Copy Link
Add to Bookmark
Report
AIList Digest Volume 2 Issue 115
AIList Digest Friday, 7 Sep 1984 Volume 2 : Issue 115
Today's Topics:
LISP - QLAMBDA & Common Lisp,
Expert Systems - AGE Contact & Expository Writing Assistant,
Books - Lib of CS and the Handbook of AI,
AI Tools - Statistical Workstations and Time-Series Lisp,
Binding - Jim Slagle,
Speech Recognition - Semantics,
Philosophy - Induction vs. Deduction & Causality,
Seminars - A Calculus of Usual Values & Week on Logic and AI
----------------------------------------------------------------------
Date: Thu, 6 Sep 84 8:54:58 EDT
From: "Ferd Brundick (VLD/LTTB)" <fsbrn@BRL-VOC.ARPA>
Subject: QLAMBDA
Does anyone have any information on a new LISP called QLAMBDA ??
It is a "parallel processor" language being developed by McCarthy at
Stanford and is supposed to run on the HEP (Heterogeneous Element
Processor). Since we have one of the original HEPs, we are interested
in any information regarding QLAMBDA. Thanks.
dsw, fferd
Fred S. Brundick
USABRL, APG, MD.
<fsbrn@brl-voc>
------------------------------
Date: 13 Aug 84 8:21:00-PDT (Mon)
From: pur-ee!uiucdcsb!nowicki @ Ucb-Vax.arpa
Subject: Re: Common Lisp - (nf)
Article-I.D.: uiucdcsb.5500009
I am also interested in such info. We have Sun-2's running 4.2 and I am
interested in obtaining Common Lisp for them.
-Tony Nowicki
{decvax|inuxc}!pur-ee!uiucdcs!nowicki
------------------------------
Date: Wed 5 Sep 84 17:28:19-CDT
From: Charles Petrie <CS.PETRIE@UTEXAS-20.ARPA>
Subject: AGE
Call Juanita Mullen at (415)497-0474 for a good time in obtaining
Stanford programs such as AGE. It'll cost you about $500.
CJP
------------------------------
Date: 5 Sep 1984 14:08:13 PDT
From: Bill Mann <MANN@USC-ISIB.ARPA>
Subject: Clarification on the non-existence of the Expository Writing Assistant
I've gotten several inquiries asking for the Expository Writing Assistant
Program that I wished for in a previous message. Unfortunately, it
doesn't exist. I'm convinced from studying text generation that we have
ENOUGH TECHNICAL INFORMATION about the structure of text, the functions
of various parts and how parts are arranged that such a program could be
written. My own writing practise, which now in effect simulates such a
program, indicates that the program's suggestions could be very helpful.
An introduction to the text structures I have in mind was presented at
the 1984 ACL/Coling conference at Stanford in July. The paper was
entitled "Discourse Structures for Text Generation."
Right now I have no plans to create the assistant.
Sorry, folks.
Bill Mann
------------------------------
Date: 4 Sep 84 16:36:13-PDT (Tue)
From: ihnp4!houxm!vax135!cornell!uw-beaver!ssc-vax!adcock @ Ucb-Vax.arpa
Subject: Re: Lib of CS intro offer: Handbook of AI Vols 1-3 for $5
Please note that the Handbook of AI is a REFERENCE book. It is not
meant to be read from cover to cover.
Also, this is the only books on AI that the Lib of CS sells.
[I disagree with the first point. The Handbook is also an excellent
tutorial, although it does lack illustrations. I enjoyed reading it
cover to cover (although I admit to not having finished all three
volumes yet). The second point is largely true, although they have
offered The Brains of Men and Machines, Machine Perception, LISPcraft,
and a few other related books. -- KIL]
------------------------------
Date: Fri 7 Sep 84 10:15:02-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: Statistical Workstations and Time-Series Lisp (Tisp)
Anyone interested in statistical workstations should look up the
August IEEE Computer Graphics and Applications article
"A Graphical Interface to an Economist's Workstation" by Thomas
Williams of Wagner, Stott and Company, 20 Broad Street, New York,
NY 10005. He describes a prototype for time-series analysis that
was quickly assembled from standard Interlisp-D functions on the
Xerox 1108. Apparently the economists of the International
Monetary Fund took to it immediately, and Williams sees no problems
in extending its capabilities to better support them. His company
is also working on a workstation for professional securities traders.
-- Ken Laws
------------------------------
Date: 5 Sep 1984 13:42-EDT
From: Russ Smith <smith@NRL-AIC>
Subject: Binding - Jim Slagle
As of September 10, 1984 Dr. Slagle will have a new address:
Professor James R. Slagle
University of Minnesota
136 Lind Hall
207 Church Street, S.E.
Minneapolis, MN 55455
(612) 373-7513
(612) 373-0132
slagle%umn-cs.csnet@csnet-relay.arpa (possibly...)
------------------------------
Date: 5 Sep 84 10:00:24-PDT (Wed)
From: ihnp4!fortune!polard @ Ucb-Vax.arpa
Subject: Re: Understanding speech versus hearing words
Article-I.D.: fortune.4138
<fowniymz for dh6 layn iyt6r> [Phonemes for the line-eater. -- KIL]
Which hip was burned?
Which ship was burned?
Which chip was burned?
and Which Chip was spurned?
all sound the same when spoken at the speed of conversational speech. This
is evidence that in order to recognize words in continuous speech
you (and presumably a speech-recognition apparatus) need to understand
what the speaker is talking about.
There seem to be two reasons why understanding is necessary
for word recognition in continuous speech:
1. The existence of homonyms. This is why "It's a good read."
sounds the same as: "It's a good reed," and why the two sentences
could not be distinguished without a knowledge of the context.
2. Sandhi, or sound changes at word boundaries. The sounds at the
end of a word tend to blend into the sounds at the beginning of the next
word in conversation, making words sound as if they ran into each other
and making words sound differently than they would when said in isolation.
The resulting ambiguities are usually resolved by context.
Speech rarely occurs without some sort of context, and even then
the first thing that usually happens is to establish a context for what
is to follow.
To paraphrase Edsgar Dijkstra: "Asking whether computers will
understand speech is like asking whether submarines swim."
--
Henry Polard (You bring the flames - I'll bring the marshmallows.)
{ihnp4,cbosgd,amd}!fortune!polard
------------------------------
Date: Wed 5 Sep 84 10:54:11-PDT
From: BARNARD@SRI-AI.ARPA
Subject: induction vs. deduction
Tony Hasemer's comments on causality and its relationship to inductive
versus deductive logic are very well-taken. It's time for people in
AI to realize that deduction is quite limited as a mode of reasoning.
Compared to induction, the mathematical foundations of deduction are
well-understood, and deductive systems are relatively easy to
implement on computers. This no doubt explains its popularity in AI.
The problem arises when one tries to extend the deductive paradigm
from toy problems to real problems, and must confront exceptions,
borderline cases, and, in general, the boggling complexity of the
state space.
While deduction proceeds from the general (axioms) to the specific
(propositions), induction proceeds from the specific to the general.
This seems to be a more natural view of human intelligence. By
observing events, one recognizes correlations, and infers causality
and other relationships. To be sure, the inferences may be wrong, but
that's tough. People make mistakes. In fact, one of the weaknesses
of deduction is that it does not permit one to draw conclusions that
may be in error (assuming the axioms are correct), but that represent
the best conclusions under the circumstances.
Visual illusions provide good examples. Have you ever wondered why
you see a Necker Cube as a cube (one of the two reversals), and not as
one of the other infinite number of possiblities? Perhaps we learn of
cubes through experience (an inductive explanation), but the effect
also occurs with totally unfamiliar figures. A more general inductive
explanation holds that we see the simplest possible figure (the
Gestalt principle of Pragnanz). A cube, which has right angles and
equal-length sides, is simpler than any of the other possiblilities.
The concept of "simple" can be made precise: one description is
simpler than another if it can be encoded more economically. This is
sometimes called the principle of Occam's Razor or the principle of
Minimum Entropy.
Steve Barnard
------------------------------
Date: 6 Sep 84 07:39 PDT
From: Woody.pasa@XEROX.ARPA
Subject: Causality
Food for thought:
All the arguments for and against cause and effect and the workings of
Causality have been based around the notion that the cause 'A' of an
effect 'B' are time-related: we assume that for A to affect B, A must
come before B in our perseption of time.
But does this have to be the case? Mathematics (inductive and
deductive logic) are time-independent identities; by assuming that
Causality may be a time-dependent phenomina on the basis of
time-independent arguments is at best wishful thinking.
What's wrong with event A affecting event B in event A's past? You
can't go back and shoot your own mother before you were born because you
exist, and obviously you failed. If we assume the universe is
consistant [and not random chaos], then we must assume inconsistancies
(such as shooting your own mother) will not arise. It does not,
however, place time constrictions on cause and effect.
- Bill Woody
Woody.Pasa@XEROX.Arpa [Until 7 September 1984]
** No net address ** [After 7 September 1984]
------------------------------
Date: Fri, 7 Sep 84 00:35:19 pdt
From: syming%B.CC@Berkeley
Subject: Seminar - A Calculus of Usual Values
From: chertok@ucbkim (Paula Chertok)
Subject: Berkeley Cognitive Science Seminar--Sept. 11
COGNITIVE SCIENCE PROGRAM
Fall 1984
Cognitive Science Seminar -- IDS 237A
SPEAKER: L.A. Zadeh
Computer Science Division, UC Berkeley
TITLE: Typicality, Prototypicality, Usuality,
Dispositionality, and Common Sense
TIME: Tuesday, September 11, 11 - 12:30pm
PLACE: 240 Bechtel Engineering Center
DISCUSSION: 12:30 - 2 in 200 Building T-4
The grouping of the concepts listed in the title of this
talk is intended to suggest that there is a close connection
between them. I will describe a general approach centering
on the concept of dispositionality which makes it possible
to formulate fairly precise definitions of typicality and
prototypicality, and relate these concepts to commonsense
reasoning. These definitions are not in the classical
spirit and are based on the premise that typicality and pro-
totypicality are graded concepts, in the sense that every
object is typical or prototypical to a degree. In addition,
I will outline what might be called a calculus of usual
values.
------------------------------
Date: Thu, 6 Sep 84 16:45:49 edt
From: minker@maryland (Jack Minker)
Subject: WEEK ON LOGIC AND AI
WEEK of
LOGIC and its ROLE in ARTIFICIAL INTELLIGENCE
at
THE UNIVERSITY OF MARYLAND
OCTOBER 22-26, 1984
The Mathematics and Computer Science Departments at the University
of Maryland at College Park are jointly sponsoring a Special Year in
Mathematical Logic and Theoretical Computer Science. The week of
October 22-26 will be devoted to Logic and its role in Artificial
Intelligence. There will be five distinguished lectures as follows:
Monday, October 22: Ray REITER
"Logic for specification: Databases
conceptual models, and knowledge representation
languages"
Tuesday, October 23: John McCARTHY
"The mathematics of circumscription"
Wednesday, October 24: Maarten VAN EMDEN
"Strict and lax interpretations of rules in logic programming"
Thursday, October 25: Jon BARWISE
"Constraint logic"
Friday, October 26: Lawrence HENSCHEN
"Compiling constraint checking programs in deductive databases"
All lectures will be given at:
Time: 10:00 AM - 11:30AM
Location: Mathematics Building, Room Y3206
The lectures are open to the public. If you plan to attend kindly
notify us so that we can make appropriate plans for space.
Limited funds are available to support junior faculty and graduate
students for the entire week or part of the week. To obtain funds,
please submit an application listing your affiliation and send either
a net message or a letter to:
Jack Minker
Department of Computer Science
University of Maryland
College Park, MD 20742
(301) 454-6119
minker@maryland
------------------------------
End of AIList Digest
********************
