Atul Kamble

AI & Robotics | Prev @Tykhe_ventures IIT Alum

[Atul Kamble @atulmorr]

https://t.co/ZLtXK96PaJ

[World Labs @theworldlabs]

Introducing Atlas:

The world's first multimodal world model that generates image and video frames with pixel-perfect camera control and reconstructs them in 3D.

Model the world, move the camera, and simulate space & time.

[Nikesh Arora @nikesharora]

The AI Business model trap: LLMs want cash flow to fund the race to AGI or the next model. Enter free consumer AI - they are losing a lot of money on the breadth of models to serve consumers for free! They are caught in the post training data trap, free consumer usage feeds post training needs, it can't be right to stop serving customers for free?

But they need money for the compute:

The monetization challenge is being pointed to Enterprises.

Phase 1 - seemed easy, value capture in coding, the most bottom up motion in enterprise - with low customization per customer. Developers continue to train coding, tasks and eventually will train flawless skills.

Phase 2 is where the challenge lies, showing true enterprise value. The promise of efficiency, accuracy, elimination of resources - that requires a different approach, build depth with harnesses, context, memory, solving for edge cases with deterministic guardrails! Build skill libraries - enter FDEs. Yes,FDEs will train the enterprise Waymos of the world.

The risk - high token pricing for enterprises while consumers for free! Yes for consumer distribution businesses (aka Google, Meta, Apple, etc) it makes sense to hold on the distribution with free AI.

If you want to win enterprise, you should be forward pricing tokens. The cheaper the tokens for enterprises it will allow for experimentation, workflow reimagination - instead CIOs are busy restricting AI use and working on making the use more efficient!

Paradox: They still haven't fully understood and embraced the value of AI in the enterprise.

If I were them:
1. Cut token pricing now, else send enterprises to secure opensource and end up with friction filled routing layers.
2. Show me how enterprises can use their context, training and data as their competitive advantage.
3. Build tools for rapid edge case learning and reducing false positives.

@HarryStebbings
@sama @DarioAmodei @demishassabis

[Ethan Blake @EBlakeAI]

Interesting trend in embodied AI:

We're seeing fewer papers focused purely on scaling models.

And more papers focused on adding structure.

Reasoning supervision (nuReasoning)

Explicit geometry (Triangle Splatting SLAM)

Task-level evaluation (RescueBench)

The question is no longer just how large a model can become.

It's what kinds of structure help it understand and interact with the world.

#EmbodiedAI #Robotics

[Jitendra MALIK @JitendraMalikCV]

I want to offer some unsolicited advice to computer vision researchers jumping into robotics. Don't focus too much on VLMs, VLAs etc. That's fine, but the real action is at the sensorimotor level. Most of the open problems in robotics are in manipulation, which is about hand-object interaction, and contacts and forces are central. Proprioception and tactile sensing are as important as vision. Don't get seduced by cherry-picked demos. You can't do robotics without doing robotics.

[Atul Kamble @atulmorr]

@candyyueliu Congrats guys. @candyyueliu would like to chat. can you DM

[Atul Kamble @atulmorr]

https://t.co/EaNtj2fBrE

[Atul Kamble @atulmorr]

@HarryXu12 Congratulations Huazhe

[Atul Kamble @atulmorr]

@LagannMikhail DMed you

[Atul Kamble @atulmorr]

Is Your AI Saving Your Time or Atrophying Your Mind? https://t.co/zHm7ouK6ps

[Atul Kamble @atulmorr]

@yeticheese What exactly are you solving for with SNN's?

[Atul Kamble @atulmorr]

Beautiful.

[David Senra @davidsenra]

This conversation with @RickRubin is special. Here's a 2 minute preview that I’ve watched 15 times.

The episode will be out tomorrow:

[Atul Kamble @atulmorr]

@AdamGillman X

[Damian Player @damianplayer]

McKinsey says the bottleneck on humanoid robots isn’t AI.

it’s the magnets, gearboxes, and sensors inside every actuator.

and China controls 70% of the entire component supply chain.

[Atul Kamble @atulmorr]

@Jason I'm in

[SpaceX @SpaceX]

Announcing TERAFAB: the next step towards becoming a galactic civilization https://t.co/xTA70LOU0e

[sarah guo @saranormous]

Caught up with @karpathy for a new @NoPriorsPod: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects

02:55 - What Capability Limits Remain?
06:15 - What Mastery of Coding Agents Looks Like
11:16 - Second Order Effects of Coding Agents
15:51 - Why AutoResearch
22:45 - Relevant Skills in the AI Era
28:25 - Model Speciation
32:30 - Collaboration Surfaces for Humans and AI
37:28 - Analysis of Jobs Market Data
48:25 - Open vs. Closed Source Models
53:51 - Autonomous Robotics and Atoms
1:00:59 - MicroGPT and Agentic Education
1:05:40 - End Thoughts

[travis kalanick @travisk]

Atoms.

https://t.co/nbCnIkI3RD

[Vinod Khosla @vkhosla]

The right place for AI in law is the enterprise, not law firms which are conflicted if the cost of legal services goes down rapidly. AI makes it possible to dramatically reduce this business overhead.