The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Robin Braun, VP of AI at HPE, and Luke Norris, CEO of Kamiwaza, delve into AI's role in transforming smart cities and enterprises. They explore the 'Agentic Smart City' project in Vail, focusing on automating website accessibility and digitizing legacy data. The discussion highlights the significance of private cloud infrastructure, fresh data, and practical use cases that deliver real ROI. Learn how innovative technologies can enhance fire detection and urban monitoring, reducing costs and improving compliance in complex workflows.

Carina Hong, founder and CEO of Axiom, discusses her groundbreaking work on creating an AI mathematician. She highlights the convergence of advanced reasoning capabilities, formal proof languages like Lean, and breakthroughs in code generation as key trends revitalizing AI in mathematics. Carina delves into the challenges of translating informal proofs into machine-verifiable formats, the concept of self-improvement through conjecturing and proving, and the potential applications in high-stakes software verification.

Hung Bui, Technology VP at Qualcomm and former head of VinAI Research, dives into high-efficiency generative AI models. He discusses the transition from complex GANs to streamlined diffusion models, revealing his team's breakthroughs with SwiftBrush and SwiftEdit, which allow rapid text-to-image generation and editing. Hung explains their innovative distillation framework that creates a single-step model from a multi-step teacher, and how this paves the way for real-time, on-device AI. The conversation also touches on privacy in personalized agents and efficiency challenges in AI deployment.

In this conversation, Alexandre Pesant, AI lead at Lovable, delves into vibe coding, a transformative practice in software development. He explains how this approach shifts focus from typing code to expressing intent through higher-level abstractions. Alex shares insights on coding agents and the critical role of context engineering. He discusses Lovable’s evolution from complex agent systems to simpler workflows and back as technologies improved. Listeners also gain perspectives on AI's impact on engineering roles and the importance of robust evaluations in creating successful AI-native development tools.

Kunle Olukotun, a Stanford professor and chief technologist at SambaNova Systems, dives into reconfigurable dataflow architectures for AI. He explains how this innovative approach enhances AI inference by dynamically matching hardware to model dataflows, leading to reduced bandwidth bottlenecks. Kunle also highlights the benefits of fast model switching and efficient multi-model serving, crucial for low-latency applications. Plus, he explores future possibilities of using AI to create compilers for evolving hardware setups, offering insights into significant performance improvements.

Jacob Buckman, co-founder and CEO of Manifest AI, dives deep into the world of long-context transformers. He discusses innovative techniques like windowed attention and the revolutionary Power Retention approach, which melds attention and recurrence for astonishing training speeds. Buckman also shares insights on Manifest AI's open-source tools, Vidrial and PowerCoder, and explores the significance of metrics in measuring context utility. Learn about the balance between state and weight for optimal compute architectures and the future potential of context length in AI.

Illia Polosukhin, co-author of the groundbreaking 'Attention Is All You Need' paper and co-founder of Near AI, dives into the intersection of decentralization and private AI. He shares his journey from Google to revolutionizing blockchain with the NEAR Protocol. Topics include confidential computing for user data protection and the risks of AI centralization. Illia emphasizes a trust-building approach through open model training and formal verification. He also explores tokenized incentives for data contributions and the future of privacy in AI deployment.

Oliver Wang, a Principal Scientist at Google DeepMind, shares insights on the transformative capabilities of the Gemini 2.5 Flash Image, codenamed 'Nano Banana.' He explores the evolution from specialized image generators to integrated multimodal agents, highlighting how Nano Banana generates and edits images while preserving consistency. Oliver discusses the balance between aesthetics and accuracy, unexpected creative applications, and the future of AI models that could ‘think’ in images. He also warns about the risks associated with training on synthetic data.

In this discussion, Aditi Raghunathan, an assistant professor at Carnegie Mellon University, tackles the limitations of large language models (LLMs). She presents insights from her award-winning paper on enhancing creativity beyond next-token prediction. Aditi introduces the innovative 'Roll the dice' method to foster randomness and 'Look before you leap' for deeper thought processes. The conversation also covers the paradox of 'catastrophic overtraining' and her pursuit of more controllable models through concepts like 'memorization sinks.' Her research aims to reshape our understanding of AI adaptability.

Join Animesh Koratana, founder and CEO of PlayerZero, as he delves into the exciting world of AI-assisted coding tools. Discover how rapid advancements in AI have created a gap between code generation speed and effective maintenance processes. Animesh discusses the innovative use of code simulations to build a memory bank of past bugs and predictive models to enhance software reliability. He also explores the future of the software development lifecycle, emphasizing the need to adapt organizational workflows for an AI-driven landscape.