What is Needed to Teach a Computer to Read?

[Peter Olcott]

How can an ontology be automatically populated?
If we are to design an ontology that knows how to learn where do we start?
What is the minimum meta-knowledge required to know how to learn?

How do we find this meta-knowledge, where do we look?
How could we represent this meta-knowledge within the ontology?

[DKleinecke]

Now you have your research goals defined. Go to it.

Let's us know when you get some results.

[Dissitra]

naaaa!...wiv tried that a decade ago...but if you really really want
to do something new or interesting in that sphere?

....naaa.. on second thoughts.....forget that... somebody will call
you mad instead!

[Peter Olcott]

I posted these questions as the starting point of the process of
reverse-engineering their answers.

[Peter Olcott]

One element of the meta knowledge that the ontology must represent is a
knowledge of {how to learn syntax}.
How would syntax be specified? (different types of connections between
nodes in a di-graph?)
How would it learn new syntax? (natural language interface?)

[Franz Gnaedinger]

You have a long way to go, five times around the Milky Way,
so you better take a Harley than a bike ... How about a
realistic project for the breaks on your long long long long
long journey? For example you might scan books or the web
for sentences containing the word time, gather each 'time',
plus the four preceeding and the four succeeding words,
like so (from the first chapter of a book by Alexander
MacCall Smith):

a long time ago, of the father walking
had not had the time to say it, Yes
minds most of the time. They say that men
all at the same time. You do not need
everything at the same time, in the same place
garage at his usual time, which was five minutes
had with underneath. Some time later that day, Mr

In the above samples, the most frequent word in the
vicinity of 'time', apart from articles, is the word 'same'.
If that word should occur most frequently in a million
samples, one could assume that time is a relativistic
term, having to do with synchronicity ... I would like
to know the most frequent word going along with time.

[Peter T. Daniels]

That is in fact how the series of "frequency dictionaries" published
by Routledge are arranged. For the 5000 most common words in English
(the one I have), the most frequent collocation-words, before and
after, are listed in frequency order.

[Dissitra]

it seems like an expense to create a machine like that...how are you
funded?

[Peter Olcott]

I am doing this as a hobby, with the wish of being able to convert this
into some kind of employment, eventually.

I have been fascinated with the idea of machine comprehension of natural
language ever since I first read the Heinlein book: "The Moon is a Harsh
Mistress" (about thirty-five years ago).

It seems that the *only* thing required for machine comprehension of
natural language would be an ontology that is sufficiently populated
with knowledge organized in such a way as to facilitate reasoning.

If a sharp focus is placed on the subset of this knowledge required to
automate the process of populating the ontology, this goal may be
achieved in minimal time.

I postulate that it is possible to reverse-engineer the design of this
desired functional result, moving progressively inward towards
increasing specificity on the basis of the broad goal.

{Categorically Exhaustive Reasoning} divides up the solution space into
broad categories, and eliminates whole categories of infeasible or sub
optimal potential solutions, thereby reducing the set of alternatives
that require further investigation.

[Mok-Kong Shen]

Apparently the researchers don't yet fully know what is needed.
BTW, there is a group comp.ai.nat-lang.

M. K. Shen

[casey]

On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
> Apparently the researchers don't yet fully know what is needed.

A program that learns the way we do.

[Peter Olcott]

I will add them to the list of cross-postings.

Here is a recap of the prior points:

If we are to design an ontology that knows how to learn where do we start?

We would have to start with the meta-knowledge of {how to learn
syntax}.** We would aim to encode this meta-knowledge directly within
the ontology. It would be encoded within the ontology such this
knowledge within the ontology can control the process of {learning syntax}.

Some representation of syntax would also be required. This
{representation of syntax} would be entirely encoded as data, such that
changes to the data will change the grammar.

** Why focus on syntax first?
Syntax and logical entailment would seem to form all of the connections
between every language element therefore form the basis of all
Compositionality.

[Peter Olcott]

We would have two different kinds of syntax:
1) Natural Language syntax would be required to process ordinary text.
2) The (much simpler) syntax of the internal semantic representation
must be specified.
A mapping from 1) to 2) would also be required.

[Peter Olcott]

On 9/1/2012 5:06 PM, casey wrote:
> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
>> Apparently the researchers don't yet fully know what is needed.
> A program that learns the way we do.
>

The key aspect of {learning the way we do} is knowing enough about
natural language to be able to populate an ontology from reading
ordinary text. We do not need a fully populated ontology for this, we
only need a BootStrap degree of natural language understanding. Since
all of knowledge is constructed on the basis of connections between
language elements (Linguistic Principle of Compositionality) we should
start with a way for the ontology to learn the structure of these
connections.

It seems to me that the set of these connections can be exhaustively
divided into two categories:
1) Syntax
2) Logical Entailment

http://plato.stanford.edu/entries/compositionality/
If we define Compositionality to include every source of meaning
{lexical, contextual} (excluding speaker intent) and we define syntax
broadly to include every connection between elements of language besides
logical entailment, then (at least) most of the arguments against
Compositionality would cease to exist.

[Burkart Venzke]

Am 02.09.2012 00:06, schrieb casey:
> On Sep 2, 7:55 am, Mok-Kong Shen<mok-kong.s...@t-online.de> wrote:
>> Apparently the researchers don't yet fully know what is needed.
>
> A program that learns the way we do.

That could have been my answer, too.

[Burkart Venzke]

Am 02.09.2012 15:26, schrieb Peter Olcott:
> On 9/1/2012 5:06 PM, casey wrote:
>> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
>>> Apparently the researchers don't yet fully know what is needed.
>> A program that learns the way we do.
>>
> The key aspect of {learning the way we do} is knowing enough about
> natural language to be able to populate an ontology from reading
> ordinary text. We do not need a fully populated ontology for this, we
> only need a BootStrap degree of natural language understanding.

Only something for a boot strap for natural language understanding, ah,
if this is all... ;)

I think it is not good possible to learn natural language without
understanding (or better: learning) something about the world, too.

> Since
> all of knowledge is constructed on the basis of connections between
> language elements (Linguistic Principle of Compositionality) we should
> start with a way for the ontology to learn the structure of these
> connections.

What are the atoms of this language structure? Words without any
understanding of them? Good luck... it will be never perfect (relatively
to our human language understanding).

Burkart

[casey]

On Sep 2, 11:26 pm, Peter Olcott <OCR4Screen> wrote:
> On 9/1/2012 5:06 PM, casey wrote:> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
> >> Apparently the researchers don't yet fully know what is needed.
> > A program that learns the way we do.
>
> The key aspect of {learning the way we do} is
> knowing enough about natural language to be
> able to populate an ontology from reading
> ordinary text.

We do not simply learn by reading ordinary text.

Our concepts first must be extracted from examples
before they can be codified and used to understand
new things via language, written or spoken, and
that means understanding how those concepts are
formed, represented and used in the brain.

A computer program may learn via reading:

"Jim has brown hair and blue eyes."
"Jill had red hair and brown eyes."

And we may ask a question.

"Does Jill have blue eyes?"

A program can decode the sentence and reply.

"No, Jill does not have blue eyes."

Programs can search and do logic on information in
a data base *but it is not knowing as we know*.

We may give it category knowledge.
Jill is a person.
Blue is a color.
Hair is part of a human.

But we learn those things via our senses using
real examples, not sentences, and it is not
stored in lists but as a rich interconnection
with everything else we know in a way we do
not yet understand.


jc

[Peter Olcott]

On 9/2/2012 4:13 PM, Burkart Venzke wrote:
> Am 02.09.2012 15:26, schrieb Peter Olcott:
>> On 9/1/2012 5:06 PM, casey wrote:
>>> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
>>>> Apparently the researchers don't yet fully know what is needed.
>>> A program that learns the way we do.
>>>
>> The key aspect of {learning the way we do} is knowing enough about
>> natural language to be able to populate an ontology from reading
>> ordinary text. We do not need a fully populated ontology for this, we
>> only need a BootStrap degree of natural language understanding.
>
> Only something for a boot strap for natural language understanding,
> ah, if this is all... ;)

This is really all there is to it, yet to make BootStrap Natural
Language is a difficult problem.

>
> I think it is not good possible to learn natural language without
> understanding (or better: learning) something about the world, too.

Since we can learn about the world by reading about the world, a machine
can too, iff (if and only if) is has the minimum prerequisite
meta-knowledge. The exact nature of this missing meta-knowledge can be
reverse-engineered.

>
>> Since
>> all of knowledge is constructed on the basis of connections between
>> language elements (Linguistic Principle of Compositionality) we should
>> start with a way for the ontology to learn the structure of these
>> connections.
>
> What are the atoms of this language structure? Words without any
> understanding of them? Good luck... it will be never perfect
> (relatively to our human language understanding).
>

There will eventually be a meaning postulate for every piece of human
knowledge. It would seem that the only {atoms} of this language
(everything is essentially defined in terms of everything else) would be
the hierarchy of connections between {units of meaning}. Everything is
either a relation or a property of something else. Even the concept of
existence itself is broken down into its properties and relations.

> Burkart
>

[Peter Olcott]

On 9/2/2012 4:46 PM, casey wrote:
> On Sep 2, 11:26 pm, Peter Olcott <OCR4Screen> wrote:
>> On 9/1/2012 5:06 PM, casey wrote:> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
>>>> Apparently the researchers don't yet fully know what is needed.
>>> A program that learns the way we do.
>> The key aspect of {learning the way we do} is
>> knowing enough about natural language to be
>> able to populate an ontology from reading
>> ordinary text.
> We do not simply learn by reading ordinary text.
>
> Our concepts first must be extracted from examples
> before they can be codified and used to understand
> new things via language, written or spoken, and
> that means understanding how those concepts are
> formed, represented and used in the brain.

Which is essentially the process of encoding meaning postulates within
an ontology.

> A computer program may learn via reading:
>
> "Jim has brown hair and blue eyes."
> "Jill had red hair and brown eyes."
>
> And we may ask a question.
>
> "Does Jill have blue eyes?"
>
> A program can decode the sentence and reply.
>
> "No, Jill does not have blue eyes."
>
> Programs can search and do logic on information in
> a data base *but it is not knowing as we know*.
>
> We may give it category knowledge.
> Jill is a person.
> Blue is a color.
> Hair is part of a human.
>
> But we learn those things via our senses using
> real examples, not sentences, and it is not
> stored in lists but as a rich interconnection
> with everything else we know in a way we do
> not yet understand.

Right that is the key missing piece, we must understand exactly how we
encode the {rich interconnection with everything else}. That is the part
that I have begun to work out here in this forum. The answer to this can
be reverse-engineered.


>
> jc

[Mok-Kong Shen]

Am 03.09.2012 04:38, schrieb Peter Olcott:

> Since we can learn about the world by reading about the world, a machine
> can too, iff (if and only if) is has the minimum prerequisite
> meta-knowledge. The exact nature of this missing meta-knowledge can be
> reverse-engineered.

As layman I surmise that a machine couldn't have the concept of
meta-knowldege to begin with.

M. K. Shen

[Peter Olcott]

Of course not, a machine lacking an ontology (when measured against its
comprehension of the meaning of words from natural language) begins with
an effective IQ of zero . Some BootStrap meta-knowledge would have to
be created manually. If we focus on finding this minimal set of
meta-knowledge then it will be much easier to find than if we are not
focusing on finding this set.

The *only* thing that is required within the BootStrap ontology is the
meta-knowledge of how to learn more knowledge (including learning more
meta-knowledge).

[Franz Gnaedinger]

On Sep 1, 1:24 pm, "Peter T. Daniels" <gramma...@verizon.net> wrote:
>
> That is in fact how the series of "frequency dictionaries" published
> by Routledge are arranged. For the 5000 most common words in English
> (the one I have), the most frequent collocation-words, before and
> after, are listed in frequency order.

Why can't you make your messages more informative?
You got a frequency dictionary, so why didn't you tell me
what word or words appear most often in the vicinity of time?

I half guessed that such a program might already exist,
however, it could be further developed. Classical
computers are good at such things. Reading is the
same as talking and writing, in reverse order, but also
achieved by cooperation, by scanning a spoken or
written text for various patterns. What words appear
together? For example snake bush says there is a snake
hiding in the bush, keep away. Then there are the peaks
in the Rupert Ruhstaller tension diagram that point out
the words of the highest importance, quickly informing
a human reader about the gist of what is being said.
I am convinced that the human mind scans language
along different patterns, each model simple, strong
when they work together, cooperate. As a computer
crack I would invest my time in developing one such
model. When there are a couple of different scanning
models available, then they may be combined.
Such an approach is far better than an impossible
dream.

[Peter T. Daniels]

On Sep 3, 9:48 am, Franz Gnaedinger <f...@bluemail.ch> wrote:
> On Sep 1, 1:24 pm, "Peter T. Daniels" <gramma...@verizon.net> wrote:
>
>
>
> > That is in fact how the series of "frequency dictionaries" published
> > by Routledge are arranged. For the 5000 most common words in English
> > (the one I have), the most frequent collocation-words, before and
> > after, are listed in frequency order.
>
> Why can't you make your messages more informative?
> You got a frequency dictionary, so why didn't you tell me
> what word or words appear most often in the vicinity of time?

Maybe if you had asked nicely, I would have.

[Adam Funk]

On 2012-09-01, Franz Gnaedinger wrote:

> You have a long way to go, five times around the Milky Way,
> so you better take a Harley than a bike ... How about a
> realistic project for the breaks on your long long long long
> long journey? For example you might scan books or the web
> for sentences containing the word time, gather each 'time',
> plus the four preceeding and the four succeeding words,
> like so (from the first chapter of a book by Alexander
> MacCall Smith):
>
> a long time ago, of the father walking
> had not had the time to say it, Yes
> minds most of the time. They say that men
> all at the same time. You do not need
> everything at the same time, in the same place
> garage at his usual time, which was five minutes
> had with underneath. Some time later that day, Mr
>
> In the above samples, the most frequent word in the
> vicinity of 'time', apart from articles, is the word 'same'.
> If that word should occur most frequently in a million
> samples, one could assume that time is a relativistic
> term, having to do with synchronicity ... I would like
> to know the most frequent word going along with time.

It sounds like you want a searchable corpus with "KWIC" (keyword in
context) output.


--
A lot of people never use their intiative because no-one
told them to. --- Banksy

[António Marques]

Is it workable, though? For instance, an empty Word document doesn't start
off with 0 bytes plus Word's knowledge of hoW to expand that. It starts with
some *data* structures already in place, a template if you will. This is
actually very common. IOW, the software doesn't know how to generate its
data from scratch, though it knows how to use it from a certain core, so the
'how to' is not the only part needed, there's also a little bit of bootstrap
*data* needed. In a way like that cake or whatever that they have in England
where they use a little bit of the previous year's cake.

[Peter Olcott]

This seems to be saying the same thing that I said using different words.
I envision the system that comprehends the meaning of words to be data
driven on the basis of encoded {Meaning Postulates} .

[Mok-Kong Shen]

Am 03.09.2012 15:02, schrieb Peter Olcott:

> Of course not, a machine lacking an ontology (when measured against its
> comprehension of the meaning of words from natural language) begins with
> an effective IQ of zero . Some BootStrap meta-knowledge would have to
> be created manually. If we focus on finding this minimal set of
> meta-knowledge then it will be much easier to find than if we are not
> focusing on finding this set.

Sorry for a layman's view: Wouldn't the machine that should "understand"
that minimal meta-knowlege need a second level minimal meta-knowledge,
and so forth and so forth?

M. K. Shen

[Burkart Venzke]

Am 03.09.2012 04:38, schrieb Peter Olcott:

> On 9/2/2012 4:13 PM, Burkart Venzke wrote:
>> Am 02.09.2012 15:26, schrieb Peter Olcott:
>>> On 9/1/2012 5:06 PM, casey wrote:
>>>> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
>>>>> Apparently the researchers don't yet fully know what is needed.
>>>> A program that learns the way we do.
>>>>
>>> The key aspect of {learning the way we do} is knowing enough about
>>> natural language to be able to populate an ontology from reading
>>> ordinary text. We do not need a fully populated ontology for this, we
>>> only need a BootStrap degree of natural language understanding.
>>
>> Only something for a boot strap for natural language understanding,
>> ah, if this is all... ;)
> This is really all there is to it, yet to make BootStrap Natural
> Language is a difficult problem.
>>
>> I think it is not good possible to learn natural language without
>> understanding (or better: learning) something about the world, too.
> Since we can learn about the world by reading about the world,

Who is "we"? Little children? Aborigines? Most of the people 500 years ago?
Individually, children first have to learn to read and to understand,
this is not innate. And we have been living of thousands of years
without the ability of reading, it cannot be trivial.

> a machine
> can too, iff (if and only if) is has the minimum prerequisite
> meta-knowledge.

Theoretically, you are right...

> The exact nature of this missing meta-knowledge can be
> reverse-engineered.

...but how do you want to manage this reverse-engineering?

>>> Since
>>> all of knowledge is constructed on the basis of connections between
>>> language elements (Linguistic Principle of Compositionality) we should
>>> start with a way for the ontology to learn the structure of these
>>> connections.
>>
>> What are the atoms of this language structure? Words without any
>> understanding of them? Good luck... it will be never perfect
>> (relatively to our human language understanding).
>>
> There will eventually be a meaning postulate for every piece of human
> knowledge. It would seem that the only {atoms} of this language
> (everything is essentially defined in terms of everything else) would be
> the hierarchy of connections between {units of meaning}. Everything is
> either a relation or a property of something else. Even the concept of
> existence itself is broken down into its properties and relations.

What are your first, foundamental knowledge items? Only "empty"
words/notions?
How can an AI (machine) know or comprehend something?

Burkart

[Burkart Venzke]

Nice idea but it is not mandatory if the machine can understand (and
learn later) something about the world without (further) meta-knowledge.
Basics might be a kernel of comprehension, some senses and communication
abilities, some inate aims to reach (or something like ethical values)...

Burkart

[Burkart Venzke]

What kind of "Meaning Postulates"? How shall they work? Are they fix or
can they be expanded by learning?

Burkart

[Peter Olcott]

On 9/3/2012 4:51 PM, Burkart Venzke wrote:
> Am 03.09.2012 04:38, schrieb Peter Olcott:
>> On 9/2/2012 4:13 PM, Burkart Venzke wrote:
>>> Am 02.09.2012 15:26, schrieb Peter Olcott:
>>>> On 9/1/2012 5:06 PM, casey wrote:
>>>>> On Sep 2, 7:55 am, Mok-Kong Shen <mok-kong.s...@t-online.de> wrote:
>>>>>> Apparently the researchers don't yet fully know what is needed.
>>>>> A program that learns the way we do.
>>>>>
>>>> The key aspect of {learning the way we do} is knowing enough about
>>>> natural language to be able to populate an ontology from reading
>>>> ordinary text. We do not need a fully populated ontology for this, we
>>>> only need a BootStrap degree of natural language understanding.
>>>
>>> Only something for a boot strap for natural language understanding,
>>> ah, if this is all... ;)
>> This is really all there is to it, yet to make BootStrap Natural
>> Language is a difficult problem.
>>>
>>> I think it is not good possible to learn natural language without
>>> understanding (or better: learning) something about the world, too.
>> Since we can learn about the world by reading about the world,
>
> Who is "we"? Little children? Aborigines? Most of the people 500 years
> ago?
> Individually, children first have to learn to read and to understand,
> this is not innate. And we have been living of thousands of years
> without the ability of reading, it cannot be trivial.

It is not trivial, yet all humans have this capability.

>
>> a machine
>> can too, iff (if and only if) is has the minimum prerequisite
>> meta-knowledge.
>
> Theoretically, you are right...
>
>> The exact nature of this missing meta-knowledge can be
>> reverse-engineered.
>
> ...but how do you want to manage this reverse-engineering?

Proceeding from the broad goal (machine comprehension of natural
language), and work backwards to what this requires.

>
>>>> Since
>>>> all of knowledge is constructed on the basis of connections between
>>>> language elements (Linguistic Principle of Compositionality) we should
>>>> start with a way for the ontology to learn the structure of these
>>>> connections.
>>>
>>> What are the atoms of this language structure? Words without any
>>> understanding of them? Good luck... it will be never perfect
>>> (relatively to our human language understanding).
>>>
>> There will eventually be a meaning postulate for every piece of human
>> knowledge. It would seem that the only {atoms} of this language
>> (everything is essentially defined in terms of everything else) would be
>> the hierarchy of connections between {units of meaning}. Everything is
>> either a relation or a property of something else. Even the concept of
>> existence itself is broken down into its properties and relations.
>
> What are your first, foundamental knowledge items? Only "empty"
> words/notions?
> How can an AI (machine) know or comprehend something?

The foundational knowledge items are the (differing types of)

connections between {units of meaning}.

A Machine can comprehend anything on the basis of the {Meaning
Postulates} within its ontology.

>
> Burkart

[Peter Olcott]

I envision something like Predicate Logic as the basis for the
specification of {Meaning Postulates}.

I would venture to guess that the {Meaning Postulates} themselves may
not change whereas learning would mostly be the case of adding more
{Meaning Postulates}.

[DKleinecke]

On Sep 3, 4:41 pm, Peter Olcott <OCR4Screen> wrote:
> On 9/3/2012 4:51 PM, Burkart Venzke wrote:

> > Individually, children first have to learn to read and to understand,
> > this is not innate. And we have been living of thousands of years
> > without the ability of reading, it cannot be trivial.
>
> It is not trivial, yet all humans have this capability.

Sadly that is not true.

There are people who do not read and there are people who do not talk.
We cope with their existence by called them handicapped and thereafter
ignoring their existence. I think this population might be very
insightful to work with - and maybe someone has - but I know of no
linguistic study of mute people (the ones I have met seem to
understand but not respond) or people with poor speech skills.

[Peter Olcott]

On 9/3/2012 8:44 PM, DKleinecke wrote:
> On Sep 3, 4:41 pm, Peter Olcott <OCR4Screen> wrote:
>> On 9/3/2012 4:51 PM, Burkart Venzke wrote:
>>> Individually, children first have to learn to read and to understand,
>>> this is not innate. And we have been living of thousands of years
>>> without the ability of reading, it cannot be trivial.
>> It is not trivial, yet all humans have this capability.
> Sadly that is not true.

All humans normal humans have the innate ability to learn to read.

[Peter T. Daniels]

On Sep 3, 9:49 pm, Peter Olcott <OCR4Screen> wrote:
> On 9/3/2012 8:44 PM, DKleinecke wrote:> On Sep 3, 4:41 pm, Peter Olcott <OCR4Screen> wrote:
> >> On 9/3/2012 4:51 PM, Burkart Venzke wrote:
> >>> Individually, children first have to learn to read and to understand,
> >>> this is not innate. And we have been living of thousands of years
> >>> without the ability of reading, it cannot be trivial.
> >> It is not trivial, yet all humans have this capability.
> > Sadly that is not true.
>
> All humans normal humans have the innate ability to learn to read.

Some people can only learn to read a very little bit. There are many
kinds of reading disorders ("dyslexia" per se is not a single
syndrome, just as there's no one thing that is "cancer"), and some are
apparently insuperable.

Reading is unlike speaking, however, in that there does not seem to be
any "critical period" for learning to read as there is for acquiring
language -- as far as the psychologists can tell me, people who learn
to read as adults do not show deficiences by comparison with those who
learned to read as children.

[Franz Gnaedinger]

On Sep 3, 4:59 pm, "Peter T. Daniels" <gramma...@verizon.net> wrote:
>
> Maybe if you had asked nicely, I would have.

I wrote a reply to Peter Olcott, and you chimed in with one
of your half-informative messages.

Goethe called reading a highly demanding art. How
can a machine that works along the lienar principle
of the universal Touring machine replace the web logic
of a neural network, the brain, consisting of one hundred
billion neurons, each connected with one thousand others,
which equals, mathematically, a space of twelve dimensions?
Peter Olcott says he solved some problems. I advise him
right from the begin to find an application for his solutions,
a modest machine or program that works. But he clings
to his impossible dream, forever talking on the meta-level.
A system can work fine on the meta-level, but the test,
and often the crash, comes when a system is applied to
the real world. Peter Olcott never speaks of actual language,
and that makes me skeptical.

[Mok-Kong Shen]

I notice however your "if". A "kernel of comprehension" has a meaning
for a subject that "can" somehow "comprehend" in the first place,
isn't?

M. K. Shen

[Curt Welch]

Franz Gnaedinger <fr...@bluemail.ch> wrote:

> On Sep 3, 4:59=A0pm, "Peter T. Daniels" <gramma...@verizon.net> wrote:
> >
> > Maybe if you had asked nicely, I would have.
>
> I wrote a reply to Peter Olcott, and you chimed in with one
> of your half-informative messages.
>
> Goethe called reading a highly demanding art. How
> can a machine that works along the lienar principle
> of the universal Touring machine replace the web logic
> of a neural network, the brain, consisting of one hundred
> billion neurons, each connected with one thousand others,
> which equals, mathematically, a space of twelve dimensions?

I take it you are not a programmer?

This is done all the time in software.

It's not a question of whether it's possible, it's only a question of how
much total computation power is needed. Nobody knows the real answer to
that, but there are plenty of good educated guesses to pick from. At this
point in time, most people believe we have enough processing power
available if someone knew what to build, and had the money to do it.

Neurons are very slow information processors which fire only a few times
per second on average. A high speed CPU has no problem emulating millions
of them in real time so you buy a few million CPUs if that is really
needed, and that gives you the same sort of power as 100's of billions of
neurons with trillions of synapses.

Most likely however, we will find better ways to structure the solution
when the machine is built out of transistors instead of neurons, and it
won't need to be anywhere near that size to duplicate the information
processing function of the brain that gives us our ability to do everything
we do, including reading and talking.

> Peter Olcott says he solved some problems. I advise him
> right from the begin to find an application for his solutions,
> a modest machine or program that works. But he clings
> to his impossible dream, forever talking on the meta-level.
> A system can work fine on the meta-level, but the test,
> and often the crash, comes when a system is applied to
> the real world. Peter Olcott never speaks of actual language,
> and that makes me skeptical.

Yeah, it has always struck me that the people who focus on language tend to
get themselves a bit lost in that they fail to answer the most important
question, which is why read or talk at all?

Before you solve the "language" problem, you must solve the problem of what
the purpose of the machine is. "to read and understand" is not a valid
answer to that question. Watson at least has a valid well defined goal of
being able to answer Jeopardy style questions correctly. But that is not
at all human-like. It's no more human like than writing a program to play
chess.

To understand human language use, we have to go beyond the language issue
to understand what language is, and why we use it.

Language is just one many behaviors we use to help us survive. How does
the brain decide between picking up a rock and throwing, vs speaking a few
words? Language does not exist in isolation. It's just one of many
optional behaviors the brain has to pick from in order to reach it's goals.
You can't make a machine talk like a human, unless you have also, at the
same time, solved the big problem of AGI. You have to solve the big
problem of how the brain makes all its behavior decisions.

Making moves in a chess game, is a highly simplified subset of the bigger
problem of making "intelligent" action selections. Answering Jeopardy
questions, is also a fairly simplified subset of the bigger problem of
making intelligent action decisions.

But true human language use, is not a simplified subset of the big problem.
It's identical to the big problem of solving AGI. You can't solve the full
language problem, without solving the full problem of general intelligence.

And if you want to solve the general problem of intelligent, it's a mistake
to focus only on our language use because it tends to put blinders on us
caused by our pre-conceived notions about language (which are often wrong).

For example, when we write a book, we might think the purpose is to
communicate knowledge. And it's not wrong to think that, but it's highly
narrow minded to think that way and ignores the important larger pictures.

We don't write books to just communicate knowledge. We write books so we
can put food on our table. We write books so we can protect ourselves from
future pain. We read books, and gain knowledge, so we can increase the
odds of creating less pain and more pleasure in our future. We use
language as a tool for manipulating our environment, just like we use a
hammer, and saw, as a tool to manipulate our environment. We use language
to try and manipulate the behaviors of other humans. We certainly see that
in full swing in a political season like we have now in the US.

If you want to make a machine that uses language like humans do, you can
not ignore the larger questions of how the brain makes all its action
decisions.

If we pick a simple goal, like they did for Watson, then we have not
duplicated full human language use, any more than building a chess playing
machine, can be seen as duplicating human-level intelligence.

If we really want to understand language, we should stop studying language,
and look at full human behavior instead. No one will be able to understand
language usage, until we solve the big problem, of full general human
intelligent behavior.

If you don't want to work on that, then you are not really working on true
language at all.

--
Curt Welch http://CurtWelch.Com/
cu...@kcwc.com http://NewsReader.Com/

[Peter T. Daniels]

On Sep 4, 12:47 pm, c...@kcwc.com (Curt Welch) wrote:

[An excellent discussion overall, but since you mention it, and
there's apparently an audience of computer people ...]

> Before you solve the "language" problem, you must solve the problem of what
> the purpose of the machine is. "to read and understand" is not a valid
> answer to that question.  Watson at least has a valid well defined goal of
> being able to answer Jeopardy style questions correctly.  But that is not
> at all human-like.  It's no more human like than writing a program to play
> chess.

Watson won only because it cheated. Its reaction time was set to be
slightly quicker than that of the two human contestants; if they had
been able to ring in first, they would have been able to question
considerably more answers correctly than Watson did.

And Ken Jennings in fact had revealed to TV Guide that his main
winning strategy was to ring in even before he had retrieved the
correct question -- thus shutting out the opponents who, naturally
enough, wanted to be sure before they committed themselves to possibly
losing the value of the question.

Has this flaw been recognized by the IBM team?

[Curt Welch]

"Peter T. Daniels" <gram...@verizon.net> wrote:

> On Sep 4, 12:47=A0pm, c...@kcwc.com (Curt Welch) wrote:
>
> [An excellent discussion overall, but since you mention it, and
> there's apparently an audience of computer people ...]
>
> > Before you solve the "language" problem, you must solve the problem of

> > wh=

> at
> > the purpose of the machine is. "to read and understand" is not a valid

> > answer to that question. =A0Watson at least has a valid well defined
> > goal=
> of
> > being able to answer Jeopardy style questions correctly. =A0But that is
> > n=
> ot
> > at all human-like. =A0It's no more human like than writing a program to
> > p=

> lay
> > chess.
>
> Watson won only because it cheated. Its reaction time was set to be
> slightly quicker than that of the two human contestants; if they had
> been able to ring in first, they would have been able to question
> considerably more answers correctly than Watson did.
>
> And Ken Jennings in fact had revealed to TV Guide that his main
> winning strategy was to ring in even before he had retrieved the
> correct question -- thus shutting out the opponents who, naturally
> enough, wanted to be sure before they committed themselves to possibly
> losing the value of the question.
>
> Has this flaw been recognized by the IBM team?

Really, who cares? You sound like someone whining over the fact
that the steam drill cheated when it competed against John Henry. The
details and "truth" is not important (even for the legend of John Henry).
The machines will beat us at everything we can do, because we are weak bags
of meat that have no hope of being "better" than the machines in the long
run.

The fact is, Watson was able to answer most questions correctly. If you
change the game, take out the button pushing, and let it compete against
the whole world, only on it's ability to answer questions correctly, its
still going to beat something over 99% of the human population just on it's
ability to answer the questions. It's ability to beat humans in this
limited domain is unquestioned. When Turing made up his famous Turning
test, he rightful pointed out that the computer should be considered "as
good as humans", when it could win only half the games - that is, when the
humans and the computers won the same number of games against each other.
Using that scoring approach, Watson is clearly far smarter than the
"average" human, and that's really all that's important. In the limited
domain of the game of Jeopardy, the computers are clearly better than the
average human - by a wide margin. Whether it's fair to say it's better
than ALL humans, is spiting hairs.

In addition, are you aware that Watson had to push the same mechanical
button as the humans? It used a solenoid to press the same button the
humans were pressing. And as far as I know, it was programmed not to push
the button, until it found a good answer, so it was not trying to cheat
just by pressing it instantly, and then hoping it would later find the
right answer. It only buzzed in when it had a high confidence answer. So
I believe your suggestion that it was "programmed" to "buzz in" too soon,
is bogus anyway.

The fact is, reaction time is part of the game, and because the machine was
so much better at this part of the game than the humans, the computer had
to be handicapped, just to make it fair for the humans. That's not an
example of how the computers "cheated",. It's just another example of how
pitiful humans are compared to the machines.

More details can be found in this article:

http://www.kurzweilai.net/the-buzzer-factor-did-watson-have-an-unfair-advantage

You see that humans were able to beat Watson even in the cases where Watson
knew the answer because the humans have an advantage over the computer in
that they could listen to the voice of the person reading the clues.
Watson could not - he had no ears. By listening to the voice of the
announcer reading the clue, the human could anticipate when the light would
come on, and buzz-in only two milliseconds after the light, where as
without the audio clues, human reaction time to a light alone, is more like
190 ms. Watson's reaction time to the light was in the 5 to 10 ms range.
So all in all, the reaction time issue was relatively equal between Watson
and a skilled Jeopardy player. But only because the humans were given the
extra audio clues that Watson did not have access to.

The obvious fact is, computers can "think" a million times faster than a
human and no one should be surprised by this fact in this day and age.

Before long, there will be machines smarter than humans at EVERYTHING a
human can do, not just better at games like chess, or Jeopardy. The fact
that the machines will be better than us at everything, should not still be
a surprise to anyone.

[António Marques]

Curt Welch wrote (04-09-2012 19:16):
> computers can "think" a million times faster than a human

MIPS are not 'thought'. 'Thought' is high level. Between 'thought' and MIPS
you have countless layers. Since MIPS have not been determined for humans
and 'thought' is absent from computers, your statement is meaningless.

[Peter T. Daniels]

On Sep 4, 2:16 pm, c...@kcwc.com (Curt Welch) wrote:

> The fact is, Watson was able to answer most questions correctly.  If you
> change the game, take out the button pushing, and let it compete against
> the whole world, only on it's ability to answer questions correctly, its
> still going to beat something over 99% of the human population just on it's
> ability to answer the questions.  It's ability to beat humans in this
> limited domain is unquestioned.

I'll grant you, it could probably do a better job on "its" and "it's"
than that.

[Burkart Venzke]

The idea of using Predicate Logic is already some decades old...

> for the specification of {Meaning Postulates}.

...the main problem is the real specification of your "Meaning Postulates".
Which are they? What are they able to describe or to fulfill?

[Burkart Venzke]

All? But this point is not important for me...

>>> a machine
>>> can too, iff (if and only if) is has the minimum prerequisite
>>> meta-knowledge.
>>
>> Theoretically, you are right...
>>
>>> The exact nature of this missing meta-knowledge can be
>>> reverse-engineered.
>>
>> ...but how do you want to manage this reverse-engineering?
> Proceeding from the broad goal (machine comprehension of natural
> language), and work backwards to what this requires.

This is your goal, right. But how do you really want to manage it?
Try to explain it concretely and you will realize the problems.

>>>>> Since
>>>>> all of knowledge is constructed on the basis of connections between
>>>>> language elements (Linguistic Principle of Compositionality) we should
>>>>> start with a way for the ontology to learn the structure of these
>>>>> connections.
>>>>
>>>> What are the atoms of this language structure? Words without any
>>>> understanding of them? Good luck... it will be never perfect
>>>> (relatively to our human language understanding).
>>>>
>>> There will eventually be a meaning postulate for every piece of human
>>> knowledge. It would seem that the only {atoms} of this language
>>> (everything is essentially defined in terms of everything else) would be
>>> the hierarchy of connections between {units of meaning}. Everything is
>>> either a relation or a property of something else. Even the concept of
>>> existence itself is broken down into its properties and relations.
>>
>> What are your first, foundamental knowledge items? Only "empty"
>> words/notions?
>> How can an AI (machine) know or comprehend something?
> The foundational knowledge items are the (differing types of)
> connections between {units of meaning}.

Where does the different types come from? Who defines them or do you
think they are "natural", however?

> A Machine can comprehend anything on the basis of the {Meaning
> Postulates} within its ontology.

"Comprehend" or "deduce"? If you only/primarily think of comprehension:
In which sense is it really more than deduction?

[Burkart Venzke]

I am not sure what you mean by 'that "can" somehow "comprehend" in the
first place'. Do you want to express that "can" (ability) is necessary
to "comprehend"? Or the other way around?

When we think of little children we know that the "can" (see, listen,
touch, feel, move...) and that they can relate input of different senses
which lead to comprehension.

Burkart

[Peter Olcott]

Right and arithmetic is much older than that, yet will not be replaced
by anything better.
There are many details to this {something like} predicate logic that
have not yet been elaborated.

>
>> for the specification of {Meaning Postulates}.
>
> ...the main problem is the real specification of your "Meaning
> Postulates".
> Which are they? What are they able to describe or to fulfill?

They are defined by Montague, in his Montague Grammar (more aptly
Montague Semantics).

The best that I can tell some extension of Montague's work can fully
specify every subtle detail of anything that can every be expressed in
words. Also it expresses these concepts using enormously simpler**
syntax, and the syntax can be organized to facilitate reasoning.

** Simpler than natural language.

[Peter Olcott]

Even Chimps can be taught to read and write.

>
>>>> a machine
>>>> can too, iff (if and only if) is has the minimum prerequisite
>>>> meta-knowledge.
>>>
>>> Theoretically, you are right...
>>>
>>>> The exact nature of this missing meta-knowledge can be
>>>> reverse-engineered.
>>>
>>> ...but how do you want to manage this reverse-engineering?
>> Proceeding from the broad goal (machine comprehension of natural
>> language), and work backwards to what this requires.
>
> This is your goal, right. But how do you really want to manage it?
> Try to explain it concretely and you will realize the problems.

Any purely analytical problem no matter how complex can always be broken
down into a hierarchy of simple steps that all fit together seamlessly
to solve the enormous problem.

The most important thing is to see how humans organize the set of all
knowledge. We don't have to know anything about the physiology, we only
need to know how everything {units of meaning} is connected together.
The second most important thing is to find the most efficient way to
exhaustively and correctly document natural language syntax.

>
>>>>>> Since
>>>>>> all of knowledge is constructed on the basis of connections between
>>>>>> language elements (Linguistic Principle of Compositionality) we
>>>>>> should
>>>>>> start with a way for the ontology to learn the structure of these
>>>>>> connections.
>>>>>
>>>>> What are the atoms of this language structure? Words without any
>>>>> understanding of them? Good luck... it will be never perfect
>>>>> (relatively to our human language understanding).
>>>>>
>>>> There will eventually be a meaning postulate for every piece of human
>>>> knowledge. It would seem that the only {atoms} of this language
>>>> (everything is essentially defined in terms of everything else)
>>>> would be
>>>> the hierarchy of connections between {units of meaning}. Everything is
>>>> either a relation or a property of something else. Even the concept of
>>>> existence itself is broken down into its properties and relations.
>>>
>>> What are your first, foundamental knowledge items? Only "empty"
>>> words/notions?
>>> How can an AI (machine) know or comprehend something?
>> The foundational knowledge items are the (differing types of)
>> connections between {units of meaning}.
>
> Where does the different types come from? Who defines them or do you
> think they are "natural", however?

Here is one type of connection between {units of meaning}
The concept of {plural} combined with the concept of an instance of a
{physical thing} derives a set of more than one {physical things}.

>
>> A Machine can comprehend anything on the basis of the {Meaning
>> Postulates} within its ontology.
>
> "Comprehend" or "deduce"? If you only/primarily think of
> comprehension: In which sense is it really more than deduction?

One does not deduce that liquid water is wet, one refers to the internal
meaning postulate for liquid water, and finds one of its properties is
{wet}.

[Burkart Venzke]

Could you post them here?

> The best that I can tell some extension of Montague's work can fully
> specify every subtle detail of anything that can every be expressed in
> words. Also it expresses these concepts using enormously simpler**
> syntax, and the syntax can be organized to facilitate reasoning.
>
> ** Simpler than natural language.

Where is the connection between a single word (isn't it an atom of the
Grammar?) and an object in the world? Can a machine/AI learn such a
world from being told by referencing to the object?

[Burkart Venzke]

Am 05.09.2012 00:39, schrieb Peter Olcott:
> On 9/4/2012 3:13 PM, Burkart Venzke wrote:
>> Am 04.09.2012 01:41, schrieb Peter Olcott:
>>> On 9/3/2012 4:51 PM, Burkart Venzke wrote:
>>>> Am 03.09.2012 04:38, schrieb Peter Olcott:
>>>>> a machine
>>>>> can too, iff (if and only if) is has the minimum prerequisite
>>>>> meta-knowledge.
>>>>
>>>> Theoretically, you are right...
>>>>
>>>>> The exact nature of this missing meta-knowledge can be
>>>>> reverse-engineered.
>>>>
>>>> ...but how do you want to manage this reverse-engineering?
>>> Proceeding from the broad goal (machine comprehension of natural
>>> language), and work backwards to what this requires.
>>
>> This is your goal, right. But how do you really want to manage it?
>> Try to explain it concretely and you will realize the problems.
> Any purely analytical problem no matter how complex can always be broken
> down into a hierarchy of simple steps that all fit together seamlessly
> to solve the enormous problem.

Yes, but how? There are a lot of possible break downs.
You need a concrete algorithm, not only the idea that is may be possible.

> The most important thing is to see how humans organize the set of all
> knowledge. We don't have to know anything about the physiology, we only
> need to know how everything {units of meaning} is connected together.

Do you think that there is a unique way how everything is connected
together?
Do you think that knowledge itself is always clear (and not unsure etc.)?

Is the concept of plural defined or learned? If learned: How?

>>> A Machine can comprehend anything on the basis of the {Meaning
>>> Postulates} within its ontology.
>>
>> "Comprehend" or "deduce"? If you only/primarily think of
>> comprehension: In which sense is it really more than deduction?
> One does not deduce that liquid water is wet, one refers to the internal
> meaning postulate for liquid water, and finds one of its properties is
> {wet}.

Where does the internal meaning postulate for liquid water initially
comes from? Definition?

[Peter Olcott]

The concept is best build up within the mind within the context of his
whole framework:
http://www.amazon.com/Formal-Semantics-Introduction-Cambridge-Linguistics/dp/0521376106/ref=sr_1_sc_2?s=books&ie=UTF8&qid=1346799399&sr=1-2-spell&keywords=formal+sematnics
The above was the first book that I read about Montague Semantics.

>
>> The best that I can tell some extension of Montague's work can fully
>> specify every subtle detail of anything that can every be expressed in
>> words. Also it expresses these concepts using enormously simpler**
>> syntax, and the syntax can be organized to facilitate reasoning.
>>
>> ** Simpler than natural language.
>
> Where is the connection between a single word (isn't it an atom of the
> Grammar?) and an object in the world? Can a machine/AI learn such a
> world from being told by referencing to the object?

The atoms of the system that I am envisioning are {units of meaning} and
the connections between them. {units of meaning} do not perfectly map to
word boundaries.

All of those things that can only be known by the first-hand direct
experience of the actual physical sensation, the machine that I am
envisioning, will never know. Everything that can be expressed within
words can be fully understood by this machine.

[Peter Olcott]

On 9/4/2012 5:56 PM, Burkart Venzke wrote:
> Am 05.09.2012 00:39, schrieb Peter Olcott:
>> On 9/4/2012 3:13 PM, Burkart Venzke wrote:
>>> Am 04.09.2012 01:41, schrieb Peter Olcott:
>>>> On 9/3/2012 4:51 PM, Burkart Venzke wrote:
>>>>> Am 03.09.2012 04:38, schrieb Peter Olcott:
>>>>>> a machine
>>>>>> can too, iff (if and only if) is has the minimum prerequisite
>>>>>> meta-knowledge.
>>>>>
>>>>> Theoretically, you are right...
>>>>>
>>>>>> The exact nature of this missing meta-knowledge can be
>>>>>> reverse-engineered.
>>>>>
>>>>> ...but how do you want to manage this reverse-engineering?
>>>> Proceeding from the broad goal (machine comprehension of natural
>>>> language), and work backwards to what this requires.
>>>
>>> This is your goal, right. But how do you really want to manage it?
>>> Try to explain it concretely and you will realize the problems.
>> Any purely analytical problem no matter how complex can always be broken
>> down into a hierarchy of simple steps that all fit together seamlessly
>> to solve the enormous problem.
>
> Yes, but how? There are a lot of possible break downs.
> You need a concrete algorithm, not only the idea that is may be possible.

It is entirely incorrect to even begin to look for an algorithm at this
early stage of design.
I have already begun to divide the problem space into its simpler
components.
1) Determine the structure of knowledge.
2) Determine the details of natural language syntax.

It is learned based on having its definition applied to concrete instances.

>
>>>> A Machine can comprehend anything on the basis of the {Meaning
>>>> Postulates} within its ontology.
>>>
>>> "Comprehend" or "deduce"? If you only/primarily think of
>>> comprehension: In which sense is it really more than deduction?
>> One does not deduce that liquid water is wet, one refers to the internal
>> meaning postulate for liquid water, and finds one of its properties is
>> {wet}.
>
> Where does the internal meaning postulate for liquid water initially
> comes from? Definition?

Sets of meanings are assigned to arbitrary word labels.

[DKleinecke]

On Sep 4, 4:25 pm, Peter Olcott <OCR4Screen> wrote:

> 2) Determine the details of natural language syntax.

Isn't that exactly what linguists have been trying to do ever since
syntax was identified as a field of research? [It turns out I have no
idea when syntax was recognized as different than morphology.] Do you
have an attitude toward any of Chomsky's proposals ? Optimality Theory?

[Peter Olcott]

I have not read read very much. Most of my ideas are ideas that I came
up with beginning thirty years ago. I finally have a means of expressing
these ideas using (Montague Semantics) . Montague Semantics and its
possible extensions have been the focus of my recent investigation.

It seems that the key to solving both of the above two goals

1) Determine the structure of knowledge.

2) Determine the details of natural language syntax.

Can be simultaneously fulfilled by the complete elaboration of
Compositionality.

Fully elaborating natural language syntax should not be that difficult.
There has already been great process towards this goal using statistical
methods.
I have ideas here too.

[Peter T. Daniels]

On Sep 4, 6:39 pm, Peter Olcott <OCR4Screen> wrote:

> Even Chimps can be taught to read and write.

?

[Curt Welch]

It was hardly meaninglessness. It was a simple reference to the fact that
computers can do many mental tasks millions of times faster than any human
can, like sum numbers for one of many examples.

[Curt Welch]

"Peter T. Daniels" <gram...@verizon.net> wrote:

> On Sep 4, 2:16=A0pm, c...@kcwc.com (Curt Welch) wrote:
>
> > The fact is, Watson was able to answer most questions correctly. =A0If
> > yo=

> u
> > change the game, take out the button pushing, and let it compete
> > against the whole world, only on it's ability to answer questions
> > correctly, its still going to beat something over 99% of the human

> > population just on it=
> 's
> > ability to answer the questions. =A0It's ability to beat humans in this

> > limited domain is unquestioned.
>
> I'll grant you, it could probably do a better job on "its" and "it's"
> than that.

Exactly!

[Mok-Kong Shen]

I mean that "can" of "can see" is somehow given in nature, we don't
know though how, sadly. A manine registers a picture in pixels and
does some computations, of course, but that's all. It does not "feel"
the content of the picture. That's that problem. It seems we can't
create that capability in a machine.

M. K. Shen

[Franz Gnaedinger]

My word. Here you are with my definition of language
from 1974/75:

Language is the means of getting help, support and
understanding from those we depend upon in one way
or another, and every means of getting help, support
and understanding may be called language, on whatever
level of life it occurs ...

A rescent estimate I read of says that we all depend on
a million people. If you don't grow wheat and bake bread
you depend on farmers and bakers, on millers, on truck
drivers and road builders and engineers designing
agricultural machines and and and. Human word language
mirrors the web of all the many interdependencies.

Language may be considered the intelligence of life.
Together, coordinated by language, we achieve much more
than if we were all on our own. For example we succeeded
in building computers, wonderful instruments even a moron
such as me learned to work with. Yes, I am a confessing
computer moron. My brother Steve is an informatician,
and I never understood what he is doing. Until I once told
him about my maxim of understanding early civilization:
Simple yet complex. He beamed and said that is exactly
his aim: to make a program so very simple that people ask
him What did you do over the past weeks? And they find out
when they see how his grogram that looks like almost
nothing works perfectly for a wide range of tasks, whereas
the previous program they had had worked fine for one task
and failed in many others. And sometimes my brother is
astonished about the pathways I find on a PC, he would
not have thought of that. In early 2006 Google hired,
I applied as a 'professional moron'. I can test a device
or program like the moron I am, and at the same time
observe professionally what happens, then propose
a solution. Google replied they don't need someone
of my abilities for the time being. My brother says they
should have employed me, for professional morons
is exactly what the IT branchs needs. (By the way,
I really applied using the term professional moron.)

You said a lot in your message, and I have not very
much online time left here, in one of my libraries.
I wish to say that I have a high respect for computer
programmers, but I am also a challenge for them.
Computers don't care when you unplug and dismantle
them, therefore they have no real language, simulating
language is most cumbersome on classical computers,
and artificial neural networks can only be simulated,
on a small scale. Things will change with large
artifical networks, the memistor developed by hp
is a promising step. For the time being, computers
are really helpful as experts, in matters of writing by
providing spelling help, dictionaries and lexica,
and specific analyses. Ricardo Mansillo of the
Free University of Mexico ran a biological taxonomy
program on Homer's Odyssey and found that it
contains material of a dozen or sixteen or more bards.
That is a fine result, and useful for me. Classical
computers are very good at such tasks. But could
a computer find out about the knowledge of ancient
civilizations and history encoded in the Odyssey?
For example, the Trojan war was caused by beautiful
Helen. Who could believe that? I do, for Helen is
a symbol - the symbol of tin, her white arms of
tin ingots, her long glittering robes she made herself
of the glittering tin ore cassitterite, her thread of tin
wire, by then cut out of hammered tin foil. Her husband
xanthos Menelaos is a symbol of copper, the color
xanthos covering all hues of copper ore - and now
I see that my online time is running out, more next time.

[Franz Gnaedinger]

> when they see how his program that looks like almost

(Continuation, got me some more online time in another
library)

Well, beautiful Helen of the white arms, cause of the
Trojan war symbolizes tin, a precious metal in the Bronze
Age, Mycenaean bronze requiring twelve or even fifteen
per cent of tin (modern bronze five percent), but there
is no tin in Greece. The Mycenaean tin came from
Central Asia, and perhaps also from the Ore Mountains
in Cenral Europe, and was in either case bound to pass
Troy, where the Trojans laid hands on the precious cargo,
abducting Helen, as it were. Helen's husband xanthos
Menelaos symbolized copper, the color xanthos
representing all hues of copper ore, yellow red brown.
Their daugher, lovely Hermione who resembled golden
Aphrodite, symbolized bronze, alloy of copper and tin,
of a golden shine when freshly cast. Menelaos had
a slave woman for a mistress, symbol of andrasit,
a natural alloy of copper and zinc - zinc in enslaved
form, as it were. Their son was strong late come
Megapenthes, symbol of brass, alloy of copper and
zinc, harder than bronze and arriving late in the
'family' of metals. - Would a computer be able to do
hermeneutic work? I doubt it, but, as I said, it can
provide data and carry out special analyses. For
example a taxonomy program ran on Chaucer
revealed that the dry vesion is the oldest, while
the juicy and saucy versions are younger. I don't
say that you should not dream, my suggestion and
advice is that you go on a pair of legs, one imagination
and the other realism, more modest but real projects
that work.

[Arnaud F.]

***

You missed that point in your f**g books on writing systems, fraud !

A.

[Peter Olcott]

http://en.wikipedia.org/wiki/Yerkish

[António Marques]

Curt Welch wrote (05-09-2012 06:19):
> =?UTF-8?B?QW50w7NuaW8gTWFycXVlcw==?= <anton...@sapo.pt> wrote:
>> Curt Welch wrote (04-09-2012 19:16):
>>> computers can "think" a million times faster than a human
>>
>> MIPS are not 'thought'. 'Thought' is high level. Between 'thought' and
>> MIPS you have countless layers. Since MIPS have not been determined
>> for humans and 'thought' is absent from computers, your statement is
>> meaningless.
>
> It was hardly meaninglessness. It was a simple reference to the fact
> that computers can do many mental tasks millions of times faster than any
> human can, like sum numbers for one of many examples.

'Sum numbers' is not one of many examples, it's about nearly all that they
can do. 'Mental tasks' is poorly defined. The reason it is meaningless is
that you're comparing apples to oranges. Yes, computers can sum numbers.
Most processors understand an instruction that takes two values from
somewhere and places the result somewhere. That's what you refer to when you
say they can do 'millions' of things in a short amount of time. However,
that's not what happens when you have x+y in a high level programming
language. x+y in smalltalk is a method call, to be resolved by polymorphism
(according to the types of the arguments - integer, float...), to be
expressed in bytecode, to run in a virtual machine, which is implemented as
a completely different program than the one it's running, which, ok, will
run on a processor and may be ultimately transformed into something that
includes an 'add' instruction - buried in a sea of others. For other high
level languages, similar scenarios apply. High level languages are needed
because they have _expressiveness_ which is lacking in lower level ones. Not
unlikewise, when you do a 'mental' computation, yourself, you're not doing
it the low level way, you're going through a lot of layers. The lowest
levels for humans have not been understood, just as the highest levels for
computers have not (yet?) been developed. There is no guarantee that our
lowest levels are similar to those of computers, just as there is no
guarantee that something similar to out highest levels can be implemented on
top of those we already have for computers. There are a lot of problems
which are quite trivial for us to solve and where computers are either at a
loss or have very suboptimal solutions (just as computers seem have the edge
when it comes to bare arithmetic).

[Peter T. Daniels]

What does that have to do with anything like reading and writing? None
of the 1970s nonhuman primate experiments yielded anything like
language in chimpanzees, gorillas, etc.

[Arnaud F.]

***

WASPs can speak,
so can apes.

A.

[Curt Welch]

Franz Gnaedinger <fr...@bluemail.ch> wrote:

> On Sep 4, 6:47=A0pm, c...@kcwc.com (Curt Welch) wrote:
> >
> > Yeah, it has always struck me that the people who focus on language

> > tend =

> to
> > get themselves a bit lost in that they fail to answer the most
> > important question, which is why read or talk at all?
> >
> > Before you solve the "language" problem, you must solve the problem of

> > wh=

> at
> > the purpose of the machine is. "to read and understand" is not a valid

> > answer to that question. =A0Watson at least has a valid well defined
> > goal=
> of

> > being able to answer Jeopardy style questions correctly. =A0But that is
> > n=
> ot

> > at all human-like. =A0It's no more human like than writing a program to
> > p=

> lay
> > chess.
> >
> > To understand human language use, we have to go beyond the language
> > issue to understand what language is, and why we use it.
> >

> > Language is just one many behaviors we use to help us survive. =A0How
> > doe=

> s
> > the brain decide between picking up a rock and throwing, vs speaking a

> > fe=
> w
> > words? =A0Language does not exist in isolation. =A0It's just one of

> > many optional behaviors the brain has to pick from in order to reach

> > it's goal=

> s.
> > You can't make a machine talk like a human, unless you have also, at

> > the same time, solved the big problem of AGI. =A0You have to solve the

> > big problem of how the brain makes all its behavior decisions.
> >
> > Making moves in a chess game, is a highly simplified subset of the

> > bigger problem of making "intelligent" action selections. =A0Answering

> > Jeopardy questions, is also a fairly simplified subset of the bigger
> > problem of making intelligent action decisions.
> >
> > But true human language use, is not a simplified subset of the big

> > proble=
> m.
> > It's identical to the big problem of solving AGI. =A0You can't solve
> > the =

> full
> > language problem, without solving the full problem of general

> > intelligenc=

> e.
> >
> > And if you want to solve the general problem of intelligent, it's a

> > mista=

> ke
> > to focus only on our language use because it tends to put blinders on
> > us caused by our pre-conceived notions about language (which are often

> > wrong=

> ).
> >
> > For example, when we write a book, we might think the purpose is to

> > communicate knowledge. =A0And it's not wrong to think that, but it's
> > high=

> ly
> > narrow minded to think that way and ignores the important larger

> > pictures=
> .
> >
> > We don't write books to just communicate knowledge. =A0We write books
> > so =
> we
> > can put food on our table. =A0We write books so we can protect
> > ourselves =
> from
> > future pain. =A0We read books, and gain knowledge, so we can increase
> > the odds of creating less pain and more pleasure in our future. =A0We

> > use language as a tool for manipulating our environment, just like we

> > use a hammer, and saw, as a tool to manipulate our environment. =A0We
> > use langu=
> age
> > to try and manipulate the behaviors of other humans. =A0We certainly
> > see =

> that
> > in full swing in a political season like we have now in the US.
> >
> > If you want to make a machine that uses language like humans do, you
> > can not ignore the larger questions of how the brain makes all its
> > action decisions.
> >
> > If we pick a simple goal, like they did for Watson, then we have not
> > duplicated full human language use, any more than building a chess

> > playin=

> g
> > machine, can be seen as duplicating human-level intelligence.
> >
> > If we really want to understand language, we should stop studying

> > languag=
> e,
> > and look at full human behavior instead. =A0No one will be able to
> > unders=

> tand
> > language usage, until we solve the big problem, of full general human
> > intelligent behavior.
> >
> > If you don't want to work on that, then you are not really working on

> > tru=

> e
> > language at all.
>
> My word. Here you are with my definition of language
> from 1974/75:
>
> Language is the means of getting help, support and
> understanding from those we depend upon in one way
> or another, and every means of getting help, support
> and understanding may be called language, on whatever
> level of life it occurs ...

That works for me.

> A rescent estimate I read of says that we all depend on
> a million people. If you don't grow wheat and bake bread
> you depend on farmers and bakers, on millers, on truck
> drivers and road builders and engineers designing
> agricultural machines and and and. Human word language
> mirrors the web of all the many interdependencies.

Yes. We are a big society these days and our life style depends on a
great number of people all doing their part.

> Language may be considered the intelligence of life.
> Together, coordinated by language, we achieve much more
> than if we were all on our own.

Yes, I actually argue that society itself is a super-intelligence, as
separate from our individual intelligence as much as human intelligence is
separate from the intelligence of a single neuron.

> For example we succeeded
> in building computers, wonderful instruments even a moron
> such as me learned to work with. Yes, I am a confessing
> computer moron. My brother Steve is an informatician,
> and I never understood what he is doing. Until I once told
> him about my maxim of understanding early civilization:
> Simple yet complex. He beamed and said that is exactly
> his aim: to make a program so very simple that people ask
> him What did you do over the past weeks? And they find out
> when they see how his grogram that looks like almost
> nothing works perfectly for a wide range of tasks, whereas
> the previous program they had had worked fine for one task
> and failed in many others. And sometimes my brother is
> astonished about the pathways I find on a PC, he would
> not have thought of that. In early 2006 Google hired,
> I applied as a 'professional moron'. I can test a device
> or program like the moron I am, and at the same time
> observe professionally what happens, then propose
> a solution. Google replied they don't need someone
> of my abilities for the time being. My brother says they
> should have employed me, for professional morons
> is exactly what the IT branchs needs. (By the way,
> I really applied using the term professional moron.)

:)

> You said a lot in your message, and I have not very
> much online time left here, in one of my libraries.
> I wish to say that I have a high respect for computer
> programmers, but I am also a challenge for them.
> Computers don't care when you unplug and dismantle
> them,

They do if you program them correctly. Give them arms and program the
correctly, and then watch them try to block you from trying to unplug them.
If you saw a computer do that, you would likely sense it cared about being
unpluged. It's not some innate ability that is missing from our hardware
to makes it "not care". It's just that it's not programmed to care.

> therefore they have no real language, simulating
> language is most cumbersome on classical computers,
> and artificial neural networks can only be simulated,
> on a small scale.

Well, they can be simulated on very large scales these days. The people
working on actual neural simulators say they are getting close to full
brain simulation.

The bigger problem is that we don't know what aspects need to be simulated,
and which can be ignored.

> Things will change with large
> artifical networks, the memistor developed by hp
> is a promising step.

Yeah, if people knew for how to do this, they could just build custom
hardware to do it instead of using generic CPUs. But, you shouldn't
underestimate just how mind-blowing fast our CPUs are these days with their
large amounts of parallel processing. May things one might think would run
faster in custom hardware, actually runs just as fast, when implemented in
software in a generic CPU. There's a huge amount of engineering invested
in our CPUs and it's hard to find problems that can be solved for less
money, with custom hardware these days.

> For the time being, computers
> are really helpful as experts, in matters of writing by
> providing spelling help, dictionaries and lexica,
> and specific analyses. Ricardo Mansillo of the
> Free University of Mexico ran a biological taxonomy
> program on Homer's Odyssey and found that it
> contains material of a dozen or sixteen or more bards.
> That is a fine result, and useful for me. Classical
> computers are very good at such tasks. But could
> a computer find out about the knowledge of ancient
> civilizations and history encoded in the Odyssey?
> For example, the Trojan war was caused by beautiful
> Helen. Who could believe that? I do, for Helen is
> a symbol - the symbol of tin, her white arms of
> tin ingots, her long glittering robes she made herself
> of the glittering tin ore cassitterite, her thread of tin
> wire, by then cut out of hammered tin foil. Her husband
> xanthos Menelaos is a symbol of copper, the color
> xanthos covering all hues of copper ore - and now
> I see that my online time is running out, more next time.

Yeah, I think computers will be able to do all that sort of abstract
thinking in the not too distant future. They certainly can't do much of it
today, but we are getting closer.

How much a machine could understand if all it had for input was language
input is hard to grasp. We base so much of our understanding in our
sensory knowledge of the real world, it's hard to know just how much could
be understood if written text was the computers only sensory knowledge. I
think it would be able to understand and talk intelligently about a lot,
but clearly, some concepts would be very hard for it to understand.

[casey]

Which is why we know Watson isn't thinking like a human.

I suspect Curt can outperform Watson in real world problem solving..

The above is a common mistake and in Curt's case is due to personality
not ignorance where I suspect he types away without paying much
attention to what his fingers are doing. It is a common phonetic
mistake because that IS the way a human brains work compared with the
way our current compute programs work. Indeed it is one reason we use
computers as a spelling or grammar Nazi.

Now is it spelt grammar or grammer? Damn what has happened to my
perfect computer memory I must be human after all.

Spelling itself is decided by common use and until codified in
dictionaries wasn't even an issue.

Some personalities check their tie is straight before they go out and
others check their spelling and/or grammar (if they are any good at
it) before the hit send.


jc

[casey]

Current computer programs can also be dumb as some humans that read
what is written rather than what was meant.
I meant to write "before *they* hit send". Even proof reading is
difficult for humans because we tend to see what is expected not what
is actually there and that is not a flaw in the way we think it is how
we handle vast quantities of incoming data.

jc

[Burkart Venzke]

I see, you think about image processing. The usual way (at least when I
studied 20 years ago) of analyizing the pixels to find edges, zones etc.
bottom up is not the natural (human) way in my mind.

> It does not "feel"
> the content of the picture. That's that problem. It seems we can't
> create that capability in a machine.

One difference is that the machine usually cannot learn to see, humans
can and do. To "feel" content needs to have much experience -> learning.

Burkart

[Burkart Venzke]

Aren't there some central points you can write here? I don't want to
read more than 300 pages first.

> The above was the first book that I read about Montague Semantics.
>
>>
>>> The best that I can tell some extension of Montague's work can fully
>>> specify every subtle detail of anything that can every be expressed in
>>> words. Also it expresses these concepts using enormously simpler**
>>> syntax, and the syntax can be organized to facilitate reasoning.
>>>
>>> ** Simpler than natural language.
>>
>> Where is the connection between a single word (isn't it an atom of the
>> Grammar?) and an object in the world? Can a machine/AI learn such a
>> world from being told by referencing to the object?
> The atoms of the system that I am envisioning are {units of meaning} and
> the connections between them. {units of meaning} do not perfectly map to
> word boundaries.

Do you have (an) example(s) for "units of meaning"?

> All of those things that can only be known by the first-hand direct
> experience of the actual physical sensation, the machine that I am
> envisioning, will never know.

Hm? You don't want the machine to have experience in the real world?

> Everything that can be expressed within
> words can be fully understood by this machine.

If a machine really have no contact to the real world it will never
understand anything like a (theoretical) human without any senses.

[Peter Olcott]

Whatever.

[Burkart Venzke]

OK, you are not so far for more concrete steps.
I am thinking of strong AI since my studies 25 years ago. Without real
algorithms it seems to me that strong AI cannot be very difficult. But
when implementing a real learning algorithm I can realize the real
problems (currently that (abstract) "safe facts" do not really exists
when learning from the world (inductively), only ideas/theories which
may be correct but also may be wrong (partially)).

So you want to define all the concepts first like "plural"?

>>>>> A Machine can comprehend anything on the basis of the {Meaning
>>>>> Postulates} within its ontology.
>>>>
>>>> "Comprehend" or "deduce"? If you only/primarily think of
>>>> comprehension: In which sense is it really more than deduction?
>>> One does not deduce that liquid water is wet, one refers to the internal
>>> meaning postulate for liquid water, and finds one of its properties is
>>> {wet}.
>>
>> Where does the internal meaning postulate for liquid water initially
>> comes from? Definition?

> Sets of meanings are assigned to arbitrary word labels.

As above: Do you define first the whole set of meanings?
If so: Do you think that humans think and learn in that way?

[Peter Olcott]

I do not have that much initiative.
Basically every concept is defined using something like predicate logic,
and if it is not defined it does not exist.
Here are some key terms {truth conditional propositions} {model
theoretic} {possible worlds}.

>
>> The above was the first book that I read about Montague Semantics.
>>
>>>
>>>> The best that I can tell some extension of Montague's work can fully
>>>> specify every subtle detail of anything that can every be expressed in
>>>> words. Also it expresses these concepts using enormously simpler**
>>>> syntax, and the syntax can be organized to facilitate reasoning.
>>>>
>>>> ** Simpler than natural language.
>>>
>>> Where is the connection between a single word (isn't it an atom of the
>>> Grammar?) and an object in the world? Can a machine/AI learn such a
>>> world from being told by referencing to the object?
>> The atoms of the system that I am envisioning are {units of meaning} and
>> the connections between them. {units of meaning} do not perfectly map to
>> word boundaries.
>
> Do you have (an) example(s) for "units of meaning"?

The concept of {Plural} when combined with {Physically_Existing_Thing}
each of these are a {unit of meaning} and so is the result of the
combination.

>
>> All of those things that can only be known by the first-hand direct
>> experience of the actual physical sensation, the machine that I am
>> envisioning, will never know.
>
> Hm? You don't want the machine to have experience in the real world?

It will never be able to actually appreciate the beauty of a rainbow,
but, it will be able to talk as if it does.

>
>> Everything that can be expressed within
>> words can be fully understood by this machine.
>
> If a machine really have no contact to the real world it will never
> understand anything like a (theoretical) human without any senses.

All of its contact will be through words, and these will be enough.

[Peter Olcott]

Ah very good we are on about the same wave length.

I propose that there exists a means of self-sustaining machine learning
that is not stochastic, and is capable of learning everything on its own
such that it will eventually have the sum total of all human knowledge.
This machine must begin with a kernel of knowledge, {BootStrap Natural
Language Understanding} that would provide the basis for the {knowledge
of gaining more knowledge}.

The would be the mission of the first one of these two paths:

1) Determine the structure of knowledge.

Exhaustively specify all Compositionality of logical entailment.

The other path that would be further developed:

2) Determine the details of natural language syntax.

Exhaustively specify all Compositionality (besides logical entailment}

>
>>>>>> A Machine can comprehend anything on the basis of the {Meaning
>>>>>> Postulates} within its ontology.
>>>>>
>>>>> "Comprehend" or "deduce"? If you only/primarily think of
>>>>> comprehension: In which sense is it really more than deduction?
>>>> One does not deduce that liquid water is wet, one refers to the
>>>> internal
>>>> meaning postulate for liquid water, and finds one of its properties is
>>>> {wet}.
>>>
>>> Where does the internal meaning postulate for liquid water initially
>>> comes from? Definition?
>
>> Sets of meanings are assigned to arbitrary word labels.
>
> As above: Do you define first the whole set of meanings?

The order that knowledge is specified to the ontology will aim to
minimize the effort to get to self-sustained learning.
Besides that the order does not matter.

> If so: Do you think that humans think and learn in that way?

We can skip how humans learn and goes straight to the organization of
{units of meaning} that is equivalent to human comprehension. We can
think of this as differing types of connections between {units of
meaning}. All it seems to break down to individuals (including sets),
properties, and relations.

[Peter Olcott]

> That would be the mission of the first of these two paths:

> 1) Determine the structure of knowledge.
> Exhaustively specify all Compositionality of logical entailment.
>
> The other path that would be further developed:
> 2) Determine the details of natural language syntax.
> Exhaustively specify all Compositionality (besides logical entailment}
>

> Determining the minimum set of prerequisite concepts that must be
> manually specified would be another sub-task of the project. It might
> be able to learn something as basic as plural, on its own.

>>
>>>>>>> A Machine can comprehend anything on the basis of the {Meaning
>>>>>>> Postulates} within its ontology.
>>>>>>
>>>>>> "Comprehend" or "deduce"? If you only/primarily think of
>>>>>> comprehension: In which sense is it really more than deduction?
>>>>> One does not deduce that liquid water is wet, one refers to the
>>>>> internal
>>>>> meaning postulate for liquid water, and finds one of its
>>>>> properties is
>>>>> {wet}.
>>>>
>>>> Where does the internal meaning postulate for liquid water initially
>>>> comes from? Definition?
>>
>>> Sets of meanings are assigned to arbitrary word labels.
>>
>> As above: Do you define first the whole set of meanings?
> The order that knowledge is specified to the ontology will aim to

> minimize the effort to get to achieve self-sustained learning.

> Besides that the order does not matter.
>
>> If so: Do you think that humans think and learn in that way?

> We can skip how humans learn and go straight to determining the
> organization of {units of meaning} equivalent to human comprehension.

> We can think of this as differing types of connections between {units

> of meaning}. It all seems to break down to individuals (including
> sets), properties, and relations.
>

[Peter T. Daniels]

> >>http://www.amazon.com/Formal-Semantics-Introduction-Cambridge-Linguis...

Mr. Olcott, meet Mr. Turing.

> >> Everything that can be expressed within
> >> words can be fully understood by this machine.
>
> > If a machine really have no contact to the real world it will never
> > understand anything like a (theoretical) human without any senses.
>
> All of its contact will be through words, and these will be enough.

How does it encounter "words"? I thought computers only know "on" and
"off."

[Franz Gnaedinger]

On Sep 5, 8:29 pm, c...@kcwc.com (Curt Welch) wrote:
>
> That works for me.

You are the first one in sci.lang to accept my definition of
language. Beware, from now on you'll have Peter T. Daniels
on your heels ;-)

> Yes.   We are a big society these days and our life style depends on a
> great number of people all doing their part.
>

> Yes, I actually argue that society itself is a super-intelligence, as
> separate from our individual intelligence as much as human intelligence is
> separate from the intelligence of a single neuron.
>

> :)

I hope they had a laugh at Google's. I employed myself
as a professional moron in their service, from time to time
giving them advice in form of an open letter - you can pray
to God without actually knowing whether God really exists,
whereas I know for sure that Google exists, they opened
their European agency in my hometown of Zurich, the first
place was twenty minutes to walk from where I live.

> They do if you program them correctly.  Give them arms and program the
> correctly, and then watch them try to block you from trying to unplug them.
> If you saw a computer do that, you would likely sense it cared about being
> unpluged.  It's not some innate ability that is missing from our hardware
> to makes it "not care".  It's just that it's not programmed to care.

But for the time being, neural networks can only be simulated
on a small scale on classical computers. The memistor
developed by Hewlett and Packard simulates a synapse,
I see this as the promising way to go. Claude Shannon who
had humour built a little device, a chest with a button: when
you press the button, the little chest opens up, a hand comes
out, presses the button, unplugging itself, the hand falls back,
the chest closes, and rests peacefully until the next visitor
can't help but press the button again. So we have a machine
that unplugs itself, but I never saw a machine defending itself
and abandoing mathematical logic in doing so, valuing life
higher than any logical theorem.

> The bigger problem is that we don't know what aspects need to be simulated,
> and which can be ignored.

Give up on the classical computer (for a while) and focus
on neural networks, look out for what you can find about
hp's memistor. The future of computing, in my opinion,
are hybrids of classical computers and artificial neural
networks, and quantum computers.

> Yeah, if people knew for how to do this, they could just build custom
> hardware to do it instead of using generic CPUs.  But, you shouldn't
> underestimate just how mind-blowing fast our CPUs are these days with their
> large amounts of parallel processing.  May things one might think would run
> faster in custom hardware, actually runs just as fast, when implemented in
> software in a generic CPU.  There's a huge amount of engineering invested
> in our CPUs and it's hard to find problems that can be solved for less
> money, with custom hardware these days.

When you talk to a computer moron you have to make it
really very simple, I understand bahnhof.

> Yeah, I think computers will be able to do all that sort of abstract
> thinking in the not too distant future.  They certainly can't do much of it
> today, but we are getting closer.

Computers are thinking tool, mind-scopes (pun on
telescope and microscope), expanding out mental abilities,
as a shovel prolongs our arms. A shovel does not replace
the arm, and a computer does nopt replace the mind.

> How much a machine could understand if all it had for input was language
> input is hard to grasp.  We base so much of our understanding in our
> sensory knowledge of the real world, it's hard to know just how much could
> be understood if written text was the computers only sensory knowledge.  I
> think it would be able to understand and talk intelligently about a lot,

> but clearly, ...

Knwoledge works on many interwoven levels in a web
or network, thinking there are "units of meaning" is
reducing language to playing Lego.

Seems that a part of your reply is lost here, the part where
you say that the brain can be simulated on a computer.
You must be referring to a big project from the university
of Geneva, the promise to simulate the brain entirely.
Many experts are more than skeptical, and consider
the big promise a hype in order to get EU fundings
in the height of billions of Euros.

[Mok-Kong Shen]

To accumulate experiences in "feeling" it has to be able to feel somehow
basically/primitively in the first place. And one is then back to
square one, IMHO.

M. K. Shen

[casey]

A computer *program* can know as much about its binary switches as we
know about our neurons.

Computers can only do ONE thing, carry out a series of steps in a
program.

What a program can do, including "knowing" something, is still an open
question.

jc

[Dissitra]

find me xxx

[Peter Olcott]

Computers will know words through the connections within an ontology.

[Dissitra]

like some leek she shall certainly know their farmer labourer

and homsexuals shat certaily be aware of their own geometricies-ess


still no money in that crap?

try an abortion

[Burkart Venzke]

OK, my idea of your "feel" was the "intuitive" comprehension of the
content of the picture, "intuitive" in the sense of using much knowledge
seemingly unconsciously.

On the other hand, we cannot feel the content of a picture as we can
feel the burning sun or the wind. But we can associate the colors, forms
or interpretated elements of the picture with more or less directly with
our natural feelings. This is a point machines as non-biological
creatures are not able to do directly (except we implement them feelings
explicitely).

However, I don't think that we want perfect copies of humans; artifical
intelligence without (real) emotions should be enough. So, an
unemotional analysis of a picture based on learned criteria may be enough.

Burkart

[Burkart Venzke]

I see, this is a (set of) model(s) without any inductive aspects.

>>> The above was the first book that I read about Montague Semantics.
>>>
>>>>
>>>>> The best that I can tell some extension of Montague's work can fully
>>>>> specify every subtle detail of anything that can every be expressed in
>>>>> words. Also it expresses these concepts using enormously simpler**
>>>>> syntax, and the syntax can be organized to facilitate reasoning.
>>>>>
>>>>> ** Simpler than natural language.
>>>>
>>>> Where is the connection between a single word (isn't it an atom of the
>>>> Grammar?) and an object in the world? Can a machine/AI learn such a
>>>> world from being told by referencing to the object?
>>> The atoms of the system that I am envisioning are {units of meaning} and
>>> the connections between them. {units of meaning} do not perfectly map to
>>> word boundaries.
>>
>> Do you have (an) example(s) for "units of meaning"?
> The concept of {Plural} when combined with {Physically_Existing_Thing}
> each of these are a {unit of meaning} and so is the result of the
> combination.
>>
>>> All of those things that can only be known by the first-hand direct
>>> experience of the actual physical sensation, the machine that I am
>>> envisioning, will never know.
>>
>> Hm? You don't want the machine to have experience in the real world?
> It will never be able to actually appreciate the beauty of a rainbow,
> but, it will be able to talk as if it does.

It cannot appreciate the beauty of a rainbow, not even it cann see it
for itself. So it can only talk about it what it "heard" (was being
told/learned/implemented) from others (for example the programmer).

>>> Everything that can be expressed within
>>> words can be fully understood by this machine.
>>
>> If a machine really have no contact to the real world it will never
>> understand anything like a (theoretical) human without any senses.
> All of its contact will be through words, and these will be enough.

For me, words (as only abstract items) are not enough.

[Burkart Venzke]

Am 06.09.2012 21:40, schrieb casey:
> On Sep 6, 1:39 pm, "Peter T. Daniels"<gramma...@verizon.net> wrote:
>> On Sep 5, 6:34 pm, Peter Olcott<OCR4Screen> wrote:
>>
>>
>>
>>
>>
>>> On 9/5/2012 4:55 PM, Burkart Venzke wrote:
>>
>>>> Am 05.09.2012 01:02, schrieb Peter Olcott:
>>>>> On 9/4/2012 5:41 PM, Burkart Venzke wrote:
>>>>>> Am 05.09.2012 00:26, schrieb Peter Olcott:
>>>>>>> On 9/4/2012 3:02 PM, Burkart Venzke wrote:
>>>>>>>> Am 04.09.2012 01:45, schrieb Peter Olcott:
>>>>>>>>> On 9/3/2012 5:10 PM, Burkart Venzke wrote:
>>>>>>>>>> Am 03.09.2012 19:10, schrieb Peter Olcott:

Right.

> Computers can only do ONE thing, carry out a series of steps in a
> program.

...as humans carry out their series of steps determined by their brain
and body.

> What a program can do, including "knowing" something, is still an open
> question.

Additionally, programs can learn so that their series of steps become
unpredictable.

"Knowing" is really difficult, even philosophical seen. What can we
really know? Or how precisely and/or surely?

Burkart

[Peter Olcott]

Yet that is all that we have between us: You and I.

[casey]

On Sep 7, 7:55 am, Burkart Venzke <b...@gmx.de> wrote:
> [...]

> "Knowing" is really difficult, even philosophical seen. What can we
> really know? Or how precisely and/or surely?

Does a light seeking robot know where the light is?

When does an act of reacting to something become knowing that
something?

jc

>
> Burkart
>
>

[Burkart Venzke]

It might be possible. But a good teacher (as for children) will save a
lot of time (the time of human evolution more or less).

>> that is not stochastic,

I have been hoping so, too, but because I think of inductive learning as
the real basis, it is not really possible. For example, you can learn
that Paris is the capital city of France, but if you do so you have to
believe your teacher and he might be wrong, even might lie...

>> and is capable of learning everything
>> on its own such that it will eventually have the sum total of all
>> human knowledge. This machine must begin with a kernel of knowledge,
>> {BootStrap Natural Language Understanding} that would provide the
>> basis for the {knowledge of gaining more knowledge}.

Something like that, that's true. We should not forget to guide the
machine to "good knowledge", for example to help humans instead of
fighting against them.

I fear that an explicite, finally structure is not perfect and/or
flexible enough.

>> The other path that would be further developed:
>> 2) Determine the details of natural language syntax.
>> Exhaustively specify all Compositionality (besides logical entailment}
>>
>> Determining the minimum set of prerequisite concepts that must be
>> manually specified would be another sub-task of the project. It might
>> be able to learn something as basic as plural, on its own.

This sounds better. I don't know exactly what "natural language syntax"
is but even language should be learned on a small communication basis by
interaction with a speaking (communicating) human who additionally uses
world items in combination with words so that words can really be
connect to the world.

>>>>>>>> A Machine can comprehend anything on the basis of the {Meaning
>>>>>>>> Postulates} within its ontology.
>>>>>>>
>>>>>>> "Comprehend" or "deduce"? If you only/primarily think of
>>>>>>> comprehension: In which sense is it really more than deduction?
>>>>>> One does not deduce that liquid water is wet, one refers to the
>>>>>> internal
>>>>>> meaning postulate for liquid water, and finds one of its
>>>>>> properties is
>>>>>> {wet}.
>>>>>
>>>>> Where does the internal meaning postulate for liquid water initially
>>>>> comes from? Definition?
>>>
>>>> Sets of meanings are assigned to arbitrary word labels.
>>>
>>> As above: Do you define first the whole set of meanings?
>> The order that knowledge is specified to the ontology will aim to
>> minimize the effort to get to achieve self-sustained learning.
>> Besides that the order does not matter.
>>
>>> If so: Do you think that humans think and learn in that way?
>> We can skip how humans learn

We could but I think that we still can learn a lot of human's learning.

>> and go straight to determining the
>> organization of {units of meaning} equivalent to human comprehension.
>> We can think of this as differing types of connections between {units
>> of meaning}. It all seems to break down to individuals (including
>> sets), properties, and relations.

As far as you do not define/explain here what "units of meaning" etc
are, it is too abstract/unimaginative for me.

[Peter Olcott]

When people acquire knowledge this knowledge is almost never stochastic.
I will use your own example:
Do most people feel that there is an extremely good chance that Paris is
the capitol of France?
or Do most people simply comprehend that Paris is defined as the capitol
of France?

>
>>> and is capable of learning everything
>>> on its own such that it will eventually have the sum total of all
>>> human knowledge. This machine must begin with a kernel of knowledge,
>>> {BootStrap Natural Language Understanding} that would provide the
>>> basis for the {knowledge of gaining more knowledge}.
>
> Something like that, that's true. We should not forget to guide the
> machine to "good knowledge", for example to help humans instead of
> fighting against them.

Because of the Chinese room thought experiment, this machine will never
have a will of its own, so we must infuse it with something. Wde have to
be careful when we do this or we could possibly get the result of
Asimov's I Robot.

It already has its own perfect structure, we only need to discover this,
to reverse-engineer it.

>
>>> The other path that would be further developed:
>>> 2) Determine the details of natural language syntax.
>>> Exhaustively specify all Compositionality (besides logical entailment}
>>>
>>> Determining the minimum set of prerequisite concepts that must be
>>> manually specified would be another sub-task of the project. It might
>>> be able to learn something as basic as plural, on its own.
>
> This sounds better. I don't know exactly what "natural language syntax" is

a specific example would be: English Grammar

> but even language should be learned on a small communication basis by
> interaction with a speaking (communicating) human who additionally
> uses world items in combination with words so that words can really be
> connect to the world.

I was envisioning that most of its knowledge would be from books, and
articles. It would also have the ability to ask questions.

>
>>>>>>>>> A Machine can comprehend anything on the basis of the {Meaning
>>>>>>>>> Postulates} within its ontology.
>>>>>>>>
>>>>>>>> "Comprehend" or "deduce"? If you only/primarily think of
>>>>>>>> comprehension: In which sense is it really more than deduction?
>>>>>>> One does not deduce that liquid water is wet, one refers to the
>>>>>>> internal
>>>>>>> meaning postulate for liquid water, and finds one of its
>>>>>>> properties is
>>>>>>> {wet}.
>>>>>>
>>>>>> Where does the internal meaning postulate for liquid water initially
>>>>>> comes from? Definition?
>>>>
>>>>> Sets of meanings are assigned to arbitrary word labels.
>>>>
>>>> As above: Do you define first the whole set of meanings?
>>> The order that knowledge is specified to the ontology will aim to
>>> minimize the effort to get to achieve self-sustained learning.
>>> Besides that the order does not matter.
>>>
>>>> If so: Do you think that humans think and learn in that way?
>>> We can skip how humans learn
>
> We could but I think that we still can learn a lot of human's learning.

The reasoning that is required to fully integrate the many dimensions of
a piece of knowledge is required.
Rote memorization is entirely moot, an machine needs never forget any
detail even if only encountered once.

>
>>> and go straight to determining the
>>> organization of {units of meaning} equivalent to human comprehension.
>>> We can think of this as differing types of connections between {units
>>> of meaning}. It all seems to break down to individuals (including
>>> sets), properties, and relations.
>
> As far as you do not define/explain here what "units of meaning" etc
> are, it is too abstract/unimaginative for me.

Pretty much the sense meanings of morphemes.
http://en.wikipedia.org/wiki/Morpheme

[casey]

On Sep 7, 8:09 am, Peter Olcott <OCR4Screen> wrote:
> On 9/6/2012 4:45 PM, Burkart Venzke wrote:

[...]

> >>> If a machine really have no contact to the real world it will never
> >>> understand anything like a (theoretical) human without any senses.
> >> All of its contact will be through words, and these will be enough.
>
> > For me, words (as only abstract items) are not enough.
>
> Yet that is all that we have between us: You and I.

But you both share the same world unlike a machine without contact to
a real world.

jc

[casey]

On Sep 7, 8:53 am, Peter Olcott <OCR4Screen> wrote:
> [...]

>
> Because of the Chinese room thought experiment, this machine will never

> have a will of its own, so we must infuse it with something. We have to

> be careful when we do this or we could possibly get the result of
> Asimov's I Robot.

This is about how the machine values a set of possible moves.

All machines that make decisions (like a chess playing program)
must be able to value its moves.

The idea of a "will" is really about what move you have decided to
make.

A lot of these words are colored by being conscious or unconscious.

My fingers *know* where the keys are when I touch type but the
conscious
part of me does not. All I *know* is what I want to type and the
subconscious
parts of the brain carry it out.

The only difference between me knowing and the finger control circuits
knowing is the former is conscious knowing and I can talk about it.

> I was envisioning that most of its knowledge would be from books, and
> articles. It would also have the ability to ask questions.

And how is this knowledge to be represented in the machine?

> Rote memorization is entirely moot, an machine needs never forget any
> detail even if only encountered once.

Even if everything was memorized (stored) how would it be recalled?

I get the feeling from this thread you guys haven't actually
started coding any of these thoughts?

jc

[Dissitra]

I wonder when the Chinese experiment was done. Was this after the that
Shanghai revolution or before in normal native popululance of Chinese,
he?

[Peter Olcott]

That only means that it has to be told everything until it knows enough
to go out on its own surfing the web, reading books, and articles. It
will also have the ability to ask questions, and/or request reading
material.

[Peter Olcott]

On 9/6/2012 6:55 PM, casey wrote:
> On Sep 7, 8:53 am, Peter Olcott <OCR4Screen> wrote:
>> [...]
>>
>> Because of the Chinese room thought experiment, this machine will never
>> have a will of its own, so we must infuse it with something. We have to
>> be careful when we do this or we could possibly get the result of
>> Asimov's I Robot.
> This is about how the machine values a set of possible moves.
>
> All machines that make decisions (like a chess playing program)
> must be able to value its moves.
>
> The idea of a "will" is really about what move you have decided to
> make.

Yes that is exactly it. It will follow a hierarchy of abstract goals,
probably including a pinnacle goal at the top of the hierarchy. I think
that the goal for humanity would be to maximize the duration, and depth
of joy.

> A lot of these words are colored by being conscious or unconscious.

What I am learning by studying Linguistics is that there are two aspects
to denotation:
a) A reference to a thing in the world.
b) A set of properties that selects a set of things in the world.
What the concept of {consciousness} is depends on the set of properties
that are assigned to the term "consciousness".

> My fingers *know* where the keys are when I touch type but the
> conscious
> part of me does not. All I *know* is what I want to type and the
> subconscious
> parts of the brain carry it out.
>
> The only difference between me knowing and the finger control circuits
> knowing is the former is conscious knowing and I can talk about it.

I think that these aspects may be moot to ontology design.

>
>> I was envisioning that most of its knowledge would be from books, and
>> articles. It would also have the ability to ask questions.
> And how is this knowledge to be represented in the machine?

Different types of connections between nodes of a di-graph.

>
>> Rote memorization is entirely moot, an machine needs never forget any
>> detail even if only encountered once.
> Even if everything was memorized (stored) how would it be recalled?

Traversals through the knowledgebase nodes. There are a pair on ontologies:
a) Knowledge Base
b) Specific Situation.

The Specific Situation ontology is optimized for reasoning, and is
constructed on the basis the the Knowledge Base and the Discourse.

> I get the feeling from this thread you guys haven't actually
> started coding any of these thoughts?
>
> jc
>
>
>
>

I am working on the architectural design of self-sustaining machine
learning from natural language words, right here right now as I answer
these posts. Although I am doing this for enjoyment, I do expect to make
substantial progress. I have been thinking about these things since at
least 1986.

[Mok-Kong Shen]

Am 06.09.2012 23:35, schrieb Burkart Venzke:

> However, I don't think that we want perfect copies of humans; artifical
> intelligence without (real) emotions should be enough. So, an
> unemotional analysis of a picture based on learned criteria may be enough.

But that casts IMHO essential doubt on whether we could build machines
that can "understand" and hence acquire languages in the sense as we do.

M. K. Shen

[António Marques]

PO thinks knowledge can be acquired exclusively from language plus a certain
kernel of it. Not only the jury is out on that, we haven't got anywhere on
the matter of how to encode the required kernel, or even if it's encodable
at all. PO thinks we communicate through language only and forgets that we
have an awful lot in common and language may simply be a catalyzer.

[Peter Olcott]

Language is the structure of thought.

Conceptual comprehension is essentially nothing more than knowing the
meaning of words.
Every possible conceptual meaning that can ever be expressed can be
expressed within an ontology.

A machine will be able to demonstrate the functional equivalent of human
comprehension as soon as its ontology is sufficiently populated, and is
organized to facilitate every type of reasoning that humans are capable of.

The required kernel of meta-knowledge needed to facilitate fully
self-sustained LearningByReading can be reverse-engineered.

[Arnaud F.]

Le vendredi 7 septembre 2012 16:34:12 UTC+2, Peter Olcott a écrit :


>
>
> Language is the structure of thought.
>
>
>
> Conceptual comprehension is essentially nothing more than knowing the
>
> meaning of words.
>
> Every possible conceptual meaning that can ever be expressed can be
>
> expressed within an ontology.
>
>
>

***

> A machine will be able to demonstrate the functional equivalent of human
>
> comprehension as soon as its ontology is sufficiently populated,

***

That guy is a complete loony.

A.
***

and is
>
> organized to facilitate every type of reasoning that humans are capable of.
>
>
>
> The required kernel of meta-knowledge needed to facilitate fully
>
> self-sustained LearningByReading can be reverse-engineered.

***

That kind of pseudo-philosophical profund crap is a must-collect.

A.

[Mok-Kong Shen]

Am 07.09.2012 16:34, schrieb Peter Olcott:

> The required kernel of meta-knowledge needed to facilitate fully
> self-sustained LearningByReading can be reverse-engineered.

Reverse-engineered from what? From the neural network of the
human brain??

M. K. Shen

[Peter Olcott]

No those paths are dead-ends. Reverse-engineered on the basis of the
fundamental structure of the universal set of conceptual knowledge.

[Arnaud F.]

Le vendredi 7 septembre 2012 17:00:38 UTC+2, Peter Olcott a écrit :


>
> No those paths are dead-ends. Reverse-engineered on the basis of the
>
> fundamental structure of the universal set of conceptual knowledge.

***

http://world-web-trolls-and-idiots.blogspot.fr/2012/09/peter-olcott.html

A.