Podcasts

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.

Generally AI - Season 2 - Episode 4: Coordinate Systems in AI and the Physical World

In this podcast, Roland Meertens and Anthony Alford discuss coordinate systems, both in AI and the physical world. They explore how a library's classification systems mirror the concept of embeddings in AI, where documents are organized based on similarity and how AI tools like RAG use vector spaces to efficiently retrieve the right content.

Generally AI - Season 2 - Episode 3: Surviving the AI Winter

Roland Meertens and Anthony Alford discuss the historical cycles of AI "summers" and "winters": periods of optimism and decline in AI research. The conversation follows the story of neural networks, to the resurgence of AI with backpropagation and deep learning in the 2010s. They also explore the potential for a future "AI Winter", as technological advances face both hype and skepticism.