----- Original Message -----
Sent: Sunday, May 07, 2006 9:07 AM
Subject: RE: [agi] Logic and Knowledge
Representation
>> John said: The human brain, the only
high-level intelligent system currently known, uses language and logic for
abstract reasoning, but these are based on, and owe their existence to, a more
fundamental level of intelligence -- that of pattern-recognition,
pattern-matching, and pattern manipulation.
I agree with this wholeheartedly. But on the
next point we diverge in our thinking.
>> John said: In evolution on
earth, sensory-motor-based intelligence came first, and the use of
language and logic only later. It seems to me that the right path
to true AI will also use sensory-motor patterns as the basic building blocks
of knowledge representation. A typical human being's knowledge of
the letter "A" involves recognition of graphical representations of the
symbol, memories of its sound when spoken, procedural or muscle memory of
how to speak and write it, and memories of where it is commonly
found in its linguistic context. A system should be capable of
recognizing symbols visually or auditorially (and possibly
of generating them through motor outputs) before it should be
expected to comprehend them.
Any thoughts or
arguments? Or am I just repeating something everyone already
knows? (I honestly don't know.)
>>
Speech recognition and visual recognition are
separate problems from knowledge representation/pattern
recognition.
Hellen Keller was blind and deaf but with some help
was able to achieve knowledge representatation and pattern recognition without
the use of either hear or sight.
Think of the senses as the input/output devices and
yes an infant's brain must first learn to control those input output devices
before it is able to learn and communicate with the world outside
itself.
But an artificially intelligent entity already has
access to an ASCII data stream that is can do input/output to communicate
outside itself.
Of course because a picture is worth a thousand words
a program that can also do visual recognition has access to a larger data
store than one that does not.
My opinion on the most probable route to a true
AI Entity is:
1. Build a better fuzzy pattern representation
language with an inference mechanism for extracting inducible information from
user inputs. Fuzziness allows the
language to understand utterances with
misspellings words run together etc...
2. Build a bot based on said
language
3. Build a large knowledgebase which captures a large
enough percentage of real world knowledge to allow the bot learn from natural
language data sources i.e. the web.
4. Build a pattern generator which allows the bot
learn the information it has read and build new patterns itself to represent
the knowledge.
5. Build a reasoning module based on Bayesian logic
to allow simple reasoning to be conducted.
6. Build a conflict resolution module to allow the
Bot to resolve/correct conflicting information or ask for help with
clarification to build correct mental model.
7. Build a goal and planning module which allow the
Bot to operate more autonomously to aid in the goals which we give it i.e..
achieve singularity.
Steps 1 and 2 an took me a couple
years.
3 is an ongoing effort. Into my fourth year now with
28000 patterns.
Hint: if the pattern recognition language is good a
single pattern should be able express all the ways of expressing a single
thought in a single pattern.
This makes the patterns longer and more complex but
reduces overall work by not forcing the bot master to write thousands of
patterns to account for all possible ways to express a single thought.
My 28000 patterns would requires match at several
orders of magnitude more inputs correctly than competing solutions including
misspellings, ungrammatical inputs
etc.
This transforms the difficulty of step 3 from being
an totally intractable problem to a doable but still difficult/work
intensive problem.
Step 4 is keeping me awake at night thinking about
it.
Steps 5, 6 and 7 don't sound that difficult to
me right now but that's only because I haven't thought about them in enough
detail.
People have challenged the top down approach saying
that such a bot would lack grounding or the ability to tie it's knowledge to
real world inputs.
But it should not be difficult to use a commercial
voice recognition engine to transform voice inputs
into ASCII inputs. And the fuzzy recognizer for be able
to
compensate many times for the mistakes that the voice
recognition software makes in recognizing a word or two in the input
stream.
Is anyone interested in discussing the use of
formal logic as the foundation for knowledge representation schemes for
AI? It's a common approach, but I think it's the wrong path.
Even if you add probability or fuzzy logic, it's still insufficient for true
intelligence.
The human brain, the only high-level intelligent
system currently known, uses language and logic for abstract reasoning, but
these are based on, and owe their existence to, a more fundamental level of
intelligence -- that of pattern-recognition, pattern-matching, and pattern
manipulation.
Philosophers have grappled with the question of
the source of knowledge for as long as there have been philosophers, and one
of the best accepted answers in modern philosophy is sensory experience.
Sensory experience, including proprioception and awareness of motor outputs,
in addition to the ordinary five senses, is the material that knowledge is
built out of. The brain constructs its logical formulations out of the
basic building blocks of the sights, sounds, and feels of linguistic
symbols. The symbols themselves (letters of an alphabet, words in a
language, etc.) are built up out of lower-level sensory patterns.
In evolution on earth, sensory-motor-based
intelligence came first, and the use of language and logic only
later. It seems to me that the right path to true AI will also use
sensory-motor patterns as the basic building blocks of knowledge
representation. A typical human being's knowledge of the letter "A"
involves recognition of graphical representations of the
symbol, memories of its sound when spoken, procedural or muscle memory of
how to speak and write it, and memories of where it is commonly
found in its linguistic context. A system should be capable of
recognizing symbols visually or auditorially (and possibly
of generating them through motor outputs) before it should be
expected to comprehend them.
Any thoughts or arguments? Or am I just
repeating something everyone already knows? (I honestly don't
know.)
J.P.
To unsubscribe, change your address, or temporarily deactivate your
subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
To unsubscribe, change your address, or temporarily deactivate your
subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]