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Buzzwords like Artificial Intelligence,
Machine Learning, Deep-learning
have all in recent years gained
a lot of attention. This is in many
parts thanks to the large, internet-
based corporations that utilize them
for extremely interesting tasks
like image or speech recognition,
natural language processing and
more while Most of us use such
systems on a daily basis.
When thinking about Machine
Learning, the first thing that comes
to our mind is the cloud hosted
on enormous data centers with
thousands of servers. For many
applications this is the standard and
has been for many years. However,
with recent increase in hardware
availability and performance,
thanks to the advent of the Internet
of Things, and decrease in cost, a
vast range of use cases are moving
from the cloud directly to the
probably the most important, and
surprisingly often neglected, step is
to identify the problem that we are
trying to solve. Truly understanding
the issue is crucial to choosing the
right angle and technology to tackle
it. Is what you are trying to address
a complex classification task that
requires a deep neural network with
many hidden layers?
Machine learning is not limited
to deep learning and neural
networks only. There are plenty
of so-called "classical" machine
learning algorithms, for example
k-means, support vector machines,
statistical models, that are often
less resource-intensive and may
in fact be an optimal solution. It
is there important to experiment
and be able to fail-quickly in order
to move forward with a more
appropriate approach. That said,
the deep learning is what has been
Artificial Intelligence: It's all about the Cloud or it’s
on the Edge ?
Ł
ukasz Grzymkowski, Arrow Electronics
edge. In this paradigm shift, the
node devices are becoming more
autonomous as the intelligence
shifts closer to the field and away
from the cloud, where the events
take place. This has already enabled
very interesting applications,
like autonomous drones, ADAS
systems in automotive, smart
mobile robots and this is certainly
just the beginning. In the following
article, we will provide an overview
of what a system designer must
consider when working on an
artificial intelligence in the edge
solution. The typical flow comes
down to understanding the task to
be solved, choosing the algorithm,
training and deploying a model for
inference.
The Goal: defining the
problem
When working on any solution,
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