Is machine learning missing a digital trick – Why AI hasn’t evolved enough – Dr Edward Williams


Artificial Intelligence and Machine Learning is a vital technology for modern digital solutions, with its uses ranging from decision support, real-time augmentation and data analytics. There is a tendency to think of it is a relatively recent innovation, however it has a long and sometimes chequered history, with many false dawns along with its many successes. What we think of as AI today is a collection of different techniques and algorithms, each of them suited to solving different problems. Some of them, often referred to as deep learning algorithms, have their origins as far back as the 1960s and 1970s, and despite many refinements remain fundamentally the same. While many of their limitations have been addressed, they remain a black box; you may teach them all you like, but you can never be certain what they have learnt.

This has very real and practical implications today. There have been high profile cases where AI/ML deployments have become mired in controversy, and ultimately rolled back. An industry has emerged around the proper and ethical use of AI, focussed on ensuring that where the technology is used – especially where decisions are being made – that we can be certain it is not basing those decisions on spurious or biased data.

This talk looks at some of the history of machine learning, and also looks at the role it plays within digital transformation initiatives. Digital transformation differs from other “technology enabled change” programmes by focussing first and foremost on the customer, putting them at the heart of how we design and manage products and services. It is this which has allowed many digital programmes to succeed. For machine learning to thrive, it has to learn the same lessons – it has to put the user first and foremost



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