Raj Reddy Carnegie Mellon University Keynote Speech MSRA 21 st Century Computing Shanghai China Aug 30 2019 Back to the Future Future AI Topics Will Be Similar to Those in the Past 50 Years Ago ID: 935153
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Slide1
Back to the FuturePast, Present and Future of AI
Raj Reddy
Carnegie Mellon University
Keynote Speech MSRA 21
st
Century Computing, Shanghai China
Aug 30, 2019
Slide2Back to the FutureFuture AI Topics Will Be Similar to Those in the Past
50 Years Ago:
List of Research Projects at Stanford AI Labs (SAIL), 1963-1969
Robotics:
Led to Vision and Robotics Industry
Mobile Robotics:
Mars Rover and Stanford Cart
Image Understanding:
Led to Vision and Robotics at CMU and Penn
Knowledge Engineering:
Led to Expert Systems, Knowledge Engineering, Knowledge Based Systems Industry, and Early Applications of AI
Speech
Led to the DARPA Speech Understanding Project during the years 1971-76
Most influential branch of Speech Recognition Industry: Dragon Systems, Apple, Microsoft. Indirectly IBM and Bell Labs
Language Understanding:
Question Asking and Dialog Modeling
Computer Music:
Led to Yamaha adopting digital synthesis for consumer products
Other AI Projects:
Chess, Symbolic Mathematics, Correctness of Programs, Theorem Proving, Logical AI, Common Sense
Computer Science:
Time Sharing, LISP, DEC Clones, Graphical Editors, Pieces of Glass, Theory of Computation
Slide3Back to the Future Lead to Dramatic Breakthroughs
1970-2000 Breakthroughs
World Champion Chess Machine:
IBM Deep Blue
Accident Avoiding Car:
1995 CMU No Hands Across America
Robotics:
Disaster Rescue Robots
Speech Recognition Systems:
Dictation Machine
Computer Vision:
Medical Image Processing
Expert Systems:
Rule Based Systems
2001-2018 Breakthroughs
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
World Champion Poker:
CMU No Limit Texas
Hold’em
Poker
Slide4Reflections on AI and CS at Stanford 1963 to 1969
Slide51960s: The Golden Age of SAILRobotics
Computer Vision
Knowledge Engineering
Speech
Language Understanding
Computer Music
Chess, Symbolic Mathematics, Correctness of Programs, Theorem Proving, Logical AI, Common Sense
Time Sharing
LISP
DEC Clones: Foonly, Graphical Editors, Pieces of Glass, Theory of Computation
Slide6The Hand Eye ProjectInteraction with the Physical World
Early work by
Karl Pingle, Bill Wichman, Don Pieper
Main Project Team
Jerry Feldman, R. Lou Paul, Marty Tenenbaum, Gerry Agin, Irwin Sobel, etc.
Robotic Hands
Bernie Roth and Vic Scheinman
Started in 1965
Using the PDP1 and later the PDP6
Led Machine Vision and Robotics Industry
Via SRI and Vic Scheinman
Slide7Image Analysis and UnderstandingImage AnalysisManfred Hueckel, Ruzena Bajcsy, and Tom Binford
Led to Vision and Robotics at UPenn
Image Understanding
Natural Scenes and Face Recognition
Mike Kelly and Raj Reddy
Led to Vision and Robotics at CMU
Slide8Mobile RoboticsMars Rover and Stanford CartMarvin Minsky (visiting)
Mars Explorer project 1964
Les Earnest
Bruce Baumgart
Lynn Quam
Hans Moravec
Rod Brooks (later in the seventies)
Influenced direction of programs at SRI and MIT
Slide9Capturing Expertise Heuristic Dendral: Representation, acquisition and use of knowledge in chemical inferenceProject Team
Ed Feigenbaum, Josh Lederberg, Bruce Buchanan, Georgia Sutherland et al.
Started in 1965
Led to
Expert Systems, Knowledge Engineering
Knowledge Based Systems Industry
Early Applications of AI
Slide10SpeechSpeech Input to Computers
Started in 1964 as a class project
Using a PDP1 with drum memory and a display
By the end of 1964 we had a vowel recognizer running
Project team in the sixties
Raj Reddy, Pierre Vicens, Lee Erman, Gary Goodman, Richard Neely
Led to the DARPA Speech Understanding Project during the years 1971-76
Most influential branch of Speech Recognition Industry: Dragon Systems, Apple, Microsoft
Indirectly IBM and Bell Labs
Slide11Language UnderstandingParsing and Understanding of Natural Language: Question Asking and Dialog ModelingComputer Simulation of Belief systems
Ken Colby, Lawrence Tesler, Horace Enea et al
Parsing of Non-Grammatical Sentences
Colby, Enea et al
Conceptual Parsing
Roger Shank
Led to Language Processing Industry
via Shank and associates
Led to other Language Processing groups at Yale and UCLA
CMU, UMass, Berkeley, etc.
Influential strand of Language research
Slide12Computer MusicComputer Synthesis of MusicStarted in 1964 on PDP1
John Chowning
Leland Smith
Andy Moorer
Impact
Led to Yamaha adopting digital synthesis for consumer products
Establishment of a Center in Computer Music in Paris
Slide13Other AI ProjectsChess and other game playing programsKalah: R. Russell
Chess: McCarthy, Barbara Huberman (Liskov)
Checkers: Art Samuels
Symbolic Mathematics
Algebraic Simplification: Wooldridge and Enea
Reduce: Tony Hearn
Proving Correctness of Programs
Correctness of Programs: McCarthy and Painter
Equivalence of Programs: Kaplan and Ito
Properties of Programs: Zohar Manna
Theorem Proving
David Luckham and John Allen
Use of Predicate Calculus as a Representation for AI
McCarthy, Cordell Green et al
AI and Philosophy
McCarthy and Pat Hayes
Programs with Common Sense
McCarthy, later by Doug Lenat
Slide14Non-AI Research at SAILProgramming Languages
LISP
Symbolic Computation
Dynamic Storage Allocation and Garbage Collection
Forerunner of Functional Programming
SAIL
LEAP Associative Data Structure
Feldman and
Rovner
Time Sharing and Real Time Systems
Graphics
Scan Line Graphics!
User Interfaces
Graphics Text Editors And Graphical Debugging
Theory of Computation
Semantics of Programming Languages
What do strings of symbols representing programs … denote!
Data Spaces (aka Data Structures)
Team: Earnest, Russell,
Weiher
, Poole, Panofsky, Sauter, Baumgart,
Quam
,
Swinehart
et al
Slide15Non-AI Research at SAIL (Cont)Representation of Time Dependent and Simultaneous Processes
Speed of Computation (aka Computational Complexity)
Storage of Information (aka Databases)
Syntax directed computation such as computations described by productions and rule based systems
Equivalence of programs
Halting problem for practical cases
Slide16Other InnovationsFilm ReportsEllis D. Kropotechev and Zeus, his Marvelous TSS, Gary Feldman
Butterfinger, Gary Feldman
Hear Here, Raj Reddy, Dave Espar, and Art Eisenson
Avoid, Gary Feldman and Don Peiper
#?+@, Anon
Use of displays and video terminals
Early use of Laser Printing
Slide17Looking back: What we missed!Personal Computers!
Alan Kay’s dynabook vs Apple and PCs
Internet and the WWW
ARPAnet in 1968 with Stanford as one of the initial nodes
Moore’s Law and VLSI
Graphics
Human Computer Interaction
UI design
Slide18Looking Back: Off in Timing!Speech
Vision
Robotics
Natural Language
Slide19Future OpportunitiesKnowledge as a Service (KaaS
)
Slide20Near Term Societal Impact of AIExisting AI Technology Can be Used to
Empower the 3 Billion People at The Bottom of the Pyramid
Slide21Bottom of the Pyramid: 3 Billion People3 Billion People with Incomes of less than $3 a day The Bottom of The Pyramid is The Largest, But Poorest Socio-economic Group.
Globally that is the 3 billion people who live on less than say $2.50 per day.
Most of Them Are Also Semi-literate, i.e., Cannot Read, Write and/or Understand Any Language
Cannot use Keyboard or Touch based Computing Apps
For a Semi-literate Person, the Only Acceptable Mode of Communication is Speech
Voice Computing a la Amazon Echo and Enhancements is the Key
Personal Assistants that Require Only Speech based Interaction is Essential for Such Populations
Slide22Technology Exists to Create Voice-Only AppsSpeech to Speech Exists (Microsoft, Facebook and Others)
BTW, Current Implementations are Based on Incorrect Business Assumptions
Available only for Commercial Languages
Will Never Result in Killer Apps
English to Chinese Speech to Speech Translation Demonstrated in 2012
Text Based “Translate” App of 2016 has to become Speech Based
Languages Supported Based on Commercial Viability
Not Need based
Unlikely to result in Killer App
Apps Tailored to Semi-literate Populations Will Become Killer Apps
1 Minute Learning Time; Two clicks; and Spoken Dialog
All Such Apps Will Require Speech Recognition, Spoken Dialog, and Speech to Speech Translation (No Keyboard or Touch)
Speech to Speech Translation
Entertainment (Movies) and Education (Khan Academy)
Translate Live Dialog
QA Dialog (Siri and Cortana)
eCommerce
and
eBanking
English Language Learning - Detect Pronunciation Errors
Slide23Applications for the Bottom of the PyramidVoice Computing (No Keyboard or Touch) Can Help The Semi-literate to Read Newspapers, Watch Foreign Language Movies, Listen to Khan Academy Lectures, Vote Online and Order Groceries Online
A Mobile App for Entertainment and Education
Dynamic Real-time Translation of a Video Dialog from English to Telugu
Text to Speech App for Newspaper Reading Assistant
Enabling Digital Democracy
Vote Online (Authentication, Authorization and Audit)
Ecommerce and
eBanking
Voice Authentication, Authorization and Audit
Learning Without a Teacher
Tutor for Listening and Speaking English
Illiterate Populations Will be the Biggest Source of Customers for Speech Based Apps in the Future
Slide24Typical Apps for the Semi-literate: NextGen Siri and Alexa
An Intelligent Agent That Anticipates What You Want To Do And Helps You To Do It Using Local Language and Clarification Dialog
Entertainment and Education: Streaming Video Translation
Alexa play Hamlet (BBC Shakespeare)
Reading Newspapers: Text to Speech Translation and/or Synthesis
Alexa read China Daily
Buying and Selling:
Voice Dialog Management
Alexa order usual brand Rice, Meat and Vegetables
Communication: Voice and/or Video Email, Chat
Alexa call my Grandson in Shanghai
Banking: Monitor Bank account, Pay Bills
Alexa charge my mobile device with 1000 rupees
Online Voting
Voice Dialog to enable the Authorization, Authentication and Audit
Semi-literate Populations Will be the Biggest Source of Customers for Speech Based Apps in the Future
Resulting Doubling of Net Users and Quadrupling of Economic Activity
Slide25AI : Near term Future (2 to 3 Years)Cognition Amplifiers That Enhance Human Capabilities
Do Tasks Faster and with Less Effort
Slide2626Cognition Amplifiers in Service of Society
A Cognition Amplifier (COG) is an Intelligent Agent that anticipates what you want to do and helps you to do it
Cognition Amplifiers Enhance Human Capabilities
Do Tasks Faster and with Less Effort
A Cognition Amplifier (COG) is a
Personal
Enduring
Autonomic Intelligent Agent that
Anticipates what you want to do and does it
Always On, Always Working, Always Learning
Slide27Examples of COGs in Service of SocietyCOGs personalized and mass customized agents as part of Knowledge as a Service (
KaaS
) may be Used by Everyone on the Planet for tasks such as
Buying and selling: Transact with multiple providers
Email: Filter spam, understand and respond to actionable email
News: Based on topic preferences, novelty, collaborative filtering
Banking: Monitor bank account, Credit Cards, Pay Bills
Travel: Flights, hotel, schedule disruptions, cancellations
Each Person May Have Thousands of Cogs as Personal Assistants
Slide28AI: Longer Term Future (5 to 10 Years)Guardian Angels That Enable Humans to Do Tasks They Cannot Do Today.
Super-Human AI?
Slide2929Guardian Angels in Service of SocietyDiscover and Warn Humans About Unanticipated Events Impacting Safety, Security, and Happiness
Guardian Angels Enable Humans to Do Tasks They Cannot Now Do.
Super-Human AI?
A Guardian Angel (GAT) is a
Personal
Enduring
Autonomic
Intelligent Agent
Always On, Always Working, Always Learning
Slide30GATs in Service of SocietyGATs are personalized and mass customized agents as part of
KaaS
for
Just-in-Time Warnings: Hurricanes, Earthquakes, Extreme Weather
Act as Coach in Health and Education Matters
Accident Alerts and Rerouting; Transport Strikes
Scarcity of Essential Resources: Food, Energy, Water etc.
Assume Everyone on the Planet has Personalized Guardian Angels (GATs)
Slide31Personalization and Customization of Guardian AngelsCloud Based Guardian Angel Platform Linked to User Smart Phone
Family of Personalized Guardian Angels
Subscribe to Local, National and Global Sources and Act on Relevant Information
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.
Slide32Knowledge Source PublishersGlobal, National and LocalKnowledge Source DistributorsReTweet Model
Global
DataBase
of Humankind
KaaS
Publish/Subscribe Eco System of
Guardian Angel Global Infrastructure Platform
Guardian Angels
Activity Monitoring and Intention Awareness
User
Cloud Based
User Infrastructure
Platform
Slide33In the Future …AI will Enable
KaaS
Human Machine Augmented Intelligence
AI and
KaaS
Will Become the Backbone of Emerging Industries
Technology, Tools, and Techniques of
KaaS
will be the Main Activity of AI
Sub-Human Level AI >>> Human Level AI >>> Super Human AI
We have a million times more computing power than 1980s!
By 2040, We Will Have
Million Times More Computing Power
Million Times More Memory
Million Times More Band Width
As McCarthy Said in 1980, We May Need 1.7
Einsteins
, 3
Maxwells
and 0.7 Manhattan project to get to Super Human AI