Machine learning’s development stems from the mid-20th century where it was defined by Arthur Samuel as a “field of study that gives computers the ability to learn without being explicitly programmed”.
Indeed, the technology provides computers with the ability to learn from exposure to data, rather than being explicitly programmed – like a human brain. The technology’s function is to learn and improve internal procedures and external delivery across a range of industries, from entertainment to cyber security, to manufacturing and finance.
Machine learning is an automated process that enables software-based systems to analyse huge data sets and recognise patterns. Using these patterns, the software is able to reprogram and improve itself – without human intervention. Artificial intelligence (AI) is different in that it produces predictable outcomes without digging deeper – it doesn’t learn, it simply repeats.
In the last couple of years businesses have acquired machine learning startups in a bid to usurp their rivals, but this is the year that the real-world impact of machine learning technologies will be felt.
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Industry is moving from acquisition to implementation. Such is the impact of this AI-based tech that, as mentioned, it is transforming nearly all aspects of business operations. This is not restricted to the private sector, with the UK government rolling out machine learning schemes to improve various departments within the public sector. Although this ambition is not without its challenges.
Consumer brands like Amazon, Netflix and Facebook have successfully adopted the technology, helping them to consistently improve their customer experience. B2B companies, by comparison, are having limited success.
To successfully adopt in the style of companies like Amazon, businesses need to remember that machine learning doesn’t solve problems by itself. The technology delivers actionable insights to users based on the data they’ve been given. To get the most out of this technology businesses need a human touch, and a good, reliable data set. This will not only impact the delivery of services, but also drastically improve internal operations: bridging departments and producing business transparency.
Machine learning does have some way to go the impact of this tech has begun to be felt. It is not about replacing man with machine in different industries, but using the technology to improve business capabilities.
Machine learning and cyber security
Cyber security is one industry to be particularly impacted by the use of machine learning. The technology takes the heavy-lifting out of tasks. Machine learning algorithms that continually adjust the baseline means we can continually adapt to a changing risk environment.
Indeed, it is vital cyber security companies adopt this type of technology to finally get ahead of the cyber criminals who have been winning the cyber war for so long.
Machine learning and travel
The immediate impact of driverless cars is in sight. Within five years they will be a more than common presence on roads across the globe. Machine learning will be essential for these self-driving cars to monitor the situation around the vehicle in real-time.
>See also: Machine learning set to unlock the power of big data
Equally, machine learning has a role to play in international travel. With the repetition of tasks you find in corporate travel machine learning embedded in online services could enable anticipating preferences, automatically booking recurring trips, or categorising your travel expenses.
Machine learning and manufacturing
The manufacturing industry is being revolutionised by the internet of things devices, which generate huge amounts of data to help improve efficiency and drive innovation.
However, without machine learning this data is under utilised. The technology will be able to detect anomalies to prevent machine failures and also help drive robot proof of concepts to drive optimisation.
Machine learning and finance
Financial institutions will increasingly lean on machine learning to devise new business opportunities, deliver customer services and even detect banking fraud as it is taking place. Portfolio management companies today use traditional methods like analysing margin profiles, free cash flow, return ratios, growth profile, pricing power that are available through reports. The AI-based tech in the meantime can help to scout social media, websites and other sources, analysing unstructured data to get some additional inputs and make better decisions.’
Machine learning and retail
Similar to the impact on global brands like Amazon, Netflix and Facebook, machine learning has the ability to truly transform the retail industry with one word: personalisation. Traditionally, retailers would use loose market research based on age, gender and location to determine whether to target their audience.
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With machine learning it allows retailers to identify why individual shoppers shop when they do, and buy what they do in incredible detail. It will help change the idea of customer service.
Machine learning, entertainment hospitality and fashion
Machine learning, like in retail, can help optimise the entertainment experience for viewers at home. In this context of TV, most consumers have viewing patterns that can be mapped to provide highly personalised results to searches. This is more accurate than user-based profile creation or thumbs up/down ratings that are both error-prone and do not automatically take into account users’ changing tastes and preferences over time.
Similarly in the hospitality industry machine learning technology is transforming the sector. With the emergence of cloud computing, hotels can have access to revenue management systems, which when leveraged with machine learning can automatically collect large and complex data sets. This is then converted ‘into a more manageable size and easily understood format, and determine the best possible room rate in real-time, as the market changes.
Fashion, again, is an industry being heavily impacted by machine learning. The future success of the business relies on staying at the forefront of change. The industry on the cusp of a machine learning revolution where the technology will help deliver accurate predictions about style and tastes, while being able to identify items that appeal to individual consumers.
Machine learning and digital marketing
Whilst speculation surrounding new ways to utilise machine learning bubble away, it’s clear that adoption rates across the industry are likely to increase over the coming year. Marketers should see this as a key growth area for marketing overall, as well as specifically for paid search.
>See also: How data and machine learning are turning the tables on mobile attackers
Machine learning and business communications
Over the next two years, machine learning will become increasingly integrated within workplace communications systems, helping to analyse complex patterns in user data and, ultimately, improving productivity across each interaction.
Already, Fuze – for example – is working with data scientists to prototype such an idea, using a small sample of internal communications data to uncover seemingly inconsequential patterns in how users prefer to communicate and interact in order to improve the overall productivity of the company
Machine learning, 2018 and beyond
It is evident that machine learning is already impacting these industries listed above, and a whole lot more. Business leaders recognise the potential machine learning brings. So much so that machine learning specialists command the highest salaries among developers in the UK and Ireland, averaging £56,851.
Machine learning is beginning to affect us both at work and at home, and the possibilities for industries are truly transformative. Enterprises will soon find machine learning at the heart of decision making in core business processing and automation, as bots increasingly take on tasks for IT and business processes. As infrastructure becomes able to support increasingly fast and effective machine learning applications, 2017 will be the year businesses begin to see machine learning as a core technology, moving out of pilot schemes and towards full scale operation.