The chances of a big federal AI rules push in the US are looking as slim as ever these days. But Miriam Vogel says the technology is far from a free-for-all, regulation-wise.

In fact, the longtime president and CEO of the nonprofit EqualAI and chair of the White House’s National AI Advisory Committee said to expect plenty of court cases to shape how the law relates to AI in coming years. Vogel recently co-authored a new book, Governing the Machine, which lays out a general playbook for creating AI that’s above-board legally and ethically.

We spoke to Vogel about how AI regulation might come from the courts, how to build trust in AI when distrust abounds, and what she’s learned from working with the government.

This conversation has been edited for length and clarity.

It doesn’t seem like there’s going to be any major federal AI regulation legislation anytime soon, but some states have been attempting to fill that void. What does the current regulatory landscape in the US mean for AI governance?

That is really one of my motivations in writing this book. I talked to too many audiences who thought that AI is completely unregulated. Yes, it’s true—there’s not a significant number of AI-focused specific regulations in the US. But as a lawyer, I recognized all the areas of law that would be applicable to AI that we’ve only started to see.

Keep reading here.—PK

Construction? Check. Commissioning chase? Check.

Now it’s on to the next step for battery materials startup Sila as it officially starts operations at its Moses Lake factory in Washington. The company, which makes silicon anodes for batteries, this month celebrated the milestone ahead of the planned launch of commercial production at its first automotive-scale plant later this year.

“The start of operations is really the beginning of running chemistry, running material through the plant, and really the next couple months are now dialing in the production recipes before we start shipping material to customers,” Sila CEO and co-founder Gene Berdichevsky told Tech Brew. “It’s a huge step.”

Keep reading here.—JG

In the nationwide treasure hunt to secure more geothermal power, some developers have pivoted from siphoning energy out of traditional geothermal wells to creating geothermal wells in parts of the country where they exist deeper under the surface. But where other developers zig, Zanskar zags.

The Utah-based geothermal developer uses AI to discover overlooked energy in geothermal wells that were previously thought to be tapped out, and thus far the company’s found success. Earlier this year, Zanskar announced it had used its AI models and new drilling techniques to harvest more power from a geothermal field in New Mexico that had been abandoned by the rest of the industry. In an interview with Tech Brew, co-founder and CTO Joel Edwards said Zanskar’s AI models have found more conventional geothermal hotspots in three years than the entire industry has been able to in the last three decades.

Edwards said the company predicts where geothermal resources are located and teams go out to test the areas for underground heat. Data pertaining to the area—whether it’s a hotspot or not—is then fed into an AI model, alongside datasets on rock types, fault lines, and the planet’s gravitational field. Eventually, the model creates a digital twin of earth’s geothermal system, learns how to identify geothermal characteristics, and can understand layers of physical data that can be tough for humans to grok.

“Our regional models are now better than humans,” Edwards told us. “Our models are now predicting where the hotspots are, and our field teams are out testing those predictions.”

Keep reading here.—TC

Two years in, ChatGPT’s “black box” vs. “white box” decision-making remains a mystery. Discover why transparency and clear explanations of these AI systems matter for building trust and ensuring reliable business applications.