a team at ist austria and mit designed an alternative intelligent system
that learns to drive a car autonomously it uses camera signals as inputs and controls the car
using only 19 control neurons imitating the nematodes nervous system to process information
efficiently this new intelligent system is more robust more interpretable and faster
to train than current deep neural network architectures with millions of parameters
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researchers are always looking for new ways to build intelligent models we all know that
really deep supervised models work great when we have sufficient data to train them but one of
the hardest things to do is to generalize well and do it efficiently we can always go deeper
but it has a high computation cost so as you may already be thinking there must be another
way to make machines intelligent needing less data or at least fewer layers in our networks
one of the most complicated tasks that machine learning researchers and engineers are currently
working on is self-driving cars this is a task where every option needs to be covered
and completely stable to be able to be deployed on our roads this process of training a self-driving
car typically requires many training examples from real humans as well as really deep neural
networks able to understand these data and reproduce the human behaviors in any situation
researchers from ist austria and mit have successfully trained a self-driving car
using a new artificial intelligence system based on the brain of tiny animals such as thread worms
they achieved that with only a few neurons able to control the self-driving car compared to the
millions of neurons needed by the popular deep neural networks such as inceptions resnets or vdg
their network was able to completely control a car using only 75 000 parameters
composed of 19 control neurons rather than millions since it is so small it doesn't
have to be a black box like the deep models where we do not completely understand what's
going on at each stage of the network indeed it can be understood by humans as professor
radu grozu head of the research group says the nematode c elegans for example lives its life
with an amazingly small number of neurons and it still shows interesting behavioral patterns
this is due to the efficient and harmonious way the nematode's nervous system processes
the information nematode's nervous system proves that there is still room for improvement for deep
learning models if nematodes can show interesting behavior while having this extremely small
number of neurons after evolving to this near optimal nervous system structure we definitely
can reproduce this on a machine too this neural system allows the nematodes to perform locomotion
motor control and navigation which is exactly what we aim for in applications like autonomous driving
following this neural system they developed new mathematical models of neurons and synapses
called liquid time constant or ltc neurons as professor thomas henzenger said
one way to make the network simpler was to make it sparse meaning that not every cell is connected
to every other cell when a cell is activated the others are not which reduces the computation time
since all the deactivated cells will not send any output or a zero output which is
extremely faster to compute they also changed the way each cell work as dr ramin hassani says
the processing of the signals within the individual cells follows different mathematical
principles than previous deep learning models let's now enter a little deeper into how this new
system works it basically consists of two parts at first there's a compact convolution neural network
which is used to extract structural features from the pixels of the input images
using such information the network decides which part of the image is important are interesting
and passes only this part of the images to the second system which they call a control
system that steers the vehicle using decisions made by a set of biologically inspired neurons
this control part is also called neural circuit policy or ncp basically it translates the data
from the compact convolution model outputs to only 19 neurons in an rnn structure inspired by
the nematode's nervous system controlling the vehicle and allowing it to stay into the lanes
following the architecture shown here you can find more details about the implementations of these
ncps networks in their paper or clear guide they made on google collab both are linked in
the description below this is where the biggest reduction in parameters happens
matthias leschner explains that ncps are up to three orders of magnitude
smaller than what will have been possible with previous state-of-the-art models
both of these systems are trained simultaneously and work together to create the self-driving car
being so small and simple they were able to see where the system was focusing its attention
on the images fed they discovered that having such a small network extracting the most important part
of the picture made the few decision neurons focus exclusively on the curbside and the
arisen which is a unique behavior among artificial intelligence systems that are currently analyzing
every single detail of an image using way too much information than needed just take a second
to look at how little information is sent into the ncp network compared to other types of networks
just by looking at this image we can see that it is clearly more efficient and faster to compute
than current approaches plus while noise is a big problem for current approaches such as rain or
snow in lane keeping applications their ncp system demonstrated strong resistance to input artifacts
because of its architecture and novel neural model keeping their attention on the horizon
even if the input camera is noisy their novel approach is more robust faster to train due
to its small size and more interpretable since it enables clear visualization of the activity inside
the neural network it's a new generation of deep models and a huge step in the field of artificial
intelligence and brings new perspectives by following the biological nervous system as it
is not already impressive enough they also made the code publicly available they even created
complete tutorials on how to create and import their new ncp networks using the ltc neurons
and how to stack ncp with other types of layers everything is available through their github which
i linked in the description box below of course this was just a simple overview of this new paper
i strongly recommend to read the paper linked in the description for more information
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