Archy de Berker began his journey with machine learning in the context of academic neuroscience, interested in how machine learning can help us understand the brain. Today, he is the Head of Data and Machine learning at CarbonChain, driven by his desire to apply machine learning to climate change solutions.
In today’s episode, Archy De Berker, Head of Data and Machine learning at CarbonChain, explains how he and his team calculate carbon footprints, some of the challenges that they face in this line of work, the most valuable use of machine learning in their business (and for climate change solutions as a whole), and some important lessons that he has learned throughout his diverse career so far!
Key Points From This Episode:
- An overview of Archy’s career trajectory, from academic neuroscientist to Head of Data and Machine learning at CarbonChain.
- The foundational mission of CarbonChain.
- Archy explains how machine learning can be applied in the context of energy storage as a climate change solution.
- Industries that CarbonChain focuses on.
- How Archy and his team calculate carbon footprints.
- A key challenge for carbon footprinting.
- Where machine learning provides the most value for CarbonChain.
- The importance of the field of document understanding.
- A story from Archy’s time at Element AI that highlights the value of having technical people working as close to the design and data generation as possible.
- Why Archy chose to move into the product management realm.
- Additional ways that machine learning can help solve climate change issues.
“We build automated carbon footprinting for the world’s most polluting industries. We’re really trying to help people who are buying things from carbon-intense industries figure out where they can get lower carbon versions of the same kind of products.” — @ArchydeB [0:02:14]
“A key challenge for carbon footprinting is that you need to be able to understand somebody’s business in order to tell them what the carbon footprint of their activities is.” — @ArchydeB [0:13:01]
“Probably the most valuable place for machine learning in our business is taking all this heterogeneous customer data from all these different systems and being able to map it onto a very rigid format that we can then retrieve information from our databases for.” — @ArchydeB [0:13:24]
Links Mentioned in Today’s Episode: