A really fun experience as attending the opening discussion in IBM Technology Forum, in W Hotel, Taipei, regarding the future enterprise AI solutions and the system requirements, August 24, invited by IBM Taiwan. It is indeed a well-organized and impressive event.

It’s a whole new and nice experience to face the key enterprise customers and IBM sales team — most of all hear their needs (and expectations) for the existing and future AI functions. The customers are really nervous on how to leverage the advanced technologies.

Along with my experiences in helping the enterprises setup their AI/DL teams, I shared my personal view on how to be the AI-savvy companies with key factors such as:
– Talents $$$$$; effective management
– Data (private)
– Strategies, products/services
– Domain experts
– Computation resources (in house, cloud-based, or hybrid)
– Standing on experts’ shoulders (open source, APIs, consultancy, etc.)
– Supports from the executives

Meanwhile, I do feel proud to promote the Watson Visual Recognition APIs with the potential enterprise customers. I am also surprised that the Morgan AI trailer project, I contributed during my sabbatical leave in IBM TJ Watson (2016-2017), had brought such advantages for the IBM sales and marketing teams.


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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