Getting a computer to read text is supposedly impossible, or really
tricky, but they made watson play jeopardy, isnt that a good sign that a
logic calculation is all thats necessary for say a chat bot you cant
tell isnt a person?
Im thinking about it, and all the neural networks or sense symbolization
under the sun wont get you the main meat of an intelligent program,
which is logical reactions to things.
If you just have a database of this’s and that’s, then the reaction
would be to access, what do i like to do, how do i solve this problem,
and how do i reply to this user speaking to me?
I dont think its a dead end at all, I think its damn possible to get a
computer to react logically, its what computers are good at!
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Have you seen AIML? It’s pretty
much all about pairing up categories of inputs with categories of
logical responses as described.
Also, what does any of this have to do with willpower?
well, willpower as in i am free to act in any way possible.
alice is a bit of a bimbo, i actually mean something that wouldnt run on
your average computer.
heres some drivel-> (now published :D)
DIFFERENT IDEAS IVE HAD
WHAT THE HIERACHICAL DATABASE OF COMMONS GAVE ME, TOY WISE.
matching 2 visions, cause to effect.
matching 2 text boxes, replying to each other, each is each others
cause and effect.
matching joystick movements, to a vision.
LOGIC FORMATION DATABASE, which is yet to even be visualized by me
the two can be combined, and it makes the effect deduced, instead of
just matched and played back.
and the idea that, i can turn this database of commons, into a logically
formulated database of experiences, granting me the power
to make new experiences from old experiences using simple logic.
Watson wasn’t just about logic though. The trick that really made Watson
possible, and got them from 60% accuracy to 90% accuracy, was to combine
a bunch of different programs that would each try to evaluate not only
the answer to the question, but also how certain the program was of its
answer. Jeopardy questions fall into vague, often-repeated categories,
and by having the sub-programs identify which categories they were good
at and only get used for those categories, they were able to vastly
improve the machine’s accuracy.
Now I do think that principle could be more widely exploited. One of the
more successful frameworks for neural networks is something called ‘ART’
(Adaptive Resonance Theory), where you have a hierarchy of networks all
responding to input, and the relative weights of the networks’ responses
also evolve according to a neural network algorithm based on their
success. When none of the networks respond well to a given input, a new
sub-network is automatically created, and the process continues - this
means that the algorithm dynamically creates a hierarchy of specialists
based on categories it observes in the input set. That said, it still
has all the classic problems of neural networks - it takes many
repetitions to learn and data representation is extremely important, as
well as eliminating noisy or meaningless inputs. Its also only able to
deal with input-to-output mapping problems with fixed input and output
sizes like most other neural networks.