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AI and rural communities

  • by JW

How are rural communities using AI – and what do they have to gain? 


Yes, even in these parts, AI might be a help rather than a hindrance or a threat…

A year ago, Nature suggested that rural areas missing out on AI opportunities. Behavioural data scientist Ganna Pogrebna believes the AI revolution is overlooking remote communities – with a couple of her observations produced here:

I’m based in rural Australia where we often face flooding and forest fires; there are projects going on at the moment that seek to use AI to advance disaster management in remote communities… I’m optimistic that things will improve. Farmers and local businesses see that AI has the potential to ensure their products reach where they’re most needed...

And writing in this week’s Rural Worlds in the UK, Jessica Sellick asks: What does AI mean for rural communities?. Here’s just one small section from a very well-considered piece:

How are rural communities using AI – and what do they have to gain? 

McKinsey has estimated that generative AI technologies have the potential to add between $2.6 trillion and $4.4 trillion annually to the global economy. AI is already found across a wide range of sectors and services. Some examples of how AI is being developed or deployed in a rural context include: 

  • Agriculture: choosing optimum crops for weather conditions, monitoring crops, improving resource efficiency, monitoring livestock and detecting the early signs of disease, employing automated workers or tasks (e.g. robotic fertilisers). 
  • Transport: monitoring deliveries or traffic status, self-driving or autonomous vehicles, or ‘pay with your face’ to use public transport. 
  • Education: for adaptive tutoring or instructional assistants that explain difficult concepts to students; to undertake automated routine tasks around lesson planning; and drafting communications with parents/carers and the local community.  
  • Healthcare: medical imaging, patient monitoring, or identifying patients at higher risk of developing certain conditions. 
  • Policing: gathering intelligence, predicting crime and facial recognition tools.
  • Weather: to generate scenarios or develop algorithms that mine social media posts to learn from the language used and the pictures shared to deduce whether a weather event is happening and to what extent. 

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