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Deployable AI可輕易佈署的人工智慧

HTC DeepQEdzer L. Wu Ph.D.

2018.05.30

DeepQ—Quest for Cure

• HTC DeepQ於 2016 年成立，是由跨領域專家和工程師所組成，例如電腦科學、軟體工程、法規、使用者經驗、設計等，藉由虛擬實境／擴增實境、大數據和人工智慧之技術開發並提供精準醫療的產品和服務，以降低成本且提高效果的個人化醫療。

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Quality

AccessCost

DeepQ -- Leadership

• 史丹佛大學電子工程學博士張智威博士在2003年獲得加州大學的教授終身職。

• 擔任 Google中國研究院院長期間領軍開發的機器學習技術，已被使用超過三萬次。

• 2012年，張博士回到台灣，接任DeepQ總裁，希望培養一支醫界加科技界的精英隊伍，以打造出台灣醫療界的AlphaGo為目標。

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XPRIZE tricorder competition

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Optical Sense Test Flow:Otitis Media

YES

NO

Transfer Useful Features for Otitis Media

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Our Deep + Transfer Learning for Otitis Media [ACM MMHealth 2017]

80.11%Accuracy 90.96%

HTC DeepQ XPRIZE tricorder#5 the result

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AI chat bot #1

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Symptom checker: DeepQ Solutions[NIPS RL 2015, AAAI 2018]

• Hierarchical Reinforcement Learning– A committee of doctors

– Master agent picks a part/system agent to select a symptom to probe

• Consider Context– Who (demographic, heredity, medical

history)

– Where (distribution of diseases by locations)

– When (seasonal diseases)

DeepQ – Quest for Cure

• Paradigm Shift– Tricorder

– Symptom Checker

– Precision Medicine (AI)

– Precision Surgery (VR)

• Scalable Algorithms– For Big Data

– For Not-so-big Data

– Small Footprint

• Architecture & Execution

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What have we learned?

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Privacy is important in certain applications

Data is the new oil that drives decision

AI training requires time & experience

AI is Hard, AI Deployment

is Harder

-- Andrew Ng

~Non-IT Company Using AI~

– Many companies are figuring out how to use AI, but this is not easy. The technology is still complex, and few teams understand AI well enough to implement it effectively. Outside the IT industry, almost no companies have enough access to AI talent.

DeepQ AI platform

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Standalone versionCloud version

Data is the New OilMedicine

Industry

FinanceDataDecision

MT or AT?

Auto TransmissionAuto Tuning

Manual TransmissionManual Tuning

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Publication list

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[ACM RS 08] PFP: Parallel FP-Growth for Query Recommendation, H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Y. Chang, ACM Recommendation Systems, October 2008

[TIST 10] PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline Processing, ACM Transactions on Intelligent Systems and Technology, 2011.

[NIPS 07] Parallel Support Vector Machines, E. Y. Chang, et al., NIPS 2007.

[PAMI 10] Parallel Spectral Clustering, W.-Y. Chen, Y. Song, H. Bai, Chih-Jen Lin, and E. Y. Chang, IEEE Transactions on PatternAnalysis and Machine Intelligence (PAMI), 2010.

[NIPS 12] Large Scale Distributed Deep Networks. J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, Q.V. Le, M.Z. Mao, M.A. Ranzato, A. Senior, P. Tucker, K. Yang, A. Y. Ng, NIPS 2012.

[VLDB 14] Big Data, Small Footprint: The Design of a Low-Power Classifier for Detecting Transportation Modes (with Open Source dataset), M. Yu, T. Yu, C.-J. Lin, and E. Y. Chang, VLDB 2014.

[NIPS 15] SpeeDO, Parallelizing SGD for CNN, Z.Y. Zheng, R. Jiang, G. Wu, C. Choi, and E. Y. Chang, NIPS Learning System Workshop, 2015.

[EMBC 15] Transfer representation learning for medical image analysis, Chuen-Kai Shie, Chung-Hisang Chuang, Chun-Nan Chou, Meng-Hsi Wu, Edward Y Chang, IEEE EMBC, 2015.

[AAAI 18] Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning, HC Kao, KF Tang, EY Chang, AAAI 2018.

[ACM MM 17] Aristo/VivePaper, An Augmented Reality Platform, Edward Y. Chang, et. al., ACM MM, 2017.

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Wrap up of DeepQ platform

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Acknowledgement

• Jason Chou

• Kevin Tang

• HaoCheng Kao

• Jake Wu

• Aaron Hu

• VivePaper team

• DeepQ team

• Prof. CK Peng (Harvard)

• Jimmy Huang (Mackey)

• DBG team

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