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AIList Digest Volume 4 Issue 157
AIList Digest Wednesday, 25 Jun 1986 Volume 4 : Issue 157
Today's Topics:
Discussion Lists - The Structure of AI, Knowledge Science, and
6th-Generation Computing,
Theory - Parallelism
----------------------------------------------------------------------
Date: Thu, 19 Jun 86 11:48:46 edt
From: Tom Scott <scott%bgsu.csnet@CSNET-RELAY.ARPA>
Subject: Ken's Plea for Help!!!!
The moderator of the AI-List has made an impassioned plea for
help. I would like to help, but before I offer to start a new
Arpanet, Csnet, or UUCP newsgroup, I'd like to put forth an
organization to Ken's list of possible new newsgroups. This
organization comes from the Japanese side of the Pacific, and is
outlined by Brian Gaines in a recent article, "Sixth Generation
Computing: A Conspectus of the Japanese Proposals" ("SIGART
Newsletter", January 1986, pp. 39-44).
Figure 1 of the article, complemented by the fundamental
topics that I've added for the sake of completeness, cuts the cake
thusly:
Theoria | Praxis | Techne
------------ | -------------------- | --------------------
| Expert systems | Pattern recognition
Physiology | | Cognition
| Machine translation | Learning
Psychology | systems | Problem solving
| | Natural language
Linguistics | Intelligent CAD/CAM | Image processing
| systems | Speech recognition
Logic | | Man-Machine interface
| Intelligent robotics |
=============================================================
| Managerial | Expert systems
Epistemology | cybernetics |
| Decision support | Development languages
Modern logical| systems | and environments
metaphysics | Information |
| retrieval systems | Computing/knowledge
Vedic Science | | machines
=============================================================
THE UNIFIED FIELD OF ALL POSSIBILITIES
This is the world of the sixth generation: knowledge science
and knowledge systems. The fifth generation, which deals mainly with
the daily realities of knowledge engineering and expert systems, as
well as with the advanced research and development of VLSI
architectures for the processing of Prolog code and database systems,
is distinct from the sixth generation.
To get a better feel for these distinctions, I'd like to
suggest the following homework assignment for new newsgroup
moderators: (1) Read Brian's article. (2) Read the abstract of the
paper that I'll be presenting to the sixth-generation session at the
1986 International Conference on Systems, Man, and Cybernetics
(Atlanta, October 14-17); the abstract is appended to this message.
(3) Think before you flame; then write back to me or to this newsgroup
and share your thoughts.
We are children of the cybernetic revolution and we are
witnessing the rising sunshine of the Age of Enlightenment.
Tom Scott CSNET: scott@bgsu
Dept. of Math. & Stat. ARPANET: scott%bgsu@csnet-relay
Bowling Green State Univ. UUCP: cbosgd!osu-eddie!bgsuvax!scott
Bowling Green OH 43403-0221 ATT: 419-372-2636
* * * Abstract of the sixth-generation SMC paper * * *
KNOWLEDGE SCIENCE
The Evolution From
Fifth-Generation Expert Systems
To Sixth-Generation Knowledge Systems
Theory, practice, technology--these are the makings of a full vision
of knowledge science and sixth-generation knowledge systems. Prior to
the establishment of research and development projects on the Fifth
Generation Computing System (FGCS), knowledge science did not exist
independent of knowledge engineering, and was conceptualized only in
technological terms, namely, expert systems and "machine architectures
for knowledge-based systems based on high-speed Prolog and relational
database machines" (Gaines 1986).
Although the design and development of fifth-generation
machines and expert systems will continue for years to come, we want
to know now what can be done with these ultra-fast architectures and
expert systems. What kinds of knowledge, other than the knowledge of
domain experts in fifth-generation expert systems, can be acquired and
encoded into sixth-generation knowledge systems? What can be done on
top of fifth-generation technology? How can fifth-generation
architectures and expert-system techniques be extended to build
intelligent sixth-generation knowledge systems?
Beyond the fifth generation it is necessary to envision
practical applications and theoretical foundations for knowledge
science in addition to the technological implementation of machine
architectures and expert systems. This paper discusses the full
three-part vision of knowledge science (theoria, praxis, and techne)
that is emerging around the world and has been treated by the Japanese
under the title Sixth Generation Computing System (SGCS).
Theoria: As indicated in Brian Gaines's article, "Sixth
Generation Computing: A Conspectus of the Japanese Proposals"
("ACM-SIGART Newsletter" January 1986), the theoretical foundations of
knowledge science are arranged in levels, proceeding downward from
physiology to psychology to linguistics to logic. Continuing in this
direction toward deeper foundations, the field of knowledge science
embraces epistemology and modern logical metpahysics. On the
empirical side of the deep foundations is the probability-based
epistemology of pragmatism, explicated in Isaac Levi's "The Enterprise
of Knowledge" (1980); on the transcendental side are Immanuel Kant's
"Critique of Pure Reason" (1781-87) and Edmund Husserl's "Formal and
Transcendental Logic" (1929). A simplified diagram of the four main
divisions of mind, based on one sentence of the Critique ("Beide sind
entweder rein, oder empirisch": B74), is:
Understanding Sensibility
|
E Knowledge Images
m of --------> Objects
p objects |
|
----------------------+-----------------------
T |
r Pure concepts Schemas Pure forms of
a (categories) --------> intuition
n and principles | (space and time)
s |
Praxis: The SGCS project is also concerned with the practical
applications of knowledge science. These applications are organized
under four headings: expert systems, machine-translation systems,
intelligent CAD/CAM, and intelligent robotics. Another way of
organizing the applications of knowledge science in terms familiar to
the IEEE Systems, Man, and Cybernetics Society is: managerial
cybernetics, organizational analysis, decision support, and
information retrieval. Stafford Beer's "The Heart of Enterprise"
(1979) is the focal point of our discussion of knowledge-science
praxis.
Techne: The SGCS project targets eight technological areas as
the basis for the future research and development of sixth-generation
knowledge systems: pattern recognition, cognition, learning, problem
solving, natural language, image processing, speech recognition, and
man-machine interfacing. To fully realize the R&D potential of these
eight areas, sixth-generation knowledge scientists must be on friendly
terms with the following areas of expertise from fifth-generation
knowledge engineering:
(1) Expert systems.
(a) Concepts and techniques for the acquisition,
representation, and use of knowledge.
(b) The software engineering of knowledge systems,
including a methodology for the building of expert
systems and the management of expert-system
development teams.
(c) Expert systems and shells.
(2) Three levels of systems and software.
(a) Production systems (e.g., ITP, Prolog, and OPS83).
(b) Traditional AI/KE languages (e.g., Lisp and Prolog).
(c) Development environments and utilities (e.g., Unix, C,
and Emacs).
(3) The knowledge engineer's technical intuition of a
computational knowledge machine.
(a) Lambda Consciousness, based on the idea of a Lisp
machine.
(b) Relational database machines.
(c) Prolog machines.
The paper includes observations from the experience of the
University of Wisconsin-Green Bay in its attempts to establish a
regional knowledge-engineering and knowledge-science resource center
in the Northeastern Wisconsin area.
* * * Finis * * *
------------------------------
Date: 19 Jun 1986 2240-PDT (Thursday)
From: Eugene miya <eugene@ames-aurora.arpa>
Subject: Fed up with all this `talk' about parallelism
The following are some ideas I have been thinking about with the help
of one co-worker. I plan to post this to several groups where I
regard parallelism discussions are significant such as parsym, ailist,
and net.arch. The ideas are still in formation.
>From the Rock of Ages Home for Retired Hackers:
--eugene miya
NASA Ames Research Center
eugene@ames-aurora.ARPA
"You trust the `reply' command with all those different mailers out there?"
{hplabs,hao,dual,ihnp4,decwrl,allegra,tektronix,menlo70}!ames!aurora!eugene
draft:
The Mythical MIPS (MegaFLOPS)
(pardons to Fred Brooks)
"Introduction"
That's it! I'm tired of hearing about all this parallelism out there.
Too many people are talking about parallelism without truly understanding
what it is. There appear to be conceptual as well as syntactic and
semantic problems.
One problem is that the world is not always parallel. "Thinking" is
not always parallel: dependency "chains" are created in logic for instance.
Another problem is that we think much of the world is parallel,
but some "levels" of parallelism are not interchangeable. It appears
there are serially connected parallel processes with serial bottlenecks
between processes (not necessary Petri nets).
Looking at snapshots,
<Blind men ("including" me) trying to describe the elephant>
I see two communities who are not communicating:
physical scientists see "spatial" parallelism: all those difference
equations over a given space, they see meshes, but the computer science people
(typically the AI and compiler people) see "syntactic" parallelism,
they tend to see syntax trees like data flow graphs, for instance.
[I do note that rectangular meshes turned on their corners do represent
`trees.']
"The Concept"
Parallelism is a geometric concept: lines not meeting and equidistant (well..).
Parallelism is not a given. `Dependence' prevents `decomposition.'
>From Fred Brooks:
If it takes a female 9 months to have offspring, then nine females can
have one in one month. If a computation takes 9 units of time,
then . . . Are the units interchangeable or should we make a distinction
in unit type? Are we confusing work and effort?
"Terminology"
Consider the terminology parallelism, concurrency, multiprocessing,
multitasking (this one is really loaded), nonsequential (non-von), etc.
There is a lot of different terminology to describe parallelism.
I don't think it's necessary to standardize the terminology, but
perhaps we should? For instance:
Would you place a "tightly-coupled problem" on a
"loosely-coupled" multiprocessor?
First obvious question is "what's a `tightly coupled problem?'"
How do you measure the parallelism? Is it just the count of the number
of parallel execution streams?
A problem of parallelism is just the degree of decompositibility:
even in uniprocessor computer systems, there is such a degree of
asynchronous inefficiency, that CPUs wait, that work is really distributed
all over the place.
Let's change the terminology for a moment to try and better understand
the issues. Rather than use parallel and multiprocess (or concurrent)
Let's try "cooperative" and "coordinated" like we would take regions
around a point, we might be able to study the neighborhood around the word
`parallel.' Is there a difference between the two. Diane Smith
asserts there is. I think there may be.
Cooperative computing implies working together to achieve a single goal.
Coordinated computing implies more that processes don't bump heads
(separate goals) but work in a common environment (coordinate).
There is the obvious third factor of communications. There may also be
levels and different types of communications such as control interaction
versus bulk transfer. Better adjectives might exist, perhaps changing
words do better, but history effects will bias those of us working
on this.
"Classifications of parallelism"
There are an obscene number of classifications:
Flynn's system: SISD, SIMD, MIMD...
Kuck's modification: execution streams distinct from instruction
streams: SIME(MD), MIME(MD), etc.
Handler's commentary that there were lots of commentaries and little work
Prolog et al AND-parallelism and OR-parallelism
Then there is temporal parallelism: pipelining: parallelism, but different
Parallelism is all cars starting forward the moment the light turns
green (without regard for any cars head). Pipelining is waiting
for the car ahead of you to start rolling.
I also see three conditions of parallelism: parallelism is not constant.
It's like snow and it's many forms: powder, neve, firn, sastrugi, and
the Eskimo words. I see
Constant parallelism: spatial parallel is a good example,
the number of parallel streams does not basically change
thru time. Gauss-Seidel and other iterative solutions
to systems of equations? AND-parallelism (can be coarse or
fine grained (what ever grain means)).
Converging parallelism: The number of parallel streams
is reducing, perhaps approaching serial: data flow graphs
of dot products, of the summation step of a matrix multiply,
a Gauss-Jordan (elimination, or direct solution) is another example.
Must be fine-grained.
Diverging parallelism: (perhaps several types): many forks,
OR-parallelism, fractal. Like diverge series, this type of
parallelism has problems. (Can be fine or coarsed grained?)
The real key is the quantitative characterization (at some level)
of parallel-ism. Are we to only count streams?
While it is largely a matter of communications/coupling, how do
we evaluate the communications needs of an algorithm as opposed to an
architecture?
What are we going to do with 2-D flow charts where we need to
express forking and branching on the same 2-D plane?
Oh well! Still searching for an honest definition.
"Socio/politico/economic commentary"
Recent economically based events in parallel processing are amazing.
The number of companies actively marketing hypercube arcitectures
and Crayettes is stagering. Machine with Cray class power are not
surprising, this is inevitable. Cray instruction set compatable machine
offerings is what is surpising about this. There are so few Crays (100)
out there, that the half dozen or more companies who say they are
offering such guarantee failure.
More surprising are the number of hypercube architectures. Admittedly,
hypercubes offer very nice connectivity features, but only one person
has a good perspective: Justin Rattner, Intel, who offered the
machine as an experimental testbed not a Cray alternative.
What with all this talk about parallelism, it is surprising there are not
more companies marketing, designing, etc., mesh-type architectures
ala ILLIAC IV style architectures. That spatial model of parallelism (SIMD)
is probably the easier to build if not program. This latter point is worth
some debate, but as noted many models of parallelism are spatially based.
Only the MPP, the DAP, and it seems the Connection Machine to a somewhat lesser
extent are based this way (albeit more connections).
It would be argued by some that this is for more limited applications
but again those are spatially based problems tned to dominate. Why no
68Ks or 32Ks in a mesh? Is it all marketing hype? How could the money be
better directed (for research purposes since obviously some of this
money is bound to go into failed experiments [necessitated by
empirical work]), can we spread out the "cost" to develop new architectures.
Ah yes, reinventing the ILLIAC again.
"A few asides:" [From S. Diane Smith]
When asked about the interconnection network in MPP compared to
that of the STARAN, Ken Batcher replied, "We learned that you didn't
need all that (the multistage cube/omega) for image processing, that
a mesh was good enough."
You could look at MPP as a second generation parallel processor,
even if the processors are only 1 bit wide. They got rid of
a number of "mistakes" that they learned about through STARAN.
The "tightly coupled" .vs. "loosely coupled" debate went on
7-8 years ago before everyone got tired of it. It was sort of
the analog of the RISC vs. CISC debates of today. The net result
was sort of an agreement that there was a spectrum, not a dicotomy.
There have been one or two papers on classification, none very satisfying.
I'll see if I can't find them.
The latest thing you see in parallel processing is the "real"
numerical analysts who are actually putting the problems on
machines. Until very recently, with a few exceptions from the ILLIAC
days, most parallel numerical analysis has been theoretical.
Diane. . .
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End of AIList Digest
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