Dear OpenCog group,

I think it would be better if you choose some more altruistic example, when
you go public with this. Like connecting a disease with a cure in medicine,
or alerting humanity for problems about polluting the Earth by unsolvable
materials, or running out of the oil resources that only cause wars anyway,
or something else. I think there must be a better use than showing off an
"intelligent" killing machine. I mean something really influent, smart and
ambitious, decent of a unit that should overpass us by intelligence.

Thank you for your time,
Ivan

2017-01-04 9:17 GMT+01:00 Ben Goertzel <b...@goertzel.org>:

> Misgana etc.,
>
> Summarizing our discussion in the office today...
>
> 1)
> Load ConceptNet and WordNet into the Atomspace (this should take many
> GB but there are instances on AWS with loads of GB of RAM)
>
> 2)
> Experiment A)
> -- feed the system 10 articles on insects to read
> -- feed the system 5 articles on poisons to read [but not on
> insecticide -- other kinds of poisons]
> -- see if insecticide-related Atoms pop up in the Attentional Focus
> (they should)
>
> 3)
> Experiment B1)
> -- feed the system 10 articles on insects to read
> -- feed the system 5 articles on poisons to read [but not on
> insecticide -- other kinds of poisons]
> -- feed the system one article on insects
>
> Experiment B2)
> -- feed the system 10 articles on insects to read
> -- feed the system 5 articles on poisons to read [but not on
> insecticide -- other kinds of poisons]
> -- feed the system one article on cars
>
>
> Here what we want to observe is whether in B1, the switch of attention
> from poisons back to insects, is faster than in B2, the switch of
> attention from poisons to cars
>
> 4)
> Now, take this same Atomspace with ConceptNet and WordNet in it, and
> load in Simple English Wikipedia.   The goal is not to have the system
> remember SEW, but rather to have it build HebbianLinks based on the
> SEW articles it is reading.   We can have the Forgetting agent run, so
> that the Atoms read from prior SEW articles will be forgotten to make
> room for the Atoms from newly read SEW articles.... (i.e. the new
> sentences from SEW articles will have high STI but low LTI, whereas
> the Atoms from WordNet and ConceptNet will have high LTI and thus be
> unlikely to get forgotten...)
>
> Then, re-run experiments A and B on this Atomspace with all the
> HebbianLinks in it
>
> An interesting parameter to play with here, is the amount of STI
> spreading that goes along HebbianLinks versus other links
>
> This gives a chance to play with the role of weak links in stabilizing
> networks, as discussed e.g. in the excellent book
>
> https://www.amazon.com/Weak-Links-Universal-Stability-
> Collection/dp/3540311513
>
> A hypothesis is that the presence of the weak HebbianLinks in the
> Atomspace will cause the behavior on experiments A and B to be better
> (i.e. more insecticide stuff in the AF in experiment A; more rapid
> switch back to insects in experiment B) ...
>
> ....
>
> These experiments should help us tune ECAN to work sensibly on large,
> moderately  messy Atomspaces ... and from here we should be able to
> move on to using ECAN to help provide guidance to PLN for common-sense
> inferences...
>
> -- Ben
>
>
> --
> Ben Goertzel, PhD
> http://goertzel.org
>
> “I tell my students, when you go to these meetings, see what direction
> everyone is headed, so you can go in the opposite direction. Don’t
> polish the brass on the bandwagon.” – V. S. Ramachandran
>
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