No Priors: Artificial Intelligence | Technology | Startups

This week on No Priors, we have a host-only episode. Sarah and Elad catch up to discuss how tech history may be repeating itself. Much like in the early days of the internet, every company is clamoring to incorporate AI into their products or operations while some legacy players are skeptical that investment in AI will pay off. They also get into new opportunities and capabilities that AI is opening up, whether or not incubators are actually effective, and what companies are poised to stand the test of time in the changing tech landscape. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Show Notes:  (0:00) Introduction (0:16) Old school operators AI misunderstandings (5:10) Tech history is repeating itself with slow AI adoption (6:09) New AI Markets (8:48) AI-backed buyouts (13:03) AI incubation (17:18) Exciting incubating applications (18:26) AI and the public markets (22:20) Staffing AI companies  (25:14) Competition and shrinking head count

Believe or not, we’re almost halfway through 2024. Sarah and Elad have spent the first of this year talking with some of the most innovative minds in the AI industry, so we’re taking a look at some of our favorite No Priors conversations so far featuring Dylan Field (Figma); Emily Glassberg-Sands (Stripe); Brett Adcock (Figure AI); Aditya Ramesh, Tim Brooks and Bill Peebles (OpenAI’s Sora Team); Scott Wu (Cognition); and Alexandr Wang (Scale). Watch or listen to the full episodes here: Build AI products at on-AI companies with Emily Glassberg Sands from Stripe Designing the Future: Dylan Field on AI, Collaboration, and Independence The argument for humanoid robots with Brett Adcock from Figure OpenAI’s Sora team thinks we’ve only seen the "GPT-1 of video models" Cognition’s Scott Wu on how Devin, the AI software engineer, will work for you The Data Foundry for AI with Alexandr Wang from Scale Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Show Notes:  (0:00) Introduction (0:46) Emily Glassberg Sands on the Future of AI and Fintech (4:23 Dylan Field on AI and Human Creative Potential (9:03) Brett Adcock on Running Figure AI’s Hardware and Software Processes (12:43) OpenAI’s Sora Team on Artists’ Creative Experiences with their Model (17:43) Scott Wu Gives Advice for Human Engineers Co-Working with AI (21:06) Alexandr Wang on How Quality Data Builds Confidence in AI Systems

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. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @krandiash | @_albertgu Show Notes:  (0:00) Introduction (0:28) Use Cases for Cartesia and Sonic  (1:32) Karan Goel & Albert Gu’s professional backgrounds (5:06) State Space 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 Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil |  @joshua_xu_ 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 Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mikeknoop 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 Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @tengyuma 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.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @garrytan 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.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alexandr_wang (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

Mikey Shulman, the CEO and co-founder of Suno, can see a future where the Venn diagram of music creators and consumers becomes one big circle. The AI music generation tool trying to democratize music has been making waves in the AI community ever since they came out of stealth mode last year. Suno users can make a song complete with lyrics, just by entering a text prompt, for example, “koto boom bap lofi intricate beats.” You can hear it in action as Mikey, Sarah, and Elad create a song live in this episode.  In this episode, Elad, Sarah, And Mikey talk about how the Suno team took their experience making at transcription tool and applied it to music generation, how the Suno team evaluates aesthetics and taste because there is no standardized test you can give an AI model for music, and why Mikey doesn’t think AI-generated music will affect people’s consumption of human made music.  Listen to the full songs played and created in this episode: Whispers of Sakura Stone  Statistical Paradise Statistical Paradise 2 Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MikeyShulman Show Notes:  (0:00) Mikey’s background (3:48) Bark and music generation (5:33) Architecture for music generation AI (6:57) Assessing music quality (8:20) Mikey’s music background as an asset (10:02) Challenges in generative music AI (11:30) Business model (14:38) Surprising use cases of Suno (18:43) Creating a song on Suno live (21:44) Ratio of creators to consumers (25:00) The digitization of music (27:20) Mikey’s favorite song on Suno (29:35) Suno is hiring

This week on No Priors hosts, Sarah and Elad are catching up on the latest AI news. They discuss the recent developments in AI like Meta’s new AI assistant and the latest in music generation, and if you’re interested in generative AI music, stay tuned for next week’s interview! Sarah and Elad also get into device-resident models, AI hardware, and ask just how smart smaller models can really get. These hardware constraints were compared to the hurdles AI platforms are continuing to face including computing constraints, energy consumption, context windows, and how to best integrate these products in apps that users are familiar with. Have a question for our next host-only episode or feedback for our team? Reach out to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  Show Notes:  (0:00) Intro (1:25) Music AI generation (4:02) Apple’s LLM (11:39) The role of AI-specific hardware (15:25) AI platform updates (18:01) Forward thinking in investing in AI (20:33) Unlimited context (23:03) Energy constraints

Scott Wu loves code. He grew up competing in the International Olympiad in Informatics (IOI) and is a world class coder, and now he's building an AI agent designed to create more, not fewer, human engineers. This week on No Priors, Sarah and Elad talk to Scott, the co-founder and CEO of Cognition, an AI lab focusing on reasoning. Recently, the Cognition team released a demo of Devin, an AI software engineer that can increasingly handle entire tasks end to end. In this episode, they talk about why the team built Devin with a UI that mimics looking over another engineer’s shoulder as they work and how this transparency makes for a better result. Scott discusses why he thinks Devin will make it possible for there to be more human engineers in the world, and what will be important for software engineers to focus on as these roles evolve. They also get into how Scott thinks about building the Cognition team and that they’re just getting started.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ScottWu46 Show Notes:  (0:00) Introduction (1:12) IOI training and community (6:39) Cognition’s founding team (8:20) Meet Devin (9:17) The discourse around Devin (12:14) Building Devin’s UI (14:28) Devin’s strengths and weakness  (18:44) The evolution of coding agents (22:43) Tips for human engineers (26:48) Hiring at Cognition

AI-generated videos are not just leveled-up image generators. But rather, they could be a big step forward on the path to AGI. This week on No Priors, the team from Sora is here to discuss OpenAI’s recently announced generative video model, which can take a text prompt and create realistic, visually coherent, high-definition clips that are up to a minute long. Sora team leads, Aditya Ramesh, Tim Brooks, and Bill Peebles join Elad and Sarah to talk about developing Sora. The generative video model isn’t yet available for public use but the examples of its work are very impressive. However, they believe we’re still in the GPT-1 era of AI video models and are focused on a slow rollout to ensure the model is in the best place possible to offer value to the user and more importantly they’ve applied all the safety measures possible to avoid deep fakes and misinformation. They also discuss what they’re learning from implementing diffusion transformers, why they believe video generation is taking us one step closer to AGI, and why entertainment may not be the main use case for this tool in the future.  Show Links: Bling Zoo video Man eating a burger video Tokyo Walk video Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @_tim_brooks l @billpeeb l @model_mechanic Show Notes:  (0:00) Sora team Introduction (1:05) Simulating the world with Sora (2:25) Building the most valuable consumer product (5:50) Alternative use cases and simulation capabilities (8:41) Diffusion transformers explanation (10:15) Scaling laws for video (13:08) Applying end-to-end deep learning to video (15:30) Tuning the visual aesthetic of Sora (17:08) The road to “desktop Pixar” for everyone (20:12) Safety for visual models (22:34) Limitations of Sora (25:04) Learning from how Sora is learning (29:32) The biggest misconceptions about video models