“A.I. do”- the marriage of AI and networking – part 1

Of all that is written about artificial intelligence (AI), one of the resounding conclusions is that AI, machine learning and automation will increasingly take on functions within society and business that were once the reserve of skilled human beings – and in some cases, highly skilled human beings.

Setting the arguments for and against this evolution to one side, what we will see is a democratisation of skills which will have the potential to overcome some of the most complex engineering challenges known to both man and machine.

In part one of this two-part series on the marriage of AI and networking, I’ll discuss the impact AI will have on designing the networks that keep people and businesses connected in the future.

Democratising skills with algorithms

Designing a network is one of the most incredibly complex feats of engineering known to man. There are only a few people on the planet who have the necessary skills, knowledge and experience to embark on such a project.

One argument in favour of using AI to help improve the network design process is that this knowledge, experience and skill becomes accessible to the many rather than restricted to the few. By pouring all our existing expertise, data and research into an AI algorithm, this knowledge can be shared and used more creatively as well as augmented and developed further.

So, not only will the number of people and companies that are able to build and innovate with networks increase, the networks which are designed and developed will be capable of far more than their predecessors.

We are already seeing networks becoming increasingly complex due to their global and multi-layered nature, with wireless networks such as superfast 4G or Low Power Internet of Things (IoT) networks built on top of the global fibre IP backbone.

A couple of years ago, you could have argued that network complexity was increasing faster than the rate of technological innovation. However, AI is the key to designing networks which are future proofed against the increasing demands new technologies such as autonomous driving, virtual reality and the IoT will put on the world’s networks.

If it ain’t broken…

While the process of designing a network is highly mathematical and logical, it is still “human” in the sense that somebody does it. This person, amazingly talented as they are, is still prone to human facets such as habit, personal preference and bias. They say “old habits die hard” and psychologically humans are pre-disposed to continue with ways of working which have stood them in good stead previously. This is summed up by another idiom: “don’t fix it if it isn’t broken.”

These predispositions as well as other variables such as time and budget restrictions can prevent humans from weighing up every single possible alternative when they are already aware of a process which has worked just fine in the past. The natural inclination is to focus on the things which are in most obvious need of improvement. This is what sets humans and machines apart.

Machine learning will overturn the human approach to design and find ways of designing networks which are faster, more cost-effective and produce better results than we are currently capable of. The sheer relentlessness of the computing power with AI will achieve means that there are potentially design and management techniques which humans are yet to discover, but self-improving intelligent algorithms will harness over time.

In part 2 of this blog series, I will look beyond AI’s role in the initial design stage and examine how it will be used to improve the way networks are managed and maintained.

Read one of my previous blogs where I discuss how technology will be able to guide our lives in the future.