In this episode, you’ll hear about Meeri's incredible career, insights from the recent AI Pact conference she attended, her company's involvement, and how we can articulate the reality of holding companies accountable to AI governance practices. We discuss how to know if you have an AI problem, what makes third-party generative AI more risky, and so much more! Meeri even shares how she thinks the Use AI Act will impact AI companies and what companies can do to take stock of their risk factors and ensure that they are building responsibly. You don’t want to miss this one, so be sure to tune in now!

Key Points From This Episode:

• Insights from the AI Pact conference.
• The reality of holding AI companies accountable.
• What inspired her to start Saidot to offer solutions for AI transparency and accountability.
• How Meeri assesses companies and their organizational culture.
• What makes generative AI more risky than other forms of machine learning.
• Reasons that use-related risks are the most common sources of AI risks.
• Meeri’s thoughts on the impact of the Use AI Act in the EU.

Quotes:

“It’s best to work with companies who know that they already have a problem.” — @meerihaataja [0:09:58]

“Third-party risks are way bigger in the context of [generative AI].” — @meerihaataja [0:14:22]

“Use and use-context-related risks are the major source of risks.” — @meerihaataja [0:17:56]

“Risk is fine if it’s on an acceptable level. That’s what governance seeks to do.” — @meerihaataja [0:21:17]

Links Mentioned in Today’s Episode:

Saidot

Meeri Haataja on LinkedIn

Meeri Haataja on Instagram

Meeri Haataja on X

How AI Happens

Sama

In this episode, Dr. Zoldi offers insight into the transformative potential of blockchain for ensuring transparency in AI development, the critical need for explainability over mere predictive power, and how FICO maintains trust in its AI systems through rigorous model development standards. We also delve into the essential integration of data science and software engineering teams, emphasizing that collaboration from the outset is key to operationalizing AI effectively.

Key Points From This Episode:

• How Scott integrates his role as an inventor with his duties as FICO CAO.
• Why he believes that mindshare is an essential leadership quality.
• What sparked his interest in responsible AI as a physicist.
• The shifting demographics of those who develop machine learning models.
• Insight into the use of blockchain to advance responsible AI.
• How FICO uses blockchain to ensure auditable ML decision-making.
• Operationalizing AI and the typical mistakes companies make in the process.
• The value of integrating data science and software engineering teams from the start.
• A fear-free perspective on what Scott finds so uniquely exciting about AI.

Quotes:

“I have to stay ahead of where the industry is moving and plot out the directions for FICO in terms of where AI and machine learning is going – [Being an inventor is critical for] being effective as a chief analytics officer.” — @ScottZoldi [0:01:53]

“[AI and machine learning] is software like any other type of software. It's just software that learns by itself and, therefore, we need [stricter] levels of control.” — @ScottZoldi [0:23:59]

“Data scientists and AI scientists need to have partners in software engineering. That's probably the number one reason why [companies fail during the operationalization process].” — @ScottZoldi [0:29:02]

Links Mentioned in Today’s Episode:

FICO

Dr. Scott Zoldi

Dr. Scott Zoldi on LinkedIn

Dr. Scott Zoldi on X

FICO Falcon Fraud Manager

How AI Happens

Sama

Jay breaks down the critical role of software optimizations and how they drive performance gains in AI, highlighting the importance of reducing inefficiencies in hardware. He also discusses the long-term vision for Lemurian Labs and the broader future of AI, pointing to the potential breakthroughs that could redefine industries and accelerate innovation, plus a whole lot more.

Key Points From This Episode:

• Jay’s diverse professional background and his attraction to solving unsolvable problems.
• How his unfinished business in robotics led him to his current work at Lemurian Labs.
• What he has learned from being CEO and the biggest obstacles he has had to overcome.
• Why he believes engineers with a problem-solving mindset can be effective CEOs.
• Lemurian Labs: making AI computing more efficient, affordable, and environmentally friendly.
• The critical role of software in increasing AI efficiency.
• Some of the biggest challenges in programming GPUs.
• Why better software is needed to optimize the use of hardware.
• Common inefficiencies in AI development and how to solve them.
• Reflections on the future of Lemurian Labs and AI more broadly.

Quotes:

“Every single problem I've tried to pick up has been one that – most people have considered as being almost impossible. There’s something appealing about that.” — Jay Dawani [0:02:58]

“No matter how good of an idea you put out into the world, most people don't have the motivation to go and solve it. You have to have an insane amount of belief and optimism that this problem is solvable, regardless of how much time it's going to take.” — Jay Dawani [0:07:14]

“If the world's just betting on one company, then the amount of compute you can have available is pretty limited. But if there's a lot of different kinds of compute that are slightly optimized with different resources, making them accessible allows us to get there faster.” — Jay Dawani [0:19:36]

“Basically what we're trying to do [at Lemurian Labs] is make it easy for programmers to get [the best] performance out of any hardware.” — Jay Dawani [0:20:57]

Links Mentioned in Today’s Episode:

Jay Dawani on LinkedIn

Lemurian Labs

How AI Happens

Sama