There is a huge buzz around artificial intelligence (AI) and its subsets such as machine learning and natural language processing. This hype has largely been created by the transformational impact it is having on businesses. The speed at which organisations can develop AI based technology and increase their value is key to having a competitive advantage. Whilst many organisations are still trying to understand exactly what AI is capable of, this article summarises some of the key benefits that are already being realised.
People think that AI is a robot that can do things a smart person would, knowing everything and being able to answer every question. This is what television and movies have led us to believe. Creating these ‘conscious’ machines is the goal of researchers and professionals but we are not close to that yet.
AI is classified into two groups. General AI is the concept explained above where machines can intelligently solve problems without human input. A machine with general AI capabilities would have cognitive abilities and interpret the environment around it. It would be able to process this information far quicker than any human could leading to these sci-fi ideas of superior beings. General AI currently is beyond our reach but as the volume of data in the world grows and computing power increases, we will get closer.
In the here and now, we are in a time of what is called narrow AI. A machine with narrow AI capabilities is one that operates from a predefined set of rules. This could be a Netflix recommendation engine or a voice command system like Alexa. Both are examples of artificial intelligence but are fed from criteria or training data to function. A good example is a driverless car which although impressive, is still narrow AI as it is given a set of rules to operate by. Until a car can understand the environment and think for itself, this will always be the case.
Narrow AI applications are driven by two subsets of AI known as machine learning and deep learning. The best way of explaining the link between the three is that AI is an all-encompassing term, inside of which is machine learning and then within that we get more complex deep learning. In this article, we focus on machine learning which is what most businesses are using to deliver tangible benefits.
Source: Semantic Software
Machine learning is often described as a method for realising AI. A computer or machine is loaded with vast amounts of data which it will use to train itself. The data might be labelled initially to make things easier. For example, if we want a machine to recognise photos of cats, we may load it with thousands of images of cats and dogs, labelling them appropriately. The machine will take a new image and find the label it matches to learn whether it is a cat or not.
Machine learning is the process of enabling machines to learn through data. The predictions the machine makes from that data is what we know as AI. If we go back to the example of Alexa. Alexa receives a voice command, interprets that using an algorithm (known as natural language processing or NLP), matches the result against all existing data stored in the cloud to find the appropriate response and sends that back as a reply. Alexa gives the impression of being a cognitive machine but is far from it.
There are four common machine learning methods.
⦁ Supervised learning
This method takes existing data and trains a model to work out how to classify a new piece of data. For example, it could hold data on the symptoms of diabetes and when it receives blood test results of a new patient, it is able to diagnose accurately from the data. It will classify the patient as having diabetes or not having diabetes.
⦁ Unsupervised learning
Unlike, supervised learning, these models will attempt to classify data without any prior knowledge. The algorithms look to find patterns themselves and put data into groups. A common example is something like customer purchasing behaviours. The algorithm won’t have existing labels and will decide on its own how to classify the data, often known as clustering. Imagine going to a party where everybody is a stranger. Your mind will probably classify people based on age, gender or clothing. You don’t know them but have still worked out the classifications.
⦁ Semi-supervised learning
As title suggests, this is a mix between supervised and unsupervised learning. In our data, some items are labelled but some are not. Where you have vast amounts of data this can be quite common. A semi-supervised model would have some labelled data to know that classification does exist. It is then trained on unsupervised data to define the boundaries of what it is looking at and potentially specify new classifications that the human did not specify when labelling.
⦁ Reinforced learning
This application is about positive and negative rewards for certain behaviours. This will be a common method in robotics where machines learn to optimise behaviour from experiencing positive or negative results. For example, if a robot found a TV remote and decided to throw it, it would break and be a negative result. However, pressing a button turns the TV on a produces a positive result so it continues to do it. The robot will continue this process until finding the best possible result.
Whilst every AI-based project is unique as they all run from different datasets and rules, there are key algorithms that you will find in the library of virtually every Data Scientist.
The theory is great but adapting that to your business without a huge budget, wealth of resource and range of technology is an obstacle where smaller companies don’t have the investment of a Facebook or Google. The examples below show how machine learning methods are being used within common business processes to help gain a competitive advantage.
Customer Experience and Service
One of the most common forms of AI is the conversational chatbot. These are messaging apps, speech-based assistants or voice activated devices that are used to automate communication and create a very personalised customer experience. These Internet of Things (IoT) based applications can process vast amounts of data instantly meaning they can make faster and more accurate responses than a human would ever be able to.
Similar personalisation that makes best use of data can be used in marketing. This is where we get emails that are relevant to us and social media ads that just happen to be something we are interested in. In some cases, each customer can even see different website homepages depending on their likely preferences and what will interest them the most.
Utilising AI in these ways is a great way to ensure customer loyalty through a personalised experience.
Businesses that have been established for a long time tend to have several manual processes. AI is a natural partner to optimise these efforts given its efficiency at handling routine tasks, improving interfaces, willingness and speed to do monotonous tasks and ability to handle massive amounts of data.
There are some obvious processes like using robotics in factories, managing conditions in product storage, processing payments and registering customer requests but these only touch the surface of the possibilities. Doctors can use AI devices to dictate clinical notes which automatically fills in the relevant forms and orders a prescription. Lawyers will use AI to process contracts and agreements in a split second that may have taken them days or weeks.
Essentially, anything that can be turned into a digital format has the potential for automation.
Cost reductions and operational efficiency
An improved customer experience and process automation will have a clear knock-on effect when it comes to reducing business costs.
In the context of efficiency, where AI can process vast amount of data so accurately, it can greatly reduce the number of business errors, improve workflows to increase production outputs and free up employee time for higher-level tasks. As an example, in healthcare, unnecessary tests as estimated to cost $210 billion per year in the US alone. Even if AI reduces this by half it is doing an amazing job.
Gartner predicts that 85% of service interactions will take place between human and AI in 2020. This reduces to cost of business call centres and allows businesses to focus their staff elsewhere. For example, they may be redeployed into jobs that support the AI and digital technology. More to the point, AI is never sick and doesn’t need a holiday!
Whilst AI can be costly to deploy, in the long run the savings will heavily outweigh the investment.
Data security and fraud
AI can be used to help identify fraudulent transactions and prevent unauthorised access to data. In an exponentially growing digital world, this is especially important when it comes to defending cyber-attacks. Powerful algorithms can find malware and combat spam for example. Machine learning will detect irregular patterns in the data and inform businesses when there is a potential threat.
As well as this we are seeing the increased utilisation of identity checks other than passwords such as facial recognition and fingerprint technology. These unique identifiers based on unstructured data are far more difficult to hack and offer a great layer of protection for businesses.
Predictive analytics could be called the heartbeat of AI. Traditionally, management information would report on what has happened in the business e.g. we sold 100 pairs of shoes yesterday. Machine learning algorithms will make decisions on what is going to happen in the future. It will do this by finding patterns in data and making decisions based on that. For example, it can predict when a customer is next likely to want to buy a pair of shoes and ensure you are at the front of the queue when they come to market.
Another example comes in supply chain management where machine learning can predict when stock is likely to run out or whether there is going to be product surplus.
AI is being used in businesses to create personalised training plans. Some companies could have huge knowledge bases that take staff weeks or even months to learn. AI has been shown to cut this in half by presenting content to the learner in the way that best suits them. This could include the order they learn items in, the length of time between when learners are presented with repeat information or the type of material such as written, visual and audio. Training is both more useful and enjoyable.
Whilst many applications are still relatively immature, companies need to prepare for investment in AI if they are going to keep up with the competition. It should be made clear that AI is not going to replace humans but instead, it will provide the technology to run monotonous, tedious and repetitive jobs, allowing people to focus on what they are best at. For example, AI will help diagnose disease so doctors can concentrate on caring for patients. AI will deliver learning curriculums, so teachers can worry about helping the students.
To make the most of these powerful efficiencies, AI should be considered as a means of augmenting and not replacing human capabilities.