[ExI] Toy brain can count to five
Stuart LaForge
avant at sollegro.com
Wed Jun 12 01:02:17 UTC 2019
In order to get a feel for the capabilities of machine intelligence, I
have been playing around with a neural network platform called Simbrain.
Simbrain is an awesome free software that lets you play with
encapsulated graphical neurons on your computer screen. You can create
neurons with a push of a button and wire them together using your
mouse. It's like a lego set or tinker toys for AI and machine
learning, relatively intuitive to use, and requiring very little in
the way of coding or math to get up and running designing toy brains.
It is written in Java so it is platform independent and it is
downloadable here:
https://www.simbrain.net/
Ok, so being inspired with all the research being done on the numeracy
and math skills of honeybees, I designed a 55 neuron brain as a
network with 5 input neurons, 45 neurons in 3 hidden layers, and an
output layer with 5 neurons labelled "1" through "5". So then I set
about seeing if I could use the back propagation algorithm to train my
tiny brain to count to five.
Basically I feed it a training set consisting of examples of all the
possible ways that the 5 input neurons can be lit up. For example, I
input all 5 ways that 1 input neuron out of 5 input neurons can be lit
up, i.e. the 1st neuron can be lit or the 2nd or the 3rd etc. Then I
correspond it to the output neuron labelled with the numeral "1". I do
the same thing for all the ways that 2 out of 5 input neurons can be
lit up and associate those with the output neuron labelled "2". I do
the same thing all the way up to all 5 input neurons lit up. (There
are 10 ways to make 2 out 5 neurons light up, 10 ways to make 3 out of
5 neurons light up, 5 ways to make 4 neurons light up and just 1 way
to make 5 out 5 input neurons light up.)
So then I use back propagation to train the neurons for about 20,000
iterations, randomizing the weights and activations a few times to
escape from local minima to get down to an error rate below 1%.
And then I tested it and it worked like a charm. It could seemingly
associate the reality of between one and five objects, in this
specific case activated input neurons, and with the specific output
neuron that symbolized that specific number. Sort of like training a
child to point to the numeral representing the number of blocks you
had set down before him or her.
So that was mildly interesting but not particularly impressive
because, I had literally gave it every possible way to make those
input neurons light up and the enumerated output neurons those
patterns corresponded to. To program a computer to do that would be
trivial. My tiny brain could simply be memorizing data without
actually thinking about it or understanding the concept, like a simple
lookup table.
So then, I went through my training data and pruned away various
patterns so that I could test the brain against patterns it had never
seen before. And I trained my brain with this pared down data and then
tested it against patterns of lit up input neurons it hadn't been
trained on. Nonetheless it did quite well at counting the activated
neurons in patterns it hadn't seen before all the way down to almost
half of the original training set. Taking out 4 of the patterns where
2 input neurons out of 5 were activated, reduced the activation of the
neuron which represented "2" but it nonetheless got the right answer.
The fewer the training examples of a given number, the less able the
toy brain was able to figure out what the number was.
For example, if I removed the one way that all 5 input neurons could
be lit up at once from the training set, upon presenting it with that
pattern of all 5 being lit up, it did not even try to infer that that
novel pattern could be the only previously unused output neuron (the
one labelled "5"). Instead it lit up none of the output neurons, thus
declining to answer.
So my conclusion is that neural networks are pretty damn good at
deductive reasoning and interpolation but pretty damn lousy at
induction and extrapolation.
If anybody downloads the software and wants a copy of my toy brain to
play with, then email me offlist and I will send it as a zip file.
Stuart LaForge
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