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ABOUT 60 LEADERS
'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks from AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
Can AI help us solve humanity’s burning problems like climate change?
Professor Eleni Mangina
In the era of IoT (Internet of Things), Artificial Intelligence has already proven that optimisation models can orchestrate industrial and commercial buildings from an energy consumption point of view. In the broader context of climate change, and considering that renewable energy sources (i.e., wind and solar) are variable by nature, AI prediction models can help with the energy stability issue for the grid and balance the supply with the demand. Grid integration with AI is required to deliver a reliable electricity system and assist to get to Net Zero Carbon.
There are ongoing developments in the area of Machine Learning that attempt to tackle climate change from within the energy sector. For example, from the Demand Side Management (DSM), by processing historical energy consumption and weather datasets, AI algorithms can derive forecasts of electricity production from alternative sources and use them to optimise the demand. As a result, the power generation can be maximised leading to a better-managed electricity grid. The latter can be quite diverse given the different sources of energy generation (solar, wind, geothermal, remnant fossil fuels, stored hydro). In terms of Demand Response (DR), the consumers are empowered with their own IoT-based management tools and they can shift their electricity providers and/or usage based on the energy market prices (on the basis of response times, services offered, and different business models).
In the supply chain sector, based on metrics from the International Energy Agency (IEA), transport emissions must be decreased by 43% before 2030. Freight logistics operations in Europe are struggling with ways to reduce their carbon footprints to adhere to regulations on governing logistics while satisfying the increasing demand for sustainable products from the customers. AI can empower efficient, optimised logistics through the continuous processing of streams of data captured from IoT devices in real-time. Physical Internet algorithms have proved to perform better in terms of reducing emissions and improving the logistics’ efficiency, especially when the sample sizes are large. But this would require a shift to an open global supply web, through digital twins, with real-time data utilised in simulation and machine learning models, and intelligent decision-making algorithms.
However, technology on its own is not the solution. This vision can become a reality only if we take into account the hurdles of Machine Learning, as it is not a panacea for climate change. AI is a tool to help us fight climate change and we need to follow a cost-benefit approach, given that AI has its own growing carbon footprint. Current research estimates indicate the ICT sector to be responsible for between 1.4% to 23% of the global emissions by 2030 (depending on the energy sources).
While Machine Learning algorithms can identify faults and predict weather and levels of energy production and consumption, the global society needs to be aware that certain actions are needed as well. AI can strengthen the applications towards smart decision-making for decarbonising buildings and transport, and allocate renewable energy, but these toolkits will be effective only when human beings are aware of the long-term benefit - and this might require a change of behaviour in terms of energy consumption. Humanity needs to be educated and be able to adapt to the needed changes.
The process of adopting AI technologies and adapting energy consumption patterns should be orchestrated by global organisations that reassure the public in terms of the ethical concerns from the data collected and transparently describe how AI is used, while also tackling the challenges the technology poses. Many researchers address separately digital technology and sustainable development. Now is the right time to join forces and utilise AI capabilities to shape a sustainable environment - we all need to act now globally.