Impact AI Greener Home Upgrades with Ankur Garg from BlocPower
Climate change is one of the most pressing issues of our time, and today’s guest, Ankur Garg, and his team at BlocPower are using machine learning technology to mitigate it. BlocPower is a climate technology company that is focused on making buildings in low and middle-income areas more environmentally friendly. Their area of expertise lies in developing products and services to lower or eliminate the barriers that prevent access to energy efficiency and electrification retrofits. And this all starts with gathering, checking, annotating, and understanding enormous amounts of data (BlocPower currently has over 40 terabytes of data in its data lake!)
In this episode, Ankur talks about the innovative ways in which BlocPower deals with its data, the challenges that they face when it comes to the size and scope of its datasets, why machine learning technology is central to the work they do, and how they measure the impact of their technology.
Key Points:
- Ankur’s career journey prior to joining BlocPower.
- Why Ankur decided to join BlocPower.
- The inspiring work that BlocPower is doing to contribute to solving the problem of climate change.
- The central role that machine learning plays in BlocPower’s approach.
- Ankur gives examples of some of the different types of machine learning models that BlocPower uses.
- The size of BlocPower’s data lake and the types of data stored within it.
- BlocPower’s innovative approach to annotating data.
- The importance of high-quality training data sets in the machine learning space.
- Challenges that Ankur and his team face when training machine learning models on their core dataset.
- Technological advancements that have allowed BlocPower to achieve what it has.
- How BlocPower measures the impact of its technology.
- What Ankur believes the future holds for BlocPower
Quotes:
“Climate change is one of the primary problems of our generation, and BlocPower is making a huge dent in solving that.” — Ankur Garg
“Machine learning really excels at ingesting huge volumes of data and to be able to infer key relationships between these data points to come up with an optimal output or a solution.” — Ankur Garg
“Labeling the data and annotating is extremely critical. If your training data set is not of a good quality, no matter what algorithm you use, it won't really perform well.” — Ankur Garg
“You need a lot of high-quality data for machine learning and artificial intelligence to be productive.” — Ankur Garg
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