Predicting the machine-learning future

31 July 2017
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By Rob Webster
Data scientist

From real-time predictions of pro cycling race scenarios, to self-driving cars – here’s what machine learning will offer digital businesses.
We interact with machine-learning systems every day. Without knowing it. From online adverts to the products that Amazon suggests. From the automated sales calls, to the voice recognition systems on our smartphones … these all use machine learning in some way.

We incorporated machine learning and predictive analytics in the pro cycling technology solution we’ve delivered this year. We took historical data, such as past performances of individual riders and previous race outcomes, and combined that with live analysis of race stages. This allowed us to make predictions of race scenarios, such as whether the peloton is likely to catch the breakaway or not.  

This technology is transforming a broad spectrum of industries, even though it may be less obvious than with sporting predictions. In fact, most industries are already benefitting from machine learning. 

Cars, doctors, and schools

One of the most publicly visible implementations of machine learning is the development of self-driving cars. Machine learning is, at its heart, advanced pattern recognition. In the example of self-driving cars, the system is taught to recognise elements of the landscape, such as a stop sign or traffic light, and act appropriately. 

Each interaction with the environment adds more data to the system, allowing it to refine and improve the car’s driving ability. The more information the systems governing self-driving cars have about the way other vehicles, pedestrians, and animals behave, the better the system will become.
Even car manufacturing and servicing will benefit from machine learning in future. In this video, BMW shows how this type of data, gathered from RFID devices, is already helping them keep track of cars throughout their processes. Eventually, machine learning will help to predict and avoid production bottle necks before they occur, improving the services delivered to customers.

Eventually, machine learning may help to predict and avoid car manufacturing problems before they occur

In healthcare, IBM uses the massive computing power of its Watson technology to optimise oncology treatments for cancer patients. The system analyses the available data to find evidence-based treatment options and helps oncologists provide cancer patients with individualised treatment.  

Of course, this underlines the importance of constant and reliable data streams feeding into the system. Watch this video to see how one hospital group,ISPPC in Belgium, uses the Internet of Things to collect medical and other streams of data to create integrated, centralised hospital management. Machine learning will in future add to hospitals’ ability to provide even more intuitive care to patients.

Machine learning may in future help hospital groups such as ISPPC analyse data to provide more intuitive patient care

Machine learning can also enhance the provision of quality education. In the US, 30% of first-year university students don’t return for their second year*. Through analysis of class attendance records, academic performance, and additional demographics, research has shown it’s possible to predict which students are most at risk. Interventions can then be put in place to assist them.

Old school steps up

Some of the greatest potential benefits of machine learning won’t come from Silicon Valley. Rather, it’s coming from the use of the technology by companies that provide services to the industrial, commercial, and agricultural sector, that have access to lots of data which can be leveraged to drive machine learning systems. 

We’ve already seen examples of this with lift manufacturers leveraging sensor data to predict when break-downs are going to occur and schedule preventative maintenance. We’ve also seen farm equipment manufacturers starting to lay claim to the data generated by their equipment, providing them with the data needed to drive sophisticated machine-learning systems. 

For any company looking to get real value out of their investment in machine learning, a few key elements need to be considered: 
• Firstly, data quality matters. The cleaner the data the better the quality of the intelligence that will be provided. The amount of data matters, as more data will help the system find patterns, but poor-quality data will be counter-productive.
• Secondly, agility is vital. Machine learning systems don’t exist in a vacuum and, to produce the best results, they need to be constantly tweaked to achieve the desired outcomes.
• Finally, understand what you want to achieve. Machine learning systems produce their best results when they are set against clear business objectives and a culture of data-driven transformation. Failure to link the investment in machine learning to your organisation’s strategy will result in diminished returns.