With the importance of data, obtaining good quality data can be quite hard to come by. In other circumstances, data may not be available. Consequently, where there’s no data, there can be no AI. There are also some exaggerated claims made about AI; it is rare for the wholesale proclamations made about AI being proven, as set out in an interesting overview of AI from the University of Toronto.
In terms of what artificial intelligence can and cannot do going forwards, Ian Firth, VP Products, Speechmatics, tells Digital Journal what we can expect in 2021 and beyond.
Firth explains that AI has progressed in some ways, but not in others: “We can have algorithms that crush any human at chess but are unable to make a cup of tea and computer programs that can perform mathematics millions of times faster than humans but, if asked who might win the next World Cup, they wouldn’t even understand the question. ”
Moreover, he explains how AI for different applications differs: “Their capabilities are not universal. We’ve reached a point with AI where we simultaneously overestimate and underestimate the power of algorithms. When we overestimate them, we see human judgment relegated to an afterthought – a dangerous place to be. The use of a “mutant algorithm” in grading A-level results is the scandal du jour in the UK, despite the algorithm producing many results that simply violate common sense.”
There are consequences too, in over- or under-estimating the capabilities of artificial intelligence. Here Firth notes: “When we underestimate algorithms, we see entire industries crumble because they didn’t see change on the horizon. How can the traditional taxi business compete when Uber’s algorithm can get you a ride in less than 3 minutes? In 2021, expect engineers to avoid AI and algorithmic blunders by not trying to map algorithms onto the human spectrum of competence.”
There will be innovations of benefit as well, as Firth explains: “Using AI technologies – such as any-context speech recognition – to enhance what humans can do and finding the right balance between AI automation and human knowledge for real world use cases – such as customer experience and web conferencing – will begin to shape the effective use of AI for the future.”
