INTELLIGENT BRANDS // Data Centres
Universities are shaping
the advances in new data
centre technology
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U
niversity researchers have developed neural
networks – data systems that mimic the
learning patterns of the human brain – that
are adaptable and agile enough to work with compact
devices. Until now, the vast size and scope of neural
networks have made it necessary for them to run
primarily on servers with extensive memory and
power. However, a group of Massachusetts Institute
of Technology researchers has developed new chip
technology that would make the networks energy-
efficient enough to operate on smartphones.
First, the MIT group found a way to measure the
amount of energy that a neural network requires for a
given device. They then applied that knowledge to the
design of more streamlined neural networks, whose
energy use is economic enough to let the networks
function on smartphones.
This advance makes it possible for smartphones to serve a more
versatile and useful role in sophisticated IT operations.
Deep neural networks could compress complex data
Other research into neural networks suggests they could master
complex computing functions, such as those involved with air traffic
control safety. Researchers at Stanford University, Johns Hopkins
University and MIT are using deep neural networks, or networks
with the most computing power, to refine a data compression
method that might make airborne collision avoidance systems
more effective.
Sensors in these next-generation air traffic control systems use data
from planes in a certain vicinity to calculate the best, safest travel
route. Due to it being impossible to precisely predict the planes’
changing trajectories, the calculations must account for many
possible trajectories and score the risk involved with each.
The resulting data table is much too large for existing control
systems and current compression methods can shrink the database
only slightly (by a factor of five) without sacrificing reliability. That
reliability is critical since the data serves to prevent collisions, but
deep neural networks were able to compress the data table by a
factor of 1,000, and millions of simulations yielded fewer collisions
and alerts.
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“Having this neural network that can represent this gigantic amount
of data and compress it by a factor of 1,000 or more opens up the
door for a lot of other applications,” said Kyle Julian, a Stanford
graduate student who led the research in a Stanford magazine
article. For example, compressing data may make it easier to use
artificial intelligence to analyse data sets.
Machine learning, data power up neural networks
As neural networks handle more data and get better at computing,
they can also work with machine learning to perform industry-specific
tasks. “The data and the computational capability are increasing
exponentially, and the more data you give these deep-learning
networks and the more computational capability you give them, the
better the result becomes, because the results of previous machine-
learning exercises can be fed back into the algorithms,” data scientist
Jeremy Howard said in an interview with McKinsey Quarterly.
Frequently, this makes it easier for organisations to spot patterns or
classify data so they can better segment their customers according
to basic characteristics (behavioural and demographic) and in theory,
target them more accurately with offers and messaging.
For example, machine learning can gather information from real-
time telecommunications network operations – such as the amount
and location of traffic, the types of calls being made and who’s
making them – to create better calling plans for subscribers. n
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