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人工智慧產業發展趨勢蔣以仁
臺北醫學大學大數據科技及管理研究所 教授/所長
學士後大數據科技及管理學程 主任
何謂人工智慧︖運用科技所製造之智慧機器，尤其是智慧電腦程式。讓機器具備智慧能做人類能做的事。（JohnMcCarthy,1956）
嘗試模擬及重複人類思行緒為：思考、說話、感應、與推論
人工智慧進化
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困境精確計算的不可行
Gödel‘s不完備定理
TuringTest
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AI復甦DENDRAL– Feigenbaum
IntelligentAgent◦SearchEngine◦Recommendation◦BusinessIntelligence
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人工智慧衝擊
經濟, 文化, 社會, … 無盡的崩解
勞工 - McKinsey 58%工作自動化
Martin Ford, Rise of the Robots
Elon Musk, artificial
intelligence... 潛藏的威脅
AI 驅動 Unprecedented Era
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超越時空的壓縮新的破壞創新
極致收斂於不同的領域
全方位的連結擴充
指數加速的自動化– smart sensors and the 26 billion IoT devices by 2020
(11 trillion USD by 2025)
AoE: Evidence
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Singapore self-driving Taxis September 2016
Norwegian Telenor AI and Big Data Lab
Telefonica, BigML AI selects startups
Deep Knowledge Ventures, AI votes on investments
GE survival on software and AI Baidu, AskADoctor, 520 diseases, refers specialists
Baidu, StockMasterpredicts market trends Controversy: AI bias
PredixCloud
今日工廠
明日工廠
商品的翻轉
HarvardBusinessReview
Big data —from the lab to the clinic and back
•Calliope A. Dendrou, Gil McVean & Lars Fugger, Neuroinflammation — using big data to inform clinicalpractice,NatureReviewsNeurology 12, 685–698 (2016)
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Industrial Damage DetectionIndustrialdamagedetectionreferstodetectionofdifferentfaultsandfailuresincomplexindustrialsystems,structuraldamages,intrusionsinelectronicsecuritysystems,suspiciouseventsinvideosurveillance,abnormalenergyconsumption,etc.◦ Example:AircraftSafety
◦ AnomalousAircraft(Engine)/FleetUsage◦ Anomaliesinenginecombustiondata◦ Totalaircrafthealthandusagemanagement
KeyChallenges◦ Dataisextremelyhuge,noisyandunlabelled◦ Mostofapplicationsexhibittemporalbehaviour◦ Detectinganomalouseventstypicallyrequireimmediateintervention
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異常偵測Detectingoutliersinaimagemonitoredovertime
Detectinganomalousregionswithinanimage
Usedin◦ mammographyimageanalysis◦ videosurveillance◦ satelliteimageanalysis
KeyChallenges◦ Detectingcollectiveanomalies◦ Datasetsareverylarge
Anomaly
50 100 150 200 250 300 350
50
100
150
200
250
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異常偵測• N1 andN2 areregionsofnormalbehavior
• Pointso1 ando2 areanomalies
• PointsinregionO3areanomalies
X
Y
N1
N2
o1
o2
O3
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重要挑戰• Definingarepresentativenormalregionischallenging• Theboundarybetweennormalandoutlyingbehavior isoftennotprecise
• Theexactnotionofanoutlierisdifferentfordifferentapplicationdomains
• Availabilityoflabeled datafortraining/validation• Maliciousadversaries• Datamightcontainnoise• Normalbehavior keepsevolving
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AspectsofAnomalyDetectionProblem• Natureofinputdata• Availabilityofsupervision• Typeofanomaly:point,contextual,structural• Outputofanomalydetection• Evaluationofanomalydetectiontechniques
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PointAnomalies• Anindividualdatainstanceisanomalousifitdeviatessignificantlyfromtherestofthedataset.
X
Y
N1
N2
o1
o2
O3
Anomaly
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ContextualAnomaliesAnindividualdatainstanceisanomalouswithinacontextRequiresanotionofcontextAlsoreferredtoasconditionalanomalies
*Xiuyao Song,Mingxi Wu,ChristopherJermaine,SanjayRanka,ConditionalAnomalyDetection,IEEETransactionsonDataandKnowledgeEngineering,2006.
NormalAnomaly
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CollectiveAnomaliesAcollectionofrelateddatainstancesisanomalousRequiresarelationshipamongdatainstances◦ SequentialData◦ SpatialData◦ GraphData
Theindividualinstanceswithinacollectiveanomalyarenotanomalousbythemselves
AnomalousSubsequence
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類型Anomaly Detection
Contextual Anomaly Detection
Collective Anomaly Detection
Online Anomaly Detection
Distributed Anomaly Detection
Point Anomaly Detection
Classification BasedRule BasedNeural Networks BasedSVM Based
Nearest Neighbor BasedDensity BasedDistance Based
StatisticalParametricNon-parametric
Clustering Based OthersInformation Theory BasedSpectral Decomposition BasedVisualization Based
*AnomalyDetection– ASurvey,VarunChandola,Arindam Banerjee,andVipin Kumar,ACMComputingSurvey,2019
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參考資料• AIassessesbreastcancerrisk30timesfaster
http://www.forbes.com/sites/janetwburns/2016/08/29/artificial-intelligence-can-help-doctors-assess-breast-cancer-risk-thirty-times-faster/#7b717af556e2
• GE,rebornasasoftwarestartupusingAIhttp://www.nytimes.com/2016/08/28/technology/ge-the-124-year-old-software-start-up.html?_r=0
• Worldleading2025ChinaAIindustryhttp://www.chinadaily.com.cn/business/tech/2016-08/27/content_26615174.htm
• GlobalAIMarket2015:127B;2016:165B;2018:200B• Audrey--NASA's New Self-Learning AI Could Save First
Respondershttp://motherboard.vice.com/read/this-nasa-ai-will-sense-danger-save-firefighters-and-learn-from-mistakes
• Voicerecognition3xfasterthantypinghttp://www.npr.org/sections/alltechconsidered/2016/08/24/491156218/voice-recognition-software-finally-beats-humans-at-typing-study-finds?utm_medium=RSS&utm_campaign=storiesfromnpr
參考資料• The world's first self-driving taxis will be picking up passengers
in Singapore in September 1http://www.cbc.ca/news/technology/driverless-taxi-nutonomy-1.3735375
• AI bias http://motherboard.vice.com/read/its-our-fault-that-ai-thinks-white-names-are-more-pleasant-than-black-names
• NorwegianTelcocreatesAIandBigDatalabhttps://www.telecomtvtracker.com/insights/telenor-supports-norwegian-entrepreneurship-and-artificial-intelligence-research-6448/
• TelefonicaandBigML usingAItoselectstartupshttps://www.telefonica.com/es/web/press-office/-/telefonica-open-future_-and-bigml-create-preseries-a-joint-venture-for-early-stage-investment
• DeepKnowledgeVenturesappointsAIlikeaboardmembertomakeinvestmentdecisionhttp://www.itbusiness.ca/blog/hong-kong-vc-firm-appoints-ai-to-board-of-directors/48815
• Satelliteimagesandmachinelearningcanmappovertyhttp://bit.ly/2bxEv3w
謝謝聆聽!QUESTIONSANDCOMMENTS
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