Towards Real-World Causal Intelligence Founded by @huang_biwei
Joined April 2026
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Aether AI (Causal Intelligence) retweeted
Had a great time chatting with @CharlieFink and @VirtualTedS.
Reasoning before acting is a fundamental part of Physical AI. Counterfactual reasoning gives a robot a way to consider possible consequences before committing to an action.
Making that reasoning transparent is equally important if we want to understand why a particular action was chosen.
“What if I do this action? What will happen?”
That’s the core of counterfactual reasoning in Physical AI.
Our founder @huang_biwei joined @CharlieFink and @VirtualTedS on @AIXRPodcast to talk 10+ years of causal AI research, how it’s shaping our causal world models, and why Physical AI is our first proving ground.
Check out the full episode in the comments.
Aether AI (Causal Intelligence) retweeted
The hard part often comes one step earlier: how do we recover the relevant causal variables in the first place? In robotics, these variables are often not given directly. They have to be learned from inputs such as pixels, trajectories, and raw sensor streams before we can model how actions change the system.
What makes a world model causal?
As a robotic gripper closes around an object, the contact conditions change. Friction, grasp point, speed and angle can all affect the outcome.
Our framework treats causality as an explicit system of causal variables, causal structure and causal dynamics in latent space.
Which condition would you vary first?
#CausalWorldModels
Aether AI (Causal Intelligence) retweeted
This got me thinking. Scaling has brought real progress, but how far can prediction take us as AI systems move toward more general intelligence? AI needs to uncover the causal mechanisms behind patterns. Causal mechanisms, interventions, and counterfactuals are key to robust generalization, and ultimately, more general intelligence.
Judea Pearl (@yudapearl) is a Turing Award winner famous for his contributions to artificial intelligence and causal reasoning.
We talked about the greatest scientist of all time, his major breakthroughs and his predictions for AI today. Full conversation also on my YouTube, Spotify, and Apple Podcasts.
Timestamps:
00:00 Intro
00:54 How he got into AI
11:17 Greatest scientist of all time
20:15 What people thought of AI in the 80s
26:23 Entering academia and researching AI
34:52 The invention of Bayesian networks
46:28 Pioneering work in causality
55:38 The causal hierarchy
59:34 LLMs and predictions
01:20:12 A restless mind pays
01:24:36 Advice for his younger self
01:26:37 Outro