The document is a cover page for a presentation on Notational Engineering and the Search for New Intellectual Primitives. It includes the title, author, date, and location of the presentation. The contents section lists the proposal and bio on pages 1-2 and slides without text on pages 3-31. The document is licensed under a Creative Commons license.
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A presentation from Functional Tricity meetup where I present the philosophy underlying the Scheme programming language
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https://telecombcn-dl.github.io/2017-dlsl/
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https://telecombcn-dl.github.io/2017-dlsl/
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http://www.meetup.com/FunctionalTricity/events/231861585/
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https://telecombcn-dl.github.io/2017-dlsl/
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The document summarizes a presentation given at the ACM Special Interest Group on Design of Communication Conference in Seattle, Washington from October 3-5, 2012. The presentation discusses defining analog and digital qualities in interactive media, providing characteristics for developing analog-like and digital-like systems, and analyzing example projects on an analog-digital continuum. The presentation aims to help classify and develop styles of interaction between users and systems.
https://telecombcn-dl.github.io/2017-dlsl/
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The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
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Chapter 1 Complexity 4
1.1 The Structure of Complex Systems 6
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May 21-25, 2000: "Why We Don't Understand Complex Systems". Poster session, presented at the International Conference on Complex Systems, sponsored by the New England Complex Systems Institute.
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Symbols and Search : What makes a machine intelligentAshwin P N
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This document discusses varieties of self-awareness and their uses in natural and artificial systems. It proposes a conceptual framework for metacognition and natural cognition. The document contains slides for presentations on this topic, including:
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This document discusses developing a shared vocabulary for systems engineering by migrating content from documents to a wiki environment. It addresses challenges like justifying an open approach, determining the best structure for the wiki, and establishing governance models for multi-author teams. The paper proposes an integrated architecture using techniques like text extraction, morphological analysis, and integrating different dimensions to develop and manage the shared vocabulary in a wiki.
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Four ways to represent computer executable rulesJeff Long
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in Systems Research and Cybernetics (IIAS). Paper published in conference proceedings.
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4. The Notation is
the Limitation
Notational Engineering and the
Search for New Intellectual Primitives
Jeffrey G. Long
September 25, 2002
jefflong@aol.com
5. Proposed outline
P d li
1: Background on the general problem:
representation and notational systems
2: Overview of Ultra-Structure: an approach to
complex systems using a new abstraction
3: Example: The Reviewers Assistance System
September 25, 2002 Copyright 2002 Jeff Long 2
7. Many, if not most, of our current problems arise from
y, , p
the way we represent them
We may have pragmatic competence in using certain kinds
of complex systems but we still don’t really understand
them theoretically
– economics, finance, markets
– medicine, physiology, biology, ecology
This is not because of the nature of the systems, but rather
because our analytical tools – our notational systems and
the abstractions they reify -- are inadequate
September 25, 2002 Copyright 2002 Jeff Long 4
8. Complexity is not a property of systems; rather,
perplexity is a property of the observer
Systems appear complex under certain conditions; when
better understood they may still be “complicated” but they
are tractable to explanation
Using the wrong, or too-limited, an analytical toolset
creates these “complexity barriers”; they cannot be
breached without a new notational system
b h d ih i l
These problems cannot be solved by working harder,
using faster computers, or moving to OO techniques; they
do not arise due to lack of effort or lack of factual
information
September 25, 2002 Copyright 2002 Jeff Long 5
9. So far we have explored maybe 12 major
abstraction spaces
September 25, 2002 Copyright 2002 Jeff Long 6
10. Notational systems facilitate perception, cognition and
communication
Each primary notational system maps a different
“abstraction space”
– Abstraction spaces are incommensurable
– Perceiving these is a uniquely human ability
Acquiring literacy in a notation is learning how to see
a new abstraction space
Having acquired such literacy, we see the world
differently and can think about it differently
September 25, 2002 Copyright 2002 Jeff Long 7
11. Notational Theory Offers a New Intellectual Synthesis
Broadened to include all notational systems (not just
language), it sheds light on, and integrates:
l ) i h d li h di
– Whorf’s notion of linguistic relativity,
– Chomsky’s notion of an innate linguistic capability
y g p y
– Toynbee’s notion of the evolution of civilizations by challenge
and response
– parts of numerous other theories in many areas
September 25, 2002 Copyright 2002 Jeff Long 8
12. Conclusions From Section 1
Every set of intellectual primitives, reified in a
y p ,
notational system, has limitations: these appear to us
in the form of a “complexity barrier”
Many of the problems we face now as a civilization
a e u da e ta y ep ese tat o a o otat o a
are fundamentally representational or notational
We need a more systematic way to develop and settle
abstraction spaces: notational engineering
September 25, 2002 Copyright 2002 Jeff Long 9
13. 2: O New Approach
2 One N A h
September 25, 2002 Copyright 2002 Jeff Long 10
14. Current engineering methods work well only under
certain conditions
September 25, 2002 Copyright 2002 Jeff Long 11
15. This is the area addressed by Ultra-Structure Theory
Ultra-Structure Theory is a general theory of systems
representation, developed/tested starting in 1985
F
Focuses on optimal computer representation of complex,
i l i f l
conditional and changing rules
Based on a new abstraction called ruleforms
The breakthrough was to find the unchanging features of
changing systems
September 25, 2002 Copyright 2002 Jeff Long 12
16. Unfortunately,
Unfortunately Complex and Changing Needs Exist in
Every Organization
Needs
SW & DB
time 1 time 2 time 3...
September 25, 2002 Copyright 2002 Jeff Long 13
17. The theory is based upon a different way of describing
complex systems and processes
observable
behaviors surface structure
generates
rules middle structure
constrains
form of rules
f f l deep structure
September 25, 2002 Copyright 2002 Jeff Long 14
18. As Wolfram has recently argued, rules are a very
y g , y
powerful way of describing things
Multi-notational: can include all other notational
systems
Explicitly contingent
Describe both behavior and mechanism
H d d of th
Hundreds f thousands can b represented and
d be t d d
executed by a desktop computer
September 25, 2002 Copyright 2002 Jeff Long 15
19. Hypothesis: Any type of assertion can be
reformulated into one or more If-Then rules
Natural language statements
Musical scores
Logical arguments
Business processes
Architectural drawings
Mathematical statements
M th ti l t t t
But often several “atomic” rules are needed to create
atomic
one “molecular” rule, e.g. “3 strikes and you’re out”
September 25, 2002 Copyright 2002 Jeff Long 16
20. If/Then Rules are Best Represented as Data (records)
Organized into Tables in a Relational Database
O i d i t T bl i R l ti lD t b
If A and B then consider C, D, E, F...
A B C D E F
1
2
Rule #
3
4
5
} 1 Ruleform
n
September 25, 2002 Copyright 2002 Jeff Long 17
21. Structured and Ultra-Structured data are semantically
y
quite different
Structured data separates algorithms and data, and is
good for data processing and information retrieval
tasks,e.g. reports, queries, data entry
Ultra-Structured data has only “rules”, formatted in
a manner that allows a very small inference engine
to reason with them using standard deductive logic
Th inference engine (“animation rules”) software
The i f i (“ i i l ”) f
has little or no knowledge of the external world
September 25, 2002 Copyright 2002 Jeff Long 18
22. The Ruleform Hypothesis
Complex system structures are created by not-
necessarily complex processes; and these
il l d h
processes are created by the animation of
operating rules. Operating rules can be grouped
into a small number of classes whose form is
i ll b f l h f i
prescribed by "ruleforms". While the operating
rules of a system change over time, the ruleforms
remain constant. A well-designed collection of
ruleforms can anticipate all logically possible
operating rules that might apply to the system,
and constitutes the deep structure of the system.
September 25, 2002 Copyright 2002 Jeff Long 19
23. The CoRE Hypothesis
Th C RE H th i
We can create “Competency Rule Engines”, or
CoREs,
C RE consisting of <50 ruleforms, th t are
i ti f 50 l f that
sufficient to represent all rules found among
systems sharing broad family resemblances, e.g.
all corporations. Th i d fi iti d
ll ti Their definitive deep structure
t t
will be permanent, unchanging, and robust for all
members of the family, whose differences in
manifest structures and b h i
if d behaviors will b
ill be
represented entirely as differences in operating
rules. The animation procedures for each engine
will be relatively simple compared to current
applications, requiring less than 100,000 lines of
code in a third generation language.
September 25, 2002 Copyright 2002 Jeff Long 20
24. The deep structure of a system specifies its ontology
What is common among all systems of type X?
What is the fundamental nature of type X systems?
What are the primary processes and entities involved
in type X systems?
What makes systems of type X different from
systems of type Y?
If we can answer these questions about a system,
then we have achieved real understanding
September 25, 2002 Copyright 2002 Jeff Long 21
25. Conclusions From Section 2
One example of a new abstraction is ruleforms To
ruleforms.
truly understand complex systems such as biological
systems, we must get beyond appearances (surface
structure) and rules (middle structure) to the stable
ruleforms (deep structure).
This is the goal of Ultra-Structure Theory.
September 25, 2002 Copyright 2002 Jeff Long 22
26. 3: Application Example: the
Reviewer’s Assistance System
September 25, 2002 Copyright 2002 Jeff Long 23
27. DOE Reviewer’s Assistance System Requirements
650 guides defining 65,000 topics that are or may be
classified
E
Extensive background knowledge required to interpret
i b k dk l d i d i
guidance
Guidance changes over time
Terminology in documents changes over time
The objective is advanced concept spotting, not document
understanding
September 25, 2002 Copyright 2002 Jeff Long 24
28. Normally This Would be Done Using an Expert
System Shell
ES often have trouble with >1,000 rules; RAS has
>100,000 rules
K i
Key issue i the maintainability of rules by experts
is h i i bili f l b
There are many benefits from using relational database to
store rules as data, including:
– Built-in referential integrity
– Easy report-writing and queries
– S bj t experts can maintain knowledgebase directly, without
Subject t i t i k l d b di tl ith t
relying on KE or Programmers
September 25, 2002 Copyright 2002 Jeff Long 25
29. RAS D fi
Defines G id
Guidance Concepts and All P
C t d Possible
ibl
Lexical Expressions of Those Concepts
System Define
Convert Guides Interpretations
Ready
Read Apply Document
Document Guidance Reviewed
September 25, 2002 Copyright 2002 Jeff Long 26
30. Rules Specify Relations Between Topics, Concepts, and
Tokens
T k
September 25, 2002 Copyright 2002 Jeff Long 27
31. Conclusions From Section 3
C l i F S i
A rule-based system can provide precise and rigorous
interpretation of key DOE terms and concepts
A rule-based system stored as tables in a relational
database allows creation of a knowledgebase which can
become as large as necessary
Such a knowledgebase is very easy to specify, change and
review directly by subject experts
September 25, 2002 Copyright 2002 Jeff Long 28
32. References
Long, J., and Denning, D., “Ultra-Structure: A design theory for
complex systems and processes.” In Communications of the ACM
processes
(January 1995)
Long, J., “A new notation for representing business and other rules.”
In Long, J. (guest editor), Semiotica Special Issue on Notational
Engineering, Volume 125-1/3 (1999)
Long, J., “How could the notation be the limitation?” In Long, J.
(guest editor), Semiotica Special Issue on Notational Engineering,
Volume 125-1/3 (1999)
125 1/3
Long, J., "Automated Identification of Sensitive Information in
Documents Using Ultra-Structure". In Proceedings of the 20th Annual
ASEM Conference, American Society for Engineering Management
(October 1999)
September 25, 2002 Copyright 2002 Jeff Long 29