Even now, that tiny yet extremely powerful acronym can feel as though it’s a world away from our real lives, and instead the domain of big tech giants. In 2021, however, artificial intelligence is a far cry from the Hollywood conception. In fact, it affects our daily lives in ways that few of us considered back then. But if you ever open your phone using face ID, that’s just one of many examples of how AI can work to provide a time-saving shortcut. How about social media? Did you know that AI works behind the scenes to tailor the content you see to your specific tastes? And those real-time traffic alerts you get while using Google Maps? Yet another example.
But what of AI in business? Despite some vocal concerns that AI will eventually replace human beings in the workplace, there are many ways in which it can, instead, augment the way humans work. For example, when it is used to take over the repetitive or dangerous tasks we once carried out, humans are freed up to do work that only we, specifically, can do; the creative, the empathetic and the intuitive stuff that machines cannot replace.
The way in which artificial intelligence in business really comes into its own is threefold. Firstly, in automating business processes. Secondly, in data processing which enables efficient insights and analytics into business. And third, by offering accurate predictions based on the information it has assimilated into customers’ needs. The AI business is booming. Are you ready for it?
What is AI?
Put simply, AI is the simulation of human intelligence in machines that are programmed to imitate the way we think. Like us, machines with artificial intelligence may learn and problem-solve in ways that mimic the human brain. When exposed to new data, these machines can assimilate it and adapt without intervention from humans. And yet, unlike human brains, they are designed to rationalise that which they learn in order to produce the best chance of achieving a goal. And the more data they receive, the more effective they become.
AI research dates back to 1956 when it became the focus of a workshop held on the campus of Dartmouth College, New Hampshire. Those who were present that summer would go on to lead the research into artificial intelligence for decades. Optimistically perhaps, they predicted that the creation of a machine with the adaptive intelligence of a human would be achievable within a generation; duly they were given millions of dollars to see that vision through. And yet, close to two decades later, the US and British governments cut off funding, in a move now referred to an ‘AI Winter’. And despite a resurgence of interest and investment from Japan, by the late 1980s, disillusionment had set in again. It wasn’t until the 21st-century and the commensurate sophistication in computer science that interest and investment resumed in earnest and AI began to be applied in areas such as academia and industry.
So how does it work? In 1950, Alan Turing – he of Enigma fame – asked a deceptively simple question: ‘Can machines think?’ AI is a computer science that, over many chequered decades, has sought to answer that question. But building machines that are intelligent brings as many questions as it answers, and those who work in the field are often at odds as to its precise meaning. Stuart Russell and Peter Norvig, the authors of the groundbreaking Artificial Intelligence: A Modern Approach, now in its fourth edition, define it as ‘the study of agents that receive percepts from the environment and perform actions.’ In short, they say, it can be defined in four ways: machines that think a) humanly and b) rationally; and those that act a) humanly and b) rationally.
There are two basic forms of AI. Narrow AI is the kind that is all around us – in our computers and cars and phones – and which performs tasks it has been specifically programmed to do. General AI, meanwhile, refers to the idea of machines that can carry out more than one task at a time. Both operate using the ideas of machine learning; deep learning; and neural networks.
Automation has, then, become the go-to solution for many companies across a diversity of industries across the world. Artificial intelligence technologies in business changes the way in which companies can distribute their human resources, allowing machines to provide data analytics; take over dangerous and repetitive tasks; to provide intelligent search engines and chatbots; and improve accessibility.
Machine Learning
This exciting branch of AI is everywhere in our day-to-day lives right now. The bare bones are that, as you feed AI software new data, it enables the algorithms to learn and therefore delivers targeted results. And the more data we feed, the better it gets, learning from experience, just like us. As it gets exposed to more data, the machine grows, adapts and develops of its own accord.
Adaptive machines have been in use since Turing’s Enigma but they have gained traction and sophistication in recent years. Today, they are articulated in the application of Machine Learning in areas as diverse as the recommendation engines used by Facebook and Netflix, right through to the development of self-driving cars.
When it comes to AI in business, Machine Learning is one of the areas of AI that holds most future potential. A little over a decade ago, the term Industry 4.0 came into usage to denote the digitalisaton of the industrial sector. Thus far, the industries benefitting most from Machine Learning are ceramics, automotive, energy management and food and drink markets – with many more set to mine its benefits in future.
In ceramics, the artificial intelligence is usefully applied to predict the behaviour of materials under temperature conditions and to detect quality issues and, as such, is used across the ceramic, porcelain stoneware and flooring sectors.
The automotive industry uses Machine Learning for predictive analysis of durability, as well as analysing the production process and keeping an accurate inventory at each of its facilities, thus streamlining its processes.
In the energy management sector, Machine Learning can be used effectively to analyse data to create smart grids which highlight supply and demand, predicting growth by areas.
Meanwhile, in the food and drink sector, Machine Learning allows companies to analyse the market and thereby gain an understanding of consumer trends. At the industrial end, it also improves hygiene by alerting workers as to when machines need to be cleaned. These are just a few of the examples of how how technology affects business, with greater advances being made all the time.
Deep Learning
Deep Learning is, in practice, a subset of Machine Learning. In fact, the two terms are often interchanged. However, the difference at its core is that while Machine Learning is designed to make applications improve functionality as they learn, machines will occasionally need an engineer’s guidance if they return inaccurate predictions.
Deep Learning, on the other hand, uses neural networks to determine whether a prediction is inaccurate without human assistance. Designed to continually and logically analyse data, its layered algorithms – known as an artificial neural network – are based on the human brain.
Today, Deep Learning is used in many different fields. In the business picture, it can be used for classification as well as clustering information and detecting similarities in data sets, while discerning data anomalies that do not fit into an established pattern. This, naturally, enables analytics to be as efficient and accurate as possible.
Deep Learning is already being used to excellent effect in the retail industry to build deeper connections with customers. Using the data from loyalty schemes, brands are able to make personal recommendations – both online and in the physical store. It is also a great tool for building effective social media campaigns, improving customer service with the use of chatbots, checking quality control, and, in the corporate world, assimilating data to stay ahead of the curve. Whilst Deep Learning harnesses the power of how the human brain is structured, it also works faster than any human could and without error, processing unstructured data quickly and discerning patterns. In short, it is the future of artificial intelligence in business 2021.
Artificial Intelligence And Business Today
The bottom line is that artificial intelligence applications in business are not aimed at eliminating human input, but at augmenting it. No machine can rival a human brain for ingenuity. Nor can it mimic our common sense. What it can do, however, is to provide a supportive and reliable tool with which to alleviate dangerous or mundane repetitive tasks by establishing business process automation. It can also make business predictions, check quality control and even assist with personalisation, both in marketing and in more practical ways too. For example, some brands are already using augmented reality to allow customers to try on their clothes. Personalisation is now the norm for brands across social media channels, while other businesses use AI to implement dynamic pricing, which is to say, pricing strategies that responds to demand. All this is thanks to AI software’s ability to process and analyse vast quantities of data. Once processed, it can then present synthesised courses of action in order that humans can see the logical end conclusion of every variable in the decision-making. This then streamlines the process for CEOs and business leaders.
AI Improving Business Processes
For anyone considering how artificial intelligence is used in business and whether it might work for them, it is worth knowing that investments in AI are skyrocketing, with it predicted to become a $100 billion industry by 2025. In fact, a recent survey by PwC cited some 30 per cent of those questioned as saying they believe that AI will have the biggest impact and bring about significant changes to their industry within the next five years.
As such, leading corporations are using AI to streamline and automate processes, to increase their gross revenue, improve employee advocacy and therefore result in a higher percentage of referral-based hires, and improve customer service and thus the likelihood of repeat business. And as the technology improves, so does its power to disrupt, as companies currently experimenting with Deep Learning tools show.
Automated Machine Learning
Automated Machine Learning takes the concept of Machine Learning and applies it to problems encountered in the real world. The technical side first: AutoML, as it is known, does this by running systematic processes on the raw data and then makes an informed decision as to what model is most suitable to the situation. What this means is that, thanks to the degree of automation, non-experts are able to use the machine-learning models without necessitating any lengthy training.
So, what fields can AutoML be best applied to? At present, its chief uses all relate to data assimilation, and they range from data preparation and feature engineering (using domain knowledge to extract features from raw data), to selecting statistical models in the context of the data provided, parameter optimisation and leakage detection.
The future looks set to be automated. We’ll be watching its evolution keenly.
By Nancy Alsop
May 2021
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