Generative AI is becoming increasingly unavoidable as tech companies weave the technology into all kinds of products. But how much do we actually know about the data on which these models are trained, the rules that govern them, or their resource usage?

When Stanford University researchers first looked into questions like these last October, the answer was a resounding not much. Half a year later, a reassessment found some slightly more encouraging signs, though there’s still a lot of room for improvement.

The goal of the transparency index, published by the Stanford Institute for Human-Centered Artificial Intelligence, is to encourage developers of societally consequential AI to reveal more about how these systems work. It comes as governments around the world are taking more steps to create guardrails around AI, oftentimes including disclosures about the development process and expected impact of deployment.

Authors of the Foundation Model Transparency Index graded major players in the AI arms race like OpenAI, Google, and Meta across various measures of openness in their development processes. They then assigned each model an overall score out of 100.

This time around, the average score on the index climbed to 58 points out of 100 from 37 points in October as more companies have disclosed or complied with the index’s request for information around hardware, compute power, and energy usage.

Still, the study noted that some areas are still kept under wraps.

Keep reading here.—PK

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With a few swipes and pinches of the fingers, a perfectly formed silicon wafer pops out of a machine.

The wafer—used to power any number of electronic functions—is a figment of virtual reality in this case, as is the production process and the pristine laboratory where it’s made. Tech Brew got a demo of this “virtual twin” technology during a May 16 visit to French multinational software company Dassault Systemès’ suburban Detroit tech center.

“The amount of pressure that all of the OEMs are under, all the suppliers are under, is absolutely tremendous,” Bill DeVries, Dassault’s VP of industry transformation and success, said. “EVs, autonomous driving, competitive pressure from low-cost countries…These are all factors that are driving automotive OEMs to consider new technologies.”

That’s where the virtual twin, which Dassault offers via its 3DEXPERIENCE platform, comes into play. The technology differs from digital twins, executives explained, in that it can provide a more dynamic, three-dimensional view of data.

The silicon wafer laboratory is just one example of a virtual twin solution.

Keep reading here.—JG

Consumers are less likely than they were a year ago to consider buying an EV for their next vehicle, according to a pair of new surveys.

New-vehicle buyer consideration for fully electric vehicles fell from the previous year for the first time since the start of J.D. Power’s US Electric Vehicle Consideration Survey in 2021.

According to the 2024 results, 24% of respondents are “very likely” to consider buying an EV, compared to 26% a year ago. And 58% of respondents said they are “overall likely” to consider one, down from 61%.

The results reflect “persistent growing pains” in the EV market, according to J.D. Power. They’re the latest sign that nagging concerns about affordability, a lack of new models on the market, and battery range are holding back EV adoption.

Stewart Stropp, executive director of EV intelligence at J.D. Power, noted in a press release that some 40% of car shoppers said they don’t have a “solid understanding” of incentives available for EV purchases: “Prioritizing initiatives and efforts to educate consumers about the EV proposition—including available incentives and how they work—is vital to accelerating market growth.”

Keep reading here.—JG

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