Building AI That Actually Works
AI-podden
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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Where Science Meets AI
AI-podden
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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What AI Can’t Do (Yet)...
AI-podden
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...
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AI, War, and the Human Touch
AI-podden
In this episode, we host special guest; Senior Business and Tech Correspondent at SVT and author, Alexander Norén. We discuss how AI is transforming warfare, especially through autonomous drones and decision-making systems, as explored in his documentary AI War. Alexander also reflects on AI’s impact on jobs, arguing that human-made work will gain value in the future. The conversation ends with concerns about AI-driven misinformation and the importance of trustworthy, human-centered journalism.
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The AI Revolution: Industry Disruption, Investment Shifts, and Future Challenges
AI-podden
In this episode we speak to Anders Indset, a philosopher, deep tech investor, and best-selling author. We discuss AI’s real-world impact, emphasizing Large Quantitative Models (LQMs) that optimize industry-specific data in fields like healthcare, finance, and logistics. Anders critiques Europe’s cautious investment culture, advocating for a bolder approach in AI-driven sectors. Inset stresses the need for balanced AI regulation to foster innovation while addressing global challenges like climate change.
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Data, Independence and Bias: The EU AI landscape
AI-podden
In this episode we chat to Niklas Silfverström, CEO of Klang.ai, about Europe's need for AI independence. We discuss data privacy risks, the Cloud Act, and AI bias, emphasizing the need for European infrastructure and language models. Niklas highlights how relying on American AI companies threatens sovereignty, and why investing in GPUs, data centers, and energy is crucial for Europe's competitive future. He also warns that without these efforts, Europe risks becoming a mere consumer of AI rather than a leader in the field. We should note that we use Klang.ai's wonderful platform in the...
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The AI Race: GROK 3, Open-Source Wars, and the Future of AGI
AI-podden
In this episode, we host Anders Arpteg to discuss GROK 3’s rapid innovation, the competitive AI landscape, and the rise of Chinese models. We explore Elon Musk’s influence, AI power concentration, open-source challenges, and the Tesla Agile DNA. The conversation concludes with insights into AI reasoning, AGI development, and future challenges.
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Game on with AI: How King is Levelling Up
AI-podden
In this episode, Ather speaks to Galina Esther Shubina, Senior Director of AI Strategy at King. They discuss how AI improves gameplay and development at King through AI-powered playtesting bots. Galina is excited about robotics but sees major technical challenges ahead. On AGI, she is skeptical, predicting it won’t emerge for at least 100 years due to AI’s Game on limitations in reasoning. For 2025, she expects smaller generative models and a shift toward traditional AI methods like supervised learning, which she sees as more valuable for businesses.
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Driving AI at Volvo
AI-podden
In this episode, Ather speaks to Mehrdad Mamaghani, Volvo Group’s Head of Data Science. They discuss AI’s role in predictive maintenance, financial forecasting, and sustainability. Merdad critiques the hype around AGI, emphasizing transparency and practical applications, and highlights the need for AI talent and expertise in Europe.
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AI-Podden News - December
AI-podden
Merry Christmas folks! This week, guest host (Sifted's) Mimi Billing and Ather discuss December's AI news updates. Highlights included Uber's entry into AI data labeling, Google DeepMind's Genie 2 for training AI in simulated environments, and Amazon's collaboration with Anthropic to develop a mega AI supercomputer. We covered the importance of new algorithms over mere scaling and the potential of agentic AI to bring advancements toward artificial general intelligence (AGI). The year saw record AI funding rounds, with Databricks leading at $10 billion, while disappointments included unmet...
info_outlineTed Schönbeck, Nordic CTO, Google Cloud discusses the current and future landscape of AI, emphasizing its ability to perform tasks that typically require human intelligence, with machine learning allowing systems to learn independently. Google, an "AI-first" company since 2016, has integrated AI into products like Maps and Search. Schönbeck highlights recent advancements in large language models and generative AI, particularly the use of large token windows for analyzing extensive datasets and the rise of multimodal AI, which processes diverse data types like text, images, and audio. He also addresses the challenges businesses face in scaling AI, recommending fine-tuned, specialized models over generalized large ones.