The Next Decade of Software Is About Climate - What Is the Role of ML?

What's the focus of your work these days?

I'm the chair of the writers group in the Green Software Foundation, where we are working to create a trusted ecosystem of people, standards, tools and best practices for green software. We have a 26 member organization and over 700 individual members. One part of my work is making sure that the carbon footprint decreases, that we are part of the solution. And then I'm also a software engineer at Microsoft, as backend engineer on Microsoft 365 products. I work to incorporate the characteristics of green software into Microsoft 365 products as well.

What's the motivation for your talk?

This is where things get dire, rapid climate change. As an industry we are growing fast. We used to have a small amount of the global carbon footprint, but it is increasing and there are trends saying that by 2040, we will account for 14 percent of the global emissions, which is half the size of the transportation sector. Which is ironic because we talk a lot about transportation, don't fly, buy local, all these things, but with software we don't even talk about it. And we are past the time to talk about it. We need to act on a massive scale.

How would you describe the persona and level of your target audience for this talk?

This will be in the machine learning track, so you should have some kind of relationship with machine learning, if you're a data scientist or if you're a person who's working in a machine learning team as a software engineer or a non-technical role as well. And if you're curious about green software and if you want to learn more about this very nascent area

What would you like those who attend your session to walk away with?

I want them to understand that in order to meaningfully solve the climate problem, we need massive action at massive scale. This is not the time where we can improve our tech by one, two percent and then call it the day. We need to start having this conversation at an entirely different scale.