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Big Data Driven Innovation and Next Generation AI

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Presentation on theme: "Big Data Driven Innovation and Next Generation AI"— Presentation transcript:

1 Big Data Driven Innovation and Next Generation AI
Raj Reddy Carnegie Mellon University Pittsburgh, PA 15213 Nov 8, 2017

2 AI 1.0: Use of Knowledge in Problem Solving
An Intelligent System must Learns from Experience Use Vast Amount of Knowledge Tolerate Error and Ambiguity Respond in Real Time Communicate with Humans using Natural Language Search Compensates for Lack of Knowledge Puzzles Knowledge Compensates for Lack of Search F=ma; E = mc2 Traditional Sources of Knowledge Formal Knowledge: Learnt in Schools and Universities Books, Manuals, Informal Knowledge: Heuristics from Peoples Human Encoding of Knowledge Expert Systems Knowledge Based System Rule Based Ssytem

3 Major Breakthroughs in AI of the 20th Century
Enabled by Brute-force, Heuristics, Human Coding of Rules and Knowledge, and Simple Machine Learning (Pattern Recognition) World Champion Chess Machine IBM Deep Blue Mathematical Discovery Proof Checkers Accident Avoiding Car CMU: No Hands Across America Robotics Manufacturing Automation Disaster Rescue Robots Speech Recognition Systems Dictation Machine Computer Vision and Image Processing Medical Image Processing Expert Systems Rule Based Systems Knowledge Based Systems

4 AI 2.0: Extract and Use Knowledge from New Data Driven Knowledge Sources
Paradigm Shift in Science First 3 Paradigms: Experiment, Theory, Simulation Rutherford, Bohr, Oppenheimer 4th Paradigm: Data Driven Science Create Next Generation AI systems Data Driven AI systems To Solve Previously Unsolved Problems Previously Unavailable Sources of Data Knowledge from Big Data: Data Driven Learning of Models and Algorithms Knowledge from Multiple (Cross) Media: Social Media Intelligence Gathering From All Language Sources From All the Media: Text, Speech, Image and Video Knowledge from Crowd Intelligence: Global Brain: from Individual Intelligence to Collective Intelligence Knowledge from Augmented Intelligence: Human-Machine Hybrid Intelligence for Collaborative Problem Solving Knowledge from Unmanned Autonomous Vehicles: Intelligence from Collaborating Teams of Robots Automatic Discovery of New Knowledge Machine Learning using Big Data Deep Learning

5 Major Breakthroughs in AI in 21st Century
Enabled by Big Data and Machine Learning Language Translation Google Translate: Any Language to Any Language Speech to Speech Dialog Siri, Cortana, Alexa Autonomous Vehicles CMU, Stanford, Google, Tesla Deep Question Answering IBM’s Watson Robo Soccer World Champion Poker CMU Libratus No Limit Texas Hold’em Poker

6 AI 2.0 Enables COGs and GATs Cognition Amplifiers and Guardian Angels
Cognition Amplifiers (COGs) and Guardian Angels (GATs) are Examples of Use of AI 2.0 Technologies. They Need Big Data Intelligence Cross Media Intelligence Crowd Intelligence Augmented Intelligence UAV Intelligence

7 Big Data: Drowning in Data

8 Sources of Big Data for AI 2.0
Personal Data: Data from People Health Education Food Security Energy Security Water Security Other: Medicine/Pharmaceuticals, Shelter/Housing, Sanitation National/Local Data: Data from Places and Things Transportation Telecom/Smartphone/Communication/WiFi Banking Entertainment Shopping Global Data – Data from Unplanned Events (Black Swans) Earthquakes/Tsunami Typhoons/Hurricanes/Cyclones Fire Flooding

9 What to Collect? Data Relevance: Big Data Parameters
Volume – Size and Scale Up to 40,000 sensors in the Airbus A380 7 TB per day Velocity – Data Rate and Streaming Data Sensor data collected in msec Type and No of Sensors Variety – Cross Media Data Sensors Images and videos Text data Relational business data Validity - Reliability Poor data quality Missing data Data collected doesn't suit targeted use cases Value – Usefulness and Importance Data per se is not valuable How to extract real value from data?

10 Necessary Conditions for Collection and Use of Big Data
Infrastructure Instrument Data Sources People Places and Things Computing Power: Processor, Memory and Bandwidth Multi Farm Cloud Computing Super Computers for Processing Zettabyte (1021 Bytes) Storage Farms Million Gigabit bandwidth Machine Learning and Analytics

11 Big Data from Health Monitor Life Support Parameters
Using Improvised Smart Phone Using a Low Cost Version of Apple Watch and Fitbit Body Media Implant or Capsule in the Future? Analyze the Data for Tell Tale Signals Notify and/or Warn the User of Impending Problems Guardian Angel Service Providers For Health Devices, Tools and Gat Apps

12 Big Data from Education
Keystroke Activity Monitoring Exposes Student Learning Behavior Attendance Paying Attention or Playing games? Learning Speed Problem Solving Speed Valuable Tool for Student, Parents and Educators Timely Notifications and Warnings Guardian Angel Service Providers For Education Devices, Tools and Gat Apps

13 Big Data from Emergencies Provide the Right Information to the Right People
At the Right Time In the Right Language I the Right Medium: Voice, Video and/or Text In the Right Level of Detail

14 Data to Knowledge: Machine Learning Machine Learning is the Key to Unlocking Big Data

15 Data Science and Machine Learning
interdisciplinary field about scientific methods… to extract knowledge or insights from data...uses techniques from many fields …mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning. Data Science Is a subfield of AI, it gives computers the ability to learn without being explicitly programmed Machine Learning Artificial Intelligence … Intelligent Amplifiers": Use of AI Technology to augment human intelligence Adapted from: SAP and Wikipedia 16

16 Machine Learning - Complexity and Automation Levels
Data Analytics Monitor: What Happened? Diagnosis: Why did IT Happen? Prediction: What will Happen? Prescription: What is to be Done? Source: Gartner 2014 © 2017 SAP SE or an SAP affiliate company. All rights reserved. 18

17 Role of Machine Learning in Big Data
Anomaly Detection Healthy Individuals vs. Persons with Potential Problems Classification Clustering into Groups of Similar Populations Failure Prediction Sensor-based Prediction of Future Health Problems Prescriptive Analytics and Optimization Recommend Actions and Optimize Activities for Preventive Services like Flu Shots and Screening Tests Correlations Support for Root Cause Analysis like DNA Inheritance Forecasting Predict Life Expectancy for Insurnace

18 Problems with Big Data Machine Learning
High dimensional data – Find the relevant features? Needs the involvement of domain experts and/or automatic feature selection techniques Data Quality is poor: Data is not collected to be used for Machine Learning. There are no standards with regards to sensor data. Difficult to integrate different data sources. Rare event problem: Standard Machine Learning algorithms achieve poor results. Needs special algorithms for unbalanced classes. No labels: Use unsupervised learning algorithms (e.g. anomaly detection) Use-case specific algorithms and data models: Needs flexibility and extensibility Deployment challenge: Need for an automatic system for model deployment and management

19 Machine Learning Steps in Big Data
Sensor Data Acquisition Pre-process and explore data and detect patterns and outliers. 80% of the time of data scientists is spent with pre- processing Learning Use domain user annotations as labels and sensor data as well as business data to learn machine learning models Prediction Use the learned model and apply to new data Feedback Ask domain users to annotate patterns and anomalies Recommendation Recommend steps that should be done by the domain user Action Asses recommendations and act accordingly if appropriate 1

20 Integrating Machine Learning into COGs and GATs
Data Preprocessing Domain Expert Feedback Adaptive Learning Anomaly Detection Prescriptive Analytics Problem and Failure Prediction Recommended Recovery Actions Model Management and Updating 1

21 Knowledge to Action: Intelligent Agents

22 Big Data Enables Cognition Amplifiers and Guardian Angels COGs and GATs for Everyone on the Planet
Cognition Amplifiers and Guardian Angels are two families of intelligent Agents that help with scarcity of attention problem A Cognition Amplifier (COG) is a Personal Enduring Autonomic Intelligent Agent that anticipates what you want to do and helps you to do it with less effort Buying and selling: Transact with multiple providers Filter spam, understand and respond to actionable News: Based on topic preferences, novelty, collaborative filtering Banking: Monitor bank account, Credit Cards, Pay Bills Travel: Flights, hotel, schedule disruptions, cancellations A Guardian Angel (GAT) is a Personal Enduring Autonomic Intelligent Agent that Discovers And Warns You About Unanticipated Events That Could Impact Your Safety, Security, and Happiness Just-in-Time Warnings: Hurricanes, Earthquakes, Extreme Weather Accident Alerts and Rerouting; Transport Strikes Scarcity of Essential Resources: Food, Energy, Water etc. Each Person has Thousands of Cogs and Gats as Personal Assistants

23 Architecture of Cogs and Gats Cogs and Gats Publish and Subscribe
Cogs and Gats are Mobile Apps (like a shadow) for Each Person on the Planet Unlike APPs of today, Cogs are Mass-customized to Each Individual Designed to be Non-intrusive, Autonomic, and Device Independent Always On, Always Present and Always Working Always Learning Enduring (life-long) Cogs and Gats Monitor, Analyze and Learn From Experience; Learn From Own Experience And Experience of Others And share knowledge with a community of Cogs and Gats Automated Discovery of Data and Information Sources Gats and Cogs Publish and Subscribe Anonymized Data of User Activities and Experiences Data, suitably anonymized, can be used to learn appropriate responses for every possible situation by Learning preferences by observing user choices, Learning by task similarity and user similarity, Learning by error correction and Simply learning thru clarification dialog ( does that mean yes? Would you care to define it?)

24 Technologies of Cogs and Gats Cogs and Gats Publish and Subscribe
Service Agents are created by service providers using agent templates included in the platform Download and Pay just as for Apps and Personalization Guardian Angels Can request and manage services on behalf of their masters. Every organization that wants to enable access to their services by Guardian Angels will create a Service Agent for this purpose. Opt-in “Waze” like Models Privacy Individual Other participants Legal Can be subpoena-ed Security Information falling into wrong hands

25 Publish/Subscribe Eco System of
Guardian Angels and Cognition Amplifiers Global Infrastructure Platform Activity Monitoring and Intention Awareness User Guardian Angels Cloud Based User Infrastructure Platform Knowledge Source Publishers Global, National and Local Knowledge Source Distributors ReTweet Model Global DataBase of Humankind

26 Personalization and Customization of
Guardian Angels and Cognition Amplifiers Cloud Based Guardian Angel Platform Linked to User Smart Phone Learning Learn by Watching Learn by being taught Learn by doing Learn by asking others Learn by discovery Security Multimodal authentication Continuous authentication Encryption Big Data Management Process, index, story and retrieve data Mine, Cluster, and Summarize relevant experience Dialog With humans With other agents Self Healing Detect errors Resolve errors Power Mgmt Activity Monitoring Multi-Sensor Integration Autonomic Systems Etc. Family of Personalized Guardian Angels and Cognition Amplifiers Subscribe to National and Global Sources and Act on Relevant Information

27 What Cogs and Gats Are Not
One System Fits All Designed for Mass Use User Activated APPs Assume Human in the loop always Intentional Activation Require Laptops or Smart Phones Consume Human Attention Texting instead of Voice and Vision Context Insensitive Fixed algorithms (non-changing) Non Temporal

28 Economic Impact of AI 2.0 and Big Data Guardian Angels and Cognition Amplifiers
Every person on the planet will be able to perform many daily habits more effectively using Gats and Cogs Daily habits (routines, activities) include a wide spectrum from routine tasks (such as banking and travel planning) to tasks too difficult for the user Over 80% of all human activity will done by Cogs by 2020 7 Billion People Market Vs 2 Billion Today Ultimately Humans Could be 10 Times More Efficient and Effective Global GDP is $100 Trillion Even 10% improvement will lead to $10T additional wealth creation Gat and Cog Eco System Requires Platform Providers, App Providers, and Agents for Personal Customization All of Whom Would Benefit from the Additional Wealth Creation


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