Hi,
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.
What kind of bot are you using? Do you mean a physical robot or a chat bot?
3 is an ongoing effort. Into my fourth year now with 28000 patterns.
This is interesting. Would you be willing to share a few example
patterns from your database, so that we can have a better sense of
what you're actually talking about? Right now I have only a very
vague idea....
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.
IMO, steps 5-7 are by hard the hardest ones, and contain many more
hidden difficulties than your wording reveals!
The Novamente AGI system loosely fits into your framework, though the
fit is not 100% natural as you leave out a bunch of things I think are
important
In Novamente we have put a lot of work into 5 and are in the thick of
working on 7, to use your breakdown
We are not doing your step 3, but instead are using a rule-based
language parser and semantic mapper to import basic knowledge into the
system from existing NL texts
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.
The grounding issue is not just about voice versus text --- it's about
grounding of linguistic relationships in physical relationships...
For instance, Novamente can learn that "near" is approximately
transitive via observing the 3D simulation world to which it's
connected; it can then use inference to generalize this transitivity
to other domains that it has not directly experienced, e.g. to reason
that if Rockville is near Bethesda and Bethesda is near DC then
Rockville must be somewhat near DC. I.e. the system can ground the
properties of "near"-ness in its embodied experience. Without this
kind of experiential grounding, a whole bunch of patterns that humans
learn implicitly must somehow be gotten into the AI either by explicit
encoding or by verrrrry subtle mining of relationships from a huge
number of NL texts or verrrrrrry tedious and long-winded chats with
humans...
-- Ben Goertzel
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