What is artificial intelligence?

Artificial intelligence, or AI, is technology that uses advanced computer programming and algorithms to mimic human intelligence in machines. It’s a field of computer science that has developed rapidly over the past century, transforming from a fledgling idea into a common element of everyday life for most people.

How does artificial intelligence work?

There are two key stages to explain how artificial intelligence works: 

1. Any AI machine is built upon a foundation of programming and algorithms. These are coded and written by computer scientists, and effectively train the machine, teaching it how to process and analyse the data it receives. 
2. Data processing is where the real work of an AI machine gets done. During this stage, the computer combs through data sets to analyse the information, and then completes specific tasks accordingly. For example, the machine might be tasked with spotting patterns, solving problems, or making a decision based on the data it receives.

Areas of artificial intelligence 

Artificial intelligence is a broad field. It spans narrow AI – systems designed to perform a single task – to artificial general intelligence (AGI) or strong AI, which is more complex. 

The field also has several important subsets. 

Machine learning

Machine learning is the subset of artificial intelligence that imitates human learning. Through data and sophisticated machine learning algorithms – such as supervised learning, unsupervised learning, and reinforcement learning – machines effectively develop knowledge the way a person does. They learn from the data to improve their understanding, and consequently can perform tasks, solve problems, and make decisions more effectively.

Other areas of machine learning include:

• Deep learning. Deep learning is an intricate subset of machine learning, one that more closely resembles the way human beings gain knowledge. Rather than using linear algorithms, deep learning algorithms are more complex, and are stacked in a hierarchy. Because of this, deep learning technology is incredibly accurate, and is often relied upon for predictive modelling and statistics projects.
• Neural networks. Neural network technology – including artificial neural networks (ANNs), simulated neural networks (SNNs), and convolutional neural networks (CNNs) – are inspired by the neural networks of the human brain, and similarly mimic the signalling patterns of biological neurons. They’re particularly useful for solving problems and recognising patterns within very large data sets.
• Computer vision. Computer vision technology enables machines to learn from photos, videos, and other visual data, and can be used for image recognition, image processing, object detection, and facial recognition systems. 

Natural language processing (NLP)

Natural language processing, or NLP, helps machines understand human languages. This technology also enables computer systems to understand a piece of content’s meaning or intent.

Other areas of natural language processing include:

• Speech recognition. Speech recognition technology allows machines to convert voice data into text data. This is a key component of text-to-speech and speech-to-text software.
• Sentiment analysis. Sentiment analysis technology helps machines to understand the more subjective qualities of a piece of text. For example, it might detect sarcasm or anger.

Expert systems

Expert systems work to emulate the expertise of a human subject matter expert. Using if-then rules, they simulate human judgement, decision-making skills, and problem-solving skills.

Common uses for artificial intelligence

Artificial intelligence may conjure up thoughts of androids and science fiction, but most people interact with artificial intelligence technology during the course of their day, even if they’re unaware of it. From websites and apps to technology at work, AI is part of everyday life.

For example, virtual assistants such as Alexa (developed by Amazon), Siri (developed by Apple), and Cortana (developed by Microsoft), are all popular examples of virtual personal assistants – and they all use machine learning, speech recognition and other subsets of AI to provide real-time support to people. 

Other common examples of AI systems in practice include:

• Chatbots on business websites
• Autonomous vehicles and self-driving cars
• Process automation within organisations and industries
• Product recommendations on ecommerce websites, programme recommendations on streaming services such as Netflix, and song recommendations from music platforms such as Spotify

When was artificial intelligence invented?

Artificial intelligence as we know it can trace its roots back to 1950. This is when mathematician Alan Turing first wrote about “machines that can think” in his paper Computing Machinery and Intelligence. Turing also created what’s known as the Turing test, a game used to answer a question: can machines think?

Breakthroughs and milestones in AI

• 1956. Dartmouth College hosts the Dartmouth Summer Research Project on Artificial Intelligence. This is the first AI event, and it’s where computer scientist John McCarthy introduced the term ‘artificial intelligence’. McCarthy is often called a founder of artificial intelligence, as are Turing and MIT computer scientist Marvin Minsky.

1956 is also noteworthy as the year the first AI software programme was developed.

• 2011. IBM Watson, an AI computer system, defeats two former Jeopardy! Champions.
• 2016. DeepMind’s AlphaGo beats a world-champion Go player in a five-game match.

Where does artificial intelligence go next?

The future of AI technology is unfolding now, with huge initiatives and advancements already happening in AI research, AI development, and AI applications. 

It’s suggested that in the coming years, intelligent machines and computer programmes can benefit everything from the global supply chain to healthcare systems. In fact, Forbes recently predicted that machine intelligence will enable truly personalised medicine, and will even be required to effectively address the climate crisis.

Take artificial intelligence to the next level

Deepen your understanding of artificial intelligence with the 100% online MSc Computer Science at the University of Sunderland. On this flexible master’s degree, you’ll gain a critical understanding of both machine learning and data mining – two interrelated branches of AI. And as part of one of your core modules, you’ll develop knowledge about the tools, trends, and current developments in the field of AI, as well as the relevant professional, ethical, social, and legal considerations in their application. You’ll also learn programming languages such as python, which is among the most commonly used for AI development.

This master’s degree is taught either part-time or full-time and has been designed for people who want to launch a career in computer science, but don’t have a computer science background. It’s also suitable for professionals already working in the field who want to gain an academic qualification to enhance their credentials and career prospects.