Alan Luu

AI/AR. Spatial Intelligence. Builder at Foundation Labs.

[Alan Luu @AlanMLuu]

@giffmana That would be such a cool idea! But that would require 6 DoF tracking to render realistically. I don’t think Meta Ray-Ban Displays have that capability yet, but Snap Spectacles and Rayneo X3 Pros might be able to do it.

[Alan Luu @AlanMLuu]

Interesting article from a robotics professor and a robotics company CEO discussing why they believe robotics won’t have a singular “ChatGPT moment,” but instead a “Cambrian Explosion,” a rapid wave of diverse breakthroughs advancing the field in parallel.

In my opinion, robotics should be separated into two domains: robots that focus singularly on either locomotion or manipulation, and robots that attempt to do both. I agree that the former may look more like the semiconductor revolution, where a wide range of technologies and devices evolved simultaneously, each transforming its own domain.

However, I think the latter may have a "ChatGPT moment", or in physics terms, the characteristics of a "1st order phase transition," where a continuous parameter approaches/passes a key threshold and pushes the entire system into a qualitatively different regime, i.e. discrete behavior emerges from continuous conditions.

In physics, the phase transition often comes from non-zero finite quantities appearing in things like correlation length when taking the large-N limit for statistical systems (limit of large number of particles).

In general AI systems, such as LLMs, coding models, agentic systems, and general robotics, I think the discrete transition happens when performance passes a psychological threshold such that people are willing to trust the system. The coding models are an example of this, where the critical psychological threshold was passed late last year.

https://t.co/zJlWizaRW2

[Alan Luu @AlanMLuu]

@rohanpaul_ai I mean, that's the way you'd get planet-level data, right? Through crowd-sourcing. And you offer them a compelling game to motivate them to scan the world.

[Alan Luu @AlanMLuu]

I think we as a society need to have a serious discussion around AI, data collection, and privacy. Right now, there are no clear ground rules about what data can be used for training, when generative AI should be labeled, how AI should replace vs. augment human work, etc.

Part of this ambiguity is intentional. Some companies push boundaries, operating in gray areas, skirting user expectations, and moving faster than regulation or public understanding. At scale, this erodes trust and pushes us toward a low-trust digital society.

Think about where this leads: artists afraid to use tools for fear their style will be extracted. Generated video blurring the line between truth and fabrication, capable of both faking atrocities and obscuring real ones. Workers hesitant to document their work because it might train their replacement.

Technology isn’t inherently good or evil, but it is inherently empowering. And machine learning amplifies those who already have data, platforms, and distribution. That concentration of power is what people are reacting to.

The fear isn’t really about the technology itself. It’s about exploitation. It's about what happens when more powerful systems are built in an environment without meaningful constraints, accountability, or shared benefit.

Companies think they don't need to worry about tragedy of the commons, but that's only because they don't think collectively and far enough ahead. If people are now afraid of augmented reality, how will they ever allow humanoid robots into their home?

[NewsForce @Newsforce]

POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP

Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images.

The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS.

Source: NewsForce

[Alan Luu @AlanMLuu]

@ManyATrueNerd As someone who worked at one of the 3 big memory companies, it’s because a single fab takes a couple billion dollars and 5+ years of construction. Running these at less than full capacity means financial losses, so demand must be guaranteed. AI might be a bubble so not true here.

[Alan Luu @AlanMLuu]

@cyb3rshi3ld hi

[Alan Luu @AlanMLuu]

@bayeslord It depends on your definition of “laws”, but for lay people definitions, they don’t assume the laws are stationary in cosmology. On a technical level, they separate the laws that don’t change with the laws that do, and abstract it into laws with variable dynamics/fields.

[Alan Luu @AlanMLuu]

@yacineMTB Are you talking about 3D reconstruction from 2D images at different view angles/distances? That should be NERF/Splatt, but you have to have good coverage over all viewing angles. Otherwise, there are methods using structural priors or unsupervised pretrained models.

[Alan Luu @AlanMLuu]

@ylecun Yann is making the point that looking at language alone can’t solve this problem. You have to have a world model to know that the distance would be 2xPi in flat space, but in space with positive curvature like the surface of a spheriod, distance will always be greater than 2xPi.

[Doris Lee @dorisjlee]

🤔As a former data practitioner from a non-CS background, it always puzzled me: Why were certain tools labeled big data tools for "serious work", while the #Python libraries I knew and loved #pandas, #NumPy were seen as "toy" — but not deemed for real production uses? [2/n]

[Ponder @ponderdata]

📣 As of TODAY, Ponder is in Public Beta. 🚀 Now ANYONE can start running Python data workflows (pandas, NumPy) directly in their data warehouse in less than five minutes. Just pip install ponder.

Learn more below! 🧵[1/N]

https://t.co/ydnF0SMuje

#python #pandas #datascience

[Modin Project @modin_project]

✨ New major release for Modin 0.13.0!

This release includes significant upgrades to Modin's documentation 📖,support for pandas 1.4 🐼 , new algebra and partitioning layer APIs 💠, performance improvements ⏳+ bugfixes 🐞 . (1/n)