With governments around the world currently deliberating new laws that could define the future of AI regulation, one of the common sticking points is open-source AI.

Policymakers and regulators are grappling with questions like how they can avoid stifling a culture of open-source innovation, whether open source is safe when it comes to massive AI models, and what open-source AI even is, exactly.

As chief legal officer at GitHub, a platform home to tens of millions of open-source projects, Shelley McKinley’s job description includes working with lawmakers to ensure the interests of open-source developers are represented in policy discussions.

Tech Brew spoke with McKinley about the potential effects of the EU’s AI Act and California’s AI safety bill, the effort to define open source and the possibility of eventual federal legislation.

This conversation has been edited for length and clarity.

There has been a lot of talk among open-source developers about the effects of California’s AI safety bill, which is now awaiting a signature from the governor. Do you think that lawmakers have done enough to address the issue?

We’re still waiting to see what’s going to happen with all of that as it goes to signature and with the two different California bills [SB 1047, which mandates guardrails for big foundation models, and AB 3211, which requires platforms to detect, label, and in some cases block AI-generated imagery]. There’s going to continue to be a need to weigh in as this moves forward, whether it ultimately goes into law as written, or there’s more improvements that are made along the way—that’s the kind of thing that we would be monitoring. We held an open discussion at GitHub in San Francisco about it to ensure that we heard voices from the community and from the policymakers.

Keep reading here.—PK

SVB’s outlook and analysis of the trends shaping the innovation economy is back with their H2 2024 State of the Markets report.

Explore the factors driving more capital flow to US startups this year than in 26 of the last 30 years, despite US VC investment falling below previous peaks. While many companies are finding it harder to raise, the best are making it happen—led by those capitalizing on the boom in AI.

Discover the dynamics at play as SVB breaks down the uneven recovery and the process of recalibration across venture-backed company operations, fundraising, investments, and exits.

Learn more.

The electric future may not include the familiar rumble of a V8 engine. But automotive sound designers might want to make EVs sound a little bit more like, well, cars.

That’s according to a recent study conducted by sonic branding agency Listen and market research firm Cloud Army.

The pair polled more than 400 people in the US about their preferences on car sounds. The question is timely, with automakers poised to sell millions more battery-powered vehicles in the coming years. Those vehicles have a unique characteristic that, according to Paul Amitai, Listen’s executive strategy director, presents both “an opportunity and a challenge”: They’re basically silent.

“You now have essentially a blank slate to create something that is functional, that suits the needs of pedestrians, and particularly blind and low-vision pedestrians,” he said. “But also brands see that as an opportunity to create something that is potentially distinctive, that when you hear that car driving down the street, you’ll recognize it. And that has a long history in ICE vehicles as well…You would recognize a Ford Mustang or a Harley-Davidson.”

Keep reading here.—JG

Orgs are investing in generative AI, but like your duct-taped Shark Tank idea, many projects aren’t quite ready for prime time.

One major reason: It’s really hard to do AI without data, Jim Rowan, principal and head of AI at pro-services firm Deloitte, told IT Brew.

“Do you understand where the data is coming from, how it’s being used, but also, are you using any other third-party information? And what are the usage rights associated with that? And are you allowed to do that, and what are the ramifications—all those different components that you don’t normally have to think about when you see a great demo,” Rowan said.

According to an August 2024 quarterly report from Deloitte, 67% of surveyed organizations said they are increasing investments in the intriguing output machines known as generative AI. Seven out of 10 respondents also admitted their organization has moved 30% or fewer of their GenAI experiments into production.

Rowan spoke more with IT Brew about why data keeps great demos from seeing full-scale, production-level usage.

Responses below have been edited for length and clarity.

How is an IT pro’s job challenged as a project moves from proof of concept to production?

If an organization is starting from the beginning and saying, “We’re going to have a strategy around generative AI; we’re going to form a cross-functional team, and that team is going to include the right people: IT, legal, risk, compliance, key business owners,” it can scale pretty quickly because they’ve got the right team around the table. What I’ve seen and where IT struggles is when [IT teams say], “We’ve tried out a solution in a department, but we didn’t really involve all the other stakeholders. We didn’t think we needed to tap into the legal team because it was just going to be used within this one department.”

Keep reading here.—BH

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