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4 PROFESSIONAL ENGLISH

Theory is great. What now

First things first
Simply put, artificial intelligence is the development of computer systems able to perform tasks that
normally require (super) human intelligence.
Rather than forcing people to perform increasingly complex calculations from a variety of data
sources, work in AI has concentrated on mimicking human decision-making processes and carrying
out tasks in ever more human ways to enable more predictive problem solving.
Related to this, machine learning is an application of AI. It is a toolkit of algorithms that provide
systems the ability to automatically learn and improve from experience without being explicitly
programmed to do so.
The process of learning begins with observations of data, and looking for trends, patterns and
anomalies within the data to make increasingly better correlations, inferences and predictions.
Machine learning software “learns” by discovering the processes that generate the observed
outcomes of particular inputs. Finally, machine learning provides a framework to make predictions
and recommendations as to what will improve the overall system.
Theory is great. What now?
So how can all this magic be usefully applied, in a practical way, to help IT and network staff drive
down costs, drive up productivity and deliver better user experience on the network? Machine
learning is the ideal tool to automate many of the traditional infrastructure management processes
that are performed manually. Specifically, in the context of enterprise access networks, it:
1.
Eliminates costly and cumbersome manual analysis and correlation of myriad network data
sources by network staff,
2.
Identifies specific and systemic user network performance problems across the entire IP
stack and makes recommendations and predictions on fixing them,
3.
Delivers a single source of network truth that can be used by different factions within the
network team, each responsible for their own services,
4.
Minimizes the finger-pointing among IT staff when issues arise, and
5.
Predicts potential network problems and capacity requirements before they happen.

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Because machines, not people, are staring at every client network transaction 24 hours a day,
network managers care able to determine who, what, when, where and why network problems are
occurring – and what do about them – even if they don't know where to look or what questions to
ask.
Cisco, HPE, Mist and Nyansa, the top talkers in the market giving lots of lip service to the use of AI
and machine learning. Nyansa, the only of the four with a pure-play commercial offering of
machine learning for access networks. Its Voyance network analytics platform provides a good
glimpse into what can be practically achieved through the technology's application.

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