Machine learning and artificial intelligence interviews. Released as audio and video.
Episodes
16 Sept 2026
"Voice AI advances toward human-like avatars but faces challenges like latency, emotional authenticity, and hardware limitations, with future systems balancing speed, multimodal training, and ethical considerations."
Open episode
9 Sept 2026
"Explores AI's economic challenges, diminishing returns of token-based models, linguistic foundations, inefficiencies in current architectures, open-source contributions, and the need for high-risk research to drive innovation."
Open episode
25 Aug 2026
"AI accelerates scientific research in molecular dynamics and material science through equivariant neural networks, machine learning force fields, and digital twin simulations, revolutionizing fields like carbon capture, semiconductors, and energy storage while integrating physics and machine learning for broader scientific advancements."
Open episode
27 Jul 2026
"Explores weights-based learning, treating neural network weights as input data to train new models, improving efficiency, addressing data scarcity, and enabling tasks like model compression and performance prediction, with future directions in scaling, privacy, and cross-domain knowledge transfer."
Open episode
8 Jul 2026
Digitizing scent using AI involves converting molecules into digital data, creating standardized scent representations, and reproducing odors, addressing biological complexities, leveraging graph neural networks, and exploring applications in fragrance, diagnostics, and emotion while highlighting technical and ethical challenges.
Open episode
9 Jun 2026
The podcast examines Retrieval-Augmented Generation's evolving role in AI-driven tax compliance, focusing on Spheres AI's TRAM model, challenges in processing fragmented legal data, and the need for accurate citations, taxonomy integration, and real-time compliance automation via a global tax legislation index.
Open episode
21 May 2026
Relational foundation models and graph-based machine learning, like GNNs, enable accurate predictions on structured data across biomedical research and industries by capturing complex relationships, integrating multi-scale data, and overcoming traditional limitations through automated feature extraction and hybrid modeling.
Open episode
7 May 2026
Distributional employs post-production analytics, unsupervised learning, and LLMs to analyze agent traces, detect patterns and anti-patterns like hallucinations, address distributional shifts, and generate actionable insights for AI system refinement in security and enterprise settings, emphasizing adaptive analytics and domain expertise.
Open episode
30 Apr 2026
AI inference deployment is accelerating, emphasizing inference engineering's critical role in optimizing generative models with advanced hardware and complex systems, while addressing challenges like latency, scalability, and modality-specific optimizations amid evolving industry trends and fragmented yet open-source-driven markets.
Open episode
16 Apr 2026
Capital One's *Chat Concierge* multi-agentic AI system streamlines car-buying through self-reflection, real-time APIs, and LLM-driven workflows, addressing enterprise AI challenges like governance, scalability, and legacy system integration while prioritizing compliance, observability, and flexible platform adoption.
Open episode