016-2017. I had been solidly upgraded from what I was in 2016.

Even returning to Taipei for the summer break now, we will continue the collaboration with IBM Watson in many forms. For example, I will join a panel with the IBM VP next week for the IBM Technology Forum in Taipei organized by IBM Taiwan. Stay tuned for more details.

[1] John R. Smith, Dhiraj Joshi, Benoit Huet, Winston Hsu, and Zef Cota. Harnessing A.I. for Augmenting Creativity: Application to Movie Trailer Creation. ACM Multimedia 2017.


1 2 3 5
December 5th, 2017

深度卷積網路的產品開發經驗 (一) (Advancing Convolutional Neural Networks for Industrial Products – I)

October 28th, 2017

Best Brave New Idea Paper Award in ACM Multimedia 2017

October 28th, 2017

How to Get Started in (PhD) Research (如何帶領你的PhD學生)

October 25th, 2017

Technical Debt — 沒有白吃的午餐

  十月初Intel副總兼CTO Amir Khosrowshahi 來台,有機會交流。Amir說明Intel在未來智能服務的定位。其中提到了NIPS 2015 Sculley的論文中 “Technical Debt” (技術債)的概念。 很有趣的一個名詞,用來解釋天下沒有白吃的午餐 — 智能化當然是相當重要的未來,但是也得搭配其他不可或缺的要件。  很快的把Sculley的論文看了,主要討論智能元件上線之後,對於線上的工程系統有怎樣的工程挑戰:比如說如何維持特徵值擷取版本,過多的特徵值,訓練模型的版本等等。 值得工程人員關注。  其中最有意思的是這張圖,核心智能(機器學習或是深度學習)常常只是工程系統中的一小部分,為了讓整個服務可以完整的運行,還有其他重要配合的工程部分,例如資料收集、清理、運算資源管理、運算平台、監控服務等。Intel想提供圖一上的大部分,不是只有ML核心。  每個有價值的垂直領域,都有專業的知識,需要時間、成本累積。從一年多前開始跟醫學領域的人一起合作,完全可以感受到跨領域的挑戰,令人戰戰兢兢。  技術債可以用(素質好)的人力彌補,例如挖角、併購,但是價格高。  同樣的,對於非以IT技術擅長的產業,即時是獲利非常良好的隱形冠軍們,要智能化在公司內部開始產生效益,也需要相當的時間跟花費(同樣的,有許多技術債得償還)。當然,經營層的決心是最關鍵的。  這也可能是許多(新創)公司的機會所在。尤其是專注服務於產出大量資料,以及資料單位價值高的幾個領域:例如製造、能源、健康醫療、交通、IT等,都是值得大量投資智能技術的領域。 […]

August 29th, 2017

Interview by Harvard Business Review (哈佛商業評論) on How to Grow the (AI) Talents

August 27th, 2017

Panel Discussion in IBM Technical Forum 2017 and How to be the AI-Savvy Company

August 20th, 2017

Technical Talk in Dell Technology Forum 2017 — AI Developments and Opportunities

August 20th, 2017

Amazing Year in IBM TJ Watson (IBM 華生研究中心的夢幻旅程)

August 20th, 2017

Presenting in Media Event: 商業週刊演講會 — 機器智能的在地機會

July 25th, 2017

Our CVPR 2017 Paper Highlighted in NVIDIA Corporate Blog