Natural Language Processing podcasts

Stanford linguist and computer scientist Chris Manning discusses what linguistics contributed to machine learning and why pragmatics and dialogue remain open problems for language models. The conversation examines early abstraction of verb categories in small transformers, how distributed representations shape learning, and whether language alone can support meaningful representations. It also considers language in world models, diffusion language models, and representation finetuning (ReFT), which steers frozen models through their hidden states. The final discussion asks where knowledge resides and whether concepts occupy linear subspaces.

Hosted by Ravid Shwartz Ziv, Allen Roush

Andrew Dai joins The Information Bottleneck to discuss language-model pre-training, next-token prediction, training-data quality and visual reasoning. The conversation examines counting and image understanding, world models and JEPA. Hosted by Ravid Shwartz-Ziv and Allen Roush.

Alex Smola discusses the development of voice agents, audiovisual systems and AI avatars. Topics include audio tokenization, inference cost, conversational latency, visual context and how artificial systems might learn from interaction while responding appropriately to human emotion.

Hosted by Sam Charrington

This Nature Podcast episode examines human brain organoids transplanted into mice lacking a cerebral cortex, where extensive cellular integration could create an in-vivo platform for testing therapies and studying human neurobiology. It also covers research highlights and Paper2Agent, an AI system that turns research papers into interactive virtual corresponding authors able to answer questions and support complex scientific tasks.

Google DeepMind research director Brendan O'Donoghue examines discrete diffusion as an alternative to autoregressive text generation. He discusses sample diversity, reinforcement learning, latency and potential on-device or robotics applications. The episode also addresses the costs of serving diffusion models, hardware trade-offs between computation and memory bandwidth, and practical limits on progress toward more capable AI. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Nature staff examine what an invisible watermark for AI-generated text could mean for research integrity and the detection of synthetic content, then discuss cross-cultural research showing that ticklishness is a distinct and specialized body sensation with common anatomical patterns.

Chandra Sripada presents his argument that language models and humans share meaningful features of cognition. Drawing on cognitive science, he and Sean Carroll examine what behavioral similarities can tell us about underlying mechanisms and how to assess the relationship between artificial and human intelligence.

Hosted by Sean Carroll

Pleias co-founder Pierre-Carl Langlais describes language models built from documented open and public-domain sources alongside synthetic data. The conversation examines the SYNTH dataset, gaps in common web crawls, preservation of source material and ethical choices beyond copyright status. It also covers compact deployed models, benchmark incentives, reasoning-trace access and competing approaches to national AI development. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

MIT professor Phillip Isola discusses what makes learned representations useful and why independently trained models can develop similar internal structure. He explores the platonic representation hypothesis, local clustering versus global geometry, and the neural thickets account of why pretrained networks can adapt readily to downstream tasks. Other topics include language models as world models, recurrent architectures, biological comparisons, and studying autonomous language-model agents as artificial life. Hosted by Ravid Shwartz Ziv and Allen Roush; watch the full research conversation on YouTube.

Hosted by Ravid Shwartz Ziv, Allen Roush

Alex Bowcut draws lessons from building an AI system for tax-law research. He explains why retrieval still matters with long-context models, how chunking and combined retrieval methods affect results, and why reliable citations and feedback from domain experts are central to evaluating a specialized application.

Hosted by Sam Charrington

Michael Riegler discusses current uses of AI and language models, possible future developments and the concerns accompanying their spread. Conversation in Norwegian.

Scott Clark discusses operating language-model systems whose failures are difficult to anticipate with offline evaluations. He distinguishes telemetry, known-signal monitoring and production analytics, and describes using trace representations and clustering to discover emergent behaviour and feed improvements into evaluations and training.

Hosted by Sam Charrington

Stefano Ermon discusses adapting diffusion models to language and code generation. Topics include discrete token spaces, parallel token generation, Mercury 2, and trade-offs with autoregressive models. The conversation examines training, serving and post-training challenges, controllable generation and possible directions for multimodal diffusion systems.

Hosted by Sam Charrington

Siddhant Pardeshi describes autonomous software-development systems that combine knowledge graphs and vector search to navigate large repositories. The conversation examines context engineering, agent orchestration, model-specific prompting and persistent feedback. It also considers how evaluation should reflect security, maintainability and acceptance of the resulting software.

Hosted by Sam Charrington

Sebastian Raschka reviews changes in language-model research, including reasoning-focused post-training, inference-time computation and reinforcement learning with verifiable rewards. The discussion connects these methods with self-consistency, model architectures, efficient attention and long contexts, while examining limits in continual learning and the reliability of agent workflows.

Hosted by Sam Charrington

Yejin Choi examines how data diversity, synthetic examples, imitation learning and reinforcement learning can strengthen reasoning in small language models. The conversation discusses output homogeneity, Prismatic Synthesis, training models to reason before predicting tokens, and pluralistic alignment that represents a broader range of human values.

Hosted by Sam Charrington

Aakanksha Chowdhery examines changes to pre-training that could support reasoning, planning and multi-step agent workflows. Drawing on large-model training experience, she discusses next-token prediction, attention and loss functions, trajectory data, context retrieval, error recovery and the role of scale in learning tool use.

Hosted by Sam Charrington

Munawar Hayat discusses visual grounding and object hallucination in vision-language models. Topics include attention-guided alignment, contrastive learning for combined image-and-text retrieval, and evaluating images containing multiple people. The conversation connects identity leakage and attribute blending with the constraints of efficient on-device deployment.

Hosted by Sam Charrington

Carina Hong discusses building systems for mathematical reasoning and machine-verifiable proofs. The conversation explores Lean, the scarcity of formal mathematical training data, and translating natural-language arguments into formal statements. It also examines conjecture-and-proof self-play and applications of formal verification to software and hardware.

Hosted by Sam Charrington

Jacob Buckman examines long-context transformer architectures and the balance between model weights and recurrent state. Topics include windowed, grouped-query and latent attention, Power Retention, efficient GPU kernels and a coding model using recurrence. The conversation also discusses measuring how much useful information models extract from context.

Hosted by Sam Charrington