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October 22, 2019updated 16 Aug 2022 10:01am

Artificial intelligence vs machine learning: What is the difference?

Artificial intelligence and machine learning are frequently used terms. But what do they mean and what's the difference between them?

By Tech Monitor

Artificial intelligence has firmly entered the mainstream over the past decade, going from academic research to a tool that is used by organisations around the world. But what is artificial intelligence and how does it compare to machine learning?

The two technologies are directly related. In fact, machine learning is regarded as a subset of artificial intelligence. While AI is the ability of a computer to mimic human cognitive abilities, including pattern recognition and problem-solving, machine learning is the process of directing a computer to learn from data.

“While AI and machine learning are very closely connected, they’re not the same,” according to Microsoft. “An ‘intelligent’ computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence.”

Machine learning, a subset of artificial intelligence, uses training data – such as images – to learn how to spot patterns or make predictions. (Photo by David Man & Tristan Ferne / Better Images of AI / Trees / CC-BY 4.0)

What is artificial intelligence?

Artificial intelligence means the ability of computers to perform cognitive functions that mimic the way humans think. This could be anything from a simple algorithm designed to stop and start a self-driving car, through to recognising patterns in the universe.

AI is increasingly found in all aspects of modern life. It can range from the relatively mundane such as recommendation of other products on Amazon, through to the more sinister such as emotion tracking using facial recognition, a practice widely criticised for its lack of scientific basis.

The concept of machine intelligence dates to antiquity and has featured in philosophy and storytelling for centuries. But the exploration of mathematical logic, which led to artificial intelligence, began to emerge with the work of British mathematician Alan Turing.

AI focuses on aspects of human cognition including learning, reasoning and self-correction and uses those abilities in a range of scenarios, from automating existing processes, making new processes possible and finding information that would take a human too long or require too many people to be cost effective.

Work began on the development of artificial intelligence systems in the 1940s, but the technology didn’t enter mainstream use until the mid-1990s when the focus shifted from creating artificial general intelligence, which can theoretically solve any problem, to more narrow AI solutions that are designed for specific problems.

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Mark Beccue, principal analyst for technology research company Omdia, said AI has now entered the mainstream. It has reached the “early majority” stage of its evolution, he says, where enterprise companies are beginning to scale up projects and roll-out AI solutions for widespread adoption.

What is machine learning?

Where artificial intelligence is the mimicking of human cognition, machine learning is the analysis of a significant amount of historical data to enable a computer to make predictions based on past events – it is the process a machine uses to learn and is a form of artificial intelligence.

According to IBM, machine learning “focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” The phrase was coined by IBM engineer Arthur Samuel in 1959 in relation to training a computer to play checkers.

“Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects,” IBM wrote in an article on the technology. “These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics.”

Machine learning uses computing technology to analyse vast amounts of information, in a way that a human brain would find possible, in order to make predictions. Data scientists train machine learning models using large datasets. They then optimise those models so they perform their function – whether that is spotting patterns in a certain kind of image, or making predictions from real-time data – as efficiently as possible.

Machine learning models have many applications in science, such make predictions based on past medical data or pictures of distant galaxies, as well as in industry. Financial services companies use machine learning to predict potential market outcomes based on past data, or it can be used in law enforcement to spot trends in behaviour.

The term is sometimes used interchangeably with the phrase “deep learning“, although there are differences between the two. Deep learning is a sub-field of machine learning where there is less input from humans, with much of the process automated. Traditional machine learning involves human experts labelling and determining the set of features and data inputs being analysed.

What is the difference between machine learning and artificial intelligence?

Dr Ashley Spindler, an astrophysicist who uses machine learning to label distant galaxies, told Tech Monitor that all machine learning is a form of AI, but not all AI is machine learning.

“When we think about AI, it is a really broad concept. We are referring to anything that is a computer program that mimics human behaviour. It is deliberately a broad definition. It is anything that replicates the way human beings think and act.

“When we talk about machine learning it is a very specific type of artificial intelligence. It is about taking algorithms that learn from historical data – such as facial recognition. They learn from images and they learn to map to find a property or label.

“They aren’t pre-programmed behaviours. The learning part is the most important aspect. They are mathematical models that are built on historical data.”

Dr Spindler uses machine learning to dig into astronomy data. “I take machine learning models and create predictions. We compare the results that come out of the model with stuff done by human experts and citizen science to produce a picture of the universe.”

Some AI tools work in different ways. Some have pre-programmed algorithms that haven’t been trained using data. “It is still artificial intelligence in that it mimics human behaviours, but it isn’t something gained through historical data, so is not machine learning,” says Dr Spindler.

Self-driving vehicles combine both approaches to artificial intelligence, he adds, using both machine learning and pre-programmed algorithms, such as rules that say, “I’ve detected a person in front of me, which means stop”. 

Read more: Forget the hype, we have no idea how to reach human-like artificial intelligence

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