Podcasts

Denys Linkov on Micro Metrics for LLM System Evaluation

Live from the QCon San Francisco Conference, we are talking with Denys Linkov, Head of Machine Learning at Voiceflow. Linkov shares insights on using micro metrics to refine large language models (LLMs), highlighting the importance of granular evaluation, continuous iteration, and rigorous prompt engineering to create reliable and user-focused AI systems.

Crossing the Feedback Chasm - a Conversation with Ken Finnigan

Michael Stiefel spoke with Ken Finnigan about how the lack of feedback impedes the development of software professionals. Without feedback, the right candidates are not hired, software professionals cannot improve or grow into new roles, or individuals stagnate or regress in their current positions. Feedback must also be delivered at the right time - when it can be effectively used.

Generally AI - Season 2 - Episode 6: the Godfathers of Programming and AI

Hosts discuss the Godfather of AI, Geoffrey Hinton, who developed pivotal algorithms like backpropagation, contributed to neural visualization with t-SNE, and inspired a resurgence in neural networks with AlexNet's success. They turn to John von Neumann, whose impact spanned mathematics, the Manhattan Project, and game theory, but most importantly: the von Neumann computer hardware architecture.

Namee Oberst on Small Language Models and How They are Enabling AI-Powered PCs

In this podcast, Namee Oberst, co-founder of AI Bloks, the company behind AI framework LLMWare, discusses the recent trend in Generative AI and Language Model technologies, the Small Language Models (SLMs) and how these smaller models are empowering the edge computing on devices and enabling AI-powered PC's.

Generally AI - Season 2 - Episode 5: Do Robots Dream of Electric Pianos?

Hosts discuss the use of simulation of both musical instruments and robots. They explore how software and sampling techniques allow musicians to replicate the sounds of real instruments and to design better pianos before manufacturing. They discuss how robot simulations allow testing code safely in virtual environments, avoiding costly or dangerous real-world consequences.