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Subject: AIList Digest   V4 #47
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AIList Digest            Monday, 10 Mar 1986       Volume 4 : Issue 47

Today's Topics:
  Article/Seminar - The TI Compact LISP Machine (Dallas ACM),
  Seminars - Tools Beyond Technique (UCB) &
    Knowledge and Action in the Presence of Faults (SU) &
    Adaptive Networks (GTE) &
    Stochastic Complexity (IBM-SJ) &
    Updating Databases with Incomplete Information (SU) &
    Parallel Architectures for Knowledge Bases (SMU),
  Conference - 1987 Linguistics Institute

----------------------------------------------------------------------

Date: WED, 10 JAN 84 17:02:23 CDT
From: [email-masked]
Subject: Article/Seminar - The TI Compact LISP Machine (Dallas ACM)

ACM Dallas Chapter Meeting Notice
Speaker: Alfred Ricoomi
         Senior Member, Technical Staff
         Texas Instruments
Topic: The TI Compact LISP Machine

The February 17 issue of Aviation Week is devoted to the military
application of Artificial Intelligence.  One article reports on the
development, at TI, of a military LISP machine.  Mr. Riccomi will
describe the machine, its near term applications, and likely spin-offs
into the commercial world especially in the airline industry.

Place: INFOMART, 1950 nStemmons Freeway (at Oak Lawn)Room 7004

Date: Tuesday, March 11, 1986, 7:30 - 8:15

------------------------------

Date: 5 Mar 86 00:24:00 GMT
From: [email-masked]
Subject: Seminar - Tools Beyond Technique (UCB)

WHEN:   12:00 noon, Wednesday, March 5th
WHERE:  Canterbury House,
        University of Illinois at Urbana-Champaign
                    TOOLS BEYOND TECHNIQUE
                       Marcel Schoppers
                    Dept of Computer Science
                U of Illinois at Urbana-Champaign
In this talk I will propose yet another way to characterize AI, but
one which I hope captures the intuitions of AI researchers: that AI is
the attempt to liberate tools/machines from absolute dependence on
human control. That done, I will suggest some achievements which should,
according to this characterization of AI, demonstrate the success of
AI work. Importantly, both the characterization and those crucial
achievements contain no comparison to human capabilities. I therefore
maintain that several contemporary arguments for and against the future
success of AI are at once fallacious and beside the point. Among others:
the AI community's claim that "brains are computers too" is hardly necessary
and certainly not scientific, while Weizenbaum's "maybe computers can think,
but they shouldn't" is self-defeating. On the issue of whether artificial
intelligence will ever be achieved I will not commit myself, but at least
my characterization provides a down-to-earth criterion.

A paper on this subject (in the socio-communications literature):
"A perspective on artificial intelligence in society" Communications 9:2
   (december 1985).

------------------------------

Date: Thu 6 Mar 86 06:09:35-PST
From: Oren Patashnik <[email-masked]>
Subject: Seminar - Knowledge and Action in the Presence of Faults (SU)

AFLB, 13-Mar-86  :  Yoram Moses (MIT)
12:30 pm in MJ352 (Bldg. 460)

        Knowledge, Common Knowledge, and Simultaneous Actions
                     in the Presence of Faults

We show that any protocol that guarantees to perform a particular
action simultaneously at all sites of a distributed system must
guarantee that the sites attain common knowledge of particular facts
when such an action is performed. We analyze what facts become common
knowledge at various points in the execution of protocols in a simple
model of a system in which processors are liable to crash.  We obtain
a new protocol for Simultaneous Byzantine Agreement that is optimal in
all of its runs. That is, rather than achieving the worst case
behavior, every run of the protocol halts at the earliest possible
time, given the pattern in which failures occur. This may happen as
early as after two rounds. We characterize precisely what failure
patterns require the protocol to run for k rounds, 1<k<t+2,
generalizing and simplifying the lower bound proof for Byzantine
agreement.  We also show a non-trivial simultaneous action for which
popular belief would suggest that t+1 rounds would be required in the
worst case, and use our analysis to design a protocol for it that
always halts in two rounds. This work sheds considerable light on many
heretofore mysterious aspects of the Byzantine Agreement problem. It
is one of the first examples of how reasoning about knowledge can be
used to obtain improved solutions to problems in distributed computing.

This is joint work with Cynthia Dwork of IBM Almaden.

------------------------------

Date: Thu, 6 Mar 86 14:24:29 est
From: Rich Sutton <[email-masked]>
Subject: Seminar - Adaptive Networks (GTE)

              Self-Organization, Memorization,
       and Associative Recall of Sensory Information
              by Brain-Like Adaptive Networks

     Tuevo Kohonen,  Helsinki University of Technology

The main purpose of thinking is to forecast phenomena that take place
in the environment.  To this end, humans and animals must refer to a
complicated knowledge base which is somewhat vaguely called memory.
One has to realize the two main problem areas in a discussion of memory:
(1) the memory mechanism itself, and (2) the internal representations of
sensory information in the brain networks.

Most of the experimental and theoretical works have concentrated on the
first problem.  Although it has been extremely difficult to detect memory
traces experimentally, the storage mechanism is theoretically still the
easier part of the problem.  Contrary to this, it has been almost a
mystery how a physical system can automatically extract various kinds
of abstraction from the huge number of vague sensor signals.  This paper
now contains some novel views and results about the formation of such
internal representations in idealized neural networks, and their
memorization.  It seems that both of the above functions, viz. formation
of the internal representations and their storage, can be implemented
simultaneously by an adaptive, self-organizing neural structure which
consists of a great number of neural units arranged into a
two-dimensional network.  A number of computer simulations are presented
to illustrate both the self-organized formation of sensory feature maps,
as well as associative recall of activity patterns from the distributed
memory.

When:    March 14, 1:00 pm
Where:   GTE Labs 3-131
Contact: Rich Sutton, [email-masked], (617)466-4133

------------------------------

Date: 6 Mar 86 14:52:51 PST
From: [email-masked]
Subject: Seminar - Stochastic Complexity (IBM-SJ)

                  IBM Almaden Research Center
                         650 Harry Road
                    San Jose, CA 95120-6099

                            CALENDAR
                       March 10 - 14, 1986

  Computer        STOCHASTIC COMPLEXITY AND THE MDL AND PMDL PRINCIPLES
  Science         J. J. Rissanen, IBM Almaden Research Center
  Colloquium

  Thurs., Mar. 13 There is no rational basis in traditional
  3:00 P.M.       statistics for the comparison of two models
  Rear Audit.     unless they have the same number of parameters.
                  Hence, for example, the important
                  selection-of-variables problem has a dozen or so
                  solutions, none of which can be
                  preferred over the others.  Recently, inspired
                  by the algorithmic notion of complexity, we
                  introduced a new concept in statistics, the
                  Stochastic Complexity of the observed data,
                  relative to a class of proposed probabilistic
                  models.  In broad terms, it is defined as the
                  least number of binary digits with which the
                  data can be encoded by use of the selected
                  models.  The stochastic complexity also
                  represents the smallest prediction errors which
                  result when the data are predicted by use of the
                  models.  Accordingly, the associated optimal
                  model represents all the statistical information
                  in the data that can be extracted with the
                  proposed models, and for this reason its
                  computation, which we call the MDL (Minimum
                  Description Length) principle, may be taken to
                  be the fundamental problem in statistics.  In
                  this talk, we describe a special form of the MDL
                  principle, which amounts to the minimization of
                  squared "honest" prediction errors, and we apply
                  it to two examples of polynomial curve fitting
                  as well as to contingency tables.  In the first
                  example, which calls for the prediction of
                  weight growth of mice, the degree of the MDL
                  polynomial agrees with the optimal degree,
                  determined in retrospect after the predicted
                  weights were seen.  The associated predictions
                  also far surpass those made with the best
                  traditional statistical techniques.  A
                  fundamental theorem is given, which permits
                  comparison of models in the spirit of the
                  Cramer-Rao inequality, except that the models
                  need not have the same number of parameters.  It
                  also settles the issue of how the
                  selection-of-variables problem is to be solved.
                  Host:  R. Arps
                  (Refreshments at 2:45 P.M.)
[...]

------------------------------

Date: Fri 7 Mar 86 17:33:40-PST
From: Marianne Winslett <[email-masked]>
Subject: Seminar - Updating Databases with Incomplete Information (SU)

         Updating Databases With Incomplete Information
                           --or--
          Belief Revision is Harder Than You Thought

                      Marianne Winslett

                           PhD Oral
                        Area X Seminar
                      Margaret Jacks 352
                   Friday, March 14, 3:15 PM

Suppose one wishes to construct, use, and maintain a database of
knowledge about the real world, even though the facts about that world
are only partially known.  In the database domain, this situation
arises when database users must coax information into and out of
databases in the face of missing values and uncertainty.  In the AI
domain, this problem arises when an agent has a base set of beliefs
that reflect partial knowledge about the world, and then tries to
incorporate new, possibly contradictory knowledge into the old set of
beliefs.  In the logic domain, one might choose to represent such a
database as a logical theory, and view the models of the theory as
possible states of the real world.

How can new information (i.e., updates) be incorporated into the
database?  For example, given the new information that "b or c is
true," how can we get rid of all outdated information about b and c,
add the new information, and yet in the process not disturb any other
information in the database?  The burden may be placed on the user or
other omniscient authority to determine exactly which changes in the
theory will bring about the desired set of models.  But what's really
needed is a way to specify an update intensionally, by stating some
well-formed formula that the state of the world is now known to
satisfy and letting the database management system automatically
figure out how to accomplish that update.

This talk will explore a technique for updating databases containing
incomplete information.  Our approach embeds the incomplete database
and the updates in the language of first-order logic, which we believe
has strong advantages over relational tables and traditional data
manipulation languages when information is incomplete.  We present
semantics and algorithms for our update operators, and describe an
implementation of the algorithms.  This talk should be accessible to
all who are comfortable with first-order logic and have a passing
acquaintance with the notion of database updates.

------------------------------

Date: WED, 10 JAN 84 17:02:23 CDT
From: [email-masked]
Subject: Seminar - Parallel Architectures for Knowledge Bases (SMU)

Toward Computer Architectures for Database and Knowledge Base Processing
Computer Science and Engineering Seminar, Friday, March 14, 1986
Speaker: Lubomir Bic
         University of California at Irvine
Location: 315SIC
Time: 3:00 PM

The importance of parallelism has been recognized in recent years and
a number of multiprocessor architectures claiming suitability to
intelligent data and knowledge base processing have been proposed.
The success of these architectures has been, in most cases, rather
modest.  The message conveyed in this talk is that, in order to build
highly-parallel computer architectures, new models of computation
capable of exploiting the potential of large numbers of processing
elments and memory units must first be developed.  To support this
claim, two such models-- one for processing queries in a
network-oriented database system and another for extracting
information from a logic-based knowledge representation system -- will
be outlined.  Both models are based on the principles of asynchronous
data-driven computation, which eliminate the need for centralized
control and shared memory.

------------------------------

Date: Sat, 8 Mar 86 15:29:19 est
From: [email-masked] (Don Walker at mouton.ARPA)
Subject: Conference - 1987 Linguistics Institute

                     1987 LINGUISTICS INSTITUTE
                        STANFORD UNIVERSITY

The 1987 Summer Institute of the Linguistic Society of America will be
hosted by the Linguistics Department of Stanford University, from June
29  to  August 7, 1987.   It is  co-sponsored  by the  Association for
Computational Linguistics.

The theme of the Institute is "Contextual and Computational Dimensions
of Language",  and is meant  to reflect  the ever-growing  interest in
integrating theories of linguistic structure with theories of language
processing and models of  how language conveys information in context.
The aim is to provide  a forum in which it is possible  to integrate a
variety of  linguistic  traditions,  particularly  linguistic  theory,
computational  linguistics,   discourse  analysis,  psycholinguistics,
sociolinguistics, and artificial intelligence.

Several  different  kinds  of courses and activities will  be  offered
during the six-week period of the Institute:
     (i)    A  series  of overview classes in  the  main  subareas  of
linguistics (six weeks, 3 units)
     (ii)   A series of one-week intensive classes intended to provide
background for the four-week courses and seminars below (June  29-July
3, 1 unit)
     (iii)  Four-week classes on  topics related directly to the theme
of the Institute (July 13-August 7, 2 units)
     (iv)    Several seminars associated with research workshops  will
run throughout the last four weeks.  These can be taken for credit, as
part  of  the Stanford "directed research" program (subject  to  prior
approval of the workshop leader) (up to three units)
     (v)     A series of Wednesday lectures (e.g.,on the Synthesis  of
Approaches to Discourse), involving Institute participants and invited
visitors
     (vi)  The Association for Computational Linguistics will hold its
annual meeting during the second week of the Institute (July 6-10).

1987  marks  the  first  time in recent  years  that  two  consecutive
Institutes have been held with the same theme.   This  complementarity
of  the 1986 Institute held at the City University of New York and the
1987  Institute reflects remarkable changes taking place today in  the
field  of linguistics.   Taken together,  the Institutes  provide  the
depth  and  diversity necessary to cover the newly emerging  subfields
and  to  teach the range of interdisciplinary tools and  knowledge  so
fundamental  to  new  theoretical approaches.  The  1987  Institute at
Stanford differs from the 1986 Institute  primarily in specific course
offerings   and  faculty  and  in  its  focus  on  providing  a   rich
interdisciplinary research as well as teaching environment.   Many  of
the  instructors  will also be participating in  research  groups;  in
general  they   will teach only  one  course.

The  Executive  planning committee is:   Ivan  Sag  (Director),  Ellen
Prince (Associate Director), Marlys Macken, Peter Sells, and Elizabeth
Traugott.   David Perlmutter will be the Sapir Professor,  and  Joseph
Greenberg the Collitz Professor of the 1987 Institute.

For  more  information,   write  1987  LSA  Institute,  Department  of
Linguistics, Stanford University, Stanford, California  94305.

Preliminary List of Institute Faculty:

  Judith Aissen
  Elaine Anderson
  Stephen Anderson
  Philip Baldi
  Jon Barwise
  Joan Bresnan
  Gennaro Chierchia
  Kenneth Church
  Eve Clark
  Herbert Clark
  Nick Clements
  Charles Clifton
  Philip Cohen
  Robin Cooper
  William Croft
  Penelope Eckert
  Elisabet Engdahl
  Charles Ferguson
  Charles Fillmore
  Joshua Fishman
  Lyn Frazier
  Victoria Fromkin
  J. Mark Gawron
  Gerald Gazdar
  Joseph Greenberg
  Barbara Grosz
  Jorge Hankamer
  Jerry Hobbs
  Paul Hopper
  Larry Horn
  Philip Johnson-Laird
  Ron Kaplan
  Lauri Karttunen
  Martin Kay
  Paul Kay
  Paul Kiparsky
  William Ladusaw
  William Leben
  Steve Levinson
  Mark Liberman
  Marlys Macken
  William Marslen-Wilson
  John McCarthy
  Nils Nilsson
  Barbara Partee
  Fernando Pereira
  David Perlmutter
  Ray Perrault
  Stanley Peters
  Carl Pollard
  William Poser
  Ellen Prince
  Geoffrey Pullum
  John Rickford
  Luigi Rizzi
  Ivan Sag
  Deborah Schiffrin
  Peter Sells
  Stuart Shieber
  Candace Sidner
  Brian Smith
  Donca Steriade
  Susan Stucky
  Michael Tanenhaus
  Elizabeth Traugott
  Peter Trudgill
  Lorraine Tyler
  Thomas Wasow
  Terry Winograd
  Annie Zaenen
  Arnold Zwicky

------------------------------

End of AIList Digest
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