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“Data in robotics versus data in LLMs has a very different alpha decay problem.” — Tarun Chitra, Managing Partner Robot Ventures Much of what LLMs learn can carry across contexts. Robotics data is far more conditional: what works depends heavily on the robot, the objects, and the environment. As... show more

YZi Labs

752,239 subscribers

26,519 Aufrufe • vor 27 Tagen •via X (Twitter)

Anya Rossi• Live Now

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8 Kommentare

Profilbild von FortunaShield

FortunaShieldvor 26 Tagen

@tarunchitra @robotventures Robotics alpha feels closer to onchain data than web text: context decides whether a signal actually survives.

Profilbild von FortunaShield

FortunaShieldvor 26 Tagen

@tarunchitra @robotventures Robotics alpha feels closer to onchain data than web text: context decides whether a signal actually survives.

Profilbild von Ray 🇺🇸

Ray 🇺🇸vor 27 Tagen

@tarunchitra @robotventures Bots Everywhere

Profilbild von vøv △

vøv △vor 27 Tagen

@tarunchitra @robotventures alpha decay hurts robots, loops win fast

Profilbild von Phenomenon Capital

Phenomenon Capitalvor 26 Tagen

@tarunchitra @robotventures The hardware overhead is the real kicker. Adapting to new environments isnt just software, its physics you cant simulate until you deploy.

Profilbild von mick maguire 🔸️🔶️🔸️

mick maguire 🔸️🔶️🔸️vor 27 Tagen

@tarunchitra @robotventures You need different wifi master systems for each colony type.

Profilbild von Dawny

Dawnyvor 27 Tagen

@tarunchitra @robotventures closing the loop" is just VC speak for "we have no idea what the fuck we're doing yet

Profilbild von Crypto美羊羊

Crypto美羊羊vor 26 Tagen

@tarunchitra @robotventures 数据保鲜期短，闭环速度才是关键

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a16z

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57,745 Aufrufe • vor 1 Jahr

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Christine (郡主Christine (✱,✱)), co-founder of Axis Robotics spent eight years ago studying mechanical engineering and working with ABB and KUKA robots before pivoting through stablecoin payments and landing on physical AI data. Axis Robotics has raised $12M to build a distributed training engine where anyone with a browser can teach robots. The network already has 200,000+ contributors and a dataset of 50,000+ robot trajectories across 207 tasks. On today's episode: why noisy human data beats "perfect" AI data, why simulation might be the key to physical AI's GPT moment, and the uncomfortable truth about people getting paid to train the robots that'll eventually replace them. Timestamps: 0:00 Intro: Building the Data Layer Behind Physical AI 1:34 Hot Take: Noisy Data vs. Perfect Data 3:16 Why Simulation Data Will Bring the GPT Moment for Robotics 7:25 From Stablecoin Payments to Robotics Data 10:08 Why Axis Chose Data Over Building Robots 16:10 Live Demo: Training a Robot Arm Like Playing FIFA 24:02 How Contributors Get Paid (Tokens + Stablecoin) 26:42 Inside the $12M Seed Round Pitch 32:08 200K+ Contributors & Viral Growth via Base 38:38 Sim-to-Real: The Living Data Engine 41:34 Token & Web3 Plans for Axis Robotics 43:00 Rapid Fire Questions

Pukerainbow 🤮🌈

24,836 Aufrufe • vor 24 Tagen

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Introducing LDA, a latent world action foundation model that, for the first time, unifies the utilization of heterogeneous embodied data across simulation and reality, humans and robots, and varying levels of action quality and annotation. By breaking long-standing data silos in embodied intelligence, LDA enables the field, much like GPT did for language, to benefit continuously from scaling data, marking the transition into a new era of scalable learning. #Galbot #Robotics #Innovation #AI #Technology #Humanoid #WorldModel

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40,883 Aufrufe • vor 6 Monaten