10 examples of Artificial Intelligence and Machine Learning in use today

OPINION / 27th April 2017

Far from being the preserve of science fiction, Artificial Intelligence and Machine Learning are already being used in many everyday business situations. With tech vendors offering a range of APIs and open frameworks, this technology is now increasingly accessible for developers to use to optimise back office and consumer facing systems and processes.

The following 10 examples are just some of the industries taking advantage of this technology right now:

Auto Trader

The online car marketplace is using machine learning to generate accurate valuations on second-hand vehicles. It has built a data science and insights team, which uses open-source data, to identify the make, model and year a car was manufactured. It teaches its systems the different features of a car, and how each can affect the price/valuation of the vehicles it advertises on its site.

Expedia

The online travel website, which has been in operation for 20 years, is using machine learning algorithms to improve holiday/flight recommendations for its customers. One of the biggest uses of machine learning is in its ‘search’ function, where it aims to find the perfect match for its customers’ queries. Due to flight schedules and prices continually changing, Expedia uses machine learning to adapt its ‘Best fare search’ all the time. It is also using machine learning to detect fraud.

Las Vegas Sands Corporation

The hotel giant, which owns the Venetian and Palazzo, has introduced a virtual assistant chatbot via Facebook for its guests. Rather than send an SMS or email, guests can message the chatbot/concierge to order room service.

NHS 111 non-emergency helpline

The NHS is trialling an AI powered chatbot for its 111 non-emergency helpline service in North London. The 1.2 million residents in this part of the capital can use a chatbot within an app, rather than telephone the 111 helpline, to find a diagnosis to their illness. The app, which consults a large medical database to diagnose patients, gives users tailored responses based on the information they’ve entered.

Ocado

The British online supermarket has created an in-house technology team, Ocado Technology, which includes a data science team that looks at new ways to apply machine learning and AI techniques to improve its systems. It is currently using machine learning to scan customer emails so that it can prioritise responding to them. The system can detect if a customer email is raising an issue, and will tag and colour code it so that a customer service representative can respond immediately. Elsewhere in the business, it is using 4G connected robots in its warehouses to replace barcode scanning. The computer vision system is still in its infancy, but Ocado hopes that this technology will help it deliver efficiencies in packing and delivery processes.

Rolls Royce

Rolls Royce, working with Microsoft, is using IoT and AI to monitor every jet engine in use to improve aircraft efficiency and reduce maintenance costs. Rolls-Royce engines send telemetry data to four centres around the world which monitors the condition of the parts. An inspection can be scheduled or spare parts can be directed to the right destination even before the pilots or the airline knows that one of their engines has a problem.

Royal Bank of Scotland

RBS has been trialling a natural language processing AI bot called Luvo, to answer NatWest, Ulster Bank and its own customers’ questions. Luvo can perform simple banking tasks like money transfers, forgotten PIN numbers and lost cards, and when it can’t help or understand the customer’s questions it refers them to a member of staff. RBS is the first retail bank in the UK to offer such a service, but elsewhere in Europe SwedBank and Spain’s BBVA have created similar services.

The Royal Free Hospital

The North London hospital has built an app, in conjunction with Google’s DeepMind, to help hospital staff monitor patients with acute kidney injuries. The system in use, called Streams, uses patient data scanned by the app to predict when an acute kidney injury (AKI) is likely to occur. Google’s DeepMind data, which puts the analytics into the hands of the frontline staff, is reportedly saving nurses up to two hours every day, time they can now spent face to face with patients.

In Japan last year, similar technology, provided by IBM Watson, saved a woman’s life by successfully diagnosing a rare form of leukaemia in 10 minutes, by cross-referencing the patient’s genetic changes with 20 million cancer research papers.

Uber

The car hailing company has been using machine learning algorithms for some time. It uses machine learning to predict travelling habits of its core users, improve maps and power its autonomous vehicles.

Virgin Trains

The UK-based train operator has implemented a cognitive learning technology solution to help it deal with the increasing customer correspondence it receives. Like Ocado, it is using AI to streamline the labour intensive admin tasks and decision making in handling customer emails. The machine learning is able to read, understand meaning/sentiment, categorise and then recognise key information within the customer emails, as they are received.

ORM’s view:

AI, cognitive computing and machine learning technology has the ability to revolutionise the way we work. It is already driving efficiencies in many sectors including healthcare, customer service, insurance, engineering, law, retail and many more. The likes of IBM Watson, Google’s DeepMind and Microsoft’s Cortana can all process and scan millions of pages of structured and unstructured data or pages of written information, and make sense of it in real time. These systems not only understand the information given to them, they can understand the nuances of language and the sentiment, and make decisions on the back of what they’re exposed to. IBM Watson is already working with oncologists to diagnose and prescribe tailored drug packages for cancer patients. IBM Watson Genomics can scan genetic sequencing data across 170 genes and create a recommendation report in a few minutes, something a human doctor would take a week to do.

Although machine learning and AI are in the early stages of use in some sectors, this technology will soon become widely adopted, and those that utilise it early on will have the competitive edge over those businesses that choose to ignore it. Come and talk to us about how we can help introduce machine learning into your business.

Peter Gough Peter Gough Managing Partner & Founder Peter Gough