As the recent article in Wired below explains, some businesses are finding really valuable outputs from Artificial Intelligence (AI) in order to predict customer behaviour. However they are also discovering that the computing power needed to crunch all the data and run the necessary algorithms is so substantive that it's deemed uneconomical.
The use of AI and machine learning in the travel sector has been widely discussed but narrowly deployed. DNATA owned BD4Travel has been in vanguard of successfully developing AI for personalisation purposes, but few other businesses have made an impact within travel so far. Is this likely to change in 2021? Technology is developing at an inexorable rate, but it might be that the impact of the coronavirus pandemic means that travel companies no longer have the budgets to allocate to potential advancements.
A recent survey commissioned by Linkedin in the US entitled "Emerging Jobs Report" looked at the trending job types, and number one on their list was "AI Specialist" where they found a 74% increase in demand for this job role. However it's worth noting that the survey was undertaken pre-Covid19. The pace at which demand for different job types will change in the UK is difficult to predict, but roles such as AI Specialists, Machine Learning Engineers and Artificial Intelligence Data Engineers are definitely on the rise.
EARLY LAST YEAR, a large European supermarket chain deployed artificial intelligence to predict what customers would buy each day at different stores, to help keep shelves stocked while reducing costly spoilage of goods. The company already used purchasing data and a simple statistical method to predict sales. With deep learning, a technique that has helped produce spectacular AI advances in recent years—as well as additional data, including local weather, traffic conditions, and competitors’ actions—the company cut the number of errors by three-quarters. It was precisely the kind of high-impact, cost-saving effect that people expect from AI. But there was a huge catch: The new algorithm required so much computation that the company chose not to use it.
https://www.wired.com/story/prepare-artificial-intelligence-produce-less-wizardry/
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