Machine learning – Connecting the dots

With more than US$55bn at stake by 2021, Artificial Intelligence and Machine Learning technologies are emerging as a key focus area for a range of industries across the globe. Machine learning is a branch of Artificial Intelligence that enables computer programs to learn, analyse and identify trends and patterns within large sets of data.

The rise of Machine Learning is being driven by massive increases in computing power, exponential growth in data and advances in algorithms. As a result, multiple use cases of Machine Learning are rapidly emerging, including advancements in facial recognition technologies, natural language understanding, computer vision, fraud detection, financial market analysis and personalised recommendations.

Accordingly, a number of global technology giants are developing and investing in Machine Learning technologies. Netflix uses Machine Learning to power its recommendations engine. Visa, MasterCard and Amex are using Machine Learning to detect credit card fraud. Telstra in Australia is using it to ascertain the effectiveness of its marketing spend.

Continued public confidence, support and trust will be the key to Machine Learning’s ongoing success. However, a range of challenges still need to be addressed. The first requires ensuring that Machine Learning algorithms are designed correctly and are not affected by data bias. The second concerns GDPR, data privacy and getting users to understand how their data will be used. Finally, there are legal issues to consider, including allocating responsibility for adverse outcomes or recommendations.