https://demo.allennlp.org/reading-comprehension

https://visualcommonsense.com/


Pointing at people doesn't mean they like pancakes.


https://www.youtube.com/watch?v=XTPxEwfNTJc


On 23.07.2019 19:17, twenkid wrote:
Costi, I think your reasoning is right as of the importance of the
multi-modal input for generalisation. As of ANN (as popular), they
were explicily mentioned only in several slides in the lecture about
Narrow AI and why it failed, with some hopes given to Schmidhuber's
LSTM. Slides 34-36:
http://research.twenkid.com/agi/2010/Narrow_AI_Review_Why_Failed_MTR.pdf

In a new course they would deserve more attention. :)

Perhaps current ANN  could be used for AGI if glued appropriately with
other methods (not just one ANN config. on tensorflow e.g.) or maybe
as Goodfellow suggested in the podcast on AI podcast of MIT - if a big
enough multi-modal dataset is fed with appropriate representation
(your proposal is in that direction as well). However with current NN
I assume it would be massively less efficient than with a better more
clever, or "selective", method.

If you lacked access to the computing power of the big organizations,
working their way is impossible, i.e. adding more and more resources,
while using the same or just slightly adjusted simple  algorithm. You
have to either invent something smarter which works on the less
powerful hardware available to you, or join them.

"Deep Learning" as hierarchical processing at different resolutions
(time including, all dimensions), starting from sensory input,
interactive adjustment (but more so), RL ideas (but hierarchical),
multi-modal data etc. are right directions regarding the "generality",
but the way the mainstream ANN do it yet is not what I considered
"AGI" either back then and now.


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