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AIList Digest Volume 2 Issue 002
AIList Digest Thursday, 5 Jan 1984 Volume 2 : Issue 2
Today's Topics:
Hardware - High Resolution Video Projection,
Programming Languages - LISP vs. Pascal,
Net Course - AI and Mysticism
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Date: 04 Jan 84 1553 PST
From: Fred Lakin <FRD@SU-AI>
Subject: High resolution video projection
I want to buy a hi-resolution monochrome video projector suitable for use with
generic LISP machine or Star-type terminals (ie approx 1000 x 1000 pixels).
It would be nice if it cost less than $15K and didn't require expensive
replacement parts (like light valves).
Does anybody know of such currently on the market?
I know, chances seem dim, so on to my second point: I have heard it would be
possible to make a portable video projector that would cost $5K, weigh 25lb,
and project using monochrome green phosphor. The problem is that industry
does not feel the market demand would justify production at such a price ...
Any ideas on how to find out the demand for such an item? Of course if
all of you who might be interested in this kind of projector let me know
your suggestions, that would be a good start.
Thanks in advance for replies and/or notions,
Fred Lakin
------------------------------
Date: Wed 4 Jan 84 10:25:56-PST
From: Christopher Schmidt <SCHMIDT@SUMEX-AIM.ARPA>
Subject: Re: stupid questions (i.e. Why Lisp?)
You might want to read an article by Beau Sheil (Xerox PARC)
in the February '83 issue of Datamation called "Power tools for
programmers." It is mostly about the Interlisp-D programming
environment, but might give you some insights about LISP in general.
I'll offer three other reasons, though.
Algol family languages lack the datatypes to conveniently
implement a large number of knowledge representation schemes. Ditto
wrt. rules. Try to imagine setting up a pascal record structure to
embody the rules "If I have less than half of a tank of gas then I
have as a goal stopping at a gas station" & "If I am carrying valuable
goods, then I should avoid highway bandits." You could write pascal
CODE that sort of implemented the above, but DATA would be extremely
difficult. You would almost have to write a lisp interpreter in
pascal to deal with it. And then, when you've done that, try writing
a compiler that will take your pascal data structures and generate
native code for the machine in question! Now, do it on the fly, as a
knowledge engineer is augmenting the knowledge base!
Algol languages have a tedious development cycle because they
typically do not let a user load/link the same module many times as he
debugs it. He typically has to relink the entire system after every
edit. This prevents much in the way of incremental compilation, and
makes such languages tedious to debug in. This is an argument against
the languages in general, and doesn't apply to AI explicitly. The AI
community feels this as a pressure more, though, perhaps because it
tends to build such large systems.
Furthermore, consider that most bugs in non-AI systems show up
at compile time. If a flaw is in the KNOWLEDGE itself in an AI
system, however, the flaws will only show up in the form of incorrect
(unintelligent?) behavior. Typically only lisp-like languages provide
the run-time tools to diagnose such problems. In Pascal, etc, the
programmer would have to go back and explicitly put all sorts of
debugging hooks into the system, which is both time consuming, and is
not very clean. --Christopher
------------------------------
Date: 4 Jan 84 13:59:07 EST
From: STEINBERG@RUTGERS.ARPA
Subject: Re: Herb Lin's questons on LISP etc.
Herb:
Those are hardly stupid questions. Let me try to answer:
1. Just why is a language like LISP better for doing AI stuff than a
language like PASCAL or ADA?
There are two kinds of reasons. You could argue that LISP is more
oriented towards "symbolic" processing than PASCAL. However, probably
more important is the fact that LISP provides a truly outstanding
environment for exploratory programming, that is, programming where
you do not completely understand the problem or its solutions before
you start programming. This is normally the case in AI programming -
even if you think you understand things you normally find out there
was at least something you were wrong about or had forgotten. That's
one major reason for actually writing the programs.
Note that I refer to the LISP environment, not just the language. The
existence of good editors, debuggers, cross reference aids, etc. is at
least as important as the language itself. A number of features of LISP
make a good environment easy to provide for LISP. These include the
compatible interpreter/compiler, the centrality of function calls, and the
simplicity and accessibility of the internal representation of programs.
For a very good introduction to the flavor of programming in LISP
environments, see "Programming in an Interactive Environment, the LISP
Experience", by Erik Sandewall, Computing Surveys, V. 10 #1, March 1978.
2. What is the significance of not distinguishing between data
and program in LISP? How does this help?
Actually, in ANY language, the program is also data for the interpreter
or compiler. What is important about LISP is that the internal form used
by the interpreter is simple and accessible. It is simple in that the
the internal form is a structure of nested lists that captures most of
both the syntactic and the semantic structure of the code. It is accessible
in that this structure of nested lists is in fact a basic built in data
structure supported by all the facilities of the system, and in that a
program can access or set the definition of a function.
Together these make it easy to write programs which operate on other programs.
E.g. to add a trace feature to PASCAL you have to modify the compiler or
interpreter. To add a trace feature to LISP you need not modify the
interpreter at all.
Furthermore, it turns out to be easy to use LISP to write interpreters
for other languages, as long as the other languages use a similar
internal form and have a similarly simple relation between form and
semantics. Thus, a common way to solve a problem in LISP is to
implement a language in which it is easy to express solutions to
problems in a general class, and then use this language to solve your
particular problem. See the Sandewall article mentioned above.
3. What is the difference between decisions made in a production
system and decisions made in a PASCAL program (in which IF statements
also have the same (superficial) form).
Production Systems gain some advantages by restricting the languages
for the IF and THEN parts. Also, in many production systems, all
the IF parts are evaluated first, to see which are true, before any
THEN part is done. If more than one IF part is true, some other
mechanism decides which THEN part (or parts) to do. Finally, some
production systems such as EMYCIN do "backward chaining", that is, one
starts with a goal and asks which THEN parts, if they were done, would
be useful in achieving the goal. One then looks to see if their
corresponding IF parts are true, or can be made true by treating them
as sub-goals and doing the same kind of reasoning on them.
A very good introduction to production systems is "An Overview of Production
Systems" by Randy Davis and Jonathan King, October 1975, Stanford AI Lab
Memo AIM-271 and Stanford CS Dept. Report STAN-CS-75-524. It's probably
available from the National Technical Information Service.
------------------------------
Date: 1 Jan 84 8:42:34-PST (Sun)
From: harpo!floyd!clyde!akgua!psuvax!bobgian @ Ucb-Vax
Subject: Netwide Course -- AI and Mysticism!!
Article-I.D.: psuvax.395
*************************************************************************
* *
* An Experiment in Teaching, an Experiment in AI *
* Spring Term Artificial Intelligence Seminar Announcement *
* *
*************************************************************************
This Spring term Penn State inaugurates a new experimental course:
"THE HUMAN CONDITION: PROBLEMS AND CREATIVE SOLUTIONS".
This course explores all that makes the human condition so joyous and
delightful: learning, creative expression, art, music, inspiration,
consciousness, awareness, insight, sensation, planning, action, community.
Where others study these DESCRIPTIVELY, we will do so CONSTRUCTIVELY. We
will gain familiarity by direct human experience and by building artificial
entities which manifest these wonders!!
We will formulate and study models of the human condition -- an organism of
bounded rationality confronting a bewilderingly complex environment. The
human organism must fend for survival, but it is aided by some marvelous
mechanisms: perception (vision, hearing), cognition (understanding, learning,
language), and expression (motor skill, music, art). We can view these
respectively as the input, processing, and output of symbolic information.
These mechanisms somehow encode all that is uniquely human in our experience
-- or do they?? Are these mechanisms universal among ALL sentient beings, be
they built from doped silicon or neural jelly? Are these mechanisms really
NECESSARY and SUFFICIENT for sentience?
Not content with armchair philosophizing, we will push these models toward
the concreteness needed for physical implementation. We will build the tools
that will help us to understand and use the necessary representations and
processes, and we will use these tools to explore the space of possible
realizations of "artificial sentience".
This will be no ordinary course. For one thing, it has no teacher. The
course will consist of a group of highly energetic individuals engaged in
seeking the secrets of life, motivated solely by the joy of the search
itself. I will function as a "resource person" to the extent my background
allows, but the real responsibility for the success of the expedition rests
upon ALL of its members.
My role is that of "encounter group facilitator": I jab when things lag.
I provide a sheltered environment where the shy can "come out" without
fear. I manipulate and connive to keep the discussions going at a fever
pitch. I pick and poke, question and debunk, defend and propose, all to
incite people to THINK and to EXPRESS.
Several people who can't be at Penn State this Spring told me they wish
they could participate -- so: I propose opening this course to the entire
world, via the miracles of modern networks! We have arranged a local
mailing list for sharing discussions, source-code, class-session summaries,
and general flammage (with the chaff surely will be SOME wheat). I'm aware
of three fora for sharing this: USENET's net.ai, Ken Laws' AIList, and
MIT's SELF-ORG mailing list. PLEASE MAIL ME YOUR REACTIONS to using these
resources: would YOU like to participate? would it be a productive use of
the phone lines? would it be more appropriate to go to /dev/null?
The goals of this course are deliberately ambitious. I seek participants
who are DRIVEN to partake in this journey -- the best, brightest, most
imaginative and highly motivated people the world has to offer.
Course starts Monday, January 16. If response is positive, I'll post the
network arrangements about that time.
This course is dedicated to the proposition that the best way to secure
for ourselves the blessings of life, liberty, and the pursuit of happiness
is reverence for all that makes the human condition beautiful, and the
best way to build that reverence is the scientific study and construction
of the marvels that make us truly human.
--
Bob Giansiracusa (Dept of Computer Science, Penn State Univ, 814-865-9507)
Arpa: bobgian%psuvax1.bitnet@Berkeley Bitnet: bobgian@PSUVAX1.BITNET
CSnet: bobgian@penn-state.csnet UUCP: allegra!psuvax!bobgian
USnail: 333 Whitmore Lab, Penn State Univ, University Park, PA 16802
------------------------------
Date: 1 Jan 84 8:46:31-PST (Sun)
From: harpo!floyd!clyde!akgua!psuvax!bobgian @ Ucb-Vax
Subject: Netwide AI Course -- Part 2
Article-I.D.: psuvax.396
*************************************************************************
* *
* Spring Term Artificial Intelligence Seminar Syllabus *
* *
*************************************************************************
MODELS OF SENTIENCE
Learning, Cognitive Model Formation, Insight, Discovery, Expression;
"Subcognition as Computation", "Cognition as Subcomputation";
Physical, Cultural, and Intellectual Evolution.
SYMBOLIC INPUT CHANNELS: PERCEPTION
Vision, hearing, signal processing, the "signal/symbol interface".
SYMBOLIC PROCESSING: COGNITION
Language, Understanding, Goals, Knowledge, Reasoning.
SYMBOLIC OUTPUT CHANNELS: EXPRESSION
Motor skills, Artistic and Musical Creativity, Story Creation,
Prose, Poetry, Persuasion, Beauty.
CONSEQUENCES OF THESE MODELS
Physical Symbol Systems and Godel's Incompleteness Theorems;
The "Aha!!!" Phenomenon, Divine Inspiration, Extra-Sensory Perception,
The Conscious/Unconscious Mind, The "Right-Brain/Left-Brain" Dichotomy;
"Who Am I?", "On Having No Head"; The Nature and Texture of Reality;
The Nature and Role of Humor; The Direct Experience of the Mystical.
TECHNIQUES FOR DEVELOPING THESE ABILITIES IN HUMANS
Meditation, Musical and Artistic Experience, Problem Solving,
Games, Yoga, Zen, Haiku, Koans, "Calculus for Peak Experiences".
TECHNIQUES FOR DEVELOPING THESE ABILITIES IN MACHINES
REVIEW OF LISP PROGRAMMING AND FORMAL SYMBOL MANIPULATION:
Construction and access of symbolic expressions, Evaluation and
Quotation, Predicates, Function definition; Functional arguments
and returned values; Binding strategies -- Local versus Global,
Dynamic versus Lexical, Shallow versus Deep; Compilation of LISP.
IMPLEMENTATION OF LISP: Storage Mapping and the Free List;
The representation of Data: Typed Pointers, Dynamic Allocation;
Symbols and the Symbol Table (Obarray); Garbage Collection
(Sequential and Concurrent algorithms).
REPRESENTATION OF PROCEDURE: Meta-circular definition of the
evaluation process.
"VALUES" AND THE OBJECT-ORIENTED VIEW OF PROGRAMMING: Data-Driven
Programming, Message-Passing, Information Hiding; the MIT Lisp Machine
"Flavor" system; Functional and Object-Oriented systems -- comparison
with SMALLTALK.
SPECIALIZED AI PROGRAMMING TECHNIQUES: Frames and other Knowledge
Representation Languages, Discrimination Nets, Augmented Transition
Networks; Pattern-Directed Inference Systems, Agendas, Chronological
Backtracking, Dependency-Directed Backtracking, Data Dependencies,
Non-Monotonic Logic, and Truth-Maintenance Systems.
LISP AS THE "SYSTEMS SUBSTRATE" FOR HIGHER LEVEL ABSTRACTIONS:
Frames and other Knowledge Representation Languages, Discrimination
Nets, "Higher" High-Level Languages: PLANNER, CONNIVER, PROLOG.
SCIENTIFIC AND ETHICAL CONSEQUENCES OF THESE ABILITIES IN HUMANS
AND IN MACHINES
The Search for Extra-Terrestrial Intelligence.
(Would we recognize it if we found it? Would they recognize us?)
The Search for Terrestrial Intelligence.
Are We Unique? Are we worth saving? Can we save ourselves?
Why are we here? Why is ANYTHING here? WHAT is here?
Where ARE we? ARE we? Is ANYTHING?
These topics form a cluster of related ideas which we will pursue more-or-
less concurrently; the listing is not meant to imply a particular sequence.
Various course members have expressed interest in the following software
engineering projects. These (and possibly others yet to be suggested)
will run concurrently throughout the course:
LISP Implementations:
For CMS, in PL/I and/or FORTRAN
In PASCAL, optimized for personal computers (esp HP 9816)
In Assembly, optimized for Z80 and MC68000
In 370 BAL, modifications of LISP 1.5
New "High-Level" Systems Languages:
Flavor System (based on the MIT Zetalisp system)
Prolog Interpreter (plus compiler?)
Full Programming Environment (Enhancements to LISP):
Compiler, Editor, Workspace Manager, File System, Debug Tools
Architectures and Languages for Parallel {Sub-}Cognition:
Software and Hardware Alternatives to the Von-Neuman Computer
Concurrent Processing and Message Passing systems
Machine Learning and Discovery Systems:
Representation Language for Machine Learning
Strategy Learning for various Games (GO, CHECKERS, CHESS, BACKGAMMON)
Perception and Motor Control Systems:
Vision (implementations of David Marr's theories)
Robotic Welder control system
Creativity Systems:
Poetry Generators (Haiku)
Short-Story Generators
Expert Systems (traditional topic, but including novel features):
Euclidean Plane Geometry Teaching and Theorem-Proving system
Welding Advisor
Meteorological Analysis Teaching system
READINGS -- the following books will be very helpful:
1. ARTIFICIAL INTELLIGENCE, Patrick H. Winston; Addison Wesley, 1984.
2. THE HANDBOOK OF ARTIFICIAL INTELLIGENCE, Avron Barr, Paul Cohen, and
Edward Feigenbaum; William Kaufman Press, 1981 and 1982. Vols 1, 2, 3.
3. MACHINE LEARNING, Michalski, Carbonell, and Mitchell; Tioga, 1983.
4. GODEL, ESCHER, BACH: AN ETERNAL GOLDEN BRAID, Douglas R. Hofstadter;
Basic Books, 1979.
5. THE MIND'S I, Douglas R. Hofstadter and Daniel C. Dennett;
Basic Books, 1981.
6. LISP, Patrick Winston and Berthold K. P. Horn; Addison Wesley, 1981.
7. ANATOMY OF LISP, John Allen; McGraw-Hill, 1978.
8. ARTIFICIAL INTELLIGENCE PROGRAMMING, Eugene Charniak, Christopher K.
Riesbeck, and Drew V. McDermott; Lawrence Erlbaum Associates, 1980.
--
Bob Giansiracusa (Dept of Computer Science, Penn State Univ, 814-865-9507)
Arpa: bobgian%psuvax1.bitnet@Berkeley Bitnet: bobgian@PSUVAX1.BITNET
CSnet: bobgian@penn-state.csnet UUCP: allegra!psuvax!bobgian
USnail: 333 Whitmore Lab, Penn State Univ, University Park, PA 16802
------------------------------
End of AIList Digest
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