The US Open this year has made two things very clear to its worldwide audience: 1. That a small tweak to the rules can make a big difference to women returners 2. That AI is very much artificial Sarah Burnett, Chair of BCS Women and a Top 50 influential person in UK tech, explains.

Women

A small tweak to the WTA rules means that women players, after a maternity career break, do not have to rebuild their careers and work their way up through the rankings from scratch. They can return to the professional game with their previous ranking. Perhaps inspired by Serena Williams’ return from childbirth in 2018, Tsvetana Pironkova returned in 2020 after a 3-year maternity career break and did spectacularly well.

She reached the quarter finals to take on Williams, and even managed to get a set off the six-times champion. By getting into the quarter finals, Pironkova earned a massive $425,000, increasing her lifetime prize money by nearly 10%. This shows that it only takes a small change to open opportunities to women returners. Those of us who have had to rebuild our careers after taking time off for our children, know only too well how hard it is to start all over again.

’A small change to relevant policies and practices in many verticals could make a huge difference to women returners. It’s not just the women who would benefit either but companies facing a growing shortage of skills as well.’

AI

This being the year of the pandemic, spectators were visible by their absence at the tournament. The IBM AI solution, the one that played tracks from a selection of spectator applause and reactions from last year’s tournament, was imaginative and made up for some of that absence but it was still very artificial. For most of us it only took a few minutes to sense the lack of atmosphere and the typical feelings and passions that tennis fans bring.

Understanding sentiment is a big challenge for AI and a developing field. I wonder if, we in the AI world, are getting a bit too wrapped up in our big ambitions, going for complex projects, and missing out on simpler opportunities, for example, improving the accuracy of AI in the many simple tasks that it can do. Moonshots and lofty ambitions are admirable, but simpler things can deliver big results too.

The possibilities of AI are not lost on enterprises. Many see the potential for it and want to take advantage of it to expand and grow their businesses. They need to look beyond the hype, to learn about AI for business, build skills, and start with relatively simple projects. These are very early days in enterprise AI and the old proven approach to adopting new technologies still holds:

‘Be clear about your objectives, start small, learn by doing, measure your success and shout about it to secure funding to scale.’