Page 45 - Frontier Technologies to Protect the Environment and Tackle Climate Change

Frontier Technologies to Protect the Environment and Tackle Climate Change

opportunity to accelerate the refinement and proliferation of AI-based technologies through
partnerships and projects among technologists, scientists, industry and governments.

Quantum computing and AI

Such acceleration will also be achieved partly through further development of complementary frontier
technologies such as quantum computing. As quantum computing becomes more mainstream over
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the next few decades, it is expected that the enablement and use of AI-based systems will escalate in
conjunction. Quantum computing promises a future of unprecedented computing speed.

UNESCO’s Abdus Salam International Centre for Theoretical Physics (ICTP) inaugurated the Trieste
Institute for Theoretical Quantum Technologies (TQT) in March 2019. The institute provides a hub
for the study of the future of AI on quantum devices, offering, in parallel, a convenient link to private
actors such as Google and IBM. The ICTP provides hundreds of scientists from developing countries
with advanced training and research opportunities each year. 124

Natural risk reduction using AI
Another burgeoning application of AI is in natural disaster risk reduction. While many ideas and
prototypes have already been tested, so far, they have tended to focus on the response and rescue
phase. For example, Sendai City in Japan has tested a prototype with private companies for a tsunami
alert using AI, whereby the AI system can automatically launch a drone, sending an alert through
mobile phones and radios and using facial recognition software to identify victims. The automatic
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analysis of social media tweets by means of ML algorithms and advanced semantic technique can
also make it possible to pinpoint ongoing floods and provide real-time data to enhance situational
awareness; such information can be useful to citizens and to first responders faced with natural hazard.

It is expected that future applications of AI will focus not only on disaster response but also on the
prevention phase. In the field of earthquake risk reduction, for example, there are sensors available that
can provide about 10 seconds’ advance warning of an impending earthquake. However, seismology
is not yet capable of predicting an earthquake hours, days or weeks in advance.
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Another example is the number of new modelling systems that are being tested for their ability to
forecast drought events with precision: Artificial Neural Networks (ANN), Adaptive Neural-based
Fuzzy Inference Systems (ANFIS), Genetic Programming (GP) and Support Vector Machines. The
current drawback in using AI for drought management, however, is the lack of ‘Big Data” necessary
to produce models that can provide accurate forecasting. The G-Wadi Geoserver aims to tackle this
problem (as seen in Box 6).

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