/ Saltlux, Inc. Act One Hyper-Connection Neuron - PowerPoint PPT Presentation

AI Applications 워크샵 – 생활속의 인공지능 초연결 지식과 인공지능 이 경 일 / Saltlux, Inc.

Act One Hyper-Connection

Neuron ~100 Billion # ~ # of Web Pages Synapse ~100 Trillion # ~ # of Web Links

x 1,300 200m 200m 1 : 1000

World Wide Web Network

Internet Network

Mobile Network

Social Network

Work Network (Musical work)

WordNet Network

Linked Data Network

LOD project (Linking Open Data) • W3C project for publishing open data based on semantic web • 270 data set accessing by using URI and SPARQL EndPoint • Interlinking between geo-spatial, bio, dbpedia data and etc. • data.gov, data.gov.uk, data.go.kr already introduced Linked Data

LOD project Statistics

Knowledge Network

Intellectual Activity of Human Decision Information Information Knowledge Learning Medium Information Knowledge Making Pre-Historic Era (12,000BC~3,000BC) Historic Era ( ~1,900AD)

Big Data Era ( 2000~ ) • Analytics • Prediction • Intuition • Insight Big Data (F.A.C.T) Augmented Brain?

New Collective Intelligence Era ( 2020~ )

Hyper Connected People Hyper Connected Devices Hyper Connected Machines Hyper Connected Services Hyper Connected Data Hyper Connected Knowledge

Act Two Artificial Intelligence

A Space Odyssey (1968/2001) Matrix (1999/2199) I-Robot (2004/2035) EX Machina (2015/2020?) Terminator (1984/2029) AI (2001/2090) HER (2013/2025)

Artificial Intelligence Hypes? VC investment in AI company Big Data (10K USD) Deep Learning

Big Data and AI Market Application Markets Big Data and AI Market Trends [M USD] 40,000 [IDC, EU, Market Reports, 2013] 30,000 Smart Robot / Wearable Services 20,000 10,000 0 2010 2011 2012 2013 2014 2015 빅데이터 인공지능 Big Data AI Virtual Agent / Decision Support • Big Data market will grow at a 27% compound annual growth rate (CAGR) to $32.4 billion through 2017 • Global Industry Analysts forecast the big data based Artificial Intelligence market to exceed € 27 billion by 2015 Self-driving Car / Smart Factory

AI Market Expectation? • Growing exponentially over the coming years • Apple, Google, Microsoft and Facebook invest in the AI 1980s 1990s 2010s 2000s Artificial H/W, System OS, S/W Service Platform Intelligence? Personal Assistant and QA Services Amazon echo Google Now MIT Jibo Docomo SC MS Cortana Apple Siri

Virtual Agent Market • BusinessWire (2015)  Global Market size is growing 30% per year: 0.35 (2012) to 3.1 billion dollars (2020)  Communication service to O2O(Online 2 Offline) service with Q&A  Korean market will grow up to 0.24 billion dollars (2020) • GrandViewResearch (2015), TechNavio (2014)  North America centric → Europe and Asia market is growing rapidly, up to 20% in 2018 Virtual Agent Market Size * TechNavio, 2014

Going to be almost FREE! (except data…) Cloud Infrastructure AI Algorithms Big Data

Big Value from Big Data? 35ZB 120 mins 750B 1,000,000,000 Tera = 1,000,000 Peta HD Movies = 1,000 Exta Size? = 1 Zetta 7.9ZB 1.2ZB 1.8ZB 2.5ZB 163M Years Watching for One Person 2010 2011 2012 2015 2020 20hrs 50M 3M/sec Volume Movies Tweets e-mails 3V? /min /day Velocity Variety Sensor DBMS Log Text e-Mail Office Image Audio Video

The Facts of Big Data? Big Data Technology : “Complex and large data sets that it becomes difficult to process using traditional technologies” Why is it so difficult to process? 3V x F.A.C.T !! (Fragment x Ambiguity x Context x Trustability)

What is A rtificial I ntelligence? • The science and engineering of making intelligent machines. (John McCarthy) • The study and design of intelligent agent system that perceives its environment and takes actions that maximize its chances of success. (Stuart Russell) • The study how to create computers and computer software that are capable of intelligent behavior. (Wikipedia) Voice / Images Planning (decision making) Perception Environment Learning NLP / Sensors Learning Reasoning Reasoning Actuators Planning Action Knowledge Representation Display / TTS

What is A rtificial I ntelligence? • Strong AI Artificial general intelligence. Computers can be made to think on a level at least equal to humans, that they can be conscious and experience emotions. • Weak AI Non-sentient computer intelligence or AI that is focused on one narrow task. All real-world systems labeled "artificial intelligence" of any sort are weak AI at most. Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally

Knowledge Representations? Human language written in letters: “ The Earth orbits the Natural Language sun in an ellipse ” Human Visual expression of knowledge in picture, structure Visual Language diagram, flow chart, and blueprint etc Knowledge expressed in keywords, symbols and images Tagging Machine Learning (Deep Learning) related with objects Symbolic Language Knowledge expressed in symbols : x 2 /a 2 + y 2 /b 2 = 1 Decision Tree Tree-shaped graph structure for complex decision making Combined expression in condition with various rules of Rules Language human knowledge Knowledge expression system composed of objects and Database System relations in a table format Knowledge expression of logical symbols and arithmetic Logical Language operations: Woman ≡ Person ∩ Female Semantic Network Knowledge expression of semantic relation between Machine concepts in a graph structure Knowledge expression of values or pointers for other Frame Language frames saved in slots Allows knowledge expression, machine learning Statistical Knowledge technology combination based on probability and statistics

Knowledge Representations Natural Language “ Employees working for a company are humans; the company and the employees are legal entities. The company is able to make a reservation for an employee’s trip. The trip is available by plane or train that travels in cities within Korea or the U.S.. The companies and destinations for business trip are located in the cities. Saltlux reserved OZ510 with a round trip of Seoul and New York for Hong, Kildong .” Rule Language (Rule) If someone is flying, he must be on trip. (Rule) If someone’s trip is reserved in a company, he is an employee of the company. (+ Rule) For short trip in the same country, an employee should take a train. (Deduction) Hong kil-dong whose flight is in reservation is an employee of Saltlux. (Deduction) OZ510 is a flight for the U.S. and Korea.

Building Explicit Knowledge Base Legal Entity Legal Entity Location Name Name (*) Legal Entity ID ID (*) Legal Entity kindOf DISJOINT Person Person Company Company startFrom Gender Industry Gender⊆{M,F} Industry Company Trip City Person Company Person books Age Address Age > 25 Addr ⊂ Seoul endsIn subclssOf subclssOf subclssOf kindOf instanceOf instanceOf instanceOf Employee Employee instanceOf Korean American Airplane Train Position Pos ≠ Exec. City City Employee Employee instanceOf #4831 instanceOf #4831 instanceOf instanceOf Saltlux Saltlux instanceOf Saltlux instanceOf instnaceOf Saltlux C98765 C98765 Software Software #3502 #3502 Seoul Seoul Seoul Kildong Kildong Kildong participatesIn P12345 P12345 OZ510 Kildong Male Male New york 37 37 Manager Manager Semantic Network (a) Semantic Network (b) (a) + Frame (Slots) (c) (b) + Logical Restrictions

Building Polymorphic Knowledge Base Prec ecision on Logical World (Sou (S oundness) (KBQA) NL, IR World (IRQA) Statistical Rec ecall Wor World ld (C (Completeness)

Act Three Exobrain and WiseKB

Exobrain is a future AI that can learn, think and make decision like human Understanding Understanding Search & Infer Selecting WiseQA Question Problems User’s Intention Candidates Solutions Service Question Analysis Collaborative Prediction Exobrain Project Agent and Making Decision Service Context Context Analysis Self-Evolving Exobrain KB Structured/Unstructured Big Data Language WiseKB Understanding Hybrid & Knowledge Reasoning System Learning Human-like Knowledge Learning