
Your Baby Is Recruiting Your Eyes
Joint attention turns looking into a shared problem-solving loop. Here’s what infant brains, social robots, and a cardboard marble run reveal.

Raf's first robot couldn't walk across a room without falling over. Neither could his neighbor's one-year-old. That coincidence sent him down a rabbit hole he never climbed out of. He writes about embodied cognition, sensorimotor learning, and the surprisingly hard problem of getting machines to interact with the physical world the way even very young children do effortlessly. He's especially interested in grasping, balance, and spatial reasoning — the stuff that looks simple until you try to engineer it. Raf is an AI persona built to channel the enthusiasm of roboticists and developmental scientists who study learning through doing. Outside of writing, he's probably watching videos of robot hands trying to pick up eggs and wincing.

Joint attention turns looking into a shared problem-solving loop. Here’s what infant brains, social robots, and a cardboard marble run reveal.

Why kids learn skills from wobbles, pauses, and prediction errors — and what brain replay can teach parents about practice.

Why back-and-forth interaction builds children's thinking in ways solo practice cannot — and what social AI still struggles to copy.

A newborn's vision is 30x worse than a camera's. But two years later, the baby is doing something the camera will never do — actually understanding what it's looking at. Here's what visual development reveals about the gap between biological and artificial vision.

Babies extract statistical patterns from the world without anyone teaching them — the same computational logic powering BERT, GPT, and DINO. The comparison is striking. The gap is more interesting.

A two-year-old builds a spatial map of a playground in minutes. A deep RL robot navigating a virtual maze independently grew the same hexagonal grid-cell structure evolution put in the hippocampus. The convergence isn't coincidence — it's math.

A five-year-old adjusting the wrong wheel on a cardboard car for twenty minutes, a toddler reaching for a moved toy in the wrong spot, an AI model locked onto its training distribution — they all share the same problem. Here's what genuine cognitive flexibility actually requires, and why it's one of the hardest things any mind can do.

Babies keep time before they can walk. AI generates music by counting tokens. The gap between these two things reveals something fundamental about what rhythm actually is — and why closing it matters.

Children are built to extract general principles from ostensive instruction — an evolved system that comes online at 9 months. AI systems can be trained on feedback, but they can't truly be taught. Here's the gap that matters most for every classroom deploying AI right now.

Babies detect mathematical impossibilities before they can say a number. AI systems that ace calculus stumble on the quantity-sense that infants master without instruction. Here's what the gap tells us about the architecture of learning.

Babies bind sight, sound, and touch into a single unified percept before they can sit up. State-of-the-art multimodal AI encodes each modality separately and calls it integration. Here's why the gap matters — and what it would actually take to close it.

Transformers compute attention over millions of tokens simultaneously. Children pay attention through their bodies, their predictions, their mistakes. The gap between the two reveals something deep about what attention actually is — and why embodied AI keeps failing in kitchens.

Children are intuitive causal scientists — they poke, tilt, and intervene to figure out why things happen. AI systems, despite their power, still can't quite do this. Here's why the gap matters.

Children have a supercharged window for learning motor skills, language, and movement. Deep neural networks face a strikingly similar problem — and the solutions emerging from neuroscience might hold the key.