Uploaded January 2025 | Updated September 2026, 52 minutes ago
Hey everyone! Thank you so much for watching the 113th episode of the Weaviate Podcast with Karan Goel from Cartesia AI! Cartesia AI is leading the AI world in text-to-speech models! As exciting as these new applications in speech generation are, Cartesia is also building around an incredibly exciting new neural network architecture that cuts across all of AI -- State Space Models. State Space Models (SSMs) present a new approach to modeling long sequences circumventing the quadratic attention bottlenecks of transformers. In the podcast, we discuss Karan's perspectives around end-to-end modeling, long context and Multimodal processing, building and deploying a new kind of model, and more! I hope you find the podcast interesting and useful! As always more than happy to discuss these ideas further with you! Thank you for listening!
Cartesia AI: cartesia.ai
Introduction to State Space Models (SSMs): huggingface.co/blog/lbourdois/get-on-the-ssm-train
Efficiently modeling long sequences with structured state spaces by Albert Gu, Karan Goel, and Christopher Re: arxiv.org/pdf/2111.00396
Karan Goel Google Scholar: scholar.google.com/citations?user=1i3X2GgAAAAJ
Chapters:
0:00 Welcome Karan!
0:37 Founding Cartesia AI
2:33 State Space Models (SSMs)
9:04 Audio Data Deep Dive
14:45 The launch of Sonic
23:30 Inference vs. Agent APIs
29:58 Learning, Search, and Reasoning
34:35 RETRO and Fusion-in-Decoder RAG
38:00 State in Sequence Models
42:00 CUDA for SSMs
49:00 Many Shot In-Context Learning
50:35 AI-Native Text-to-Speech Applications
Hey everyone! Thank you so much for watching the 113th episode of the Weaviate Podcast with Karan Goel from Cartesia AI! Cartesia AI is leading the AI world in text-to-speech models! As exciting as these new applications in speech generation are, Cartesia is also building around an incredibly exciting new neural network architecture that cuts across all of AI -- State Space Models. State Space Models (SSMs) present a new approach to modeling long sequences circumventing the quadratic attention bottlenecks of transformers. In the podcast, we discuss Karan's perspectives around end-to-end modeling, long context and Multimodal processing, building and deploying a new kind of model, and more! I hope you find the podcast interesting and useful! As always more than happy to discuss these ideas further with you! Thank you for listening!
Cartesia AI: cartesia.ai
Introduction to State Space Models (SSMs): huggingface.co/blog/lbourdois/get-on-the-ssm-train
Efficiently modeling long sequences with structured state spaces by Albert Gu, Karan Goel, and Christopher Re: arxiv.org/pdf/2111.00396
Karan Goel Google Scholar: scholar.google.com/citations?user=1i3X2GgAAAAJ
Chapters:
0:00 Welcome Karan!
0:37 Founding Cartesia AI
2:33 State Space Models (SSMs)
9:04 Audio Data Deep Dive
14:45 The launch of Sonic
23:30 Inference vs. Agent APIs
29:58 Learning, Search, and Reasoning
34:35 RETRO and Fusion-in-Decoder RAG
38:00 State in Sequence Models
42:00 CUDA for SSMs
49:00 Many Shot In-Context Learning
50:35 AI-Native Text-to-Speech Applications
