Download Where Government Science Policy Matters: Two National Initiatives

Where Government Science Policy Matters:
Two National Initiatives in Fundamental CS & AI
John C. Mallery
([email protected])
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
DANGEROUS IDEAS SEMINAR, March 19, 2003
(Revised April 8, 2003)
© John C. Mallery, 2003. All rights reserved.
Overview
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Introduction: Two Research
Initiatives
Tom Kalil: How to Roll Your Own
National Initiative in Science or
Technology
Ultrastable Computing Initiative
Problem: Computer Science in
Crisis
Long-range Risks to Economic &
National Security
21st Century Programming
Languages
Self-adaptive Software
Semantic Integration
Introspective Software Development
Introspective Development
Environment
Introspective Computing
Introspective Computing
Architecture
Rationalized Bootstrap Language
Introspective Language Level
M.I.T. Artificial Intelligence Laboratory
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Natural Language Understanding
Initiative
Problem: NLU in Crisis
Natural Language Research
Context
NLU Research Objectives
Impact of Successes in NLU
Substantial Recent Progress in
Natural Language Processing
Missing Ingredients: Semantics &
Reference
Prospects for Filling Gaps in
Semantics & Reference
Machine-parsable Knowledge
Sources
Q & A Based Testing & Evaluation
Performance Metrics
Initial NLU Program Structure
Conclusions
Appendix: How to Shape US
Science Policy: Selections From a
Talk at MIT AI
4/30/2017
John C. Mallery
Introduction:
Two Research Initiatives
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Reinventing computing as ultrastable computing (5-10 years)
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Self-adaptation & ultra-high productivity based on introspection
Pre-competitive basic/applied research
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Tremendous economic impact
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Market forces reinforcing the COTS status quo
Industry cannot do it now
High risk & moderate time horizons
Wide scope
Orders of magnitude reduction in costs of software development,
maintenance, & evolution
Superior reliability, flexibility, security
Breaking the natural language barrier (10-20 years)
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Acquiring useable knowledge from vast quantities of human text
Pre-competitive basic research
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Small, fragmented research community
Need to converge on cumulative paradigms
Very high risk with long time horizons
Tremendous economic impact
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Wide scope
High-productivity human utilization of text bases
Critical masses of machine useable knowledge
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Tom Kalil: How to Roll Your Own National
Initiative in Science or Technology
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Need a well-developed
idea!
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Degree of Difficulty
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What’s the goal?
Why is achieving this goal
important?
What are the mechanisms
for reaching that goal?
Why is government
involvement required?
Who else will help?
How will success be
measured?
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Level at which decision
can be made (new
DARPA start or legislation)
Support or opposition from
interest groups, agencies,
Congress, public
Level of resources
required ($, time, political
capital)
Complexity of
implementation
Precedence
Level of partisanship
Level of risk or uncertainty
about outcomes
Incremental or dramatic
change
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Ultrastable Computing Initiative
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What’s the goal?
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Government funding for research &
development
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Market forces reinforcing COTS (1960s
computing core)
Large-scale research
High development risk
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Complexity of implementation
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Precedence
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Level of risk or uncertainty about outcomes
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bipartisan
Research & development risks are
moderate
Wide-adoption risk is high
Incremental or dramatic change
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Early computers
Supercomputers (parallel)
Level of partisanship
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Working systems will be disseminated for
use by the research community
Companies will begin selling to government
& private sectors
Moderate
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How will success be measured?
$1 billion over 5-10 years
Some political capital
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Research universities & other research
institutions
Some related industry efforts & spinouts
Potential for international participation
Support from research community, agencies
Support from industry, if viewed as a precompetitive R&D subsidy
Opposition from industry, if viewed as
creating new subsidized competitors or
exposing fraudulent or negligent practices
Support from Congress & public if strategic
importance of computer leadership
understood
Level of resources
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Who else will help?
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Why is government involvement required?
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Economy
Science & Engineering
Education
National Security
Congress because new budget required
Support or opposition from interest groups,
agencies, Congress, public
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What are the mechanisms for reaching that
goal?
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Tremendous impacts
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Decision Level
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New hardware/software platforms
Human centric peripherals
Why is achieving this goal important?
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Making computers more cooperative
Revolutionary change
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Problem: Computer Science in Crisis
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Rise of COTS hardware/software has narrowed the gene pool
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1960s language & OS concepts provide the playing field
Market forces (scale, monopoly, inertia) reinforce narrowing hardware & OS choices
Appearance of progress
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Massification of computers has pulled elite centers down towards the commercial mean
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Critical applications in government dumbed down to fit COTS
Developers, researchers, & students pulled to COTS relevance
Increasingly difficult for researchers & funders to conceptualize outside the COTS box
Changing assumptions not reflected in dominant architectures
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Tremendous penetration of computers throughout society
`New’ languages introduce 1970s concepts (dynamism, GC) to the masses in the 1990s
Computation is now almost free
Humans are now the extremely expensive factor
Symbolic computing dominates numerical computing
Evolution is the norm
Security & reliability are critical
Computation is now at the center of the economy & national defense
Consequences
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Modern pyramids are being built inside the computer
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Software costs are extremely high (due to labor intensity)
Agility & adaptation are relatively low (due to primitive design foundations)
Security, brittleness, & reliability are inherent problems
Economic & national security goals are at risk
Market pressures reinforce lock-in & make transition to alternate architectures difficult
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Long-range Risks to Economic & National Security
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Loss of software industries
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High-wage US economy cannot
compete with Low-wage countries
Unless
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Large productivity increases reduce
the labor component
Proximity to markets & head offices
dominates
National security restricts location of
activity
Scale economies offset R&D costs
Anti-competitive market forces
dominate
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Fast time to market
`Inside the opponent’s decision cycle’
New technologies become feasible
due to faster computation,
development, debugging
More intensive utilization of labor
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Technologies unknown to competitors
Scale economies offset R&D costs
Faster computing
Faster software development,
certification, deployment, revision &
evolution
Lower costs of ownership due reliable
operation & high-productivity user
interfaces
Superior organizational structures for
software engineering & exploitation
Speed Kills [Adversaries]
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Individuals, companies & countries
More is achieved per time unit by:
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High labor costs mean US cannot
produce software-intensive systems
to match foreign competitors in ‘bang
for buck’
Unless
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Time is a finite resource for
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Competitive disadvantage in defense
systems
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Failure to utilize time efficiently
Higher productivity research &
education
Productive resources freed for new
economic activities
Lower unit costs
Wider product dissemination
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John C. Mallery
21st Century Programming Languages
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Design goals:
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Handle greater complexity
Provide higher certainty
Support faster development & smoother evolution
Build in self-adaptation
Deliver superior performance
Key enablers:
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Design for understanding (human & computer)
Semantic integration of code base
Introspective descriptions
Built-in problem solvers in support of self-adaptation
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Self-adaptive software
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Automatic programming revisited?
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Simplifying assumption: sequencing operators rather than
automatically programming operators
Combinatorics & dataflow more manageable
Analogous to quasi-custom mass production
More at self-reconfiguring software
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Goals are explicitly represented
Operators are explicitly annotated with meta-data:
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Means-ends analysis is used to maintain or achieve domain
objectives
Human programmers provide code, descriptions, and constraints
Programming is indirect
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Function they perform
Inputs, outputs, side effects
Performance characteristics
High-level assembly is flexibly controlled by goals and functional
metadata descriptions
But, programmers still stand behind the code
Next step down the abstraction road
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The domain descriptive vocabulary is now annotated for (semi-)
automatic assembly
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Semantic Integration of the Code Base
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Self-adaptation requires knowledge representation to store goals,
states, operator metadata, etc
Once knowledge representation becomes a standard facility of the
runtime & development environments, why not utilize it fully?
Key idea: all knowledge about programs is stored in a uniform
encoding
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Eliminates `transaction costs’ of accessing divergent data structures
Enables full introspective access to the software-development &
runtime environments
Integration across the domain, the code base, the compiler, and
the runtime environment enables further abstractive activities
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The development environment can further support the programmer,
thus reducing cognitive load & increasing effective complexity
The compiler can break abstractions and specialize functions based
on domain and runtime knowledge
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Introspective Software Development
Design Rationales
Basic Methodology
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Problem Solver Composes Operators
to Realize Goals
Humans Sequence Goals
Humans Provide Search Control
Hints
Programmers Define New Operators
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Primitives
Descriptions
Objectives
Runtime Adaptivity
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Monitor Program Execution
Evaluate Performance
Diagnose, Replan, Recompose
Execute Replacement Program
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Raise Abstraction Level
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Increase Productivity
Rapid Adaptation
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Encapsulated Computation
 Meta-Data
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Systematically Captured
Machine AccessibleRepresentation
Required for Programs to Work
Basis for Adaptation to Changing
Assumptions
Enhance Flexibility
Ultra Rapid Prototyping
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Fully Functional
Procedural Description
Dataflow Description
Linkage of Procedural & Declarative
Description
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Introspective Development
Environment
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Lisp Machine Recapitulation
Design Rationale Capture
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Tracked
Consequences Represented
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Novice to Expert
Intelligent Tutoring
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Based on Full Representation of
Code Base
Dependency Tracking for code &
Data
System Versioning Based on
Code Assumptions
M.I.T. Artificial Intelligence Laboratory
Structured to Speed Humans
to Relevant Documentation
Multiple Models of Systems
and Components
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Adaptation in Response
Drives Problem-solving
Semantic Operation on Code
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Goal-based Documentation
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Multimedia
Assumptions
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Teaches All Aspects of the
System
Extended As Programs Are
Written
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Introspective Computing
Rationalized language substrate
Heterogeneous hardware
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Reconfigurable logic
Symbolic computing
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Self-adaptive software
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Introspectively self-aware
Goal-directed programming &
adaptation
Design rationale capture
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Foundational security
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Transactional & versioned
memory
Persistent
Large-scale
M.I.T. Artificial Intelligence Laboratory
Functional justifications recorded
Supports
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Hardware-grounded
Expectation-based auditing
Adaptive response to breaches
Knowledge bases
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Parallel ready operations & data
representations
Simplified primitives & data flow
Higher level abstraction for
analysis & synthesis
Ultra-rapid programming, testing,
deployment & modification
Self-adaptive software
Smooth transition between
software & hardware
implementations
Collaboration & organizational
design
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Group design & decision-making
Information logistics
Reactive planning
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Introspective Computing Architecture
Domain Applications
Embedded Domain Languages
Introspective Programming Language
Multiple Representations
Bootstrap Programming Language
Operating System
Hardware
External Systems
Introspective
Operating System
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Introspective
Peripherals
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Rationalized Bootstrap Language
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Higher-level Abstractions
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Units for Quantities
Futures
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Inter-process Communication With
Built-in Constructs
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Networked Communication With
Built-in Constructs
Mobile Continuations
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Perceptual Loops
Modularity Institutionalized
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Uniform Interface Protocol
Interfaces Are Self-Descriptive
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Event-based
High-level Process Programming
Constructs Built-in
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Introspection
Security
Program Analysis/Verification
M.I.T. Artificial Intelligence Laboratory
Computation
Communication
Power Consumption
Process Orientation
Data Flow First-Class
Rationalized to Support:
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Test Vectors Associated With
Operators, Data, Modules
Automatic Self-Diagnosis
Computing Costs First-class
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Sharing Explicit
Transparent Networking
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Value Meta-Data
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Diagnostics First-class
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Parallel-ready
Iteration
Higher-Level Mathematics
Resources First-Class
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Represented, Indexed
Automatic Invocation
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Introspective Language Level
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Problem-Solver/Planner
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Language Support
Operator Description
Framework
Data Flow Description
Framework
Normative Constraints on
Action
Requirements Drive Action
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Executable
External Descriptions
Goal-based Programming
Bootstrapping Introspection
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Priority-based Resource
Allocation
Understand Programs
Automatic Code
Rationalization
Performance Feedback
Algorithms Library
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Building Expectations for
Runtime
Alerting When Expectations
Fail
Adaptive Repair
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Program Description
Framework
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Active Self-monitoring
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Minimize Size & Complexity
of Pre-introspective Cold
Load
Security Issues
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Natural Language Understanding Initiative
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What’s the goal?
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NL systems will be developed that:
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Bipartisan
Level of risk or uncertainty about
outcomes
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Route documents more accurately
Acquire useable knowledge from text
Support education & research
Translate semantically across languages
DARPA funding for AI
NASA `Man on the Moon’
Level of partisanship
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How will success be measured?
High
Precedence
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Research universities & other research
institutions
Some related industry efforts & spinouts
Potential for international participation
$4 billion over 10-20 years
Considerable political capital
Complexity of implementation
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High research risk
Large-scale, long duration research effort
Need to develop the research community
Support from research community,
agencies, industry
Support from Congress & public if the
tremendous payoff understood
Level of resources
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Government funding for research &
development
Who else will help?
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Congress because new budget
required
Support or opposition from interest
groups, agencies, Congress, public

Economy
Science & Engineering
Education
National Security
Why is government involvement required?
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What are the mechanisms for reaching that
goal?
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Incremental products towards the goal
Building up the research infrastructure
Tremendous impacts
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Decision Level
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Why is achieving this goal important?
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Making computers understand human
language
Research risks are high
Incremental or dramatic change
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Revolutionary change
Progressive research paradigms
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Problem: NLU in Crisis
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Considerable progress was made in natural language understanding up to 1987,
but
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After 1987 `AI winter,’ funded NL systems focused on:
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Speech (based on Markov processes)
Extraction of information from text, too often using ad hoc methods
Statistical techniques for parsing (learning from annotated corpora)
Some domain-specific applications
Result:
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But, systems erected too much ad hoc machinery to achieve `depth understanding’ on
small texts
Principled foundations for broad shallow understanding, let alone deep understanding,
were not all there
NL was too often constrained to interfacing to arbitrary databases, making semantic
perception & generation intractable
Very little work on acquiring semantic representations from text or `understanding’ those
texts
Many `schools of thought’ in computational linguistics with no clearly dominant paradigm,
together with some atheoretic regression
Yet, considerable progress in broad-coverage syntactic parsing based on large grammars
coded by hand or induced from corpora
Consequence:
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US poorly positioned to exploit (or generate) major advances in automatic creation of
knowledge representation from unrestricted text based on broad coverage shallow
understanding
US risks technological surprise from well-funded European efforts
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Natural Language Research Context
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Challenge of large-scale NL research in Europe
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$100 million Verbmobil Project in Germany
Hiatus in NLU research in US post 1987
Pent up opportunities for significant advances
Need to build foundation for large-scale NL systems
DARPA BAA-02-21 (Cognitive systems) inspired hope
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Opportunity for basic research (once again)
Relevant areas of interest
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Computational Perception
Cognitive Architectures & Integrated Cognitive Agents
Representation & Reasoning
Learning
Underlying Foundations
But, the funding is
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Ramping up slowly
Suitable for relatively small efforts
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NLU Research Objectives
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Platform for Natural Language Understanding & Cognitive
Simulation
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Full-cycle Language Understanding & Synthesis
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Modular, Extensible, Scalable
Syntactic Parsing & Generation
LF Construction & Base Structure Synthesis
Referentially-integrated Surface Semantic Representation
Deliberative Reference (deduction, induction, abduction, analogy,
metaphor)
Teleological Processing (Goal-directed text generation)
Exploration
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Surface Semantic Representations
Constructivist Perceptual Models
Reversible Parsing & Generation
Analogical & Metaphorical Reasoning
Multi-lingual Understanding & Translation
Cognitive Architectures for Linguistically-grounded Intelligence
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Impact of Successes in NLU
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Semantically grounded information retrieval & document routing
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Constraint of grammatical relations
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Knowledge acquisition from text
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Bootstrapping machine parsable text
Knowledge reuse (encoded as text)
Enables cumulative research on ultra-large knowledge bases
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Increased precision
Superior felicity of returns
Correct level of generality
Large-scale inference & Learning
Debugging ultra complex computations
Some applications:
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Knowledge-based retrieval, routing, and analysis of information
Generation of user-specific reports & texts
Superior organizational collaboration
Support for education, science & technology development
Large symbolic political models
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Value systems
Organizational decision processes
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Substantial Recent Progress in
Natural Language Processing
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Large deep grammars have been
developed (e.G. LKB, LFG)
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Wide coverage (approx. 40-60%
for unrestricted syntax)
Acceptable efficiency
Declarative formalisms have
been explored
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Linguistic communities with
shared development of grammar
resources
Reversible parsing & generation
on the horizon
Lexical semantics, word-sense
disambiguation
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Empirical methods applied to
very large text sets
Useful methods
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Part-of--speech disambiguation
Lexical components
Managing unknown words
Heavy emphasis on English
German, Japanese, French,
Dutch, Chinese, Arabic, etc
General approach is bottom-up
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Statistical approaches are
increasing robustness
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Corpora-based acquisition of
shallow grammar as aspects of
learning parsers
Target languages
Focus on careful syntactic
analysis
Wide syntactic coverage &
applicability
But some good work on
discourse analysis
Semantic integration can
improve coverage
Interleaving of shallow & deep
parsing is coming
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Missing Ingredients:
Semantics & Reference
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Bottom-up semantic representation
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Compatible with bottom-up syntax
High scalability
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Characterization of the linguistic &
referential model implemented by a
system
Platform for NL understanding &
synthesis
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Categories
Propositions
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Constructivist model of reference &
semantic perception
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Semantic corpora for system training,
calibration, and testing
Bootstrap models for incrementally
building up background knowledge
necessary to understand texts in
specific domains
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Metaphors & other tropes
Belief & counter-factual contexts
Inconsistencies
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Shallow understanding
Efficient algorithms
Parallel hardware implementation
Large hardware capacity (memory &
performance)
Robustness under error & ambiguity
Capable of handling vagaries of
surface language
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Enables higher-level research
Enables semantically-grounded
applications
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Decompositional semantics
discredited
Discrimination network models of
perception cognitively implausible
(and impractical)
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Retrieval -- higher precision, felicity,
and appropriate specificity
Acquisition of correct knowledge from
machine parsable text
Generation of documents
Effective decomposition of reference
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Selection of deliberation strategies in
reference
Supporting inference techniques
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Prospects for Filling Gaps in
Semantics & Reference
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MIT has been working on ternary relation representation technology for 30 years
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Winston Thesis
Winston & Katz Analogy System
Katz START system
Mallery RELATUS system
Bottom-up semantics matches bottom-up syntax well
Surface semantic representation
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Easy mappings in and out of syntax
New constructivist perceptual model
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Uniform model of inference & relational learning based on graph rewriting
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Deduction, induction, analogy, planning
Other inferences
New hardware architectures can help scaling up
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Graph representation of lexical items and grammatical relations
Constraint-based model of reference
Effective decomposition of reference\
Fast graph operations
Ultra-large graphs
Parallel computation
New introspective programming languages & development environments can help
cope with program & data complexity

NLU systems are extremely complex & difficult to debug
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Machine-Parsable Knowledge Sources

New model of how human knowledge is transferred to computers:
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Knowledge is organized into:
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Machine-parsable texts
Associated machine parsable background knowledge (Taxonomic knowledge
and axioms)
“Machine-parsable texts” refers to texts that adhere to a set of coding
rules defining the syntactic, semantic, and inferential coverage of a
natural language system
Explicit characterization of the machine parsing model enables
generation of texts conforming to a given model (by more capable
systems)
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Natural language texts plus specialized mathematical formalisms
NL as communications language between NL systems
Ability to downgrade a text for a less-capable system
Textual knowledge encodings can be reused because they are free of
epistemic constraints and formalisms specific to individual systems
Window of machine parsability will only expand over time
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Previously coded knowledge will remain accessible
Large-scale investments in knowledge encoding become viable
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Q & A Based Testing & Evaluation
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Correct operation & system coverage is determined by
answering a series of questions based on associated
background text
Like reading comprehension tests except these are
designed to test syntactic, semantic and inferential
processing
Comparison across systems is enabled because all
inputs, questions, and answers are in natural language
Q&A-based test vectors enable rapid detection of bugs
introduced as systems are developed
Q&A-based benchmarking can support measurement
of progress in natural language understanding
research (and thereby provide a basis for allocating
funding)
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Performance Metrics

Problem: local maxima
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Switching to new promising approaches may produce
performance drops before systems achieve higher capabilities
Rigid performance testing can force researchers to adhere to
the current `best’ approach, especially when lower performers
are defunded
Yet, no performance testing obscures progress
Solution: Measure research progress by both
performance metrics & introduction of new approaches

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Reward performance to achieve incremental progress
Reward new approaches to achieve revolutionary progress

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Assumption of reasonable expectations for superior performance
Critical analysis of approaches
Explain successes & limitations
 Seek out bases for superior, subsuming approaches

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Initial NLU Program Structure
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NLU theory & system development
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Constructivist model of semantic
perception
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LKB/HPSG parsing & generation
High-coverage statistical parsing (e.g.
Collins Parser)
Strongly theoretical parsing &
generation (e.g., Chomskian)
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Meaning-postulates (equivalent
surface structures)
Word-sense disambiguation
Discourse context
Space/time context
Document deixis
Deliberative reference


M.I.T. Artificial Intelligence Laboratory
Intelligent tutoring
Lexicon Development
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28
Learning taxonomies from text
Learning word-sense disambiguators
Learning meaning postulates
Evaluation

One-step inferences in sentential
Reference
Framework for rapid development of
knowledge bases derived from text
Supporting infrastructure
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Language-derived `expert systems’
Background `common-sense’
knowledge
Engineering design documentation
Military doctrine
Knowledge-based question
answering

Semantic analysis & referential
integration
Document routing
Document retrieval
Sentence-level indexing
Acquisition of useable knowledge
from machine parsable text
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Integrating several major existing
parsing/generation efforts
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Intelligent information access
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Sharable knowledge representation
substrate
Constraint-based construction of LF

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Building scalable surface-semantic
representations

Applications
Characterization of models linguistic
coverage
Development of test texts &
associated questions
4/30/2017
John C. Mallery
Conclusions

This talk has described two national
research initiatives in CS & AI

Ultrastable computing initiative ($1
billion over 5-10 years)


Self-adaptation & ultra-high
productivity based on introspection
Pre-competitive basic/applied
research


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Market forces reinforcing the COTS
status quo
Industry cannot do it now
High risk & moderate time horizons
Tremendous economic impact



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Wide scope
Orders of magnitude reduction in
costs of software development,
maintenance, & evolution
Superior reliability, flexibility, security
Natural language understanding
initiative ($4 billion over 10-20 years)


Acquiring useable knowledge from
vast quantities of human text
Pre-competitive basic research




These two critical technologies of the
future, but they will never come to
into existence unless government
begins working to make them a
reality today
The potential payoffs are so great
that they clearly justify the relatively
modest funding levels within the
overall US budget
Failure to pursue these initiatives in a
timely fashion can result in a
diminished US competitive position,
both economically and militarily, as
we enter further into the 21st century
and other technically-sophisticated
powers arise.
Small, fragmented research
community
Need to converge on cumulative
paradigms
Very high risk with long time horizons
Tremendous economic impact



Wide scope
High-productivity human utilization of
text bases
Critical masses of machine useable
knowledge
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Appendix:
How to Shape US Science Policy:
Selections from a Talk at MIT AI
Thomas Kalil
([email protected])
University of California at Berkeley
May 1, 2001
Shaping S&T policy

The good news …

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Ideas matter
Growing awareness of importance of S&T
People from outside can have an impact
Many public servants are hard-working, want to do
the “right thing”
S&T relatively non-partisan
The bad news …

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Interest groups can trump “public interest”
High-levels of cluelessness
Lower incentives for politicians to worry about long
term
Growing imbalance in research funding (NIH vs.
everything else)
Complacency about U.S. competitiveness
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Importance of agenda-setting

Need a well-developed idea!
What’s the goal?
 Why is achieving this goal important?
 What are the mechanisms for reaching that
goal?
 Why is government involvement required?
 Who else will help?
 How will success be measured?

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Degree of difficulty
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Level at which decision can be made (new
DARPA start vs. legislation)
Support or opposition from interest groups,
agencies, Congress, public
Level of resources required ($, time, political
capital)
Complexity of implementation
Precedence
Level of partisanship
Level of risk or uncertainty about outcomes
Incremental or dramatic change
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How to get things done
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A compelling idea
Authority and credibility of proponent(s)
Breadth, power and “intensity of preference” of
coalition
Positive media coverage
Well-funded “inside” and “outside”gov’t relations and
PR strategy
Let others take credit
Third party validators -- not seen as self-serving
Persistence
Ability to communicate w. multiple audiences
Willingness to compromise to diffuse opposition or
broaden coalition
Friends in high & low places
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Existing mechanisms for input
National level (PCAST, PITAC, NAS)
 Agency-specific (DARPA ISAT,
NSF/CISE)
 IPAs
 University and scientific society gov’t
relations
 Ad hoc

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Kalil’s suggestions for S&T community
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Take public policy & communicating with public
more seriously
Develop mechanism for aggregation &
synthesis of good ideas – include ideas from
“lunatic fringe”
Make agenda-setting part of workshop &
conferences
Willingness to engage in “demand pull” as well
as “technology push” issues
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