The Bridge - Issue 2, 2018 - 8

Feature
THE BARRIERS TO ENTRY
ARE COMING DOWN.
Getting into machine learning has become much
easier over the past decade. Many APIs are
available for all programming languages. With a
host to choose from including; Tensorflow, Caffe,
Keras, DeepLearning4J, or even MATLAB's Machine
Learning Toolbox, it requires very little time and fairly
little code to start applying machine learning. Many
tutorials are available to help people get started with
machine learning, and while there will be some
learning time for people unfamiliar with the subject,
very little knowledge of machine learning algorithms
is needed to successfully use these tools.
Another barrier that has been coming down is
the need for supercomputing. While the CPU in a
standard computer can usually run most algorithms
especially after they are trained, to effectively train
on large amounts of data in a reasonable time
requires much more power. Fortunately, many
APIs nowadays are automatically GPU accelerated,
allowing for massive speedup. Specialized hardware
is even reaching consumer levels, as can be seen
in products such as the Intel Movidius, a USB stick
that can be used to accelerate machine learning. As
well, several cloud services from companies such
as Amazon and Google offer GPU and TPU nodes
for use, allowing for models to be trained without
buying hardware.
It is important to note that these growths are not
a direct result of Moore's Law. While Moore's law
clearly benefits newer hardware, allowing for more
transistors in the same area, GPUs don't necessarily
rely on these and many of their gains are simply
from increasing core count. Furthermore, specialized
hardware simply boils down to configurations of the
transistors that allow for faster computation, at a cost
in speed for general purpose computation.

THE BRIDGE

CONCLUSION
The world is fast becoming AI centric as autonomous
vehicles are becoming more of a reality every
day, autonomous drones/delivery bots are being
developed, robots are becoming more adaptable,
and the ability of computers to sell to us is growing
dramatically. With the rapid advancement of machine
learning into nearly every area, it is fast becoming
commonplace. With the continued innovations
in machine learning algorithms and specialized
hardware, the growth in this field will continue far
beyond the limits of Moore's Law.

REFERENCES
[1] O. Russakovsky et al., "ImageNet Large Scale Visual Recognition
Challenge," Int J Comput Vis, vol. 115, no. 3, pp. 211-252, Dec. 2015
[2] J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," arXiv
preprint arXiv:1709.01507, 2017.
[3] D. Silver et al., "Mastering the game of Go with deep neural networks
and tree search," Nature, vol. 529, no. 7587, pp. 484-489, Jan. 2016.
[4] D. Silver et al., "Mastering the game of go without human knowledge,"
Nature, vol. 550, no. 7676, p. 354, 2017.
[5] S. A. Mulder and D. C. Wunsch, "Million city traveling salesman problem
solution by divide and conquer clustering with adaptive resonance
neural networks," Neural Networks, vol. 16, no. 5-6, pp. 827-832,
Jun. 2003.



Table of Contents for the Digital Edition of The Bridge - Issue 2, 2018

Contents
The Bridge - Issue 2, 2018 - Cover1
The Bridge - Issue 2, 2018 - Cover2
The Bridge - Issue 2, 2018 - Contents
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