This is an important step toward making computers much more brainlike than ever
before, but perhaps trying to mimic the brain, will not be the best route forward [
24
]
much the same way that aviation engineers realised it was much better to utilise
concepts like lift than to try to mimic feathers for aircraft. Professor Michael Jordan
states "There is nothing very neural about neural networks ... no spikes in deep-learning
systems.. no dendrites” [
24
]. Regarding backpropagation Jordan describes how the brain
does not operate the same way, though he acknowledges it led to significant progress.
Numenta’s Jeff Hawkins argues that “intelligent machines will be based on models of the
5
neocortex, not the rest of the brain”, there is a big difference. The most recent paper to
have been accepted into the Computer Vision and Pattern Recognition conference,
CVPR2015, describes how differently CNNs see the world than humans and how this can
also lead to inaccuracy. Nguyen, Clune et al of the University of Wyoming show that
CNNs, if trained with “fooled” images will give a majority false positive results. The
technique is demonstrated on www.picbreeder.org where the user is the fitness function.
The paper illustrates the significance that CNNs can be trained on whatever we want
them to be trained on, and that we can even flip the linear processes; fixed image,
tweaked parameters v. fixed parameters, altered image. The flaw itself, is inherited from
the linear nature of classifiers themselves. The CNN is trained via a repeated process of
sampling data, calculating gradients and updating. One gradient updates one parameter
to increase the correct image score, along with all other gradient-parameter updates.
This is repeated epoch after epoch on all images in the training data until a suitable
accuracy is obtained. Perhaps more efficient methods lie within self-organisation and in
the random way the brain seems to construct its own neural network. Whatever the
solution, today, the most successful artificial neural networks are deep and
multi-layered.
1.4 Neuromorphic chips
Neuromorphic chips are currently being funded by DARPA via HRL Laboratories and IBM,
to enhance AI and avoid Moores Law and its fundamental performance limit. Deep
learning will soon integrate with mobile phones due to Qualcomm bundling its Zeroth
software with its Snapdragon 820 processor - to recognize speech, music, scenes, pick
out objects and even spot patterns of behaviour via the device’s sensors.
5
http://recode.net/2015/03/02/theterminatorisnotcomingthefuturewillthankus/
16
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