Tapping onto AI to build a more sustainable future with Recursive AI

In this special episode, we are featuring That Digital Show. AI is seen as a powerful tool and enabler for businesses around the world. At the same time, more organizations are looking for ways to operate more sustainably. To combine the two, Recursive AI was established in 2020, formalizing the way AI can be used for sustainability. Whether it’s through innovation, improving productivity, providing better education, or using AI for prevention and mitigation efforts in managing climate change, Recursive AI is changing the sustainability landscape one project at a time.

In this episode, Recursive AI co-founder Tiago Ramalho puts on a new lens to the way we think about AI. He tells us how neural networks, which form the core infrastructure of AI, can simulate systems quickly, finding new and improved solutions to existing problems. He also shares how the company is predicting the future of natural disasters so that organizations can take action before it is too late.  

When it comes to sustainability, no action is too small. Listen in to find out how the organization is innovating by leveraging AI technology to solve the sustainability problems of today and tomorrow.

Dr. Tiago Ramalho, Recursive Co-founder and CEO

Tiago is the co-founder and CEO of Recursive AI, a company focused on building AI tools to help companies grow their sustainability impact. Tiago is a physicist and former Googler at Google DeepMMind with a passion for technology and sustainability. Through Recursive, Tiago is able to marry the two to help others create a greener tomorrow. 

Theo Davies

Theo is Head of Cloud Sales Excellence & Productivity at Google Cloud and host of “That Digital Show APAC”. He is a record-breaking salesperson, sales leader, coach and speaker with a 20+ year career beginning in sales. Theo is also the President of the Google Public Speaking Academy.

Interview

• Recursive AI: https://recursiveai.co.jp/en/

Hosts

Theo Davies and Paris Tran

Episode source