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Rochelle, how would you define the role of AI in society?
AI leverages computers and machines to work for us in doing highly repetitive tasks or functions that can be automated. This allows us to work more efficiently and concentrate on those activities that require unreplaceable human roles, such as emotional intelligence, human relationships and intuition.
What’s your view of the idea that AI will result in a loss of jobs?
Well, AI will undoubtedly transform society, but this will create untold opportunities for new careers. We should look at AI therefore as a tool to facilitate or enhance human abilities and tasks, rather than something that will replace us in the job market.
We have to encourage the next generation of professionals to embrace the benefits of working with AI technologies. In Earth observation at ESA for example, we’re teaching young scientists and engineers to adopt the tools of machine learning as part of their understanding of data and data processing.
Can you explain how AI advances Earth observation?
Earth observation is being helped enormously by AI. Several satellites now have AI computers onboard for filtering and processing data, and downstream there are many applications that use AI to create critical insight for end users.
Computer vision amply illustrates the contribution AI makes. We extract many features from satellite images, like vegetation or coalmines, and although computers have been doing this for some time, they traditionally use fixed algorithms or models to spot the required objects.
With AI, the model keeps evolving and learning as new data arrive, and that’s a game changer in terms of our need for automatic detection. There are huge volumes of Earth observation data produced every day, and so automation from AI is a vital aid for monitoring Earth.
In the Child Connectivity project that ESA is carrying out with Giga for instance, we are assessing young people’s access to the internet. As part of this, we use AI algorithms to recognise school buildings from Copernicus Sentinel-2 data, and in Brazil alone we were able to identify 65 000 schools that were incorrectly located in the UNICEF database.
The area of prediction is also becoming increasingly important. In another major initiative with UNICEF, our climate-data AI models were able to forecast dengue fever outbreaks one month in advance.