Machine Learning (ML) has been around, as a scientific concept at least, for a number of years now. The term can trace its origins back to the late 1950s, where it was defined as the ability for computers to learn “without being explicitly programed.” However, the term is only know gaining the kind of industry hype that it deserves.

To explain the process further, machine learning is a subset of artificial intelligence that focuses on data analysis. Computer programs trawl through huge data sets looking for patterns that they then use to adjust their program actions, without any additional human intervention required. The vast quantities of data being created today has facilitated the growth of machine learning possibilities by providing more information for computer algorithms to “learn” from.

In addition, there are two predominant types of machine learning: supervised and unsupervised. Supervised learning works with data sets where there is already a known desired outcome, which can generally be predicted through historic data. This can be used to identify credit card fraud, for example. In this case, the data available suggests certain types of behaviour by the credit card holder. When the behaviour differs from the expected norm, machine learning can identify this and prevent illegal transactions. Unsupervised learning occurs where there is no desired outcome. In this situation, the algorithm simply analysis the data and identifies any patterns. This approach is often used by online retailers to offer product recommendations.

With the amount of available data growing all the time, along with the processing power required to analyse this data, the future of machine learning looks very bright indeed. For now, here are a few example where the technology is already being put to good use.

The cutting edge of cybersecurity

The number of cybersecurity threats are growing all the time and it is impossible for human agents to manually check the entirety of the programming code that makes up the World Wide Web, our digital networks and the software we use every day.

Instead, machine learning can be used to analyse the huge quantities of data that are being transferred over computer networks, scanning for anomalous behavior. In particular, this is effective against malware threats that focus on remaining hidden in order to siphon away company data. This type of attack is often too difficult to detect manually until after it is too late. Machine learning, however, can analyse the entire network and interpret unusual behaviour as a security threat instantly.


Another benefit of using machine learning in this way is that it is self-learning. Firewalls can be used to protect networks, for example, but they must be manually updated every time a new attack is developed. Security programs that use machine learning, on the other hand, learn from new data and so, in theory, are able to respond well even to new threats.

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The latest development in SEO

Search engine optimization, or SEO, has come on a long way from those early days of the internet, where webpages were stuffed full of keywords in order to improve their page ranking. Today, modern websites are having to take a different approach in an age where search engines are using machine learning to decide how relevant a page is to a given query.

Machine learning now analysis online behaviour in much greater depth than ever before. It is taking note of how users behave on a given website, what they click on and how long they stay on a certain page. It is identifying which words and phrases are interchangeable and which linguistic subtleties can return vastly different results. It is attempting to understand the user psychology behind the search query, rather than simply which characters are typed in a search box.

Website are adapting their SEO approaches to better match up to these developments in machine learning. Schema markup language is being added to web documents in order for websites to index better against new criteria. Many sites are now incorporating a schema structured data audit and implementation into their webpage design in order to ensure that they perform better in this new age of SEO.

Keeping you healthy

Healthcare is one of the industries that has been quickest to embrace machine learning. Over the years, doctors based all over the globe have collected data on patients, diseases and treatments, just by going about their everyday work. This information has potentially life-saving applications when it comes to selecting the right treatment for each specific medical case, but it is impossible for an individual doctor or healthcare team to look through and analyse this data – which is where machine learning comes in.

Machine learning can analyse vast quantities of data and suggest effective treatments based on previous medical successes. Already we are witnessing artificial intelligence outfits, like IBM’s Watson, use this approach, but as the amount of data available increases the likelihood of machine learning leading to a medical breakthrough only increases.

Boosting speed in the legal industry

The speed with which machine learning can be used to comb through huge volumes of data is proving useful in a number of fields, including the legal industry. One of the ways that this added speed is being put to good use is in the analysis of legal documents. Machine learning can not only be used to “translate” legal terminology into language that is more easily understood, it can also scan a document and identify if a clause is missing that would normally be associated with that type of contract.

Legal teams are also using machine learning to analyse evidence in court cases. There can often be large quantities of audio, textual and visual data to sift through, which puts a great strain on legal teams and can lead to human errors. Instead, machine learning can look for certain patterns of behaviour or phrases in speech, saving time and bringing teams closer to a breakthrough in a case.

Fraud prevention

Machine learning has been used to identify fraudulent behaviour for some time now and the process continues to undergo refinement. Every action that we take online can be broken down into a series of data points, which computer programs can then use to put together a pattern of “normal” user behavior.

Despite fraudsters using increasingly sophisticated methods, machine learning can identify even the smallest difference in the user’s usual pattern of behaviour. Often these differences would be undetectable by human agents, buy ML algorithms are able to identify them and prevent fraudulent activity before any financial damage occurs.  

Autonomous vehicles

One of the reasons that autonomous vehicles are spending time on the road before becoming available to purchase is that they need time for their software to accumulate driving data. By analysing human driving behaviour across a multitude of different scenarios, it will eventually be able to react to new situations in real-time. Ultimately, this could enable autonomous vehicles to predict a dangerous manoeuvres ahead of time, react accordingly and prevent an accident. The time where self-driving cars are the only ones allowed on the road may be some way off yet, but machine learning does promise to change the automobile industry forever.

Customer service

A number of businesses are already using machine learning to take some of the burden away from their human employees. Virtual agents are instead able to learn from previous interactions and provide a high level of customer service, without human intervention at any stage.

By analyzing the products or services that you use, machine learning can immediately discern what sort of issue you are having and offer relevant assistance. Currently, customers can usually request human assistance when ML software fails to meet their needs, but those situations will occur less often as the technology develops and has access to more data.

Speech recognition

Speech recognition programs have been in existence for some time, but are only now entering mainstream usage, thanks largely to machine learning. The process of recognizing a sound wave, translating it digitally into a recognised word and responding appropriately, is extremely complex and requires access to huge data stores in order to be accurate.

Speech recognition software needs to be able to recognise different accents and ways of speaking, which produce vastly different sound waves, as the same words. It then needs access to a huge vocabulary in order to be able to respond. Machine learning lets these programs learn from their interactions with users in order to give a more accurate and personal response.

Spam filters

Machine learning is also to thank for the fact that your inbox is not swamped everyday under an avalanche of spam. Previous methods identified spam by rejecting messages based on predefined criteria, but this proved ineffective every time spammers tweaked their methods. Machine learning, instead adapts to these changes, ensuring that it can block spam emails even when the language that they use is different.

Artificial intelligence is usually seen as a futuristic development, but it is already present today in the form of machine learning and will surely expand into many more areas of life as the technology progresses.