Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) focused on building computer systems that learn from data. The broad range of techniques ML encompasses enables software applications to improve their performance over time.

Machine learning?algorithms?are trained to find relationships and patterns in data. They use historical data as input to make predictions, classify information, cluster data points, reduce dimensionality and even help?generate new content, as demonstrated by new ML-fuelled applications such as ChatGPT, Dall-E 2 and GitHub Copilot.

Machine learning is widely applicable across many industries.?Recommendation engines, for example, are used by e-commerce, social media and news organizations to suggest content based on a customer's past behaviour. Machine learning algorithms and?machine vision?are a critical component of self-driving cars, helping them navigate the roads safely. In healthcare, machine learning is used to diagnose and suggest treatment plans. Other common?ML use cases?include fraud detection, spam filtering, malware threat detection, predictive maintenance and business process automation.

While machine learning is a powerful tool for solving problems, improving business operations and automating tasks, it's also a complex and challenging technology, requiring deep expertise and significant resources. Choosing the right algorithm for a task calls for a strong grasp of mathematics and statistics. Training?machine learning algorithms?often involves large amounts of good quality data to produce accurate results. The results themselves can be difficult to understand -- particularly the outcomes produced by complex algorithms, such as the deep learning?neural networks?patterned after the human brain. And?ML models?can be costly to run and tune.

Why is machine learning important?

Machine learning has played a progressively central role in human society since its?beginnings in the mid-20th?century, when AI pioneers like Walter Pitts, Warren McCulloch, Alan Turing and John von Neumann laid the groundwork for computation. The training of machines to learn from data and improve over time has enabled organizations to automate routine tasks that were previously done by humans -- in principle, freeing us up for more creative and strategic work.

Machine learning also performs manual tasks that are beyond our ability to execute at scale -- for example, processing the huge quantities of data generated today by digital devices. Machine learning's ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery. Many of today's leading companies, including Facebook, Google and Uber, make machine learning a central part of their operations.

As the volume of data generated by modern societies continues to proliferate, machine learning will likely become even more vital to humans and essential to machine intelligence itself. The technology not only helps us make sense of the data we create, but synergistically the abundance of data we create further strengthens ML's data-driven learning capabilities.