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Machine learning for tech businesses - will you be left behind?

Matt Stokes

Organisations that use AI at scale perform 11.5% better on average than those who don't, according to Microsoft's whitepaper Accelerating competitive advantage with AI. This is has increased from 5% just one year ago.


Microsoft's research also found that in the UK alone there has been an 11% rise in the number of companies using machine learning within the past year.


What is Machine Learning?

Machine learning is a subset of artificial intelligence and enables systems to learn how to identify patterns and perform specific tasks - such as image recognition or natural language processing - without being programmed.

It enables individuals and organisations to automate routine processes or process huge volumes of data.

There are two types of machine learning tasks:

Supervised learning

Initially the programme will need to be "taught" by submitting sets of example data. For example, in the case of image recognition, an individual will need to highlight images of interest that they would like the system to learn to detect. Once enough data has been submitted, the system will start to make decisions for itself.

For example, Coeo has been working alongside an organisation that represents medical professionals to help them detect fraudulent certifications. Our consultants used a machine learning programme to detect and classify stamps on certifications and help to spot fakes.

Unsupervised learning

The programme is not given any example data and must discover patterns and structure for itself, without any set outcomes. This is particularly helpful for discovering patterns in large volumes of data where there isn’t a desired outcome, such as analysing patterns in user behaviour.


How can technology companies use machine learning?

Data Security

Organisations are increasingly using machine learning to test for vulnerabilities in their networks and identify potential instances of malware within their systems. Earlier this year Microsoft used machine learning to thwart an attack aimed at the satellite and communications industry.

Customer Service

Technology companies can use chat bots to provide consumers with answers to commonly asked questions and refer on to human operators if more involved help is needed. This improves effectiveness at dealing with customer requests and frees up technical teams.

Checking Code

Developers working on code can use machine learning to check for errors and reducing the time taken for deployment.

Improving sales efficiency

Machine learning can be used to predict outcomes, such as which type of companies are most likely to buy a product or identifying cross-selling and up-selling opportunities.

For example, we have been working with a company that sells products online to analyse customers' baskets and provide them with recommendations based on their choices.


What to consider before using machine learning

Have a plan in mind

The first step to take when engaging in machine learning is to identify a problem that you would like to solve, such as detecting fraudulent accounts.

Similarly, you should also identify a measure of what success will look like, such as a specific cost saving, a reduction in time take to process data or a higher success rate.

Ensure you have enough data

Computers may be able to perform tasks faster and more efficiently than humans, but they still can't think better than people can. You need to ensure you have enough data - if you cannot make a decision based on the information you have, a machine learning programme will not be able to do it for you.

Check you have the right data

Finally, you'll need to ensure that your data is in a format that can be inputted into the machine learning programme. As Microsoft said in Accelerating competitive advantage with AI: "This need for companies to get their data house in order is true across all sectors, with experts from the fields of finance, healthcare, retail and manufacturing united in seeing it as a critical component of any AI scaling plan".

Additionally, you should check to see if there is any bias that the system could learn from it - for example, a system used to screen potential job applications could learn to screen out females if it learns that successful applications tended to have more years of experience.


Find out more

Microsoft has a suite of Cognitive Services that you can use to build machine learning algorithms that perform tasks such as language processing, speech-to-text conversion, image identification and simple decision-making.

If you would like to know more about how Coeo can help your business benefit from machine learning, contact us on info@coeo.com


How advanced is your business at managing data?

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