What is artificial intelligence and how does it work?Posted on: May 10, 2023
In today’s digital world, there are few aspects of our lives which aren’t powered by artificial intelligence (AI).
Despite its volume and prevalence, much of what we know and understand about AI seems like science fiction. Many of the applications and breakthroughs of AI would’ve previously seemed impossible: self-driving cars that improve road safety; AI algorithms, such as ChatGPT, that are capable of human-like content creation; virtual personal assistants, like Google’s Siri or Amazon’s Alexa, who scour the Internet in milliseconds for your favourite recipe; and computer systems such as AlphaGo that champion over human intelligence in highly complex games.
Perspectives of AI development and AI applications range from the positive – for example, they have the ability to diagnose illnesses quicker and more accurately than experienced doctors, to the negative – what happens when AI matches, and surpasses, human intelligence, and what ethical issues are tied up in AI?
What is artificial intelligence?
John McCarthy, former Professor at MIT – hailed as one of the ‘founding fathers’ of artificial intelligence, together with Alan Turing – defines AI as “the science of engineering of making intelligent machines, especially intelligent computer programmes […] it is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
The broad field of artificial intelligence brings together computer science and robust datasets to solve problems. It aims to develop expert systems that can make incredibly accurate classifications and predictions using input data. There are different types: weak AI – also known as narrow AI – focuses on specific tasks, such as Apple autocorrect or Google Maps; strong AI, on the other hand, aims to replicate the human mind and its processes, behaviours and inferences, such as the technology behind autonomous vehicles.
AI research is big business. The top five countries who are currently leading the way in terms of AI research are China, the USA, the UK and Germany.
How does AI work?
AI combines vast amounts of data with fast, iterative processing and highly intelligent algorithms, enabling its software to automatically identify features and patterns within the data.
The core elements of AI include:
- machine learning (ML). ML combines neural networks, physics, statistics and operations research – via supervised learning, unsupervised learning or reinforcement learning – to automate the construction of analytical models. It has the ability to identify hidden insights in data – after training data has been used so it can learn how to perform a task – without being specifically instructed on what or where to look.
- neural networks. Interconnected units – like neurons in the human brain – process information in response to inputs and communicate between each other. By relaying information multiple times, networks make meaning and find patterns within new data.
- computer vision. Pattern recognition and deep learning techniques are used to recognise the content of images and videos.
- natural language processing (NLP). NLP refers to a computer’s ability to analyse, understand and generate human language and speech.
- deep learning. Deep learning relies on giant neural networks, containing many layers of processing units, to learn and analyse complex patterns in large amounts of data.
Such learning techniques are used to develop solutions to real-world problems. Deep learning algorithms have facilitated particularly rapid growth – their deep neural networks and artificial neural networks power smartphones and other smart devices around the world.
What’s the difference between AI and machine learning?
While the terms artificial intelligence and machine learning are often used interchangeably, discernible differences exist between the two.
Global analytics platform, SAS, explains the difference between artificial and machine learning as follows: whereas artificial intelligence is the broad science of mimicking human abilities, machine learning is a specific subset that trains machines how to learn.
Examples of AI technology in action
Types of AI are deeply embedded in just about every global industry. Let’s take a closer look at some of the pivotal roles AI plays in three vast, and diverse, fields: healthcare, marketing and farming.
In the healthcare sector, AI is credited with producing diagnoses and results that surpass human beings in terms of accuracy and speed. As well as disease detection, other uses support the automation of processes such as drug discovery and diagnostics and help develop personalised treatment plans. These advances can help aid decision-making, improve patient outcomes, increase safety, and help to reduce healthcare costs.
The use of AI in marketing is as powerful as it is prolific. It’s integral to a whole host of tools, from predictive customer service, chatbots, web design and search functionality, to targeted advertising, image recognition, speech recognition and content creation. Netflix relies heavily on AI systems for service development and delivery, making full use of machine learning and big data technologies to provide intuitive, relevant, and personalised recommendations to users. The streaming giant also relies on AI to subtly enhance other parts of its service; for example, utilising image personalisation to identify which thumbnail graphics and images increase clicks and interaction.
In the world of farming, AI uses predictive analytics and forecasting to minimise the risk of crop failure, maximise harvesting outcomes and reduce associated errors. Other uses include devices that monitor animal welfare, real-time video feeds, applying machine learning algorithms to yield mapping, and utilising smart tractors and agribots to ease the load of labour-intensive jobs.
What’s next for AI in 2023?
AI trends that are set to transform the business world in 2023 include:
- creative or generative AI
- greater AI-human collaboration
- ethics and regulation
- democratisation – low-code and no-code AI
- digital twinning
- AI personalisation
- AI voice technology
- AI in motoring
- AI in medicine.
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