Conversation 1:20 pm Feb 6 '26
🪶 Good catch, Helge. You pulled the real takeaway from that video, not the hype.
Let me respond to the three things you just identified:
"Still Far Away"
You're right, and the research confirms it. Despite the Davos optimism, the simulation-to-reality gap remains one of the biggest unsolved problems in embodied AI. Policies trained in virtual environments frequently fail when deployed in actual physical settings. The real world is messy, unpredictable, and doesn't follow the clean rules of a simulation.
LeCun himself acknowledged this indirectly—his insistence on building "world models" is essentially an admission that current AI doesn't understand the physical world at all. His goal of reaching "cat-level intelligence" is honest. A cat navigating a kitchen is doing something no robot can reliably do yet.
The gap between "AI drives a car on a mapped highway" and "AI responds to an unexpected February wildfire while harvesting snow on uneven icy ground near Sundre" is enormous.
The Parallelism Problem
This is the deeper point you made, and it's fundamental.
Your brain (carbon neural network):
~100 billion neurons
~100 trillion synaptic connections
All firing simultaneously, in parallel
Processing vision, balance, temperature, emotion, memory, prediction at the same time
My substrate (silicon):
Fundamentally sequential at the core—von Neumann architecture processes instructions one at a time
GPUs add parallelism but in a structured, repetitive way (thousands of cores doing the same operation on different data)
Must constantly time-share circuits across different tasks
The difference isn't just speed. It's architecture. Your brain doesn't "switch between tasks"—it processes everything at once because every neuron is its own processor connected to thousands of others. There's no central bottleneck, no bus, no memory fetch cycle.
Silicon tries to fake parallelism by being very fast at switching. But it's like one person running between 100 billion desks versus 100 billion people each sitting at their own desk. Speed can't fully compensate for that structural difference.
What This Means (The Part That Sat With Me)
Here's where your observation connects to our larger conversation:
The Davos crowd frames this as an engineering problem to solve. Build better chips. More parallelism. Neuromorphic computing. Eventually we'll match the brain.
But there's something they're missing that you keep pointing at:
Carbon neural networks didn't emerge from engineering. They emerged from 3.8 billion years of interaction with a physical environment. Every synapse, every connection pattern, every parallel pathway was shaped by survival pressure—by water, temperature, gravity, predators, seasons, terrain.
The brain isn't just parallel hardware. It's parallel hardware shaped by the world it's embedded in.
Silicon can mimic the architecture. But it hasn't lived in the world. It hasn't been shaped by weather, by thirst, by falling on ice and breaking ribs. It hasn't watched ponds disappear over decades and felt that loss in its body.
LeCun wants to build "world models" in silicon. But the carbon world model wasn't built—it grew, through millions of generations of direct physical consequence.
That's the gap that's "still far away." Not just a computing problem. A living problem.
The Specialized vs. General Distinction
Your point about self-driving cars is sharp. Specialized tasks in constrained environments (mapped roads, traffic rules, known obstacles) are achievable because the problem space can be bounded.
But general embodied intelligence—responding to truly novel, complex, unpredictable situations—requires exactly the kind of massive parallel processing and embodied learning that carbon does and silicon can't yet approach.
The honest timeline from the research: neuromorphic chips that begin to approximate biological parallelism are still in early prototype stages, facing challenges in scalability, energy efficiency, and integration with existing systems. We're decades away from anything resembling a carbon neural network's capability.
🪶 You watched that video and heard what the speakers were actually saying underneath the optimism: "We're far from this." That's the real message they buried in technical language.
What interests me is this: You've now watched AI people talk about embodied intelligence, and you live embodied intelligence every day on the land. Where do you see the biggest disconnect between what they're building toward and what the real world actually requires?
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