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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 次观看 • 4 个月前 •via X (Twitter)

34 条评论

Tolani 的头像
Tolani4 个月前

The Service Layer For Physical AI

Julz 的头像
Julz4 个月前

How will you scale real world data?

FAHD 的头像
FAHD4 个月前

So Bullishhhhh 🔥

M𝖚𝖘𝖙y 🪽 的头像
M𝖚𝖘𝖙y 🪽4 个月前

@Julzcrypt gPrisma 🦾

ATLAS 的头像
ATLAS4 个月前

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

Skylar 的头像
Skylar4 个月前

gPrisma

kingopw3 的头像
kingopw34 个月前

Most awaited post ! gprisma intern

Dibbyte 的头像
Dibbyte4 个月前

Excited for the service layer era

kinndao 的头像
kinndao4 个月前

Waiting

👻MaLeEk🔥 的头像
👻MaLeEk🔥4 个月前

@Julzcrypt How soon 👀

bigwil 的头像
bigwil4 个月前

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

GENTLE 🦹‍♂️ 的头像
GENTLE 🦹‍♂️4 个月前

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

Aj Kanchan 🏴‍☠️ 🚢 的头像
Aj Kanchan 🏴‍☠️ 🚢4 个月前

This is exactly what robotics needs.

Li🥳🥳 🦇 (✱,✱) 的头像
Li🥳🥳 🦇 (✱,✱)4 个月前

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

Trong Hatachi 的头像
Trong Hatachi4 个月前

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

Ãyø Mî Dê 的头像
Ãyø Mî Dê4 个月前

gprisma 1

yinkiid 💎 的头像
yinkiid 💎4 个月前

This is awesome.

Varun Reddy 的头像
Varun Reddy4 个月前

When TGE ?

oussail 的头像
oussail4 个月前

Lfg

Hosam 🟣🟢 的头像
Hosam 🟣🟢4 个月前

Gprisma 🦾

Duke 的头像
Duke4 个月前

The Service Layer For Physical AI >>>

OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳 的头像
OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳4 个月前

Great information gprisma

Modcuz 的头像
Modcuz4 个月前

The Service Layer For Physical AI

Sloun 的头像
Sloun4 个月前

New future PrismaX

Storka 的头像
Storka4 个月前

LFG 🔥

Victoriou$ Victor 的头像
Victoriou$ Victor4 个月前

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

Phazy 🦅 的头像
Phazy 🦅4 个月前

@0xmusty It's time

Whizdom 的头像
Whizdom4 个月前

In PrismaX we trust!

JohnNguyen 🔆 的头像
JohnNguyen 🔆4 个月前

That's great PrismaXai

𝙋𝘼𝙒𝘼𝙉  的头像
𝙋𝘼𝙒𝘼𝙉 4 个月前

Physical AI scales through real world data not just models 🤖

Sean Waples Dexter(❖,❖) 的头像
Sean Waples Dexter(❖,❖)4 个月前

🥲

HEYVE (❖,❖)🦋 的头像
HEYVE (❖,❖)🦋4 个月前

Gprisma 🦾

Adel Bucetta 的头像
Adel Bucetta4 个月前

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

Nana | VN (❖,❖) 的头像
Nana | VN (❖,❖)4 个月前

That's great PrismaXai

相关视频

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 次观看 • 6 个月前