A machine learning chip in the time vortex

Time for an AI rewind

Are we letting the generative AI hype blind us to more practical and reliabel uses of the technology?

Adam Tinworth
Adam Tinworth

This is an interesting quote buried in a significantly less interesting article about AI being used to come up with ideas for TV shows:

"The BBC's Springwatch and Winterwatch use a bespoke AI system that monitors live camera feeds, and has been trained to recognise, record and log different species of animals and birds as they appear in the frame," he says. "It can then tell the production team how often they appear, give behavioural insights, and generally do something that would eat up hours of human production time."

The current focus on generative AI โ€” Ai which creates based on its best guess of what it should say or draw next โ€” has somewhat obscured the vital role that more specific, more targeted machine learning algorithms can play in many everyday tasks.

This particular example is fascinating because it frees up researchers from spending literally hours combing through footage before they can start the work of storytelling around it. And it also exposes new information it would have been too time intensive to collect otherwise. This isnโ€™t about replacing people, but about taking away laborious work and freeing up their time to do more valuable creative work.

Weโ€™re letting the shiny new, but unreliable, AI tools distract us from the ones we know can work really well.

AImachine learning

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Adam is a lecturer, trainer and writer. He's been a blogger for over 20 years, and a journalist for more than 30. He lectures on audience strategy and engagement at City, University of London.

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