15th October 2018
Machines don’t add bias, people add bias to machines
UK GDP will be up to 10.3% higher in 2030 as a result of Machine Learning and AI, making it one of the biggest commercial opportunities in today’s fast-changing economy.
Our latest research of 2,000 UK consumers shows that trust in Machine Learning is growing. But if businesses want to build customer acceptance of Machine Learning and embed it within their operations, there is still some work to do.
We have analysed the key findings of our research and interpreted what they mean for technology leaders and organisations in our exclusive research report.
Our recommendation is that businesses would be well advised to focus on education, transparency and choice as they move forward using Machine Learning.
Machine Learning is the science of getting computers to act and make decisions without being explicitly programmed by a human, and instead letting them come to their own conclusions from knowledge the system has gained through statistical methods applied to data.
You can create, train, and deploy self-learning models to improve day-to-day business decisions:
Machine Learning can help you make better informed business decisions with the ability to learn from data, identify unseen patterns, and make predictions. Machine learning can be applied to many use cases across every industry, including financial services, retail, automotive and logistics. Some of our recent Machine Learning projects include:
Black Pepper came in to our business and took the time to understand our data and business domain first, in order to provide useful Machine Learning software.
Facilitating Machine Learning at JLR, a proof of concept with 84% accuracyRead the Case Study
Machine Learning (ML) uses statistical algorithms that learn from existing data to make predictions about new data. For example, an algorithm can be trained to identify if a dog is in a picture by learning what a dog is through analysis of a large set of images of dogs. Once trained, it will be able to recognize a dog in any new images it encounters. These predictions can happen in batch or in real-time, such as a self-driving car understanding the world around itself while in motion.
One application of a machine learning model is to predict how likely a customer is to purchase certain products based on a number of factors including things like previous purchases, products they’ve recently viewed, geographic location, time of day, and even the current weather.
Using the breadth of ML capabilities, organizations across a wide variety of industries, are able to gain insights from large amounts of data and leverage those insights to make better decisions and gain new competitive advantages.
Machine learning can be applied to many use cases across every industry, including financial services, government, healthcare, retail, and transportation. Financial services and government agencies leverage machine learning to improve processes and prevent fraud such as identity theft, while retailers use machine learning to personalize and optimize their end user experiences. The healthcare industry has been taking advantage of the benefits of machine learning for specific use cases, such as diagnosing and treating patient health in real-time. Transportation organizations identify patters to make more efficient public transportation routes with machine learning. From improving internal processes to optimizing user experiences, machine learning is applicable to almost every industry.