'Folk psychology' works
Raffael Cavallaro
David Longly wrote:
>We have ample evidence that people's thinking is not rational
>but most of that evidence has accumulated since the 70s, and it
>came as a surprise. As a consequence, a lot of what was taken for
>granted back in the 50s has to re-evaluated, and that includes
>some of the basic goals of AI.
This is certainly true. However, the point you consistently ignore is that
the 'folk psychology' (meaning the often irrational way people think) that
you continually deride is what actually gave rise to the science that you
laud.
This proves something you don't like admitting: That the 'folk psychology'
is a sufficient cause for a mind that can think scientifically. It may not
be an absolutely necessary cause, but we know from history that it is
sufficient.
Let me make this very clear. There was a time when all thinking was what you
deride as 'folk psychology'. Then people who came from this tradition slowly
developed scientific thought. We now live in a world where we routinely take
'folk psychologies' (i.e., freshmen) and turn them into scientists. This is
what happens every day at the better scientific and technical Universities.
This being the case, we know that, if we can create an artificial 'folk
psychology,' we can turn it into an artificial scientist relatively easily
(i.e., just sent it to MIT for 4 years).
Now, put yourself in the shoes of an AI researcher. There are 2 ways to go.
1. Focus your research on developing an artificial 'folk psychology'. This
is *guaranteed* to give you an artificial scientist as a by product.
2. Listen to David Longly, and try to develop an artificial scientist
directly. Do this in spite of the fact that there's no historical precedent
for it. This is not to say that it might not work, but that there's no
*guarantee* that it will work.
Now which course of research will this AI researcher follow? One that we
know from history can succeed, or one that *may* succeed *if* David Longely
is right?
The answer, clearly is the former, because the latter is simply an
unnecessary risk. We can have all of its benefits (i.e., an artificial
scientist) as a by product of the development of an artificial 'folk
psychology'.
This being the case, your plea that we abandon the goal of creating an
artificial 'folk psychology' is bound to be ignored, and with very good
reason.
Raf
David Longley
In article <69oijv$5...@news-central.tiac.net>
raf...@pop.tiac.net "Raffael Cavallaro" writes:
>
> David Longly wrote:
> >We have ample evidence that people's thinking is not rational
> >but most of that evidence has accumulated since the 70s, and it
> >came as a surprise. As a consequence, a lot of what was taken for
> >granted back in the 50s has to re-evaluated, and that includes
> >some of the basic goals of AI.
>
> This is certainly true. However, the point you consistently ignore is that
> the 'folk psychology' (meaning the often irrational way people think) that
> you continually deride is what actually gave rise to the science that you
> laud.
> This proves something you don't like admitting: That the 'folk psychology'
> is a sufficient cause for a mind that can think scientifically. It may not
> be an absolutely necessary cause, but we know from history that it is
> sufficient.
No..you simply don't understand.
It isn't a matter of derision. That's your imputation. Folk
psychological theory like folk physical theory is just misguided
theory. It's misguided for many reasons.
> Let me make this very clear. There was a time when all thinking was what you
> deride as 'folk psychology'. Then people who came from this tradition slowly
> developed scientific thought. We now live in a world where we routinely take
> 'folk psychologies' (i.e., freshmen) and turn them into scientists. This is
> what happens every day at the better scientific and technical Universities.
> This being the case, we know that, if we can create an artificial 'folk
> psychology,' we can turn it into an artificial scientist relatively easily
> (i.e., just sent it to MIT for 4 years).
Again this is nonsense. It takes more than 4 years, and even that
doesn't stop us behaving according to folk psychological
principles OUTSIDE of the specific training contexts (which at
university tend to be JUST verbal argument). There's a point here
which you are clearly missing. We can learn "folk psychological"
notions just as we can learn science. The point is that the
learning mechanism can produce either. As a consequence, those
processes per se are NOT what is important to INTELLIGENT
behaviour.
> Now, put yourself in the shoes of an AI researcher. There are 2 ways to go.
> 1. Focus your research on developing an artificial 'folk psychology'. This
> is *guaranteed* to give you an artificial scientist as a by product.
No it's probably just going to produce an incomprehensible mess.
> 2. Listen to David Longly, and try to develop an artificial scientist
> directly. Do this in spite of the fact that there's no historical precedent
> for it. This is not to say that it might not work, but that there's no
> *guarantee* that it will work.
If you look more closely at what I am advocating you'll see that
I don't see any future for "AI" distinct form science and
technology. The whole idea of AI is a mistake borne of some
mistaken ideas which were not seen to be mistaken at the time AI
was conceived, ie in the early 50s.
> Now which course of research will this AI researcher follow? One that we
> know from history can succeed, or one that *may* succeed *if* David Longely
> is right?
We have no evidence form history that the former plan can
succeed. All we have is technological developments inspired by
what folk believe may be characteristic of how people operate. It
really is very shallow...
> The answer, clearly is the former, because the latter is simply an
> unnecessary risk. We can have all of its benefits (i.e., an artificial
> scientist) as a by product of the development of an artificial 'folk
> psychology'.
I've suggested a particular field of application where we can put
our technology to good use in the assessment and management of
behaviour on the basis of extensional analysis. The intelligence
is in the use of science and database technology (and by that I
mean to include artificial languages all the way up through
statistics). We need to replace NI with that sort of AI.
> This being the case, your plea that we abandon the goal of creating an
> artificial 'folk psychology' is bound to be ignored, and with very good
> reason.
>
> Raf
You will not be able to create an artificial folk psychology,
because folk psychology itself is irrationally cobbled together.
I've explained how our folk psychological idioms of propositional
attitude flout basic axioms of inference through failures of
quantification in and substitutivity of identity - the sine qua
non for inference.
That there are those in the GOFAI tradition who know there is a
problem to be dealt with here can be seen by the recent work of
McCarthy. I assert that what he is trying to do is not going to
work.
Until folk face up to these issues they are just going to be
writing crypto-science fiction, or engineering under a fancy
name.
--
David Longley (check end reply line #)
Longley Consulting London, UK
Behaviour Assessment & Profiling Technology,
Research, Data Analysis and Training Services,
Small IT Systems http://www.longley.demon.co.uk
David Longley
In brief - saying "Folk psychology works" is like saying
"witchcraft works" or "alchemy works". Each might have
adventitiously effective elements within a fundamentally
superstitious and overall irrational framework - to say "it
works" isn't just inaccurate - it's STUPID.
There are plenty of STUPID people about who have nothing better
to do than argue for the status quo - after all, that's all
they're familiar with. The alternative proposed in "Frag.htm" at
the site below has been shown to be more effective and consistent
with what is known to be sound.
I suggest sceptics have a look at the first few pages of that
file before deciding where they stand on this issue - it's likely
that they'll come across something they may not have considered.
Seth Russell
David Longley wrote:
> I've suggested a particular field of application where we can put
> our technology to good use in the assessment and management of
> behaviour on the basis of extensional analysis. The intelligence
> is in the use of science and database technology (and by that I
> mean to include artificial languages all the way up through
> statistics). We need to replace NI with that sort of AI.
Fine, then propose (or point to ... URLs please) an artificial language
that is *up to the task* of interfacing us to this database (db). My
experience tells me that 1) if you know which db to use and 2) if you
know what is in the db and 3) you know the inference capabilities of the
db and 4) you have learned the specialize artificial language of that
particular db, then you may be able to retrieve useful information from
it. We have been doing that since the 60's and some of us are sick and
tired of it because we now understand first hand it's limitations. The
fact of the matter is that if we are to make any headway here we need to
switch to using natural language in a network environment - and that my
chiggy friend is AI.
Seth
See "Bozo's Conjecture" at http://www.clickshop.com/ai/conjecture.htm
And then on to the AI Jump List ...
David Longley
In article <34BFF290...@clickshop.com>
seth...@clickshop.com "Seth Russell" writes:
> David Longley wrote:
>
> > I've suggested a particular field of application where we can put
> > our technology to good use in the assessment and management of
> > behaviour on the basis of extensional analysis. The intelligence
> > is in the use of science and database technology (and by that I
> > mean to include artificial languages all the way up through
> > statistics). We need to replace NI with that sort of AI.
>
> Fine, then propose (or point to ... URLs please) an artificial language
> that is *up to the task* of interfacing us to this database (db). My
> experience tells me that 1) if you know which db to use and 2) if you
> know what is in the db and 3) you know the inference capabilities of the
> db and 4) you have learned the specialize artificial language of that
> particular db, then you may be able to retrieve useful information from
> it. We have been doing that since the 60's and some of us are sick and
> tired of it because we now understand first hand it's limitations. The
> fact of the matter is that if we are to make any headway here we need to
> switch to using natural language in a network environment - and that my
> chiggy friend is AI.
>
> Seth
> See "Bozo's Conjecture" at http://www.clickshop.com/ai/conjecture.htm
> And then on to the AI Jump List ...
>
'No predictions made about a single case in clinical work are
ever certain, but are always probable. The notion of
probability is inherently a frequency notion, hence
statements about the probability of a given event are
statements about frequencies, although they may not seem to
be so. Frequencies refer to the occurrence of events in a
class; therefore all predictions; even those that from their
appearance seem to be predictions about individual concrete
events or persons, have actually an implicit reference to a
class....it is only if we have a reference class to which the
event in question can be ordered that the possibility of
determining or estimating a relative frequency exists.....
the clinician, if he is doing anything that is empirically
meaningful, is doing a second-rate job of actuarial
prediction. There is fundamentally no logical difference
between the clinical or case-study method and the actuarial
method. The only difference is on two quantitative continua,
namely that the actuarial method is #more explicit# and #more
precise#.'
P. E. Meehl (1954)
Clinical versus Statistical Prediction
A Theoretical Analysis and a Review of the Evidence
'Finding right words of my own to communicate another's
saying is a problem of translation. The words I use in the
particular case may be viewed as products of my total theory
(however vague and subject to correction) of what the
originating speaker means by anything he says: such a theory
is indistinguishable from a characterization of a truth
predicate, with his language as object language and mine as
metalanguage. The crucial point is that there will be equally
acceptable alternative theories which differ in assigning
clearly non-synonymous sentences of mine as translations of
his same utterance. This is Quine's thesis of the
indeterminacy of translation.'
D. Davidson (1969)
On Saying That
ibid. p.100
'Much of what is called for is to mechanize as far as
possible what we now do by art when we put ordinary English
into one or another canonical notation. The point is not that
canonical notation is better than the rough original idiom,
but rather that if we know what idiom the canonical notation
is for, we have as good a theory for the idiom as for its
kept companion.'
D. Davidson (1967)
Truth and Meaning
'We think of a science as comprising those truths which are
expressible in terms of 'and', 'not', quantifiers, variables,
and certain predicates appropriate to the science in
question....To specify a science, within the described mold,
we still have to say what the predicates are to be, and what
the domain of objects is to be over which the variables of
quantification range.'
W.V.O. Quine (1954)
The Scope and Language of Science
The Ways of Paradox and other essays p.242
'Ultimately the objects referred to in a theory are to be
accounted not as the things named by the singular terms, but
as the values of the variables of quantification.'
W.V.O. Quine (1953,1961)
Reference and Modality
From a Logical Point of View p.144-145
Quantifers in natural language refer to the positions in statements
taken by pronouns. They allow us to keep track of who, or what, we are
talking about. In formal, predicate (functional) logic, they demystify
the whole process of deductive inference. In a finite universe of
discourse (which is what we have in PROBE as a database of inmates),
existential (y) quantification can be replaced by a finite series of
disjunctions (V ie ORs), just as universal (z) quantification can be
replaced by a finite series of conjunctions (U ie ANDs). Our queries
can be conceived as clauses written in disjunctive or conjunctive
normal form. When applied to the database, cases either do or do not
meet the specified conditions for class membership, and this serves as
our basis for actuarial analysis of behaviour. Since our database is
finite, we can in fact venture into considering the application of
probability quantifiers (e.g. Vickers 1988), ie the relative frequency
of individuals which meet the conditions of our logical conditions.
'The major motivating principle of probability quantifiers is
the development of probability within pure or general logic
to the extent that this is possible. The great difficulty of
precisely defining general logic can perhaps be avoided by
agreeing that however it is defined, the semantics of first-
order logic as developed by Frege and Tarski fall quite
within its confines. Then, as the above remarks suggest, the
question is just to what extent such notions as "the
proportion of objects falling under a concept" or "the
proportion of assignments satisfying a formula" can be given
a meaning in general logic.'
J. M. Vickers (1988)
Chance & Structure: An Essay on the Logical Foundations of
Probability
Probability quantifiers:principles and semantics p.153
Once this way of working with a data base becomes familiar, it is a
simple set of steps from relative frequencies to joint probabilities
and correlations, regressions and the rest of descriptive statistics
which all Prison Psychologists receive systematic training in as part
of their induction MSc training. It should also be clear that work
within the PROBE system is work in the application of extensional
logic to a specific domain within which the Prison Service employs
Behaviour Scientists to provide a technical service within. That
specificity, or specialism is defined by the selection, analysis and
use of the behaviour predicates and functions within a particular
universe of discourse.
In the case of breaches of prison rules, each instance of an
infraction is identified by the paragraph of the prison rules broken.
In turn, further aspects of the infraction can be recorded such as the
date, time and location, resulting in an n-place predicate. Individual
names, or identifiers are syntactically referred to as constants, with
arbitrary individuals being represented as variables. These are
jointly referred to as terms. A term, without variables is known as a
ground term. When we describe an individual, the descriptor is a
predicate of order n, where n is the number of terms which follow. The
predicate and its terms, together, are known as an atom. A ground
atom, is an atom without variables. Semantically, the set of ground
terms is known as the Herbrand Universe, ie the cases within our CASE
based data base which are inmates. The Herbrand base for any retrieval
we may write is the set of ground atoms that we can construct from the
predicates available (often confusingly called variables) and the
ground term in the Herbrand Universe. This encompasses all we can say
about the inmates in our database. A Herbrand Interpretation is a
subset of the Herbrand base, i.e. those assigned the value true.
Ultimately, these notions will be important when we come to use AI
techniques such as the resolution principle to milk implicit
inferences from within our database. Note, that according to the
thesis being developed in these volumes, it is only the failure of
Leibniz's Law within epistemic (intensional) contexts which makes any
of this seem remotely difficult. What we are generally concerned with
is the creation of well formed formulae, simple atomic propositions or
predicates, which when combined by logical connectives, amount to
compound propositions, or complex predicates. Such predicates are
generally described as one-place, two-place or higher indicating how
many argument positions they require. For example, age is one place
predicate, whilst associate_of, is a two place predicate. The PROBE
data dictionary (Volume 5) lists one-place, unary or monadic
predicates. Two place predicates are also referred to as relations.
Two place predicates, known as binary relations do not exist in the
schema. Individual inmates can be regarded as unary relations, a
strange notion, but one which allows one to treat individuals as
classes.
An example or two may help to make the above more concrete at this
point. Age, NIC score, report rate and index offences will suffice to
illustrate the value of working solely extensionally with relations
and classes. Comparison of the distribution of inmates by age group is
one of the population measures provided to the field week as part of
weekly analysis of the Long Term prison system. Such population
parameters readily highlight unplanned discrepancies in allocation.
That there is a functional relationship between NIC score and age, or
age and rate of disciplinary infractions was used in Volume 2 to
highlight how such relations can be used by management in the interest
of maintaining control.
Relations are clearly basic to relational data bases, and it should be
noted that one of the great changes brought about by relativity theory
was that Newtonian monadic predicates were replaced by relations
(Churchland 1989). The logic of relations with quantifiers is perhaps
the greatest breakthrough in human thought to date, and is still one
of the most difficult to fully appreciate. Frege's 'Concept Writing
Script' (his 'Begriffsschrift' or Predicate Calculus) effectively
introduced for the first time, cognition or reasoning, as a formal,
mechanical process. Here is how Carnap (1933) introduced the notion of
the new logic:
'The new logic is distinguished from the old not only by the
form in which it is presented but chiefly also by the
increase of its range....The only form of statements
(sentences) in the old logic was the predicative form:
"Socrates is a man," "All (or some) Greeks are men." A
predicate-concept or property is attributed to a subject-
concept. Leibniz had already put forward the demand that
logic should consider sentences of relational form. In a
relational sentence such as, for example, "a is greater than
b," a relation is attributed to two or more objects, (or, as
it might be put, to several subject-concepts). Liebniz's idea
of a theory of relations has been worked out in the new
logic. The old logic conceived relational sentences as
sentences of predicative form. However, many inferences
involving relational sentences thereby become impossible. To
be sure, one can interpret the sentence "a is greater than b"
in such a way that the predicate "greater than b" is
attributed to the subject a. But the predicate then becomes a
unity; one cannot extract b by any rule of inference.
Consequently, the sentence "b is smaller than a" cannot be
inferred from this sentence. In the new logic, this inference
takes place in the following way: The relation "smaller than"
is defined as the "converse" of the relation "greater than."
The inference in question then rests on the universal
proposition: If a relation holds between x and y, its
converse holds between y and x. A further example of a
statement that cannot be proved in the old logic: "Wherever
there is a victor someone is vanquished." In the new logic,
this follows from the logical proposition: If a relation has
a referent, it also has a relatum. Relational statements are
especially indispensable for the mathematical sciences. Let
us consider as an example the geometrical concept of the
three-place relation "between" (on an open straight line).
The geometrical axioms "If a lies between b and c, b does not
lie between c and a" can be expressed only in the new logic.
According to the predicative view, in the first case we would
have the predicates "lying between b and c" and "lying
between c and a". If these are left unanalyzed, there is no
way of showing how the first is transformed into the second.
If one takes the objects b and c out of the predicate, the
statement "a lies between b and c" no longer serves to
characterise only one object, but three. It is therefore a
three-place relational statement....
Restriction to predicate-sentences has had disastrous effects
on subjects outside logic. Perhaps Russell is right when he
made this logical failing responsible for certain
metaphysical errors.....Above all, we may well assume that
this logical error is responsible for the concept of absolute
space. Because the fundamental form of a proposition had to
be predicative, it could only consist in the specification of
the position of a body. Since Leibniz had recognized the
possibility of relational sentences, he was able to arrive at
a correct conception of space: the elementary fact is not
position of a body but its positional relations relative to
other bodies. He upheld the view on epistemological grounds:
there is no way of determining the absolute position of a
body, but only its positional relations. His campaign in
favor of the relativistic view of space, as against the
absolutistic views of the followers of Newton, had as little
success as his program for logic.
Only after two hundred years were his ideas on both subjects
taken up and carried through: in logic with the theory of
relations (De Morgan 1858; Pierce 1870), in physics with the
theory of relativity (anticipatory ideas in Mach 1883;
Einstein 1905).'
R. Carnap
The Old and the New Logic (1930)
In A.J. Ayer (ed) Logical Positivism (1959)
Throughout these volumes, the case is made that, for PROBE to be used
as an effective system, it will require users to analyse and manage
inmate behaviour exclusively according to an inmate's class
membership, which in turn only makes sense relative to other classes.
The monadic predicate calculus (the calculus of classes), it should be
understood:
'.. consists in characterizing the predicates by their
extension instead of according to their content. To each
predicate corresponds a certain "class" of objects,
consisting of all objects for which the predicate holds. The
case of a class containing no object is of course not
excluded here. Classes are now to be taken as the entities
dealt with by the calculus, which in this interpretation will
be called the calculus of classes.
D. Hilbert & W. Ackermann (1950)
The Principles of Mathematical Logic p.46
As stated above, a list of individuals which can occupy the positions
of an n-place, or n-ary, or degree n predicate, is known as an ordered
n-tuple (n-membered sequence), and this is ultimately what we are
concerned with as behaviour scientists. Date (1992), who along with
E.F. Codd is a major spokesman for relational theory, had this to say
about predicates:
'It is convenient to assume that the predicates "=", ">", " r "
etc, are builtin (i.e they are part of the formal system we
are defining) and that the expressions using them can be
written in the conventional manner, but of course users
should be able to define their own additional predicates as
well. Indeed, that is the whole point, as we will quickly
see: The fact is, in database terms, a user-defined predicate
is nothing more nor less than a user-defined relation.'
...
'The suppliers relation S, for example, can be regarded as a
predicate with four arguments (S#, SNAME, STATUS, and CITY).
Furthermore the expressions S(S1, Smith,20,London) and
S(S6,Green,45,Rome) represent "instances" or invocations of
that predicate that evaluate to true and false respectively.'
C. J. Date (1992)
Logic Based Database Systems: A Tutorial Part II p.378
Relational Database Writings 1989-1991
The import of this statement marks an important step on the route to
widescale practise of logical and actuarial behaviour management
rather than ad hoc clinicalism which as we have seen in Volume 1, can
only be less precise instances of the former, acceptance of this may
be limited solely by the fact that it is all so relatively new:
'Research on the relationship between database theory and
logic goes back at least to the late 1970s, if not earlier.
However, the principal stimulus for the recent considerable
expansion of interest in the subject seems to have been the
publication in 1984 of a landmark paper by Raymond Reiter,
"Towards a Logical Reconstruction of Relational Database
Theory," which appeared in a book entitled On Conceptual
Modelling: Perspectives from Artificial Intelligence,
Databases, and Programming Languages (eds. Brodie,
Mylopoulos, and Schmidt; Spinger-Verlag, 1984). In that
paper, Reiter characterised the traditional perception of
database systems as model theoretic - by means of which he
meant, speaking very loosely, that:
(a) The database is seen as a set of explicit (i.e. base)
relations, each containing a set of explicit tuples, and
(b) Executing a query can be regarded as evaluating some
specified formula (ie truth-valued expression) over those
explicit relations and tuples.
Reiter then went on to argue that an alternative proof-
theoretic view was possible, and indeed preferable in certain
respects. In that alternative view - again speaking very
loosely - the database is seen as a set of axioms ("ground"
axioms, corresponding to tuples in base relations, plus
certain "deductive" axioms, to be discussed), and executing a
query is regarded as proving that some specified formula is a
logical consequence of those axioms - in other words, proving
that it is a theorem....Consider the following query
(expressed in relational calculus)....
SPX
WHERE SPX.QTY > 250
Here SPX is a tuple variable ranging over the shipments
relation SP. In the traditional (i.e. model-theoretic)
approach, we examine the shipment (SPX) tuples one by one,
evaluating the formula "SPX.QTY > 250" for each one in turn;
the query result then consists of just those shipment tuples
for which the formula evaluates to true. In the proof
theoretic approach, by contrast, we consider the shipment
tuples (plus certain other items) as axioms of a certain
"logical theory"; we then apply theorem-proving techniques to
determine for which possible values of the variable SPX the
formula "SPX.QTY > 250" is a logical consequence of those
axioms within that theory. The query result then consists of
just those particular values of SPX.'
ibid p.267-368
Although there is a degree of confusion in terminology in the area,
Date (1992) suggests that a Deductive Database Management System is:
'a database that supports the proof-theoretic view of a
database, and in particular is capable of deducing additional
facts from the "extensional database" (i.e. the base
relations) by applying specified deductive axioms or rules of
inference to those facts. The deductive axioms, together,
together with the integrity constraints (discussed below),
form what is sometimes called the "intensional database"
(IDB), and the extensional database and the intensional
database together constitute what is usually called the
deductive database (not a very good term, since it is the
DBMS, not the database, that carries out the deductions).
As just indicated, the deductive axioms form one part of the
intensional database. The other part consists of additional
axioms that represent integrity constraints (i.e. rules whose
primary purpose is to constrain updates, though actually such
rules can also be used in the deduction process to generate
new facts)....it now becomes more important than ever that
the extensional database not violate any of the declared
integrity constraints! - because a database that does violate
any such constraints represents (in logical terms) an
inconsistent set of axioms, and it is well known that
absolutely any statement whatsoever can be proved to be
"true" from such a starting point (in other words,
contradictions can be derived. For exactly the same reason,
it is also important that the stated set of integrity
constraints be consistent.'
ibid p.394-5
One might profitably read the above with the failure of Leibniz's Law
within intensional contexts clearly in mind. Similarly, neophyte PQL
programmers soon find that the reason why most of what they want to
achieve fails to materialize is due to errors in their programming,
which invariably come down to them not specifying step by step the
logical and procedural steps of their query. Here again, the actual
user, rather than the casual reader will appreciate the didactic force
of the imperative "stay out of your head, and look at the screen". The
experienced user should appreciate that the keyboard and screen
comprise a very effective system of 'virtual' reality, which is
improved by a mouse.
One of the main advantages of a formal database system is that as
updates are made to the overall data structure, cross referencing
maintains database integrity constraints by only making updates
according to well established update rules. We have seen at length,
the problems which results from failure of substitutivity within
intensional contexts - namely, that deductive inference is not
possible. Within PROBE, deductively driven updates are currently quite
minimal, restricted essentially to PQL 'retrieval updates' which cross
update inmate cell location and prison location across relations 3 and
11. Where further updates are possible, implementation beyond
providing quality control reports has been refrained from in the
interests of maintaining a degree of user input to maintaining overall
system integrity.
Returning to the terminology of relational technology, where a
predicate is a two-place predicate, it is an ordered 2-tuple, or
ordered pair. A tuple is a row, and a relation is a set of predicates
comprising a record type (sometimes called a table). In almost all
instances, whether a retrieval generates a simple list of inmates, or
a multivariate statistical analysis (with post-processing using SPSS
for multiple or logistic regression for example), we are practically
interested in value distributions (Kerlinger and Pedhazur 1973).
Carnap (1959) summarised the situation as follows (although it should
be appreciated that Quine's austere, wholly extensionalist system
developed in Word and Object (1960) was largely a critique of the
intensionalism which remained within Carnap's "Meaning and Necessity"
program):
Intensions and Extensions of the Chief Types of Expressions
Expression Intension Extension
Sentence Proposition Truth-value
Individual constant Individual concept Individual
One-place predicate Property Class of individuals
n-place predicate (n>1) n-place relation Class of ordered n-tuples of
individuals
Functor Function Value-distribution
Carnap (1958)
Introduction to Symbolic Logic and its Applications
In an annex to a short paper entitled 'What is a Relation' Date (1992)
put the situation as follows:
'In the body of this paper, I gave the mathematician's view
of a relation as "An n-ary relation is a set of ordered n-
tuples." In this appendix, I would like to mention an
alternative view very briefly - namely, the logician's view.
In logic, an n-ary relation is simply that which is
designated by an n-place predicate in what is called the
first order predicate calculus. For example, the expression
">(A,b) is a 2-place predicate that designates the "greater
than" relation, and "SP(S#,P#,QTY)" is a three-place
predicate that designates the "shipments" relation in the
usual suppliers and parts database. In general, an n-place
predicate can be thought of as a truth-valued function with n
arguments; a given tuple appears in the corresponding
relation if and only if the function evaluates to true for
the argument values represented by that tuple.
..
When we talk about the foundations of the relational model,
we usually talk in terms of sets and set theory - a
mathematical foundation, in fact. But the forgoing indicates
that it is at least equally possible to talk in terms of a
foundation in logic - specifically, in the first order
predicate calculus - instead. And this alternative perception
does have certain arguments in its favor....some people would
argue that the true foundation of the relational model is
really the first order predicate calculus, not set theory,
and moreover that there is no real need to invoke set-
orientated ideas at all in developing and discussing the
model.'
C. J. Date (1992)
What is a Relation? A Logician's View
Relational Database Writings 1989-1991 p.54-5
Whilst initially unfamiliar, this logical notation, basic to the
predicate or functional calculus, provides an invaluable framework
when designing and managing data base management system's structure,
when planning analyses and programming automated reports. It is
certainly easier to deal with in the author's view than the more
commonly encountered set theoretic terminology, and renders the links
with work in theoretical logic (e.g. Quine 1960, 1992) much easier.
All database systems must be reduced to 'normal form' in the interests
of being able to analyse the modelled domain at its most fundamental
levels. Through Quine's critique of analyticity (1951, 1960), coupled
with the axiomatic nature of Leibniz's Law the language of science
(Quine 1954) has little choice but to dismiss intensional notions such
as 'sense' (Frege 1883), or 'individual concept' (attribute, property,
meaning, content etc; Carnap 1947; Church 1951). Intensional contexts
are indeterminate, and thereby unable to occupy positions of bound
variables (Quine 1943;1956) in any form of scientific analysis
(computer or otherwise).
In 1994, we simply do not know how to use formal logic (Information
Technology) to quantify reliably into intensional contexts (such as
the propositional attitudes), and attempts to do so using techniques
such as Repertory Grids (the 'Fragmentation Corollary' aside) and
Factor Analysis may prove to be creative rather than analytical as a
consequence. Less formally, we do not know how to reason within such
contexts without falling into rhetoric and sophistry. Until we are
shown otherwise, extensional systems render us incapable of analysing
inmates by anything other than the classes which they fall into. We
can do no more than use quantification theory to extensionally
identify the functional relations which exist between such classes,
and manage behaviour according to such functions.
Compound predicates, or n-ary relations e.g. Governor's reports can be
created such as 'Rule_Paragraph', 'Date_of_Infraction',
'Time_of_Infraction', 'Location_in_Prison' and a unique
'Inmate_Case_Identifier' (the constant, or when quantified, a variable
x). Each predicate returns one, and only one value, and together they
comprise a vector which can be analysed like the values of any simple
or atomic predicate. In this way, it is possible, using relational
technology, to define the arity of relations or predicates using the
logical connectives within a fourth generation retrieval language and
thereby expand or restrict relations or predicates to certain times,
dates, places, or to inmates with certain classes of index offence,
ages, or whatever the algorithm written, actually 'satisfies' (Tarski
1931) through the tuples meeting the specified value criteria of the
well-formed formula (wff). That is, an instance (or instantiation) of
a clause is obtained by applying a substitution to the clause, and a
substitution is an assignment of terms to variables (Kowalski 1979).
An example to illustrate the above should clarify the terminology and
illustrate the potential of working within this framework, given our
understanding of Leibniz's Law.
We will take record three of PROBE, Behavior at The Current Prison
(CURPRIS). There are (34 Records in all, several one-many (eg. reports
movements, segregation periods, attainment assessments).
Key
'Variable' 'Variable Label'
01 a NATNUM NATIONAL NUMBER
02 b PRESCAT PRESENT SECURITY CATEGORY
a 03 c EDRCPRIS EDR or NPD CURRENT PRISON
r 04 d PRISON CURRENT ESTABLISHMENT
g 05 e DOR DATE OF RECEPTION
u 06 f WINGINST CURRENT WING
m 07 g TPPSYC PSYCHIATRIC DIAGNOSIS AT CURRENT PRISON
e 08 h TPDRUGS EVIDENCE OF DRUGS THIS PRISON
n 09 i ELIST PLACED ON E LIST THIS PRISON
t 10 j NEWHOST HOSTAGE TAKER AT THIS PRISON
11 k TPR43OR RULE 43(OR) SEGREGATIONS THIS PRISON
p 12 l TPR43GO RULE 43(GOAD) SEGREGATIONS THIS PRISON
l 13 m TPC1074 CI1074/3790 TRANSFER FROM THIS PRISON
a 14 n TPSTVIO (PROVEN) STAFF ASSAULTS THIS PRISON
c 15 o TPINVIO (PROVEN) INMATE ASSAULTS THIS PRISON
e 16 p TPADJ (PROVEN) ADJUDICATIONS THIS PRISON
17 q PSYMON3 PSYMON vs F1150 FLAG(3)
18 r DATMOD03 MODIFIED
Relation Name = Curpris
Argument Positions (arity) = 18
As an 18-ary relation:
A R G U M E N T P O S I T I O N S
1 1 1
1 2 3 4 5 6 7 8 9 0 7 8
T Curpris(113386,2,01011700,LLC,05041991,A,0,0,0,0,..... ,0 10041991)
U Curpris(119085,1,01011700,LLC,14111991,Z,0,0,0,0,..... ,0 01061993)
P Curpris(122004,2,01011700,LLC,14101988,B,1,0,0,0,..... ,0 30011989)
L Curpris(132016,1,01111988,LLC,01021979,E,0,0,0,0,..... ,1 01051988)
E Curpris(132687,1,01011700,LLC,30101989,S,0,0,0,0,..... ,0 29031990)
S Curpris(133616,2,01011700,LLC,11051982,F,0,0,0,0,..... ,0 01061993)
Or as a series of binary predicates:
01 Natnum(113386,Curpris)
02 Natnum(119085,Curpris)
P 03 Natnum(122004,Curpris)
R 04 Natnum(132016,Curpris)
E 05 Natnum(132687,Curpris)
D 06 Natnum(133616,Curpris)
I 07 Prescat(113386,2)
C 08 Prescat(119085,1)
A 09 Prescat(122004,2)
T 10 Prescat(132016,1)
E 11 Prescat(132687,1)
S 12 Prescat(133616,2)
13 Etc., etc.
14 Etc., etc.
Queries can then be expressed in 'clausal form' as:
Answer(x)
Answer(x) Inmate(x, Curpris) AND Prescat(x,1)
or
Answer(x)
Answer(x) Curpris(x,y,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o)
AND
Curpris(x,1,z,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o)
Here, the value '1' is substituted for the variable b in order to list
all inmates with a value of 1 for Present Security Category (Prescat).
This presentation should make it graphically clear why some query
languages are given the name 'Query By Example'. The same format is
followed of course when instantiating queries with predicates drawn
from other relations such as Person, Utadata, Curpris, Reports and so
on. As covered at length in Volume 1 and the early parts of this
volume, the fundamental value of relational, deductive technology,
lies in the failure of effective substitutivity of identicals, 'salva
veritate', within intensional contexts. The failure of Leibniz's Law
within epistemic and other intensional contexts renders anything and
everything inferable given the violation of the law of contradiction,
or failure of truth-functionality within such contexts.
Comprehensive relational modelling and extensional deductive analysis
within a domain, or universe of discourse comprises a science of that
domain. The application of the theorems derived from analysis back
into the domain, comprises a technology. There can be nothing
controversial about this claim once the logical basis of relational
theory and scientific method are clearly understood in conjunction and
the significance of the failure of Leibnitz Law within intensional
contexts is fully appreciated.
At the end of Volume 1, and certainly within Volume 2, we used
functional notation rather than the language of relations and
predicates, so before leaving the subject, we show how functional
notation expresses predicates or relations. Recall that in his
discovery of the Predicate Calculus (his 'Begriffsschrift') in 1879,
Frege wrote that his discovery of the quantifiers was in large part a
consequence of rejecting the old Predicate-Argument notation, and
selecting instead an extended concept of the mathematicians notion of
function-argument, at the same time, we will deal with the important
issue of equality or identity.
We can express Times(x,y,z)
as x * y = z
or Father(x,y)
as x = father(y)
Relational calculus query language uses function symbols and equality:
prescat(x) = y
in place of Prescat(x,y)
winginst(x) = y
in place of Winginst(x,y)
The following is taken from Kowalski (1979), and effectively brings us
full circle to the leitmotif of these volumes, beginning with the
quote at the beginning of Volume 1: the identity of indiscernibles and
the failure of Leibniz's Law within intensional contexts.
'Equality is necessary whenever an individual has more than
one name. For example:
Jove = Jupiter .
It is also necessary, even in the relational notation, to
express that one argument of a relation is a function of the
others. For example:
x = y Father(x,z) , Father(y,z)
To show that a set of clauses S containing the equality
symbol is inconsistent, the set of clauses needs to contain
the following axioms characterising the equality relation,
for every function symbol f and every predicate symbol P
occurring in S, (including the equality symbol).
E1 x = x
E2 P(x 1 ,.....,x m ) P(y 1 ,......,y m ), x 1 =y 1 , ..., x m =y m
E3 f(x 1 ,.....,x m ) = f(y 1 ,......,y m ) x 1 =y 1 , ..., x m =y m
for example, to demonstrate that the assumptions
J1 Jekyl = Hyde
J2 father(John) = Hyde
J3 Member(father(John), birthday club)
imply the conclusion
member(Jekyl, birthday club)
it is necessary to deny the conclusion
J4 Member(Jekyl, birthday club)
and add the appropriate axioms for the equality relation:
J5 x = x
J6 Member(x 1 ,x 2 ) Member(y 1 ,y 2 ), x 1 =y 1 ,x 2 =y 2
J7 x 1 = x 2 y 1 = y 2 , x 1 = y 1 , x 2 = y 2
J8 father(x) = father(y) x = y
The following set of clauses J1-8 is inconsistent because J1-3 are
"obviously" inconsistent with the instances
Hyde = Hyde
birthday club = birthday club
Member(Jekyl, birthday club) Member(father(John), birthday club),
Jekyl = father(John),
birthday club = birthday club
Jekyl = father(John) Hyde = Hyde, Jekyl = Hyde, father(John) = Hyde
of J5-7. Clause 8 in this example does not contribute to the inconsistency.
R. Kowalski (1979)
Representation in Clausal Form: Equality
Logic for Problem Solving
In managing and designing and developing the PROBE system, special
care has been taken to ensure that these extensional, ie truth-
functional principles are followed and that referential integrity
constraints or rules on data entry are built in to optimize quality
control. Training has emphasised the pitfalls of clinical judgement
(Volume 1) as evidence of the failure of quantification within
intensional contexts. At times this has been extremely difficult,
since many users still regard databases as 'nothing more than' a
research data storage medium. This can only stem from a poor
conception of the technology behind record (table) design, the power
of 'normal form' or 'clausal form' as an artificial language, and a
very limited practical use of the such systems, e.g. the production of
simple lists rather than full relational analyses. This functional
specification is designed to suggest how the PROBE system might be
used in support of an applied behaviour science and technology, which
in turn supports effective inmate management, not, it must be said, as
an all purpose MIS. Any failure to fully appreciate these points will
inevitably lead to great financial investments with very little in the
way of productive returns. Without a sound appreciation of logic, such
systems simply will not be used effectively. This is a simple lesson
from research in descriptive (folk) psychology (Volume 1).
For those who are sceptical about the value of checklists for
instance, it is important perhaps to point out that the prison rules
can be listed as 21 paragraphs under Rule 47, ie as a series of
observation statements. An inmate will always be charged under one, or
another paragraph of the Rule (each as a separate event or offence).
The paragraphs serve as a set of declarative statements (32 binary
predicates or a 32-ary relation if the circumstances such as date and
time, place etc. are included in the tuple). The Rule 47 system
effectively operate as a behaviour checklist, or criterion referencing
system. Where no offences have occurred it is as if null entries were
entered for each inmate, date, time and place - something which is
made graphically clear when actual offences are plotted against time.
Construed from the perspective of relational theory, this removes in
one move, any objections to 'box ticking' as a means of assessment,
since it can readily be seen that all inmate management must be based
on such predicate or relational systems, albeit sometimes of quite
high arity, and therefore for memory capacity constraints, quite a
bewildering i.e. impossible task for working memory as outlined in
Volume 1 and elsewhere (Miller 1956; Attneave 1959; Cherniak 1986,
Stich 1990).
Based on this conception of a Data Base Management System, PROBE's
second phase of development work between 1991 and 1994 enabled the
system to map entire prison regimes using the relational concepts
outlined above (and as illustrated in Volume 2). A system of Sentence
Management was designed whereby staff are able to continuously define
(and up to a point, dynamically refine) the regime functions they are
responsible for supervising, be these elements of wing routines or the
requirements (performance criteria) of specific inmate activities such
as education courses, periods in prison industries, special programmes
etc. Within the PROBE Sentence Management system, staff are required
to define declarative statements (predicates/regime propositional
functions/relations) or 'Attainment Criteria' which can be assessed as
being true or false of an inmate, at specific stages of programmes, on
specified dates. Just as the truth or falsehood (guilt or innocence)
of a prison rules infraction is ascertained by an expert on the prison
rules (a Governor), so too, the level of attainment an inmate has
attained is ascertained by, ideally, an accredited, expert supervisor.
This system allowed us to expand the arity of the relations available
within the PROBE relational Data Base Management System almost
infinitely without having to make physical changes to the system's
data dictionary (the schema - Volume 5). Such a criterion referencing
system can develop flexibly, with individuals being profiled with
reference to such criteria at any stage of their prison career.
Together, therefore, the predicates/relations/functions and truth
values within PROBE serve as a Knowledge Base for the production of
comprehensive inmate career profiles which are descriptive,
declarative reports of inmate behaviour relative to fixed reference
criteria. Such extensional reports have clear reference criteria and
are produced by algorithms written using the 4GL (PQL) provided within
the DBMS. The skilled work within such a system lies in the writing of
retrievals.
Furthermore, such retrievals can be written to incorporate parameters
of the population from which the inmate is drawn, such parameters
thereby serving as reference classes. PROBE routinely provides
profiles which provide information at both the individual (Section
3.2) and group (Section 3.3) levels. As the technical work is
primarily on the design and use of PQL algorithms in the management of
inmate's as a function of the classes they fall into and the
characteristics of those classes (e.g. age group and report rate),
PROBE is basically an actuarial system (Dawes, Faust and Meehl 1989,
1993), as well as an application of Artificial Intelligence research.
Risk assessment in all areas of inmate management becomes largely a
matter of ascertaining what classes an individual belongs to, and the
characteristics of such classes. Providing that all concerned
appreciate that individual assessment must always, albeit often
implicitly, be assessment relative to some class or another, and that
class membership is a dynamic function of ongoing behaviour, it
becomes clear that the PROBE technology amounts essentially to no more
than an MIS to support effective inmate management based on actuarial
rather than clinical judgment.
As outlined above in the context of quantification, Vickers (1988) and
Lukasiewicz (1909) have generalized the Fregian concept of truth
function:
'The truth value of an indefinite proposition is "...the
ratio between the number of values of the variables for which
the proposition yields true judgements and the total number
of values of the variables" (p.17). The relative
(conditional) truth value of indefinite propositions is the
quotient of the truth value of their conjunction and that of
the antecedent. Lukasiewicz then argues that these truth
values provide an adequate account of probability, free from
many of the difficulties that plague subjectivistic and
empirical views.'
J. M. Vickers (1988)
Chance and Structure:
An Essay on the Logical Foundations of Probability p.149
Statistical technology is covered little in these volumes since most
readers will have already undertaken the course which complements
these volumes. However, for the sake of what follows it is important
that the reader appreciates that we are, at least in part, following
Vickers (1988) in his treatment of Fregian quantification:
'The major motivating principle of probability quantifiers is
the development of probability within pure or general logic
to the extent that this is possible. The great difficulty of
precisely defining general logic can perhaps be avoided by
agreeing that however it is defined, the semantics of first-
order logic as developed by Frege and Tarski fall quite
within its confines. Then, as the above remarks suggest, the
question is just to what extent such notions as "the
proportion of objects falling under a concept" or "the
proportion of assignments satisfying a formula" can be given
a meaning in general logic.'
J. M. Vickers (1988)
Chance & Structure: An Essay on the Logical Foundations of
Probability
Probability quantifiers:principles and semantics p.153
David Longley
> David Longley wrote:
>
> > I've suggested a particular field of application where we can put
> > our technology to good use in the assessment and management of
> > behaviour on the basis of extensional analysis. The intelligence
> > is in the use of science and database technology (and by that I
> > mean to include artificial languages all the way up through
> > statistics). We need to replace NI with that sort of AI.
>
> Fine, then propose (or point to ... URLs please) an artificial language
> that is *up to the task* of interfacing us to this database (db).
You still haven't grasped it have you? Each science has its own
language within the web. We KNOW that natural languages (which
are not well defined) are not truth-functional (Tarski). We have
to learn language and NATURAL languages are folk psychological. I
doubt whether research in NLP will result in much - for the same
reasons that linguistics generlaly fails to acheive much - its
subject matter is too emphemeral and idiomatic.
> experience tells me that 1) if you know which db to use and 2) if you
> know what is in the db and 3) you know the inference capabilities of the
> db and 4) you have learned the specialize artificial language of that
> particular db, then you may be able to retrieve useful information from
> it. We have been doing that since the 60's and some of us are sick and
> tired of it because we now understand first hand it's limitations.
We've been doing it since the 70s maybe - and in my book not long
enough.
The
> fact of the matter is that if we are to make any headway here we need to
> switch to using natural language in a network environment - and that my
> chiggy friend is AI.
That has got to be nonsense. We can't get Natural Language to do
a job its not up to - that's why we create the artificial
languages (of science) in the first place. They are truth
functional, and are rapidly becoming international.
David Longley
In article <69oild$6...@news-central.tiac.net>
raf...@pop.tiac.net "Raffael Cavallaro" writes:
>
> This being the case, your plea that we abandon the goal of creating an
> artificial 'folk psychology' is bound to be ignored, and with very good
> reason.
>
> Raf
And, I have done no such thing. The job of modelling "folk
psychological processes" is the business of experimental
psychology. Specifically, you will find folk working in Learning
Theory doing this. I've made considerable use of the work of
Gluck and Bower in this respect and suggest you look at that
section of "Fragments". Their work is undertaken in the tradition
of the Rescorla-Wagner model, which was basically ANN work in the
early 70s before the new look re-emerged in the mid 80s.
Such work is, as psychology should be, interested in modelling
the processes whereby NI works. It's just that those processes
are prone to biases which render the processes better named as
"Natural Assessments" (Tversky & Kahneman 1974).
When it comes down to it ANNs are not very good at what we call
"intelligent" behaviour. They're pretty bad at maths and logic.
just like most people, most of the time.
There's something interesting to be learned here - and I've had a
go at pointing out what this is. If you don't know what that is,
read the first section of "Frag.htm" at the www below.
AIBrain
David Longley wrote:
[snip]
> You still haven't grasped it have you? Each science has its own
> language within the web. We KNOW that natural languages (which
> are not well defined) are not truth-functional (Tarski). We have
> to learn language and NATURAL languages are folk psychological. I
> doubt whether research in NLP will result in much - for the same
> reasons that linguistics generlaly fails to acheive much - its
> subject matter is too emphemeral and idiomatic.
So what you're saying is that we can't get water from the ocean
because it is too big? That it is spread out too far? Nonsense.
--
AIBrain Home Page -"Artificially Intelligent Brain"
http://www.geocities.com/CapeCanaveral/Lab/7677/index.html
Anti-Spam in effect. Magic words are "Rick" or "aibrain".
"Assimilation is futile. You will be resisted." -The H Factory
"What do you mean my attention sp...what was I saying?" -Rick Harker
David Longley
> David Longley wrote:
> [snip]
> > You still haven't grasped it have you? Each science has its own
> > language within the web. We KNOW that natural languages (which
> > are not well defined) are not truth-functional (Tarski). We have
> > to learn language and NATURAL languages are folk psychological. I
> > doubt whether research in NLP will result in much - for the same
> > reasons that linguistics generlaly fails to acheive much - its
> > subject matter is too emphemeral and idiomatic.
> [snip]
>
> So what you're saying is that we can't get water from the ocean
> because it is too big? That it is spread out too far? Nonsense.
>
>
'Finding right words of my own to communicate another's
saying is a problem of translation. The words I use in the
particular case may be viewed as products of my total theory
(however vague and subject to correction) of what the
originating speaker means by anything he says: such a theory
is indistinguishable from a characterization of a truth
predicate, with his language as object language and mine as
metalanguage. The crucial point is that there will be equally
acceptable alternative theories which differ in assigning
clearly non-synonymous sentences of mine as translations of
his same utterance. This is Quine's thesis of the
indeterminacy of translation.'
D. Davidson (1969)
On Saying That p.100
'Much of what is called for is to mechanize as far as
possible what we now do by art when we put ordinary English
into one or another canonical notation. The point is not that
canonical notation is better than the rough original idiom,
but rather that if we know what idiom the canonical notation
is for, we have as good a theory for the idiom as for its
kept companion.'
D. Davidson (1967)
Truth and Meaning
Did I say anything of the sort? No, my points were 1) Natural
language is largely non-truth-functional (Tarski) and 2) given
the fact that we would have to resrict usage to some well dfined
sub-set, why not recognise that we have largely done that already
in our invention of artificial languages (and even in our
development of natural language). Which remainds me of a joke..
*- A N U T T E R L Y A B S U R D L O O K A T G R A M M A R -*
.
. -By Dave Barry.
..
.
. I cannot overemphasize the importance of good grammar..
.
What a crock. I could easily overemphasize the importance of good
grammar. For example, I could say: "Bad grammar is the leading cause of
slow, painful death in North America," or "Without good grammar, the United
States would have lost World War II."
.
The truth is that grammar is not the most important thing in the world.
The Super Bowl is the most important thing in the world. But grammar is
still important. For example, suppose you are being interviewed for a job as
an airline pilot, and your prospective employer asks you if you have any
experience, and you answer: "Well, I ain't never flied no actual airplanes
or nothing, but I got several pilot-style hats and several friends who I
like to talk about airplanes with."
.
If you answer this way, the prospective employer will immediately realize
that you have ended your sentence with a preposition. (What you should have
said, of course, is "...several friends with who I like to talk about
airplanes.") So you will not get the job, because airline pilots have to use
good grammar when they get on the intercom and explain to the passengers
that, because of high winds, the plane is going to take off several hours
late and land in Pierre, South Dakota, instead of Los Angeles.
.
We did not always have grammar. In medieval England, people said whatever
they wanted, without regard to rules, and as a result they sounded like
morons. Take the poet Geoffrey Chaucer, who couldn't even spell his first
name right. He wrote a large poem called "Canterbury Tales," in which people
from various professions - knight, monk, miller, reever, riveter, eeler,
diver, stevedore, spinnaker, etc. - drone on and on like this:
.
In a somer sesun whon softe was the sunne
I kylled a younge birde ande I ate it on a bunne.
.
When Chaucer's poem was published everybody read it and said: "My God, we
need some grammar around here." So they formed a Grammar Commission, which
developed the parts of speech, the main ones being nouns, verbs, predicates,
conjunctures, particles, proverbs, adjoiners, coordinates and rebuttals.
Then the commission made up hundreds and hundreds of grammar rules, all of
which were strictly enforced.
.
When the colonists came to America, they rebelled against British grammar.
They openly used words like "ain't" and "finalize," and when they wrote the
Declaration of Independence they deliberately misspelled many words. Thanks
to their courage, today we Americans have only two rules of grammar:
.
Rule 1. The word "me" is always incorrect.
.
Most of us learn this rule as children, from our mothers. We say things
like: "Mom, can Bobby and me roll the camping trailer over Mrs.
Johnson's cat?" And our mothers say: "Remember your grammar, dear. You
mean: 'Can Bobby and I roll the camping trailer over Mrs. Johnson's
cat?' Of course you can, but be home by dinner-time."
.
The only exception to this rule is in formal business writing, where
instead of "I" you must use "the undersigned." For example, this
business letter is incorrect:
.
"Dear Hunky-Dory Canned Fruit Company: A couple of days ago my wife
bought a can of your cling peaches and served them to my mother who has
a weak heart and she damn near died when she bit into a live grub. If I
ever find out where you live, I am gonna whomp you on the head with an
ax handle."
.
This should be corrected as follows: "...If the undersigned ever finds
out where you live, I am gonna whomp you on the head with an ax handle."
.
Rule 2. You're not allowed to split infinitives.
.
An infinitive is the word "to" and whatever comes right behind it, such
as "to a tee," "to the best of my ability," "tomato," etc. Splitting an
infinitive is putting something between the "to" and the other words.
For example, this is incorrect:
.
"Hey man, you got any, you know, spare change you could give to, like, me?"
.
The correct version is:
.
"...spare change you could, like, give to me?"
.
* * *
.
The advantage of American English is that, because there are so few
rules, practically anybody can learn to speak it in just a few minutes. The
disadvantage is that Americans generally sound like jerks, whereas the
British sound really smart, especially to Americans. That's why Americans
are so fond of those British dramas they're always showing on public
television, the ones introduced by Alistair Cooke. Americans love people who
talk like Alistair Cooke. He could introduce old episodes of "Hawaii Five-O"
and Americans would think they were extremely enlightening.
.
So the trick is to use American grammar, which is simple, but talk with a
British accent, which is impressive. This technique is taught to all your
really snotty private schools, where the kids learn to sound like Elliot
Richardson. Remember Elliot? He sounded extremely British, and as a result
he got to be attorney general, secretary of state, chief justice of the
Supreme Court and vice president at the same time.
.
You can do it, too. Practice in your home, then approach someone on the
street and say: "Tally-ho, old chap. I would consider it a great honour if
you would favor me with some spare change." You're bound to get quick
results.
[ InfoWorld, March 4, 1985. Page 8.
Viewpoint, by Darryl Rubin, Contributor ]
David Longley
Claiming that folk psychology "works" is like claiming that
herbal medicine etc. work. Those with a pharmacology background
just waste their breath trying to explain what "chemicals" are
etc. - many "herbalists" are basically latter day back-to-nature
religious atavists at best - ignorant luddites more often than
not the enlightened chemists they would like to have others
believe.
This pre-occupation with "common-sense" and all things natural is
endemic within AI too - and probably for the same sort of reason.
Seth Russell
> aib...@usa.net "AIBrain" writes:
> So what you're saying is that we can't get water from the ocean
> because it is too big? That it is spread out too far? Nonsense.
Well said.
David Longley wrote:
> Did I say anything of the sort? No, my points were 1) Natural
> language is largely non-truth-functional (Tarski)
Granted, NL is ambiguous and there is a nontrivial *mapping task*
between it and unambiguous representations of truth(s). And that's not
the only problem.
> and 2) given the fact that we would have to resrict usage to some
> well dfined sub-set,
Nope, that does not follow and is not a fact. If you ask a person a
question, and he does not understand it, what is the first thing he will
do (assuming that he is motivated to answer your question) ? Ok so
that is one strategy that helps .... how about another. If you are
vague about asking a question and consequently do not get the answer you
sought, what is the next thing you (as an intelligent person) will do
? Sorry about being cute and not answering my own questions, but me
thinks people that answer those questions for themselves will appreciate
the answers more.
> Which remainds me of a joke..
LOL ... Thanks I needed that ...but it brings me to ...
> Rule 1. The word "me" is always incorrect.
How about Rule 1a: the word "I" is always incorrect.
Me thinks that readers of sentences containing "I" always shudder and
instantly flash: Oh Shit! Here we go, another ego trip. Me, I like to
scatter more "me(s)" into my meandering to avoid this :))
David Longley
In article <34C0F1D9...@clickshop.com>
> > language is largely non-truth-functional (Tarski)
>
> between it and unambiguous representations of truth(s). And that's not
> the only problem.
>
> > well dfined sub-set,
>
> question, and he does not understand it, what is the first thing he will
> do (assuming that he is motivated to answer your question) ? Ok so
> that is one strategy that helps .... how about another. If you are
> vague about asking a question and consequently do not get the answer you
> sought, what is the next thing you (as an intelligent person) will do
> ? Sorry about being cute and not answering my own questions, but me
> thinks people that answer those questions for themselves will appreciate
> the answers more.
No - the "Natural Language" promises are invariably accompanied
by caveats which betray the nature of the real problem. Face it,
the whole venture is misguided. As you have to teach someone to
use a subset of "Natural Language", why not teach them a formal
language in the first place?
Just because it's "natural" doesn't make it optimal - "natural"
processes are the product of random processes and mutations, and
are not necessarily suited to technology.
Is it really worth arguing this point?
Seth Russell
David Longley wrote:
> Is it really worth arguing this point?
I guess not. The only response that I could make would be to repeat my
points which you have not aknowledged and I see no new information in
your post. So if you don't have any glue, guess i will be falling off
...
David Longley
In article <34C10F3A...@clickshop.com>
seth...@clickshop.com "Seth Russell" writes:
> David Longley wrote:
>
> > Is it really worth arguing this point?
>
> I guess not. The only response that I could make would be to repeat my
> points which you have not aknowledged and I see no new information in
> your post. So if you don't have any glue, guess i will be falling off
> ...
>
By all means demonstrate a reliable "natural language interface"
which doesn't demand the accommodations I claim are insidiously
demanded. I've yet to see one or hear that anyone has developed
or got anywhere near to developing one.
In my view, all areas of human endeavour are advanced by
technological research and development. In all those areas we
develop artificial languages where rules are not subject to
random variation. Those languages are not just programming
languages, they're the sciences themselves.
'We think of a science as comprising those truths which are
expressible in terms of 'and', 'not', quantifiers, variables,
and certain predicates appropriate to the science in
question....To specify a science, within the described mold,
we still have to say what the predicates are to be, and what
the domain of objects is to be over which the variables of
quantification range.'
W.V.O. Quine (1954)
The Scope and Language of Science
The Ways of Paradox and other essays p.242
Seth Russell
David Longley wrote:
> In article <34C0F1D9...@clickshop.com>
> seth...@clickshop.com "Seth Russell" writes:
> >
> > David Longley wrote:
> >
> > > Did I say anything of the sort? No, my points were 1) Natural
> > > language is largely non-truth-functional (Tarski)
> >
> > Granted, NL is ambiguous and there is a nontrivial *mapping task*
> > between it and unambiguous representations of truth(s). And that's
> not
> > the only problem.
> >
> > > and 2) given the fact that we would have to resrict usage to some
>
> > > well dfined sub-set,
> >
> > Nope, that does not follow and is not a fact. If you ask a person a
>
> > question, and he does not understand it, what is the first thing he
> will
> > do (assuming that he is motivated to answer your question) ? Ok so
>
> > that is one strategy that helps .... how about another. If you are
>
> > vague about asking a question and consequently do not get the answer
> you
> > sought, what is the next thing you (as an intelligent person) will
> do
> > ? Sorry about being cute and not answering my own questions, but
> me
> > thinks people that answer those questions for themselves will
> appreciate
> > the answers more.
>
> No - the "Natural Language" promises are invariably accompanied
> by caveats which betray the nature of the real problem. Face it,
> the whole venture is misguided. As you have to teach someone to
> use a subset of "Natural Language", why not teach them a formal
> language in the first place?
>
> Just because it's "natural" doesn't make it optimal - "natural"
> processes are the product of random processes and mutations, and
> are not necessarily suited to technology.
>
> Is it really worth arguing this point?
PS: I just though of a more interesting response. Answer is still no -
not worth arguing points. But ...
It seems to me that both our viewpoints as well as Savin's could
actually be possible from this point in history. I mean there might be
three possible futures: 1) giant databases fed by an elite clan 2)
intelligent free standing robots or 3) naturally evolving network
intelligence. My conjecture (url below) just says that 3 is more
likely to happen before 2 ... it's a prediction of the sequence of
events. Sorry, Dr Longley, for me you're universe might be interesting
to visit, but not to live in. The question I propose each of us asks
ourselves is: Which of those three possible universes we choose to live
in? So if I am correct in this analysis we can all choose by our
actions starting now.
Seth Russell
David Longley wrote:
> By all means demonstrate a reliable "natural language interface"
> which doesn't demand the accommodations I claim are insidiously
> demanded. I've yet to see one or hear that anyone has developed
> or got anywhere near to developing one.
Ahhaa ... yes that's the rub. I have been looking ... the best I've
found are posted on my jump list below. And I agree, none yet measure
up ... though some are more impressive than one might think without
looking deeper. One major point is that all the big boys are working
very hard on this: Xerox Park, Stanford, CMU, IBM even Bill. This niche
is just too much in demand and too lucrative for it to remain unfilled
for much longer.
> In all those areas we
> develop artificial languages where rules are not subject to
> random variation.
Yes this too is necessary. I see NI as an interface to "artificial
language" or to a representation of mind which is computational and
which *must* have all the properties you demand (see my comments and
pointers under Semantic Knowledge on my jump list). But if you have
no human interface to this logic, then it will die from lack of interest
simply because enough people will not be able to use it.
Peter Phelan
Raffael Cavallaro <raf...@pop.tiac.net> wrote in article
<69oijv$5...@news-central.tiac.net>...
>
> David Longly wrote:
> >We have ample evidence that people's thinking is not rational
> >but most of that evidence has accumulated since the 70s, and it
> >came as a surprise. As a consequence, a lot of what was taken for
> >granted back in the 50s has to re-evaluated, and that includes
> >some of the basic goals of AI.
>
> This is certainly true. However, the point you consistently ignore is
that
> the 'folk psychology' (meaning the often irrational way people think)
that
> you continually deride is what actually gave rise to the science that you
> laud.
> This proves something you don't like admitting: That the 'folk
psychology'
> is a sufficient cause for a mind that can think scientifically.
Hmm. It's not clear exactly what 'folk psychology' is supposed to be,
apart from David Longley's favourite perjorative.
This idea about science ultimately resting upon 'folk psychology' is
interesting. There is a view that 'third person' objectivity is *really*
real, and that subjective, first person phenomenology is somehow mythical;
or less real; or unreal. But I think this has it backwards. There are
no third persons, only first ones. Somewhere along the line, the entire
scientific edifice rests upon Newton's seeing the apple fall, or
Archimedes' sensations in the bath -- not on 'an observer' -- but an
individual, with an owned viewpoint.
David Longley
In article <01bd2713$cacaeb80$35c2...@penty.anite-systems.lu>
Peter.DELET...@anite-systems.lu "Peter Phelan" writes:
It is NOT a perjorative - it's a description.
Failure understand this is a fundamental failure to understand
the nature and limits of scientific method.