Ivan Poupyrev's banner

Ivan Poupyrev

@ipoupyrev • 5,227 subscribers

Founder, CEO at @PhysicalAI. Tech leader and executive, interaction designer, scientist. Google, Disney, Sony before. 2019 National Design Award. TED speaker.

Shorts

This thing is a beast. This is an easy mode. Sony AI

This thing is a beast. This is an easy mode. Sony AI

18,324 просмотров

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

Years ago at Disney Imagineering I built a novel sensor to make almost anything interactive. As a test you can put a single wire in the soil, and touching a leaf could play a note. I called this project Botanicus Interacticus. The physical world is full of signals and hidden behaviors, and we've never learned to read and understand them. That's why we built Archetype AI. Newton, our foundation model, finally lets us listen.

Years ago at Disney Imagineering I built a novel sensor to make almost anything interactive. As a test you can put a single wire in the soil, and touching a leaf could play a note. I called this project Botanicus Interacticus. The physical world is full of signals and hidden behaviors, and we've never learned to read and understand them. That's why we built Archetype AI. Newton, our foundation model, finally lets us listen.

96,104 просмотров • 11 дней назад

At first glance, it looks like activity recognition. But look closer: the model isn't just detecting "typing" but spontaneously distinguishing right-hand from left-hand keystrokes. We didn't train Newton, our foundation model, to do this. No explicit labels. No supervision. Archetype AI #PhysicalAI

At first glance, it looks like activity recognition. But look closer: the model isn't just detecting "typing" but spontaneously distinguishing right-hand from left-hand keystrokes. We didn't train Newton, our foundation model, to do this. No explicit labels. No supervision. Archetype AI #PhysicalAI

47,739 просмотров • 7 месяцев назад

TimeFusion is built on a new multimodal encoding and decoding framework based on Universal Tokens. This approach can easily extend to a wide range of modalities beyond time-series data and language. Here’s a preview of our ongoing work using Universal Tokens to unify images, sensor data, and language in a single embedding space. This allows dynamic cross-modal mappings—such as converting a video stream directly into physical measurements, all controlled through natural language. #PhysicalAI #multimodal

TimeFusion is built on a new multimodal encoding and decoding framework based on Universal Tokens. This approach can easily extend to a wide range of modalities beyond time-series data and language. Here’s a preview of our ongoing work using Universal Tokens to unify images, sensor data, and language in a single embedding space. This allows dynamic cross-modal mappings—such as converting a video stream directly into physical measurements, all controlled through natural language. #PhysicalAI #multimodal

36,361 просмотров • 7 месяцев назад

Latest from Archetype AI demo land: This is Newton distinguishing between two people using the same phone. But how? Everyone has a slightly different way of typing. Newton understands the micro-differences in motion, which are mapped clearly in the embedded space (on the right). We never explicitly trained Newton to do this. Our Physical AI foundation model is capable of detecting emergent behaviors — so instead of multiple vertical AI solutions, organizations can leverage Newton to generalize across hundreds of sensor types and support a broad range of use cases. #PhysicalAI #FoundationModel

Latest from Archetype AI demo land: This is Newton distinguishing between two people using the same phone. But how? Everyone has a slightly different way of typing. Newton understands the micro-differences in motion, which are mapped clearly in the embedded space (on the right). We never explicitly trained Newton to do this. Our Physical AI foundation model is capable of detecting emergent behaviors — so instead of multiple vertical AI solutions, organizations can leverage Newton to generalize across hundreds of sensor types and support a broad range of use cases. #PhysicalAI #FoundationModel

12,917 просмотров • 4 месяцев назад

Больше нет контента для загрузки