In this episode of Uncanny Valley, we discuss the economics and environmental impacts of energy-hungry data centers and whether these facilities are sustainable in the age of AI. The Stargate AI data center in Abilene, Texas.Photo-Illustration: WIRED Staff; Getty Images Tech giants have been investing hundreds of billions of dollars into AI data centers just this year alone. But as the deals pile up, so have the concerns around their viability and sustainability. Michael Calore and senior correspondent Lauren Goode sit down with senior writer Molly Taft to discuss how these energy hungry facilities actually work, the different industry interests at stake, and whether it'll all come crumbling down. The AI Industry's Scaling Obsession Is Headed for a Cliff by Will Knight OpenAI's Blockbuster AMD Deal Is a Bet on Near-Limitless Demand for AI by Will Knight How Much Energy Does AI Use? The People Who Know Aren't Saying by Molly Taft Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link. Note: This is an automated transcript, which may contain errors. It's so nice to be back in studio with you again, because our schedules were not aligning for the past few weeks. But the stars and the moon have aligned now, and here we are once again. Lauren Goode: Here we are. And I'm sure all of our listeners have just been sitting here wondering, "When are Lauren and Mike getting back together? When is the band getting back together?"

Things to Do in L.A. Tap to enable a layout that focuses on the article. Millions of Californians are getting a refund on their electric bill. Credits for Californians on October electric bills are set to go up in the coming years, according to the governor's office. This is read by an automated voice. Please report any issues or inconsistencies here .

We propose a Web-Mashup Application Service Framework for Multivariate Time Series Analytics (MTSA) that supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decision recommendations, and web portals. This framework maintains the advantage of combining the strengths of both the domain-knowledge-based and the formal-learning-based approaches and is designed for a more general class of problems over multivariate time series. More specifically, we identify a general-hybrid-based model, MTSA-Parameter Estimation, to solve this class of problems in which the objective function is maximized or minimized from the optimal decision parameters regardless of particular time points. This model also allows domain experts to include multiple types of constraints, e.g., global constraints and monitoring constraints. We further extend the MTSA data model and query language to support this class of problems for the services of learning, monitoring, and recommendation. At the end, we conduct an experimental case study for a university campus microgrid as a practical example to demonstrate our proposed framework, models, and language.

When her phone rang in February, Mashawn Cross was skeptical of the gentle voice offering help at the end of the line. "You said you do what? And you're with who?" the 52-year-old recalled saying. Cross, who wasn't working because of her ailing back and knees, was scraping by on roughly $200 a month in aid plus whatever she could make from recycling bottles and cans. Her gas and electric bills were chewing up her checks.

For all the modern conveniences technology brings to the home – Wi-Fi-enabled washing machines, powerful gaming systems and enormous smart televisions – one of the downsides is paying to power it all. In fact, home utility costs are continuing to spike for many parts of the country, with 2021 electricity prices rising at the fastest rate since 2008, says the U.S. Energy Information Administration (EIA) – already hitting Americans facing skyrocketing inflation, resulting in higher costs for many goods and services. Not only does the average household have dozens of consumer electronics products plugged into power outlets at any given time, most consume electricity when not in use. "Vampire power" – also referred to as "phantom power" or "standby power" – can account for as much as 10% of a household's electricity bill, says the Environmental Protection Agency (EPA). This can really add up.

How best to upgrade your lighting and electric devices to save money and cut your electric bill. From LED lights and Wi-Fi thermostats to water sensors and voice-controller personal assistants, the latest in-home tech could save you money. While buying the latest technology can cost a pretty penny, choosing the right gadgets could save you money. If you want evidence of this, look no further than your home. Between LED lights (that sip rather than gulp electricity) and smart thermostats (that can automate temperature when you're home or not), as well as many other "connected home" gizmos, you can reduce your monthly energy bills and reduce the odds of an expensive emergency situation, such as water damage.

Michael Schmidt is the founder and CTO of Nutonian. But despite all the talk around AI, no one seems to really understand what it is or how companies can use it. Is AI the computer that competed on Jeopardy? Will machines really take our jobs? As data volumes surge and analytic engines become more mature, has technology finally caught up with the hype?

Mark Chung was a chip guy. He spent nearly 15 years in the semiconductor industry since getting his master's and bachelor's degrees in electrical engineering at Stanford in 1999. He'd been an engineer at AMD for nearly six years designing chips like the Athlon and Opteron, then at startup PA Semi working on microprocessors that, he anticipated, would go into Apple computers (Apple later purchased the company and the designs would end up in iPhones). In 2008 Chung was a principal engineer at RMI, a company that later merged with Netlogic and was acquired by Broadcom. One month that year--a month he and his family had mostly spent out of town, he received a surprisingly large electrical bill: $560, when his typical bill was around $100.

For all its promise of delivering a bold future, the green energy industry is still decidedly low tech when it comes to moving merchandise. Solar panels are still sold door-to-door, a slow and expensive process that has impeded the industry's growth. Now, a California startup thinks it has a solution, using sophisticated data science to find consumers likely to adopt solar power and let them know just how much they can save on their electric bills. PowerScout, a machine-learning-enabled eCommerce platform for solar energy, aims to eliminate marketing costs that, according to CEO Attila Toth, can exceed the cost of the actual equipment for some green-power vendors. "This is very absurd--very crazy," Toth says.

Michael Schmidt is the founder and CTO of Nutonian. But despite all the talk around AI, no one seems to really understand what it is or how companies can use it. Is AI the computer that competed on Jeopardy? Will machines really take our jobs? As data volumes surge and analytic engines become more mature, has technology finally caught up with the hype?