In this episode, Stephen Jenkins, VP of Technology Strategy at Magna Electronics, shares practical insights into how AI is reshaping the automotive industry - from cutting development timelines with synthetic data to building smarter, more efficient autonomous parking systems. He explores the challenges of trust, safety, and explainability in AI, the current state of self-driving technology, and why full autonomy may be closer than we think.
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27:42
AI-Podden News - May (with Anders Arpteg)
In this week's episode, we host one of our favourite guests; Anders Arpteg, Head of AI and Data at GlobalConnect, to discuss how AI could enable “single person unicorns” by handling core business functions, based on his keynote at the Data Innovation Summit. The episode also explores leadership shifts at Meta and OpenAI, the growing divide between research and product focus, and a promising new approach to training AI without human data, inspired by AlphaZero.
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41:47
Building AI That Actually Works
In this week's episode, Ather chats with Magnus Hambleton, investor at byFounders and former data lead at Natural Cycles. Magnus has successfully applied transformers to improve fertility predictions in a medical setting. As an investor, he focuses on agentic AI and backs startups slightly ahead of what’s currently possible, betting on near-future model capabilities. He remains cautious about LLMs’ reliability and generalization, believing new architectures may be needed for AGI. While big tech is dominant, he sees room for startups in complex, workflow-driven applications.
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37:26
Where Science Meets AI
In this episode, Salla Franzén, Investment Manager at Navigare Ventures, returns to AI-Podden to explore how AI accelerates discovery in fields like life sciences and neuroscience. She unpacks the risks of tech hype, the need for explainable, trustworthy AI, and the importance of tools that bridge disciplinary gaps. Salla also shares insights on scientific investing, technical depth, and improving EU funding processes.
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37:42
What AI Can’t Do (Yet)...
In this week's episode we host John Lamb, Solutions Lead AI & Analytics at TietoEvry. John emphasises that while tools like LLMs are useful for automating repetitive tasks such as code generation or data prep, they fall short when it comes to reasoning and interpreting structured data. He explains that data cleaning still dominates much of a data scientist’s time and that current AI tools, though helpful, cannot yet be trusted for end-to-end analysis. John also discusses why he believes AGI is still years away, requiring a fundamental shift in technology, and shares practical advice for using AI responsibly, underscoring the importance of human judgment in any AI-driven workflow.
Sverige