At a crucial moment for climate change, these technologies show us where we're heading. I know it's a bit late to say, but it never quite feels like the year has started until the new edition of our 10 Breakthrough Technologies list comes out. For 25 years, has put together this package, which highlights the technologies that we think are going to matter in the future. This year's version has some stars, including gene resurrection (remember all the dire wolf hype last year?) And of course, the world of climate and energy is represented with sodium-ion batteries, next-generation nuclear, and hyperscale AI data centers . Let's take a look at what ended up on the list, and what it says about this moment for climate tech.

Why he thinks near-term targets can be a distraction, and what technologies he expects to power our future grid. Bill Gates doesn't shy away or pretend modesty when it comes to his stature in the climate world today. "Well, who's the biggest funder of climate innovation companies?" he asked a handful of journalists at a media roundtable event last week. "If there's someone else, I've never met them." The former Microsoft CEO has spent the last decade investing in climate technology through Breakthrough Energy, which he founded in 2015. Ahead of the UN climate meetings kicking off next week, Gates published a memo outlining what he thinks activists and negotiators should focus on and how he's thinking about the state of climate tech right now.

But there are massive challenges ahead in material demand for climate technologies, and unfortunately, recycling alone won't be enough to address them. Our climate reporter Casey Crownhart has taken a look at why recycling isn't always the answer--and what else might help instead. This story is from The Spark, our weekly newsletter covering climate technology. Sign up to receive it in your inbox every Wednesday. The news: A new robotic system called OK-Robot could train robots to pick up and move objects in settings they haven't encountered before.

What's the news?: Deep learning is behind machine learning's most high-profile successes. But this incredible performance comes at a cost: training deep-learning models requires huge amounts of energy. Now, new research shows how scientists who use cloud platforms to train algorithms can dramatically reduce the energy they use, and therefore the emissions they create. How can they do it?: Simple changes to cloud settings are the key. Researchers created a tool that measures the electricity usage of any machine-learning program that runs on Azure, Microsoft's cloud service, during every phase of their project.