P.S. To clarify and avoid understatements, as well as overstatements.
In certain respects, the following statements from the slides for the thesis describe what is true now, and are not just claims about what can be achieved in the foreseeable future:
Tala can represent unconstrained,
complex English sentences,
involving self-reference, conjecture,
and higher-level concepts – anything
that can be expressed in English,
with underspecification and
semantic annotation.
Tala sentences can describe how to
perform processes and be executable
concepts, i.e. Tala is a simple
programming language, with
variables, pattern-matching, etc.
English syntax supports complex statements involving self-reference, conjecture, and higher-level concepts. Since the syntax of Tala is based on English syntax, Tala also has the ability to syntactically represent such statements. This is true with the initial syntax design presented in Chapter 5, although developing a comprehensive English syntax is a topic for future research. It is also shown in the simulations of the demonstration system, along with the ability of Tala sentences to be executable concepts, with variables and pattern-matching.
Chapter 3 gives theoretical discussions about semantic representation and processing using English syntax within a TalaMind architecture. The demonstration system gives a very limited illustration of how semantic processing could be implemented on a computer, though fully developing TalaMind architectures to understand natural language is a topic for future research. Phil
From: philipcjacksonjr@xxxxxxxxxxx To: ontolog-forum@xxxxxxxxxxxxxxxx Date: Mon, 9 Jun 2014 14:51:53 -0400 Subject: [ontolog-forum] FW: Toward Human-Level AI
Ed, Thanks very much for your comments. I would not agree that the Tala language is "remote from anything that has been implemented on a computer or is likely to be in the foreseeable future." Rather, my thesis presents theoretical arguments intended to show it is possible in the foreseeable future, a design for the syntax of Tala, and a demonstration system to illustrate how it could be implemented on a computer, with variables, pattern-matching, etc. That said, I agree there is much more research to be done to fully implement a TalaMind architecture that could achieve human-level AI. Whether the goal is likely to be achieved, and remote from what has been achieved, may be a matter of one's perspective. I think we are closer to achieving the goal, and more likely to achieve the goal, if we take this direction. Arguably, we cannot achieve the goal by relying only on formal logic approaches: we need the ability for systems to represent and reason directly within a computer language based on the syntax of a natural language. Phil
Date: Mon, 9 Jun 2014 13:19:14 -0400 From: eslowry@xxxxxxxxxxxx To: ontolog-forum@xxxxxxxxxxxxxxxx CC: philipcjacksonjr@xxxxxxxxxxx Subject: Re: [ontolog-forum] Toward Human-Level AI
Phil Jackson describes the Tala language as follows:
*********
Tala can represent unconstrained,
complex English sentences,
involving self-reference, conjecture,
and higher-level concepts – anything
that can be expressed in English,
with underspecification and
semantic annotation.
Tala sentences can describe how to
perform processes and be executable
concepts, i.e. Tala is a simple
programming language, with
variables, pattern-matching, etc.
*******
A language like that seems remote from anything that has been
implemented
on a computer or is likely to be in the foreseeable future.
I would value translations of the example below into realistic
computer executable
language. It would help illustrate both the need for improved
computer language
and the practical possibility producing substantial improvements.
Ed Lowry
On 6/9/2014 11:49 AM, Philip Jackson wrote:
Ed,
Of course, in Tala the question can be stated directly
corresponding to:
How many states
have cities with more than a million people?
My arguments are that we need Tala as a new artificial language.
That said, I agree that the methods Kowalski describes could
play an important role in a TalaMind architecture.
Phil
Thesis: http://www.philjackson.prohosting.com/PCJacksonPhDThesis20140422.pdf
Slides: http://www.philjackson.prohosting.com/TowardHumanLevelAI20140422.pdf
(links included for readers who may be new to the thread.)
Date: Mon, 9 Jun 2014 11:14:55 -0400
From: eslowry@xxxxxxxxxxxx
To: ontolog-forum@xxxxxxxxxxxxxxxx
CC: john@xxxxxxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] Toward Human-Level AI
John and
John
John Bottoms writes:
> Tell me again why we need a new computer language?
> I would rather vote to kill off some of the languages
we already have.
We need a new programming language because the currently
used languages
(including Prolog) are deficient on 7 simplicity-related
leading edges compared
with a design circulated at IBM in 1973. See "Inexcusable
Complexity for 40 years"
on my web site. Killing off some current languages would
ALSO be a good idea.
If you don't think we need a new design that allows for
greater simplicity, please
translate
6 = count every state where populatn of some city of
it > 1000000
into your favorite language and tell us why anyone should
allow their brain to be
burdened with such irrelevant complexity. I expect those 14
tokens will require over 35
in the best current languages. The above capability was
running at DEC in 1982.
Simplicity of _expression_ is important for advanced AI
because that requires
working with information about information and the
extraneous complexities
get compounded.
It is possible to design building blocks of information
which are
well designed to be easily arranged. Fundamentals are
being ignored.
John Sowa writes:
> Anybody who has
> never written at least a few exercises with Prolog or other logic
> programming (LP) language is not qualified to express an opinion
> on this topic.
Something similar could be said about the many people who
are 40 years behind on
those 7 leading edges.
To John Bottoms: I live in the next town and would be glad
to discuss these issues further.
Ed Lowry
Bedford Mass
781 276 4098
eslowry@xxxxxxxxxxxx
http://users.rcn.com/eslowry
**********************************
On 6/8/2014 10:13 PM, John Bottoms wrote:
On 6/8/2014 3:25 PM, John F
Sowa wrote:
I came across a book by Bob Kowalski, who makes a strong argument
for model building as a basis for intelligent systems. The ideas
are further developments of his work on logic-programming systems.
Kowalski, Robert (2011) Computational Logic and Human Thinking: How
to be Artificially Intelligent, Cambridge: Cambridge University Press.
John,
There have been a number of people who have put forward
that concept. It seems to be a common basis for the
implementation myth. AKA: if you only did this then you
would have AI. This was one of the leading contributors to
the 5th Generation AI failure. The designers felt that if
you implemented logic then you would have a smart machine.
It is a variant of the Philosopher's Disease: if you
implement all the words in a dictionary, then you will
have a smart machine.
Certainly there is a need for modeling, and likewise,
there are roadblocks to doing that right now. We don't
know what type of KR to use, we don't understand how the
models should be structured, and we don't know the details
of extensibility.
There is no dearth of programming languages. We now have
as many computer languages as we do natural languages.
That is because they are increasingly easy to construct
with tools provided for just that purpose. Also, there are
efficiency justifications for creating new languages. In
fact, my natural language is not particularly efficient.
The estimates are that English is about 50%
redundant...for very good reasons. Tell me again why we
need a new computer language? I would rather vote to kill
off some of the languages we already have.
Why do you think this work is important? Or, why is this
approach more important than any other type of solution?
Does it move us forward an order of magnitude in any way?
-John Bottoms
FirstStar Systems
Concord, MA USA
You can buy it for $44.10 or download an earlier version for free:
http://www.doc.ic.ac.uk/~rak/papers/LPS%20revision.pdf
The papers cited below go into more detail about LP methods for
deriving imperative procedures from declarative statements.
Logic Programming
http://www.doc.ic.ac.uk/~rak/papers/History.pdf
Completeness of a Reactive System Language
http://www.doc.ic.ac.uk/~rak/papers/KELPS%20Completeness.pdf
Reactive Computing as Model Generation
http://www.doc.ic.ac.uk/~rak/papers/LPS%20revision.pdf
See below for excerpts from each of these sources.
John
_________________________________________________________________________
From the book:
Although the applications of Computational Logic in AI require the
use of mathematical notation, its human applications do not. As a
consequence, I have written the main part of this book informally,
to reach as wide an audience as possible. Because human thinking
is also the subject of study in many other fields, I have drawn upon
related studies in Cognitive Psychology, Linguistics, Philosophy,
Law, Management Science and English Composition.
In fact, the variant of Computational logic presented in this book
builds not only upon developments of logic in AI, but also upon many
other complementary and competing knowledge representation and problem
solving paradigms. In particular, it incorporates procedural representa-
tions of knowledge from AI and Computing, production systems from AI
and Cognitive Science, and decision analysis from Management Science,
Cognitive Psychology and Philosophy.
From the article, History.pdf
The driving force behind logic programming is the idea that a single
formalism suffices for both logic and computation, and that logic
subsumes computation.
From the article, KELP Completeness.pdf
KELPS is a first-order, sorted language, including a special sort
for time. In the version of KELPS presented in this paper, we assume
that time is linear and discrete, and that the succession of time
points is represented by the ticks of a logical clock.
From the article, LPS revisions.pdf
In this paper we propose a logic-based framework inspired by artificial
intelligence, but scaled down for practical database and programming
applications. Computation in the framework is viewed as the task of
generating a sequence of state transitions, with the purpose of making
an agent’s goals all true.
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