Our friends in the Insight Centre for Data Analytics have asked us to share details of this event with our Members:
Can we harness Artificial Intelligence (AI) in our efforts to build a more sustainable world? AI plays a significant role in the world today. Its impact has been transformational and it has been disrupting society and industry alike. Sustainability is one of the greatest challenges facing us today and requires action in a variety of areas such as clean energy, agriculture, population health, food security, transportation, consumption, industry, and the appropriate management of our natural resources. At the Insight Centre for Data Analytics, we believe that AI, data analytics, and data science, have a significant role to play in evidence-based policy-making to support sustainability, as well as providing a tool to monitor and achieve sustainability objectives across a wide range of settings.
While the United Nations Sustainable Development Goals set out a global sustainability agenda, organisations and governments have set out specific programmes of their own that provide ideal opportunities for the power of AI, data analytics and data science, to be brought to bear on addressing major challenges.
The objective of this event is to bring together a broad range of participants interested in addressing a variety of sustainability challenges across a myriad of different domains such as energy, agriculture, health, transport, manufacturing, etc., along with experts from the Insight Centre for Data Analytics, to discuss the challenges and opportunities for AI, data analytics, and data science, in the area. We will explore the opportunities for developing and deploying methods based on AI, machine learning, sensing, optimisation and decision-making, recommender systems, big data, citizen science, and many other techniques to address sustainability challenges.
We will achieve this through a combination of formal presentations, panel discussions and demonstrations on topics including but not limited to:
How machine learning and data mining can be used to predict demand for transportation services, and how optimisation methods can be used to meet that demand.
How sensing and actuation as applied to environmental monitoring and decision support tools for water management (flooding, coastal pollution, material contamination etc).
How optimising flexible car routing and scheduling (ridesharing) and other demand-responsive transport initiatives can reduce congestion.
How AI can be used to manage and optimise smart energy grids, predict and exploit sustainable energy sources such wind energy.
How we can make decisions at internet scale to manage data centres in an energy-efficient manner.
Apply optimisation and decision making to very large-scale, ubiquitous and complex data sets (big-data) in addressing large-scale societal issues such as public health, transportation, agriculture, decarbonisation and other environmental issues.