Machine learning is designed to take over the manual and repetitive processes, enabling employees to focus on the more rewarding results of model building and analysing data. This is exactly where the right tools can help staff members focus on what they really want to do – getting their teeth into meaningful data.
First choose your right machine learning model
Finding the right machine learning model can take a significant amount of time. Employees involved in data science currently dedicate the majority of their workload to cleaning and preparing data, potentially taking up 80 per cent of their time. The process typically requires trying out several different options, often depending on the knowledge and preferences of each individual data scientist. But with automated machine learning tools it is possible to leave that selection process to the machine. Machine learning tools not only do the job better, they do it in a fraction of the time and also make it possible to analyse far more dimensions of data than ever before.
Unlocking patterns from an overwhelming amount of data
Although humans are usually very good at spotting patterns, this ability can only stretch so far when there are multiple variables in play. Machine learning has the capability to work with hundreds of dimensions. This is where it really becomes clear to see relationships in data that human intelligence has no chance of understanding, nor can it extract meaningful insights.
Take looking at a graph to explore your data as an example. In two dimensions everything makes sense. In three it gets complex, but how do we look at data that has 200 dimensions?
Let’s look at a project where we used machine learning to detect the spread of malaria. In a collaboration known as HumBug between Royal Botanic Gardens Kew and the University of Oxford, we developed a real-time detection system based on a phone app that aims to detect malaria-carrying species of mosquito by identifying the sound they make. This data is combined with information on associated environmental factors drawn from high-resolution remote imaging – such as the composition of local vegetation, the distance to water and beyond.
Getting a global picture fast
This results in a huge volume of data, but by harnessing machine learning, all this data can be analysed and used by researchers to produce detailed, real-time maps of the spread of malaria. Charity workers and pharma companies alike can use this data to start to co-ordinate and target key malaria control programmes in a way they would never have been able to do before.
This is one of the key reasons we find that so many people are intrigued to learn more about how the machine interprets data and draws patterns from it. We make the processes as clear as we can, exposing all the parameters, to the benefit of users who can then see how decisions are made. As a spin-off from the University of Oxford, we’re fortunate to have access to a plethora of cutting-edge research, which we always include in our training programmes.
AI and machine learning: An employee’s best friend
AI and machine learning are quite simply making the working lives of people more satisfying, rather than taking jobs away from people. In a recent survey by the Chartered Institute of Personnel and Development (CIPD) and PA Consulting, 43% of workers at companies using AI felt they were learning new things, while a third said they were undertaking more interesting tasks. Similarly, a study this spring by the Japan Science and Technology Agency (JST) revealed a clear trend: as new information technologies such as AI are increasingly adopted, the greater the rise in employee job satisfaction.
This really should come as no surprise. When AI is acting as a hugely helpful hand that allows you to focus on more insightful work, why wouldn’t you feel satisfied?
Thinking out of the box made easy
These advanced, highly accessible tools are making the role of the data scientist far more sophisticated than before. Once people understand the potential of machine learning, they come up with novel applications – and these don’t have to be work related!
One Mind Foundry client has collected over a decade’s worth of data on the performance of his children’s football teams, but to date has only analysed this manually. Now he is exploring using the Mind Foundry platform to gain predictive insights to help improve their performance. Although analysing children’s football results won’t necessarily boost one’s employment prospects, the skills gained from these new tools certainly can.
Consider key questions
Even the smallest of organisations can benefit from machine learning and new cloud-based applications make it easy for them to take advantage of these new technologies. The key is to consider whether staff are spending valuable time on manual, repetitive tasks, rather than concentrating on what they do best. If so, machine learning can help ease this burden, but it will also do the job faster, with more precision and enable employees to use the data to make more informed decisions and unlock new ideas. Decision-makers must ultimately weigh up how much more employees could achieve if they are able to focus on more strategic thinking that drives their business forward.