I don't know enough about Novamente to say if your approach would work.  Using 
an artificial language as part of the environment (as opposed to a substitute 
for natural language) does seem to make sense.

I think an interesting goal would be to teach an AGI to write software.  If I 
understand your explanation, this is the same problem.  I want to teach the AGI 
two languages (English and x86-64 machine code), one to talk to me and the 
other to define its environment.  I would like to say to the AGI, "write a 
program to print the numbers 1 through 100", "are there any security flaws in 
this web browser?" and ultimately, "write a program like yourself, but smarter".

This is obviously a hard problem, even if I substitute a more "English-like" 
programming language like COBOL.  To solve the first example, the AGI needs an 
adult level understanding of English and arithmetic.  To solve the second, it 
needs a comprehensive world model, including an understanding of how people 
think and the things they can experience.  (If an embedded image can set a 
cookie, is this a security flaw?).  When it can solve the third, we are in 
trouble (topic for another list).

How could such an AGI be built?   What would be its architecture?  What 
learning algorithm?  What training data?  What computational cost?
 
-- Matt Mahoney, [EMAIL PROTECTED]

----- Original Message ----
From: Ben Goertzel <[EMAIL PROTECTED]>
To: agi@v2.listbox.com
Sent: Thursday, November 2, 2006 3:45:42 PM
Subject: Re: Re: [agi] Natural versus formal AI interface languages

Yes, teaching an AI in Esperanto would make more sense than teaching
it in English ... but, would not serve the same purpose as teaching it
in Lojban++ and a natural language in parallel...

In fact, an ideal educational programme would probably be to use, in parallel

-- an Esperanto-based, rather than English-based, version of  Lojban++
-- Esperanto

However, I hasten to emphasize that this whole discussion is (IMO)
largely peripheral to AGI.

The main point is to get the learning algorithms and knowledge
representation mechanisms right.  (Or if the learning algorithm learns
its own KR's, that's fine too...).  Once one has what seems like a
workable learning/representation framework, THEN one starts talking
about the right educational programme.  Discussing education in the
absence of an understanding of internal learning algorithms is perhaps
confusing...

Before developing Novamente in detail, I would not have liked the idea
of using Lojban++ to help teach an AGI, for much the same reasons that
you are now complaining.

But now, given the specifics of the Novamente system, it turns out
that this approach may actually make teaching the system considerably
easier -- and make the system more rapidly approach the point where it
can rapidly learn natural language on its own.

To use Eric Baum's language, it may be that by interacting with the
system in Lojban++, we human teachers can supply the baby Novamente
with much of the "inductive bias" that humans are born with, and that
helps us humans to learn natural languages so relatively easily....

I guess that's a good way to put it.  Not that learning Lojban++ is a
substitute for learning English, rather that the knowledge gained via
interaction in Lojban++ may be a substitute for human babies'
language-focused and spacetime-focused inductive bias.

Of course, Lojban++ can be used in this way **only** with AGI systems
that combine
-- a robust reinforcement learning capability
-- an explicitly logic-based knowledge representation

But Novamente does combine these two factors.

I don't expect to convince you that this approach is a good one, but
perhaps I have made my motivations clearer, at any rate.  I am
appreciating this conversation, as it is pushing me to verbally
articulate my views more clearly than I had done before.

-- Ben G



On 11/2/06, Matt Mahoney <[EMAIL PROTECTED]> wrote:
> ----- Original Message ----
> From: Ben Goertzel <[EMAIL PROTECTED]>
> To: agi@v2.listbox.com
> Sent: Tuesday, October 31, 2006 9:26:15 PM
> Subject: Re: Re: [agi] Natural versus formal AI interface languages
>
> >Here is how I intend to use Lojban++ in teaching Novamente.  When
> >Novamente is controlling a humanoid agent in the AGISim simulation
> >world, the human teacher talks to it about what it is doing.  I would
> >like the human teacher to talk to it in both Lojban++ and English, at
> >the same time.  According to my understanding of Novamente's learning
> >and reasoning methods, this will be the optimal way of getting the
> >system to understand English.  At once, the system will get a
> >perceptual-motor grounding for the English sentences, plus an
> >understanding of the logical meaning of the sentences.  I can think of
> >no better way to help a system understand English.  Yes, this is not
> >the way humans do it. But so what?  Novamente does not have a human
> >brain, it has a different sort of infrastructure with different
> >strengths and weaknesses.
>
> What about using "baby English" instead of an artificial language?  By this I 
> mean simple English at the level of a 2 or 3 year old child.  Baby English 
> has many of the properties that make artificial languages desirable, such as 
> a small vocabulary, simple syntax and lack of ambiguity.  Adult English is 
> ambiguous because adults can use vast knowledge and context to resolve 
> ambiguity in complex sentences.  Children lack these abilities.
>
> I don't believe it is possible to map between natural and structured language 
> without solving the natural language modeling problem first.  I don't believe 
> that having structured knowledge or a structured language available makes the 
> problem any easier.  It is just something else to learn.  Humans learn 
> natural language without having to learn structured languages, grammar rules, 
> knowledge representation, etc.  I realize that Novamente is different from 
> the human brain.  My argument is based on the structure of natural language, 
> which is vastly different from artificial languages used for knowledge 
> representation.  To wit:
>
> - Artificial languages are designed to be processed (translated or compiled) 
> in the order: lexical tokenization, syntactic parsing, semantic extraction.  
> This does not work for natural language.  The correct order is the order in 
> which children learn: lexical, semantics, syntax.  Thus we have successful 
> language models that extract semantics without syntax (such as information 
> retrieval and text categorization), but not vice versa.
>
> - Artificial language has a structure optimized for serial processing.  
> Natural language is optimized for parallel processing.  We resolve ambiguity 
> and errors using context.  Context detection is a type of parallel pattern 
> recognition.  Patterns can be letters, groups of letters, words, word 
> categories, phrases, and syntactic structures.  We recognize and combine 
> perhaps tens or hundreds of patterns simultaneously by matching to perhaps 
> 10^5 or more from memory.  Artificial languages have no such mechanism and 
> cannot tolerate ambiguity or errors.
>
> - Natural language has a structure that allows incremental learning.  We can 
> add words to the vocabulary one at a time.  Likewise for phrases, idioms, 
> classes of words and syntactic structures.  Artificial languages must be 
> processed by fixed algorithms.  Learning algorithms are unknown.
>
> - Natural languages evolve slowly in a social environment.  Artificial 
> languages are fixed according to some specificiation.
>
> - Children can learn natural languages.  Artificial languages are difficult 
> to learn even for adults.
>
> - Writing in an artificial language is an iterative process in which the 
> output is checked for errors by a computer and the utterance is revised.  
> Natural language uses both iterative and forward error correction.
>
> By "natural language" I include man made languages like Esperanto.  Esperanto 
> was designed for communication between humans and has all the other 
> properties of natural language.  It lacks irregular verbs and such, but this 
> is really a tiny part of a language's complexity.  A natural language like 
> English has a complexity of about 10^9 bits.  How much information does it 
> take to list all the irregularities in English like swim-swam, mouse-mice, 
> etc?
>
> -- Matt Mahoney, [EMAIL PROTECTED]
>
>
>
>
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