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AIList Digest Volume 1 Issue 058

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AIList Digest
 · 1 year ago

AIList Digest           Saturday, 17 Sep 1983      Volume 1 : Issue 58 

Today's Topics:
Automatic Translation - Ada,
Games - Go Programs & Foonberg's Number Problem,
Artificial Intelligence - Turing Test & Creativity
----------------------------------------------------------------------

Date: 10 Sep 83 13:50:18-PDT (Sat)
From: decvax!wivax!linus!vaxine!wjh12!foxvax1!brunix!rayssd!sdl@Ucb-Vax
Subject: Re: Translation into Ada: Request for Info
Article-I.D.: rayssd.142

There have been a number of translators from Pascal to Ada, the first
successful one I know of was developed at UC Berkeley by P. Albrecht,
S. Graham et al. See the "Source-to-Source Translation" paper in the
1980 Proceedings of Sigplan Symp. on Ada, Dec. 1980.

At Univ. S. Calif. Info. Sci. Institute (USC-ISI), Steve Crocker (now
at the Aerospace Corp.) developed AUTOPSY, a translator from CMS-2 to
Ada. (CMS-2 is the Navy standard language for embedded software.)

Steve Litvintchouk
Raytheon Company
Portsmouth, RI 02871

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

Date: 10 Sep 83 13:56:17-PDT (Sat)
From: decvax!wivax!linus!vaxine!wjh12!foxvax1!brunix!rayssd!sdl@Ucb-Vax
Subject: Re: Go Tournament
Article-I.D.: rayssd.143

ARE there any available Go programs which run on VAX/UNIX which I
could obtain? (Either commercially sold, or available from
universities, or whatever.)

I find Go fascinating and would love to have a Go program to play
against.

Please reply via USENET, or to:

Steve Litvintchouk
Raytheon Company
Submarine Signal Division
Portsmouth, RI 02871

(401)847-8000 x4018

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

Date: 14 Sep 1983 16:18-EDT
From: Dan Hoey <hoey@NRL-AIC>
Subject: Alan Foonberg's number problem

I'm surprised you posted Alan Foonberg's number problem on AIlist
since Vivek Sarkar's solution has already appeared (Prolog digest V1
#28). I enclose his solution below. His solution unfortunately omits
the special cases , 2020, and 21200; I have sent a correction to the
Prolog digest.

Dan

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

Date: Wed 7 Sep 83 11:08:08-PD
From: Vivek Sarkar <JLH.Vivek@SU-SIERRA>
Subject: Solution to Alan Foonberg's Number Puzzle

Here is a general solution to the puzzle posed by Alan Foonberg:

My generalisation is to consider n-digit numbers in base n. The
digits can therefore take on values in the range 0 .. n-1 .

A summary of the solution is:

n = 4: 1210

n >= 7: (n-4) 2 1 0 0 ... 0 0 1 0 0 0
<--------->
(n-7) 0's

Further these describe ALL possible solutions, I.e. radix values of
2,3,5,6 have no solutions, and other values have exactly one solution
for each radix.

Proof:

Case 2 <= n <= 6: Consider these as singular cases. It is simple to
show that there are no solutions for 2,3,5,6 and that 1210 is the only
solution for 4. You can do this by writing a program to generate all
solutions for a given radix. ( I did that; unfortunately it works out
better in Pascal than Prolog ! )

CASE n >= 7: It is easy to see that the given number is indeed a
solution. ( The rightmost 1 represents the single occurrence of (n-4)
at the beginning ). For motivation, we can substitute n=10 and get
6210001000, which was the decimal solution provided by Alan.

The tough part is to show that this represents the only solution, for
a given radix. We do this by considering all possible values for the
first digit ( call it d0 ) and showing that d0=(n-4) is the only one
which can lead to a solution.

SUBCASE d0 < (n-4): Let d0 = n-4-j, where j>=1. Therefore the number
has (n-4-j) 0's, which leaves (j+3) non-zero digits apart from d0.
Further these (j+3) digits must add up to (j+4). ( The sum of the
digits of a solution must be n, as there are n digits in the number,
and the value of each digit contributes to a frequency count of digits
with its positional value). The only way that (j+3) non-zero digits
can add up to (j+4) is by having (j+2) 1's and one 2. If there are
(j+2) 1's, then the second digit from the left, which counts the
number of 1's (call it d1) must = (j+2). Since j >= 1, d1=(j+2) is
neither a 1 nor a 2. Contradiction !

SUBCASE d0 > (n-4): This leads to 3 possible values for d0: (n-1),
(n-2) & (n-3). It is simple to consider each value and see that it
can't possibly lead to a solution, by using an analysis similar to the
one above.

We therefore conclude that d0=(n-4), and it is straightforward to show
that the given solution is the only possible one, for this value of
d0.

-- Q.E.D.

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

Date: Wed 14 Sep 83 17:25:38-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: Re: Alan Foonberg's number problem

Thanks for the note and the correction. I get the Prolog digest
a little delayed, so I hadn't seen the answer at the time I relayed
the problem.

My purpose in sending out the problem actually had nothing to do with
finding the answer. The answer you forwarded is a nice mathematical
proof, but the question is whether and how AI techniques could solve
the problem. Would an AI program have to reason in the same manner as
a mathematician? Would different AI techniques lead to different
answers? How does one represent the problem and the solution in
machine-readable form? Is this an interesting class of problems for
cognitive science to deal with?

I was expecting that someone would respond with a 10-line PROLOG
program that would solve the problem. The discussion that followed
might contrast that with the LISP or ALGOL infrastructure needed to
solve the problem. Now, of course, I don't expect anyone to present
algorithmic solutions.

-- Ken Laws

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

Date: 9 Sep 83 13:15:56-PDT (Fri)
From: harpo!floyd!cmcl2!csd1!condict @ Ucb-Vax
Subject: Re: in defense of Turing - (nf)
Article-I.D.: csd1.116

A comment on the statement that it is easy to trip up an allegedly
intelligent machine that generates responses by using the input as an
index into an array of possible outputs: Yes, but this machine has no
state and hence hardly qualifies as a machine at all! The simple
tricks you described cannot be used if we augment it to use the entire
sequence of inputs so far as the index, instead of just the most
recent one, when generating its response. This allows it to take into
account sequences that contain runs of identical inputs and to
understand inputs that refer to previous inputs (or even
Hofstadteresque self-referential inputs). My point is not that this
new machine cannot be tripped up but that the one described is such a
straw man that fooling it gives no information about the real
difficulty of programming a computer to pass the Turing test.

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

Date: 10 Sep 83 22:20:39-PDT (Sat)
From: decvax!wivax!linus!philabs!seismo!rlgvax!cvl!umcp-cs!speaker@Ucb-Vax
Subject: Re: in defense of Turing
Article-I.D.: umcp-cs.2538

It should be fairly obvious that the Turing test is not a precise
test to determine intelligence because the very meaning of the
word 'intellegence' cannot be precisely pinned down, despite what
your Oxford dictionary might say.

I think the idea here is that if a machine can perform such that
it is indistinguishable from the behavior of a human then it can
be said to display human intelligence. Note that I said, "human
intelligence."

It is even debatable whether certain members of the executive branch
can be said to be intelligent. If we can't apply the Turing test
there... then surely we're just spinning our wheels in an attempt
to apply it universally.

- Speaker

--
Full-Name: Speaker-To-Animals
Csnet: speaker@umcp-cs
Arpa: speaker.umcp-cs@UDel-Relay

This must be hell...all I can see are flames... towering flames!

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

Date: Wed 14 Sep 83 12:35:11-PDT
From: David Rogers <DRogers@SUMEX-AIM.ARPA>
Subject: intelligence and genius

[This continues a discussion on Human-Nets. My original statement,
printed below, was shot down by several people. Individuals certainly
derive satisfaction from hobbies at which they will never excel. It
would take much of the fun out of my life, however, if I could not
even imagine excelling at anything because cybernetic life had
surpassed humans in every way. -- KIL]

From: Ken Laws <Laws@SRI-AI.ARPA>
Life will get even worse if AI succeeds in automating true
creativity. What point would there be in learning to paint,
write, etc., if your home computer could knock out more
artistic creations than you could ever hope to master?

I was rather surprised that this suggestion was taken so quickly
as it stands. Most people in AI believe that we will someday create an
"intelligent" machine, but Ken's claim seems to go beyond that;
"automating true creativity" seems to be saying that we can create not
just intelligent, but "genius" systems, at will. The automation of
genius is a more sticky claim in my mind.

For example, if we create an intelligent system, do we make it a
genius system by just turning up the speed or increasing its memory?
That"s like saying a painter could become Rembrandt if he/she just
painted 1000 times more. More likely is that the wrong (or uncreative)
ideas would simply pour out faster, or be remembered longer. Turning
up the speed of the early blind-search chess programs made them
marginally better players, but no more creative.

Or let's say we stumble onto the creation of some genius system,
call it "Einstein". Do we get all of the new genius systems we need by
merely duplicating "Einstein", something impossible to do with human
systems? Again, we hit a dead end... "Einstein" will only be useful in
a small domain of creativity, and will never be a Bach or a Rembrandt
no matter how many we clone. Even more discouraging, if we xerox off
1000 of our "Einstein" systems, do we get 1000 times the creative
ideas? Probably not; we will cover the range of "Einstein's" potential
creativity better, but that's it. Even a genius has only a range of
creativity.

What is it about genius systems that makes them so intractable?
If we will someday create intelligent systems consistently and
reliably, what stands in the way of creating genius systems on demand?
I would suggest that statistics get in our way here; that genius
systems cannot be created out of dust, but that every once in a while,
an intelligent system has the proper conditioning and evolves into a
genius system. In this light, the number of genius systems possible
depends on the pool of intelligent systems that are available as
substrate.

In short, while I feel we will be able to create intelligent
systems, we will not be able to directly construct superintelligent
ones. While there will be advantages in duplicating, speeding up, or
otherwise manipulating a genius system once created, the process of
creating one will remain maddeningly elusive.

David Rogers DRogers@SUMEX-AIM.ARPA


[I would like to stake out a middle ground: creative systems.

We will certainly have intelligent systems, and we will certainly have
trouble devising genius systems. (Genius in human terms: I don't want
to get into whether an AI program can be >>sui generis<< if we can
produce a thousand variations of it before breakfast.) A [scientific]
genius is someone who develops an idea for which there is, or at least
seems to be, no precedent.

Creativity, however, can exist in a lesser being. Forget Picasso,
just consider an ordinary artist who sees a new style of bold,
imaginative painting. The artist has certain inborn or learned
measures of artistic merit: color harmony, representational accuracy,
vividness, brush technique, etc. He evaluates the new painting and
finds that it exists in a part of his artistic "parameter space" that
he has never explored. He is excited, and carefully studies the
painting for clues as to the techniques that were used. He
hypothesizes rules for creating similar visual effects, trys them out,
modifies them, iterates, adds additional constraints (yes, but can I
do it with just rectangles ...), etc. This is creativity. Nothing
that I have said above precludes our artist from being a machine.

Another example, which I believe I heard from a recent Stanford Ph.D.
(sorry, can't remember who): consider Solomon's famous decision.
Everyone knows that a dispute over property can often be settled by
dividing the property, providing that the value of the property is not
destroyed by the act of division. Solomon's creative decision
involved the realization (at least, we hope he realized it) that in a
particular case, if the rule was implemented in a particular
theatrical manner, the precondition could be ignored and the rule
would still achieve its goal. We can then imagine Solomon to be a
rule-based system with a metasystem that is constantly checking for
generalizations, specializations, and heuristic shortcuts to the
normal rule sequences. I think that Doug Lenat's EURISKO program has
something of this flavor, as do other learning programs.

In the limit, we can imagine a system with nearly infinite computing
power that builds models of its environment in its memory. It carries
out experiments on this model, and verifies the experiments by
carrying them out in the real world when it can. It can solve
ordinary problems through various applicable rule invocations,
unifications, planning, etc. Problems requiring creativity can often
be solved by applying inappropriate rules and techniques (i.e.,
violating their preconditions) just to see what will happen --
sometimes it will turn out that the preconditions were unnecessarily
strict. [The system I have just described is a fair approximation to
a human -- or even to a monkey, dog, or elephant.]

True genius in such a system would require that it construct new
paradigms of thought and problem solving. This will be much more
difficult, but I don't doubt that we and our cybernetic offspring will
even be able to construct such progeny someday.

-- Ken Laws ]

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

End of AIList Digest
********************

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