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Robotics doesn't have a model problem. It has a data problem. And underneath that, a deployment problem. Physical AI progresses through real-world interaction. Robots act, fail, recover, and adapt. Without shared standards, every team relearns the same lessons in isolation. Deployment standards determine whether learning compounds or resets. PrismaX...

15,330 просмотров • 4 месяцев назад •via X (Twitter)

Комментарии: 34

Фото профиля Tolani
Tolani4 месяцев назад

The Service Layer For Physical AI

Фото профиля Julz
Julz4 месяцев назад

How will you scale real world data?

Фото профиля FAHD
FAHD4 месяцев назад

So Bullishhhhh 🔥

Фото профиля M𝖚𝖘𝖙y 🪽
M𝖚𝖘𝖙y 🪽4 месяцев назад

@Julzcrypt gPrisma 🦾

Фото профиля ATLAS
ATLAS4 месяцев назад

What's the next chapter in decentralizing the service layer???

Фото профиля Skylar
Skylar4 месяцев назад

gPrisma

Фото профиля kingopw3
kingopw34 месяцев назад

Most awaited post ! gprisma intern

Фото профиля Dibbyte
Dibbyte4 месяцев назад

Excited for the service layer era

Фото профиля kinndao
kinndao4 месяцев назад

Waiting

Фото профиля 👻MaLeEk🔥
👻MaLeEk🔥4 месяцев назад

@Julzcrypt How soon 👀

Фото профиля bigwil
bigwil4 месяцев назад

Lots of cool little examples and experiments... It's time to scale now!

Фото профиля GENTLE 🦹‍♂️
GENTLE 🦹‍♂️4 месяцев назад

@Julzcrypt “physical AI only improves when robots learn from real-world deployment” Gg PrismaX

Фото профиля Aj Kanchan 🏴‍☠️ 🚢
Aj Kanchan 🏴‍☠️ 🚢4 месяцев назад

This is exactly what robotics needs.

Фото профиля Li🥳🥳 🦇 (✱,✱)
Li🥳🥳 🦇 (✱,✱)4 месяцев назад

Let's go, that's the best news!

Фото профиля Trong Hatachi
Trong Hatachi4 месяцев назад

service layer sounds right, but compounding only happens if data is portable, open standard or prisma-only and who owns the ops data?

Фото профиля Ãyø Mî Dê
Ãyø Mî Dê4 месяцев назад

gprisma 1

Фото профиля yinkiid 💎
yinkiid 💎4 месяцев назад

This is awesome.

Фото профиля Varun Reddy
Varun Reddy4 месяцев назад

When TGE ?

Фото профиля oussail
oussail4 месяцев назад

Lfg

Фото профиля Hosam 🟣🟢
Hosam 🟣🟢4 месяцев назад

Gprisma 🦾

Фото профиля Duke
Duke4 месяцев назад

The Service Layer For Physical AI >>>

Фото профиля OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳
OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳4 месяцев назад

Great information gprisma

Фото профиля Modcuz
Modcuz4 месяцев назад

The Service Layer For Physical AI

Фото профиля Sloun
Sloun4 месяцев назад

New future PrismaX

Фото профиля Storka
Storka4 месяцев назад

LFG 🔥

Фото профиля Victoriou$ Victor
Victoriou$ Victor4 месяцев назад

Beautiful to see how @PrismaXai is shaping the future of robotics

Фото профиля Phazy 🦅
Phazy 🦅4 месяцев назад

@0xmusty It's time

Фото профиля Whizdom
Whizdom4 месяцев назад

In PrismaX we trust!

Фото профиля JohnNguyen 🔆
JohnNguyen 🔆4 месяцев назад

That's great PrismaXai

Фото профиля 𝙋𝘼𝙒𝘼𝙉 
𝙋𝘼𝙒𝘼𝙉 4 месяцев назад

Physical AI scales through real world data not just models 🤖

Фото профиля Sean Waples Dexter(❖,❖)
Sean Waples Dexter(❖,❖)4 месяцев назад

🥲

Фото профиля HEYVE (❖,❖)🦋
HEYVE (❖,❖)🦋4 месяцев назад

Gprisma 🦾

Фото профиля Adel Bucetta
Adel Bucetta4 месяцев назад

the reason most robotics projects fall short is they're trying to shortcut the feedback loop by relying on simulations instead of actual world interaction. that's a data problem, but it's also an opportunity for shared knowledge and standards

Фото профиля Nana | VN (❖,❖)
Nana | VN (❖,❖)4 месяцев назад

That's great PrismaXai

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Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

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