No Priors: Artificial Intelligence | Technology | Startups Conviction | Pod People

This week on No Priors, Sarah Guo and Elad Gil sit down with Karan Goel and Albert Gu from Cartesia. Karan and Albert first met as Stanford AI Lab PhDs, where their lab invented Space Models or SSMs, a fundamental new primitive for training large-scale foundation models. In 2023, they Founded Cartesia to build real-time intelligence for every device. One year later, Cartesia released Sonic which generates high quality and lifelike speech with a model latency of 135ms—the fastest for a model of this class.

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Show Notes: 
(0:00) Introduction
(0:28) Use Cases for Cartesia and Sonic 
(1:32) Karan Goel & Albert Gu’s professional backgrounds
(5:06) Steady State Models (SSMs) versus Transformer Based Architectures 
(11:51) Domain Applications for Hybrid Approaches 
(13:10) Text to Speech and Voice
(17:29) Data, Size of Models and Efficiency 
(20:34) Recent Launch of Text to Speech Product
(25:01) Multimodality & Building Blocks
(25:54) What’s Next at Cartesia? 
(28:28) Latency in Text to Speech
(29:30) Choosing Research Problems Based on Aesthetic 
(31:23) Product Demo
(32:48) Cartesia Team & Hiring

 AI video generation models still have a long way to go when it comes to making compelling and complex videos but the HeyGen team are well on their way to streamlining the video creation process by using a combination of language, video, and voice models to create videos featuring personalized avatars, b-roll, and dialogue. This week on No Priors, Joshua Xu the co-founder and CEO of HeyGen,  joins Sarah and Elad to discuss how the HeyGen team broke down the elements of a video and built or found models to use for each one, the commercial applications for these AI videos, and how they’re safeguarding against deep fakes. 

Links from episode:

HeyGen

McDonald’s commercial


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Show Notes: 
(0:00) Introduction
(3:08) Applications of AI content creation
(5:49) Best use cases for Hey Gen
(7:34) Building for quality in AI video generation
(11:17) The models powering HeyGen
(14:49) Research approach
(16:39) Safeguarding against deep fakes
(18:31) How AI video generation will change video creation
(24:02) Challenges in building the model
(26:29) HeyGen team and company

The first step in achieving AGI is nailing down a concise definition and Mike Knoop, the co-founder and Head of AI at Zapier, believes François Chollet got it right when he defined general intelligence as a system that can efficiently acquire new skills. This week on No Priors, Miked joins Elad to discuss ARC Prize which is a multi-million dollar non-profit public challenge that is looking for someone to beat the Abstraction and Reasoning Corpus (ARC) evaluation.
In this episode, they also get into why Mike thinks LLMs will not get us to AGI, how Zapier is incorporating AI into their products and the power of agents, and why it’s dangerous to regulate AGI before discovering its full potential. 
Show Links:

About the Abstraction and Reasoning Corpus

Zapier Central

ARC Prize


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Show Notes: 
(0:00) Introduction
(1:10) Redefining AGI
(2:16) Introducing ARC Prize
(3:08) Definition of AGI
(5:14) LLMs and AGI
(8:20) Promising techniques to developing AGI
(11:0) Sentience and intelligence
(13:51) Prize model vs investing
(16:28) Zapier AI innovations
(19:08) Economic value of agents
(21:48) Open source to achieve AGI
(24:20) Regulating AI and AGI

After Tengyu Ma spent years at Stanford researching AI optimization, embedding models, and transformers, he took a break from academia to start Voyage AI which allows enterprise customers to have the most accurate retrieval possible through the most useful foundational data. Tengyu joins Sarah on this week’s episode of No priors to discuss why RAG systems are winning as the dominant architecture in enterprise and the evolution of foundational data that has allowed RAG to flourish. And while fine-tuning is still in the conversation, Tengyu argues that RAG will continue to evolve as the cheapest, quickest, and most accurate system for data retrieval. 

They also discuss methods for growing context windows and managing latency budgets, how Tengyu’s research has informed his work at Voyage, and the role academia should play as AI grows as an industry. 

Show Links:

Voyage AI

Stanford Assistant Professor of Computer Science

Tengyu Ma Key Research Papers:

Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

Non-convex optimization for machine learning: design, analysis, and understanding

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

Larger language models do in-context learning differently, 2023

Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning

On the Optimization Landscape of Tensor Decompositions


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Show Notes: 
(0:00) Introduction
(1:59) Key points of Tengyu’s research
(4:28) Academia compared to industry
(6:46) Voyage AI overview
(9:44) Enterprise RAG use cases
(15:23) LLM long-term memory and token limitations
(18:03) Agent chaining and data management
(22:01) Improving enterprise RAG 
(25:44) Latency budgets
(27:48) Advice for building RAG systems
(31:06) Learnings as an AI founder
(32:55) The role of academia in AI

Garry Tan is a notorious founder-turned-investor who is now running one of the most prestigious accelerators in the world, Y Combinator. As the president and CEO of YC, Garry has been credited with reinvigorating the program. On this week’s episode of No Priors, Sarah, Elad, and Garry discuss the shifting demographics of YC founders and how AI is encouraging younger founders to launch companies, predicting which early stage startups will have longevity, and making YC a beacon for innovation in AI companies. They also discussed the importance of building companies in person and if San Francisco is, in fact, back. 

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Show Notes: 
(0:00) Introduction
(0:53) Transitioning from founder to investing
(5:10) Early social media startups
(7:50) Trend predicting at YC
(10:03) Selecting YC founders
(12:06) AI trends emerging in YC batch
(18:34) Motivating culture at YC
(20:39) Choosing the startups with longevity
(24:01) Shifting YC found demographics
(29:24) Building in San Francisco 
(31:01) Making YC a beacon for creators
(33:17) Garry Tan is bringing San Francisco back

Alexandr Wang was 19 when he realized that gathering data will be crucial as AI becomes more prevalent, so he dropped out of MIT and started Scale AI. This week on No Priors, Alexandr joins Sarah and Elad to discuss how Scale is providing infrastructure and building a robust data foundry that is crucial to the future of AI. While the company started working with autonomous vehicles, they’ve expanded by partnering with research labs and even the U.S. government.  

In this episode, they get into the importance of data quality in building trust in AI systems and a possible future where we can build better self-improvement loops, AI in the enterprise, and where human and AI intelligence will work together to produce better outcomes. 

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(0:00) Introduction
(3:01) Data infrastructure for autonomous vehicles
(5:51) Data abundance and organization
(12:06)  Data quality and collection
(15:34) The role of human expertise
(20:18) Building trust in AI systems
(23:28) Evaluating AI models
(29:59) AI and government contracts
(32:21) Multi-modality and scaling challenges