Satya Mallick

CEO,
CEO, Ph.D. ( Computer Vision & Machine Learning ).
Author:

[Satya Mallick @LearnOpenCV]

@MartijnGonlag @michellechen We maintain the OpenCV library, and I’d love to talk to you about this. My DMs are open.

[Satya Mallick @LearnOpenCV]

Would I let a video model sign off on completed work? Only after checking how it reached the verdict.

Our Nemotron 3.5 Super VL test is a useful example: the robot completed the checklist, but the model could not support one step and still agreed. A correct verdict alone would hide that failure.

We also tested a road sign and squat counting. Three case studies—not a benchmark. Human review of references is pending.

Full video, “Nemotron 3.5 Super VL Tested: A Correct Answer for the Wrong Reason,” on LearnOpenCV:
https://t.co/yHPg8c0tQA

Video sources: NVIDIA; FurnitureBench (CLVR Lab/KAIST); FitnessScape (CC BY 3.0). Full credits in the description. Thumbnail is an illustration.

What would you check beyond the final answer?
#AI #ComputerVision

[Satya Mallick @LearnOpenCV]

I finally watched the movie.

The staring is nothing compared to the head-nod that messes your brain.

https://t.co/unBGeo4lvC

[Satya Mallick @LearnOpenCV]

When the blink detector has more independent validation than Theranos. 😅

Built with HRFFA: per-person blink counting, facial landmarks, and synchronized audio/visual markers.

[Satya Mallick @LearnOpenCV]

@gdb I am sorry to learn that PHP was your first programming language.

[Satya Mallick @LearnOpenCV]

How to create this blue text string: #̢̛ͥ̀̽̽ͨ̌̓͆̇̀̽ͦͨͫͯ̏̎̇̈̚͜҉̕͢͏͡҉̸̶̛͢͡҉̵̨̭̳̘̹̫͔͖̜̠͖̫̲̖̱̤̻̖͉

It’s a # followed by a pile of Unicode combining marks, normally used for accents. They stack above and below the character, spilling into nearby lines. X makes it blue because it recognizes a hashtag. Copy the string to try it.

[Nassim Nicholas Taleb @nntaleb]

A reminder

[Satya Mallick @LearnOpenCV]

@steipete This definitely shocked me too.

[Satya Mallick @LearnOpenCV]

What interests me about Dream-1 isn't just a new robot demo. It's the possibility of needing less training data every time the hardware changes.

Dream Labs trained it on the YAM and showed zero-shot transfer to 27 unseen robots in simulation, plus real arms. I'd want to know where that transfer breaks: unfamiliar workspaces, poor IK, and new gripper designs.

Today the scope is bimanual robots with parallel grippers. Selected, sped-up demos aren't a reliability test. I haven't run Dream-1 myself.

Footage: Dream Labs (3× simulation, 4× real robots). Research: https://t.co/B1nXB2IU29
AI voice and avatar.

What would you test first on your own robot?

#Robotics #AI

[Satya Mallick @LearnOpenCV]

@bilawalsidhu Very well deserved congratulations

[Satya Mallick @LearnOpenCV]

@prof_kamilov Are you among the 4 or the 3 or the remaining population of the world.

[Satya Mallick @LearnOpenCV]

@nntaleb Unfortunately in the case of H1B two things are simultaneously true.

It brings highly skilled people into the US AND many companies are abusing it.

[Satya Mallick @LearnOpenCV]

Tell me you don't know about CUDA (and when it was funded internally at NVIDIA) without telling me you don't know about CUDA.

[Rabadash @Rabadash2]

Do people forget this guy just made toys for gamers up until like 10 years ago and has no
expertise in this stuff ?

[Satya Mallick @LearnOpenCV]

Is Muse as smart as Dot? I tried this little personal test.

A friend's wife invited our family for his birthday party a few days. She had not sent the exact time.

I asked both Muse and Dot to check if "Richard Stallman's [not his real name, obviously] wife has sent an exact time for the birthday party"

Now, Richard and his wife do not have the same last name. So it requires some reasoning to figure out who I was talking about.

Muse said it could not find any communication about Richard's birthday.

Dot nailed it.

It figured out the relationship, and then informed me that an invitation is waiting for me with the exact time and venue.

[Satya Mallick @LearnOpenCV]

We were able to launch OpenCV 5 just because of extensive use of AI.

OpenCV is the biggest computer vision open-source library with about 2 million downloads a day, and it is maintained by about 5 FTEs + Interns.

That's impossible without extensive use of AI.

[Kenney @KenneyNL]

Don't let billionaires convince you slop is the future. They're used to managing & directing, not making things themselves. Of course they can't see the difference. They don't understand your love for the craft. Keep making personal, beautiful games.

You've got my support 💖

[Satya Mallick @LearnOpenCV]

@elonmusk @TimSweeneyEpic @garrytan @KenneyNL We were able to launch OpenCV 5 just because of extensive use of AI.

OpenCV is the biggest computer vision open-source library with about 2 million downloads a day, and it is maintained by about 5 FTEs + Interns.

That's impossible without extensive use of AI.

[Satya Mallick @LearnOpenCV]

I have had many similar experiences in my three visits in two years to China.

Except for the slow internet that foreigners experience while traveling, the China experience is breathtaking.

[DHH @dhh]

I'd never heard of the Chinese car brand that just drove us back to the hotel in Shenzhen, but the interior was as nice or nicer than a top-spec Mercedes S class and the 20-minute drive was $6. Germany should be very worried indeed.