From science fiction to the enterprise: Time to embrace AI

Steve Evans

Monday 13 February 2017

Artificial intelligence and machine learning are getting a lot of attention at the moment. What are the main use cases and why should forward-thinking enterprises look at embracing it?

The tech industry is never afraid to label something as ‘the next big thing.’ Cloud computing, social media and mobility have, quite rightly, all had their moment in the spotlight over the past few years. Right now, it is time for machine learning (ML) and artificial intelligence (AI).

Specifically, it’s the combination of the ML and AI that is getting a lot of tech experts excited. While the two are generally used interchangeably, there is a difference. AI is the concept of machines carrying out tasks in a way humans would consider to be intelligent. ML, meanwhile, is an aspect of AI that dictates – as the name suggests – that machines are given data that enables them to learn for themselves.

Stepping out of science fiction

Robots and computers that closely resemble humans in the way they think and act have been part of the sci-fi genre for decades. The Terminator is a great example, with Skynet an AI that is sufficiently advanced to realise it’s under attack and take action to protect itself.

For many, that’s where AI and ML still remain: in science fiction only. But the truth is there are many uses for them, some of which have already been embraced by millions of people in everyday life.

Personal assistants from some of the world’s biggest tech companies are playing an increasingly important role in our lives. These assistants, whether they are built into our mobile devices or run through systems in our homes, enable us to play music, listen to the news, order food or a taxi, do our shopping and so much more.

AI in everyday life

There is considerable debate surrounding whether these personal assistants are true artificial intelligence, but there’s no doubt they use elements of it and machine learning in voice recognition. The underlying AI and ML also help these assistants improve their responses and better understand the context of questions, to the point where they can often be predicted.

Video games, smart cars, recommendation engines (on streaming services and online retailers) and facial recognition software used on social platforms all use elements of AI.

Quocirca founder Clive Longbottom adds that situations where speed and intelligence are needed are key to embracing AI and ML. FAQ services on websites is one example he cites, saying: “It learns from what users are asking and improves its answers all the time.”

If customers keep asking about a product in a certain colour, the system has the capability to inform the company, so they could consider introducing one in that colour. “So it’s cutting out the cost of humans, replacing it with something that is intelligent enough to help the company,” he says.

AI in IoT and big data

In the years ahead however, Longbottom expects AI and ML to lend themselves to the Internet of Things (IoT) very well. “Going forward it will be far more of a big data problem, particularly if the IoT comes through – you cannot have human learning going on there, it’s too big a problem and it’s too fast a problem.”

So what’s happened recently that has made AI and ML such hot topics? As with so many emerging technologies, it’s a combination of developments. Huge amounts of data, near-limitless low-cost storage options and much cheaper, yet hugely more powerful computers that are able to crunch through this vast amount of data.

Analyst company IDC’s latest spending guide reveals the worldwide market for AI will increase from $8 billion (€7.5 billion) in 2016 to $47 billion (€44 billion) in 2020, stating: “Automated customer service agents, quality management investigation and recommendation systems, diagnosis and treatment systems, and fraud analysis and investigation are gaining the most traction.”

Going forward, it’s expected that “public safety and emergency response, pharmaceutical research and discovery, diagnosis and treatment systems, supply and logistics, quality management investigation and recommendation systems, and fleet management” will see a lot of AI investment, the IDC spending guide added.

Low-hanging fruit

Gartner, meanwhile, listed “AI and ML” as one of its top 10 strategic technology trends for 2017. “[It includes] more advanced systems that understand, learn, predict, adapt and potentially operate autonomously. Systems can learn and change future behaviour, leading to the creation of more intelligent devices and programs.

“Organisations seeking to drive digital innovation with this trend should evaluate a number of business scenarios in which AI and machine learning could drive clear and specific business value and consider experimenting with one or two high-impact scenarios.”

Increasingly, these scenarios are where artificial intelligence and machine learning mean businesses can automate processes and tasks that used to be carried out by humans, who can be slow, expensive and make mistakes.

“Organisations should really be looking at the low-hanging fruit of where it makes sense to get rid of the commodity process and tasks by automating them,” Longbottom says. “It has to be very high on the list. It’s now at the stage where if you don’t do it, someone else will.”

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