AI and its revolution
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Why 'Microsoft Predicts' predicted the US election so wrong? Why & What is Big data? What's 'machine learning' and why 'deep learning' is better? How could 'deep learning' do for us? The Paradigm Shift of IoT? 分成幾個部分： 1. 大數據與小數據（以美國總統選舉為例） 2. 大數據、機器學習與深度學習的關係 3. 大數據與數位廣告 4. 深度學習的應用 5. 美中台的 AI 發展 6. 人工智能＋物聯網與數位典範翻轉
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AI and its revolution
- 1. AI and Its Revolution Craig Chao Big Data Consultant & Senior Data Scientist chaocraig@gmail.com Exploring the AI development in USA/China/TW
- 2. Agenda u Let’s Start from United States Presidential Election, 2016 u Big Data - 4V u Introduction of AI u The AI Development in USA, China and Taiwan u From Things to AIoT -- Reverse business
- 3. Let’s Start from United States Presidential Election, 2016
- 9. AI = Scientific Witchcraft?
- 10. Predictions in One Thousand of Years Ago u 竹外桃花三兩枝，春江水暖鴨先知。 u 簍篙滿地蘆芽短，正是河豚欲上時。 《春江晚景》— 宋·蘇軾
- 11. Challenges of Big Data - 4V 資料量大 資料多樣性 資料輸入 和處理速度快 資料真實性
- 12. 4V & Solution Directions Challenges Directions Volume Scalability Velocity Reactive / Streaming Variety All data Veracity Knowledge engineering & Machine intelligence
- 13. The Revolution of Big Data DATA Hypotheses Statistical Analysis BIG DATA Hypotheses Machine Learning Data Mining Machine-generated Sampling, Multi-variant… All, Hyper space, … Volume, Velocity, Variety, Veracity Human-explainable
- 14. Models ßà Cases Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011 BIG DATA Hypotheses Machine Learning Data Mining Machine-generated All, Hyper space, … Volume, Velocity, Variety, Veracity deductive inductive Cases Models Models Cases
- 15. Models ßà Cases Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011
- 16. Range ‘All data’, if not exhaustive, is also ‘sampling’!
- 17. 三體 u 「射手」假說： u 有一名神槍手，在一個靶子上每隔十釐米打一個洞。設想這 個靶子的平面上生活著一種二維智能生物，它們中的科學家 在對自己的宇宙進行觀察後，發現了一個偉大的律：「宇宙 每隔十釐米，必然會有一個洞。」它們把這個神槍手一時興 起的隨意行為，看成了自己宇宙中的鐵律。 u 「農場主」 u 假說則有一層令人不安的恐怖色彩：一個農場里有一群火雞， 農場主每天中午十一點來給它們餵食。火雞中的一名科學家 觀察這個現象，一直觀察了近一年都沒有例外，於是它也發 現了自己宇宙中的偉大定律：「每天上午十一點，就有食物 降臨。」它在感恩節早晨向火雞們公佈了這個定律，但這天 上午十一點食物沒有降臨，農場主進來把它們都捉去殺了。 採樣樣本要足夠，但是卻永遠不足夠。
- 19. Why Big Data? u Internet Data u 4 V u Analysis àInference àAction u Data-driven with no people interception u Building model programmatically u Hyperparameters optimization Automation? Personalization?
- 20. 義烏指數
- 22. Key Data is the Key 山不在高，有仙則名。 水不在深，有龍則靈。
- 23. How Could Big Data Work Well?
- 24. 4R: Reach, Richness, Representation, Range Reach Richness High High Low 使用者接觸量(DAU) 資料豐富度 (Behavioral data) Range High 系統範圍 ( Affiliate of whole context) Representation 呈現形式與內容 (Format & Content)
- 25. Data Economy Traditional -> Internet Economy HighREACH RICHNESS High Low Traditional Economy Internet Economy (quality) (quantity) 聚焦、精準 擴大受眾
- 26. Reach: The Value Funnel CPM campaign: Revenue = N/1000 ⋅CPM CPC campaign: Revenue = N ⋅ CTR ⋅ CPC CPA campaign: Revenue = N ⋅ CTR ⋅ CVR⋅ CPA UU Reach (DAU) ARPU = Life-time Value
- 27. Richness Data Quality è Predictive Power
- 28. Richness: Predictive Power APP類型偏好 使用裝置 使用時間 定位區域 廣告行為偏好 Conversions Logs Behavioral Data Attribution Data
- 29. Richness u Data Quality Richness u Profile vs. Transaction u Data Utilization Richness u Call taxi (short vs. long route) u Download times vs. Activation days u Data Model Richness
- 30. Representation
- 31. Representation
- 32. Representation Brand Awareness View Rating Reach TV campaign Conversions Click Impression Request Range Mobile Campaign Actions Traffic Buzz Reach Offline Campaign Reach Richness Cross-screen Effect
- 33. 成功案例：掌握 4R 成效更優異！ Cross-screen synergy Big data synergy with Cross-screen effect +TV 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 0 5000 10000 15000 20000 25000 30000 35000 40000 Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun APP下載率 優化轉換率 App Download Rate Optimized Conversion Rate
- 34. Range ‘All data’, if not exhaustive, is also ‘sampling’!
- 35. Range
- 36. Range u Google trend, Viral install…
- 37. High 4R: Reach, Richness, Representation, Range Reach Richness High High Low 使用者接觸量(DAU) 資料豐富度 (Behavioral data) Range 系統範圍 ( Affiliate of whole context) Representation 呈現形式與內容 (Format & Content)
- 41. 望梅止渴
- 43. Simplest Model
- 45. LM & LR Source: http://www.saedsayad.com/logistic_r egression.htm 歸一化的好處在於數值具備可比性和收斂的邊界 Likelihood
- 48. Latent Factors
- 49. Matrix = Associations Rose Navy Olive Alice 0 +4 0 Bob 0 0 +2 Carol -1 0 -2 Dave +3 0 0 u Things are associated Like people to colors u Associations have strengths Like preferences and dislikes u Can quantify associations Alice loves navy = +4, Carol dislikes olive = - 2 u We don’t know all associations Many implicit zeroes Source: Sean Owen(2012), Cloudera
- 50. In Terms of Few Features u Can explain associations by appealing to underlying features in common (e.g. “blue-ness”) u Relatively few (one “blue-ness”, but many shades) (Alice) (Blue) (Navy) Source: Sean Owen(2012), Cloudera
- 51. Losing Information is Helpful u When k (= features) is small, information is lost u Factorization is approximate (Alice appears to like blue-ish periwinkle too) (Alice) (Blue) (Navy) (Periwinkle) Source: Sean Owen(2012), Cloudera
- 52. © 2015 應用數據科學實戰營 Src: AmpCamp, 2015
- 53. © 2015 應用數據科學實戰營 ALS Algorithm • Optimizing X, Y simultaneously is non-convex,hard • If X or Y are fixed, system of linear equations: convex,easy • Initialize Y with random values • Solve for X • Fix X, solve for Y • Repeat (“Alternating”) X YT
- 54. Singular Value Decomposition A m = n S k k• T’ n m •Σ
- 60. AI Timeline Ada (1842) Alan Turing (1950) The first conference on AI by John McCarthy, Marvin Minsky (1956) Demonstrated by Newell (1957) Unimation s working on GE (1961) Joseph Weizenba um (1965), E. Geigenba um (1965) Chess- playing program by Greenblatt at MIT (1968) Jack Myers Harry Pople (1979) 1980s Ian Horswil l (1993) TiVo Suggestions (2005) Apple, Google, Micorsoft (2011) Machine Learning, Deep Learning (2013 ~)
- 61. KEY MOMENTS IN DEEP- LEARNING HISTORY
- 62. KEY MOMENTS IN DEEP- LEARNING HISTORY 擊碎玻璃天花板的華裔女科學家
- 63. KEY MOMENTS IN DEEP- LEARNING HISTORY
- 66. Deep Dream
- 67. Deep Dream
- 68. Deep Dream
- 69. Deep Dream
- 70. Deep Dream
- 72. Deep Dream
- 73. Deep Dream
- 74. Artistic Style
- 75. Artistic Style
- 76. Artistic Style
- 77. LipNet
- 78. LipNet
- 79. LipNet
- 83. Google RAISR 「Rapid and Accurate Image Super-Resolution」 Upsampling （aliasing artifacts）
- 84. Google RAISR
- 85. Google RAISR
- 86. Google RAISR
- 87. Ratbot and maze
- 88. Ratbot and maze
- 89. Bird-eye camera over the maze
- 90. Digital reward map updating
- 91. Superior learning performance of Ratbots over unenhanced rats
- 92. Embed The Word
- 93. AI can Tell the Difference between Sports
- 94. CaptionBot
- 96. "I think it's a young girl throwing an orange Frisbee in the park," Microsoft's AI will tell you. "I think it's a man jumping through the air doing a trick on a skateboard," Microsoft's AI says.
- 97. AlphaGo 9–15 March 2016
- 98. AlphaGo 9–15 March 2016
- 99. Open Letter on Artificial Intelligence 08/19/15
- 100. OpenAI Gym
- 101. StarCraft II DeepMind feature layer API
- 102. Microsoft's AI Tay offends and goes offline
- 103. To Benefit People and Society
- 104. Microsoft + OpenAI
- 105. The AI Development in USA, China and Taiwan
- 106. Auto-Pilot in China
- 108. Auto-Pilot in China
- 111. Auto-Pilot in China
- 112. Auto-Pilot in China
- 113. Auto-Pilot in China
- 115. Auto-Pilot in China 隧道內部光線微弱，考察車輛在暗環境下的應對策略。 隧道內設置了「白板」障礙物。對於白板的設計，考察 攝像頭和激光雷達的識別能力。
- 116. Auto-Pilot in China
- 117. Auto-Pilot in China
- 119. Auto-Pilot in China
- 120. AI Investment of Lenovo
- 139. 云端智能 机器人
- 141. cloud-based clinical documentation solutions building technology to power the next generation of intelligent applications Clinical Documentation
- 160. AI investments u 今年Google執行長 Sundar Pichai 在財報會議上明確表示， DeepMind團隊的人工智慧技術幫助Google數據中心節省了 40%的能源利用 u 九月出席台灣半導體產業協會（TSIA）的聯發科創辦人蔡 明介就秀出數據 u 美國在自動駕駛車投資40億美元，無人機投資46億美元 u 日本產業經濟省對自動駕駛、機器人及產業保安等預計投入 145億美元 u 韓國規畫於2016年至2020年投入30億美元發展，建立國家級 AI中心 u 歐盟在2013年就啟動10億歐元「人類腦計畫」，同年，美 國宣布啟動45億美元的「腦計畫」 Src: http://www.bnext.com.tw/article/41689/ai-asian-silicon-valley-startups?utm_source=pnn
- 161. AI in Taiwan u 科技部指出，政府用於補助與人工智慧技術研發如 深度學習、人工智慧、機器學習、無人機、自動駕 駛、機器人等相關6種領域，共1,691件計畫，總經 費約近13.65億元。 u 民間 u Appier在種子輪、A輪、B輪募資總計已獲得3千萬美 元（約10億元台幣） u 創意引晴（Viscovery）在十月則獲得1千萬美元融資， 其團隊花了兩年研發出影音大數據分析技術，運用 人臉、圖片/商標、文字等七種不同的辨識技術，透 過深度學習計算影片情境及廣告投放效益。 u 盾心科技（Umbo CV）與行品科技（Skywatch Innovation） 等公司也持續在AI領域深耕茁壯 Src: http://www.bnext.com.tw/article/41689/ai-asian-silicon-valley-startups?utm_source=pnn
- 162. From Things to AIoT -- reverse business
- 163. IROS 2016 - Best Application Paper Award --11 Minutes of Up&Down with
- 164. 150 Robots in Tesla Factory
- 167. Amazon Echo
- 168. Google Home
- 169. Google Home 02:05
- 170. Alexa vs OK Google 10:00
- 171. Thousands of people to make a 49 dollars of smart device
- 172. PC Mobile Things
- 173. Tesla + Uber + X = ?
- 174. Paradigm Shift(Reverse) u Move u Data à program u Value u Things à Product Service à Personal Service u Value/revenue shift u What if phone price is near its cost or free?
- 175. Box Moving Things Users For example: Camera
- 176. IoT Service Users CloudThings IoT Service For example: Home Surveillance Low service feeHigh price
- 177. AIoT Service Users Data Cloud Training/ Inference Things AIoT Service For example: Smart Home Surveillance
- 178. AIoT Service(Reverse) Users Data Cloud Training/ Inference Things AIoT Service Low price Main revenue stream
- 179. Users Data Cloud Training/ Inference Things AIoT Service Low price Main revenue stream Assets Specificity From Servant to Master AIoT Service(Paradigm Shift) UGC Cross-platform connections
- 185. GoPro
- 186. GoPro
- 187. GoPro
- 188. 體力 腦力 Artificial Power Artificial Intelligence 蒸汽機 電力 電腦 AIR/AIoT
- 190. chaocraig@gmail.com