Proud to attend the NVIDIA AI Lab cross-team meet-up held in CVPR 2017 and have received the brand-new TESLA V100 GPUs in person from NVIDIA CEO Jensen Huang.

利用CVPR 2017在夏威夷舉行,NVIDIA舉辦了跨國的NVIDIA AI Lab交流會。也很幸運成為第一批使用TESLA V100的實驗室。

Along with #CVPR2017, NVIDIA calls for the first cross-team meeting for the 18 NVIDIA AI Labs it supported in the past year. There are research teams from Berkeley, CAS, CMU, CUHK, DFKI, MIT, NTU, NYU, Oxford, Peking University, Stanford, Tsinghua, Univ of Washington, Università della Svizzera Italiana and SUPSI, Université de Montréal, University of Tokyo, University of Toronto, AI Institute Canada, etc.

It’s exciting to meet with and learn from so many outstanding research teams. It’s a further surprise for the appearance of NVIDIA CEO Jensen Huang.

One more surprise! Jensen announced the first batch of the brand new GPU, TESLA V100, are delivered to the 18 AI teams — before the official release date. Then each PI is called up for receiving the award, which also has his own signature and the best wish in “Do Great AI“!

Note that our team had received the awards from NVIDIA since November 2016 including a DGX-1 supercomputer and multi-year unrestricted research fund.

We are proud in one of the NVIDIA AI Labs and are proud to be in #CVPR2017 for presenting two prominent papers.

The V100 includes 640 Tensor Cores, delivering 120 teraflops of deep learning performance; Volta provides a 5x improvement in peak teraflops over its predecessor Pascal, and 15x over the Maxwell architecture, launched just two years ago.


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December 5th, 2017

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October 25th, 2017

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  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。 […]

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August 27th, 2017

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August 20th, 2017

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August 20th, 2017

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August 20th, 2017

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July 25th, 2017

Our CVPR 2017 Paper Highlighted in NVIDIA Corporate Blog