Goto

Collaborating Authors

Future of AI & 5G Part 4: Driving Cleaner Economic Growth & Jobs

#artificialintelligence

Governments, investors and business leaders need to adopt practical solutions that can be deployed across the world at scale. The arrival of 5G along with wider adoption of AI technology into the physical world will make it possible to substantially enhance the opportunities to scale cleaner energy generation technologies, enable efficiency gains in manufacturing, our homes, retail stores, offices and transportation that will enable substantial reductions in pollution. Policies that incentivise the accelerated development and deployment of Industry 4.0 solutions will require politicians and regulators to better understand the opportunities that 5G alongside AI will enable. The OECD published a paper "What works in Innovation Policy" and observed that "Policies ignoring or resisting the industrial transition have proven to be not just futile but result in an innovative disadvantage and weak economic performance." Entering the new year will allow us to develop and deploy solutions for the 2020s that make use of the next industrial revolution with 5G and AI to enable dramatic efficiency gains across all sectors of the economy and to enhance renewable energy generation. The emergence of India, China and others as industrial economic powers is occurring at a time when we now know the damage that such pollution causes and hence there is a need to work together, collaboratively to solve a global problem. Embracing technological change and enhancing its capabilities to deliver better living standards alongside sustainable development is the best option for those who really want to make an impact on climate change at scale in the 2020s and beyond. I wish to thank Henry Derwent, former advisor to Prime Minister Margaret Thatcher and former CEO of IETA for his efforts to promote technological innovation and scaled up financing with Green Bonds.


Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis

arXiv.org Artificial Intelligence

Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.


Machine Learning vs. Climate Change: AI for the Greener Good

#artificialintelligence

Climate change is one of the most pressing issues of our time. Despite increasing global consensus about the urgency of reducing emissions since the 1980s, they continue to rise relentlessly. We look to technology to deliver us from climate change, preferably without sacrificing economic growth. Our optimistic--some would say techno-utopian--visions of the future involve vast arrays of solar panels, machines that suck carbon dioxide back out of the atmosphere, and replacing fossil fuels for transport and heating with electricity generated by renewable means. This is nothing less than rebuilding our civilization on stable, sustainable foundations.


AI and climate change: The promise, the perils and pillars for action - Climate-KIC

#artificialintelligence

This article was first published in Branch magazine, an online collaboration between EIT Climate-KIC, Mozilla Foundation and Climate Action.tech A global pandemic has shocked the world, leading to thousands of deaths, economic hardship and profound social disruption. While we worry about our immediate needs, we should remember that another crisis is looming: climate change. The lockdown made it clear that staying at home and slowing down the economy is far from enough to solve the climate crisis. We're still emitting more than 80 per cent as much CO2 as normal, despite having 17 per cent fewer emissions compared to 2019 -- which is one of the most significant drops in recent years (1).


Climate action focus series round-up – interviews, research summaries, webinars and more

AIHub

In December 2020 we launched a focus series AI for Good: UN sustainable development goals (SDGs). Each month we pick a different sustainable development goal (SDG) and highlight work in that area. February was the turn of UN SDG number 13: climate action. In this summary article we highlight some of work at the intersection of AI and climate science. Climate Change AI (CCAI) is a volunteer-led effort bringing together people from academia, industry, and the public sector.