By sharing ML models and training data, organisations can power-up their ML projects. Now there’s a way to do it without compromising data privacy or security

Amazing things happen when different organisations work together. It’s something that we see in business and technology, where collaboration has often helped drive new ideas or product categories forwards, and something we’ve seen over the last year or so in medicine and science, as scientists, institutions and pharmaceutical companies have worked together to fight the COVID-19 pandemic. Now, though, collaboration could also prove crucial to harnessing the power of machine learning and AI, in turn fuelling further developments in medicine, business, technology and science, but only if organisations can find a secure way to share data. To be more specific, they need a way for their machine learning models to train using data from a wider range of datasets, while reducing the risk of compromising the privacy or security of the data.

Machine learning is already revolutionising fields as diverse as finance, security, public services, manufacturing and transportation. It’s helping doctors to spot and diagnose conditions, fraud investigators to uncover money laundering and city transport planners to optimise their transport systems. But before machine learning models can analyse streams of data and a problem or recommend an action, they need to be trained using existing datasets. Generally speaking, the more data they have to work with, the more accurate and useful their models will be. Read More