Every second counts on the racetrack.

That’s one reason General Motors is integrating artificial intelligence into just about every aspect of its motorsports division.

Tech Brew recently got a peek behind the scenes of where the machine learning magic happens: GM’s Charlotte Technical Center in North Carolina. Jonathan Bolenbaugh, manager of data and analytics for GM Motorsports, gave us a virtual tour and explained how AI is changing the way the organization races.

It “took a leap of faith when we started the team,” he said. “Like, ‘Hey, we’re gonna use AI to change racing.’ And then people are like, ‘Why would we do that?’ Well, because it’s cool and it’s fun and I like winning races.”

Performance review: Bolenbaugh—whose experience spans both racing and production vehicles—formed the data and analytics team two years ago.

The idea is to use technology to assist the teams that GM Motorsports supports across racing series like NASCAR, IndyCar, and IMSA.

Data scientists and engineers use machine learning to create models based on vehicle performance data, track conditions, and competitors’ behaviors to inform race strategy. AI also is used in real-time race analysis to help the team make decisions and improve outcomes.

“It’s literally hundreds of people per car to get that out on the track,” Bolenbaugh said. “This team was founded with the intent of using technology—specifically AI and [machine learning] and some statistical methods—to give those people the tools that they need to have the peak outcome every weekend.”

Keep reading here.—JG

In recent years, car shoppers have been hit by what one industry analyst likened to a “one-two punch”: higher prices and interest rates.

“It really impacted affordability for consumers out there,” Kevin Roberts, director of industry insights and analytics at CarGurus, told Tech Brew. “Vehicle prices going up and higher interest rates really hit that monthly payment.”

At long last, some relief may be on the way, thanks to the Federal Reserve’s half percentage-point (or 50 basis-point) interest rate cut, bringing the central bank’s benchmark rate to between 4.75% and 5%. The move, which is expected to be followed by more rate cuts in the coming months, came after months of deliberations by central bankers over whether inflation had sufficiently cooled.

This reduction in borrowing costs is likely to trickle down to auto loans, albeit not overnight—and some experts say it might be good news for the EV market amid a challenging time in the auto industry’s electric transition.

“If you’ve got a higher price on an EV, higher interest rates just makes that monthly payment even more painful on that front,” Roberts said. “So, potential interest rate cuts would be welcome from that point.”

Keep reading here.—JG

Who needs 50 billion parameters in a large language model (LLM), when 3 billion or so will do just fine?

Market intelligence firm Gartner sees security benefits in “small language models”—computational machine learners with fewer than 10 billion parameters, or training variables.

“You don’t need your language model to write a poem about cats and dogs eating spaghetti under a bridge. You need it to answer an HR-related question,” Birgi Tamersoy, Gartner’s senior director analyst for AI technologies, said in a live presentation on September 12.

Known, domain-specific data—that HR-related info, for example—can be embedded into a small language model to solve a specific task, Tamersoy said.

A July 2024 survey from another market intel firm, IDC, found that 20% of IT pro respondents said they “don’t expect to use small models”; 25% have deployed them; and 26% characterize their current use of small models as “learning.” (A further 17% said “evaluating,” and 13% said “testing.”)

The specialized LLM option offers potential security benefits when the smaller computational workloads can remain nearby, according to Tamersoy.

Keep reading on IT Brew.—BH