AI for Climate Masterclass: How AI Can Help Manufacturers to Achieve Sustainability Goals [Online Event] | SGInnovate
June 25 2020

Location

Online Event
Singapore

AI for Climate Masterclass: How AI Can Help Manufacturers to Achieve Sustainability Goals [Online Event]

Presented by SGInnovate and Element AI

At the same time as we recognise the positive impact of Artificial Intelligence (AI) in boosting the global economy (by as much as an additional USD 13 trillion by 2030), it is increasingly important to understand how AI can also help society and industry to build resilience, manage disruption and be more sustainable. This is especially relevant as the world is embracing a new normal after Covid-19.

Presented by Element AI and SGInnovate, this by-invitation masterclass is designed for leaders and practitioners in manufacturing-related industries – pharmaceuticals, chemicals, automotive, semiconductors, FMCGs, etc. to learn about the challenges and opportunities for deploying AI to drive impact on an organisation’s sustainability goals whilst managing ROI. In this class, you will learn about how machine learning can optimise manufacturing processes and asset use to achieve significant energy, cost and emissions reduction. We will also explore the challenges and opportunities for deploying AI in Manufacturing - from human in the loop to reducing overall emissions and use of resources while simultaneously meeting increases in demand.

Specifically, operations directors, lead scientists, quality management heads, sustainability experts who are involved in manufacturing processes and/or sustainability related programmes at their companies should benefit from the session. Should you wish to attend this masterclass, please complete this form to indicate your interest. Attendance is limited and is based on relevant roles and experiences. 

Date: 25 June 2020 (Thursday)
Time: 3:00pm – 4:15pm (UTC+8)

Programme:

  • 3:00pm – 3:15pm: Presentation by Sherif Elsayed-Ali, Director AI for Climate, Element AI
    • Sherif will provide an overview on the current state of Enterprise AI and on the opportunities for operationalising AI to solve climate related challenges. 
  • 3:15pm – 3:30pm: Presentation by Daniel Summerbell, Research Associate - Institute for Manufacturing, University of Cambridge
    • Daniel will share some of the lessons learned from his experiences working with manufacturers to improve processes and achieve sustainability goals.
  • 3:30pm – 3:45pm: Joint presentation on the AI based approaches to driving sustainability objectives for manufacturers 
  • 3:45pm – 4:15pm: Q&A

 

Speakers' Profiles:

Sherif Elsayed-Ali, Director AI for Climate, Element AI

Sherif Elsayed-Ali leads the AI for Climate programme at Element AI, which brings Element AI’s deep expertise in machine learning to help tackle the climate emergency and solve environmental problems.

Formerly, Sherif co-founded Amnesty Tech, which leads Amnesty International’s work on the impact of technology on human rights and the potential uses of new technologies to advance
human rights protection. He held a number of other positions at Amnesty, including as director of global issues. Sherif studied engineering and international law at the American University in
Cairo and has a master's in public administration from Harvard Kennedy School.

 

Daniel Summerbell, Research Associate - Institute for Manufacturing, University of Cambridge

Dr Daniel Summerbell is a researcher at the Institute for Manufacturing in Cambridge. His research focuses on cost-effective environmental improvements in industry, with a particular focus on the cement industry. Other interests include industrial demand-side management of electricity, and material efficiency approaches. He has worked in manufacturing operations across the UK, Europe and North America, including in the consumer products, food & drink, and energy industries.

Daniel's PhD was under the supervision of Dr Claire Barlow in the Centre for Industrial Sustainability. He was looking for ways to improve the greenhouse gas emissions associated with industrial processes. Prior to his PhD he worked as an operations consultant in France, Canada, and the United States, and studied aerospace engineering at CUED, graduating in 2010.

 

Topics: AI / Machine Learning / Deep Learning