The funniest WhatsApp I saw last week was when one wit stated: ‘These days, avoiding AI at a sports conference is like playing Whamageddon at Christmas.’
For those like their festive season with an added frisson of competition, Whamageddon is where players avoid hearing Wham’s ‘Last Christmas’ until Christmas Day. Hear it and you lose. Last year, Northampton Town FC’s stadium DJ sent 7,000 people to Whamhalla after playing the song on December 2nd at the club’s fixture against Portsmouth. There were complaints; the club had to apologize.
AI in sport as a discussion topic is now equally omnipresent so hopefully this article saves you some time. There are currently two important points to make about AI in sports:
- AI isn’t a ‘one size fits all’. There are numerous types offering a range of (very impressive) capabilities. If you are speaking to someone about AI, start by asking them what type they are talking about and it will quickly be evident how much of an expert they are. If they mutter darkly about algorithms, there might be someone better to talk to
- A lot of the tech that is being talked about – and whisper this – isn’t that new, it is just that awareness of it has increased. To cut through the noise, start with what you need solving and work back from that.
You see this every few years spanning buzzwords or trends, to something that is actually very significant. AI is firmly in the latter camp but a lot of the conversation surrounding it has been scaremongering or cynical.
Anyone who has sat down and used AI thoughtfully in some shape or form knows exactly how useful it can be. You never have to write anything you don’t want to from scratch and if you are feeling a bit bolder, you can have a go at writing code. That said, there are things that have to be taken into account:
- Security – any sane company has taken measures to ensure their teams aren’t sharing sensitive data on open-source AI platforms. We were always amazed at some of the information we could unearth but things have tightened up, especially when it comes to research on individuals.
- Bias – we’ve all read stories about female Popes and black Vikings but on a practical level, be mindful. As a user, open-source AI is constantly evolving but do treat its research output with caution – the chances are it will have errors. As an ‘owner’, treat it as you would the intern: don’t give it anything meaningful until it is proved capable – and don’t let it loose on customers.
- Environmental creds – the data-led nature of AI means that it is energy-thirsty. Creating one image on Dall-E is the equivalent of charging 12 iPhones from scratch. There is a smart-sounding argument that states that AI is more likely to advance the search for sustainable solutions but you can draw your own conclusions on that.
- Fakery – this is the cause of much of the negativity associated with AI. It is very serious but is a relatively small part of the wider AI conversation. If you manage celebrities or high-level reputations, run social media channels or oversee security, you must be on top of it. For the rest of us, it is unlikely to be our immediate concern.
I am firmly ‘Team AI’. Well considered and well applied, AI is revolutionary. We’ve seen it support many award-winning entries over the past seven years and it helps the team here across a wide variety of solutions.
If you think you aren’t already using it, the chances are that you are either wrong or, if you are right, you are one of the last few resistors. I’d suggest you explore making friends with it – you can always ghost it at a later date.
In the meantime, good luck with Whamageddon 2024.
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