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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 Aufrufe • vor 4 Monaten •via X (Twitter)

34 Kommentare

Profilbild von Tolani
Tolanivor 4 Monaten

The Service Layer For Physical AI

Profilbild von Julz
Julzvor 4 Monaten

How will you scale real world data?

Profilbild von FAHD
FAHDvor 4 Monaten

So Bullishhhhh 🔥

Profilbild von M𝖚𝖘𝖙y 🪽
M𝖚𝖘𝖙y 🪽vor 4 Monaten

@Julzcrypt gPrisma 🦾

Profilbild von ATLAS
ATLASvor 4 Monaten

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

Profilbild von Skylar
Skylarvor 4 Monaten

gPrisma

Profilbild von kingopw3
kingopw3vor 4 Monaten

Most awaited post ! gprisma intern

Profilbild von Dibbyte
Dibbytevor 4 Monaten

Excited for the service layer era

Profilbild von kinndao
kinndaovor 4 Monaten

Waiting

Profilbild von 👻MaLeEk🔥
👻MaLeEk🔥vor 4 Monaten

@Julzcrypt How soon 👀

Profilbild von bigwil
bigwilvor 4 Monaten

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

Profilbild von GENTLE 🦹‍♂️
GENTLE 🦹‍♂️vor 4 Monaten

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

Profilbild von Aj Kanchan 🏴‍☠️ 🚢
Aj Kanchan 🏴‍☠️ 🚢vor 4 Monaten

This is exactly what robotics needs.

Profilbild von Li🥳🥳 🦇 (✱,✱)
Li🥳🥳 🦇 (✱,✱)vor 4 Monaten

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

Profilbild von Trong Hatachi
Trong Hatachivor 4 Monaten

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

Profilbild von Ãyø Mî Dê
Ãyø Mî Dêvor 4 Monaten

gprisma 1

Profilbild von yinkiid 💎
yinkiid 💎vor 4 Monaten

This is awesome.

Profilbild von Varun Reddy
Varun Reddyvor 4 Monaten

When TGE ?

Profilbild von oussail
oussailvor 4 Monaten

Lfg

Profilbild von Hosam 🟣🟢
Hosam 🟣🟢vor 4 Monaten

Gprisma 🦾

Profilbild von Duke
Dukevor 4 Monaten

The Service Layer For Physical AI >>>

Profilbild von OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳
OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳vor 4 Monaten

Great information gprisma

Profilbild von Modcuz
Modcuzvor 4 Monaten

The Service Layer For Physical AI

Profilbild von Sloun
Slounvor 4 Monaten

New future PrismaX

Profilbild von Storka
Storkavor 4 Monaten

LFG 🔥

Profilbild von Victoriou$ Victor
Victoriou$ Victorvor 4 Monaten

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

Profilbild von Phazy 🦅
Phazy 🦅vor 4 Monaten

@0xmusty It's time

Profilbild von Whizdom
Whizdomvor 4 Monaten

In PrismaX we trust!

Profilbild von JohnNguyen 🔆
JohnNguyen 🔆vor 4 Monaten

That's great PrismaXai

Profilbild von 𝙋𝘼𝙒𝘼𝙉 
𝙋𝘼𝙒𝘼𝙉 vor 4 Monaten

Physical AI scales through real world data not just models 🤖

Profilbild von Sean Waples Dexter(❖,❖)
Sean Waples Dexter(❖,❖)vor 4 Monaten

🥲

Profilbild von HEYVE (❖,❖)🦋
HEYVE (❖,❖)🦋vor 4 Monaten

Gprisma 🦾

Profilbild von Adel Bucetta
Adel Bucettavor 4 Monaten

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

Profilbild von Nana | VN (❖,❖)
Nana | VN (❖,❖)vor 4 Monaten

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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27,871 Aufrufe • vor 6 Monaten