I am not talking about growing knowledge from a domain base. I am
talking about growing knowledge from interactions with human users who
will provide the system with facts and relationships usually expressed
in a natural (but austere) form, which the program would then have to
integrate and explore. This idea is not radically new but the metaphor
of 'growing knowledge' makes me think of the problem in new ways. All
out radical thinking is not going to work unless it can be used to
resolve some complexity. So if you have done some work and developed a
novel idea that can work on resolving complexity to some new degree,
then that would be worthwhile to examine more closely. The idea that
some guy in France tried what I am talking about is not an assertion
that can be made without a more careful comparison of the ideas.
Looking as quickly as I can at Pitrat's blog, I found this:
"For the realization of a General Problem Solving system, a number of
meta-problems arise. The main idea of bootstrapping is that these
meta-problems will be solved by the system itself, in the same way as
it solves the problems for which it was designed."
That is the opposite of what I had said. OK, I now see where Nanograte
misunderstood me. I had used the word "seeding" and he thought that I
meant that you seed the program with domain knowledge at the start. I
can see how a non-gardener, non-farmer urbanite might come to that
conclusion. I used to grow some stuff in a garden. You have to seed
every year, and then you have to nurture the garden. You have to
harvest. You have to do something to refurbish the soil. You have to
come up with watering systems, you have to watch the weather, you have
to cover your plants to survive a first frost, you have to figure out
new ways to get better results and ways to grow stuff during the
winter. I was able to grow basil all year round by keeping it near the
furnace in the winter and putting it under florescent lamps that were
on a timer to approximate 12 hour days.
In other words my idea is that if I could write a program that would
do a lot of the heavy lifting itself, I might be able to get it to
function successfully by relying on the principle that human beings
can guide it by giving it all sorts of facts and opinions and guesses.
In my plan I would have to supply facts, but I would also have to try
to help it to think. But the program would have to be able to use this
guidance effectively. A person cannot teach the program to learn more
effectively using contemporary AI theories. But if complexity were
resolved to some extent then it would be much easier to discover how
to improve the AI programs. Talking alone isn't going to get it done.

Jim Bromer
On Fri, Sep 14, 2018 at 2:36 AM Nanograte Knowledge Technologies via
AGI <agi@agi.topicbox.com> wrote:
>
> A good and fair challenge. I'll just have to find my source.
>
> ________________________________
> From: Basile Starynkevitch <bas...@starynkevitch.net>
> Sent: Friday, 14 September 2018 5:55 AM
> To: AGI
> Subject: Re: [agi] Growing Knowledge
>
>
> On Wed, 12 Sep 2018 at 15:26, Nanograte Knowledge Technologies via AGI 
> <agi@agi.topicbox.com> wrote:
>
> Evn with all its bureaucracy, France has not (officially) and never had a 
> single head of AI research.
>
>
> However, regarding bootstrapping AI, I guess you are refering to Jacques 
> Pitrat. He is a pionneer of French AI, is a retired academics (he probably 
> was born just before WW2) and was a top-level Directeur de recherches at CNRS 
> and is still working on bootstrapping his CAIA system. He is describing on 
> his blog, http://bootstrappingartificialintelligence.fr/WordPress3/ his views 
> on AI and his system.
>
>
> Cheers.
>
> --
>
> Basile STARYNKEVITCH   == http://starynkevitch.net/Basile
> opinions are mine only - les opinions sont seulement miennes
> Bourg La Reine, France
>
> Artificial General Intelligence List / AGI / see discussions + participants + 
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