COMPLEXITY Nature of Intelligence, Ep. 4: Babies vs Machines
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Nov 6, 2024 
Linda Smith, a pioneer in infant language learning research, joins Michael Frank, an expert in cognitive development from Stanford, to explore how humans and AI learn. They discuss groundbreaking studies using head-mounted cameras to understand infant perception and the importance of social interactions in learning. The conversation highlights the contrast between the rich, multimodal experiences of babies and the data-driven methods of AI. They also challenge the effectiveness of traditional evaluations of language models, questioning their ability to truly understand language like infants do.
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Early Visual Development
- Babies' visual systems develop significantly in the first three months, impacting face recognition and object discrimination.
- Early cataract removal is crucial, as delays can permanently disrupt face perception.
Curated Visual Data
- Babies' visual experiences are curated by evolution, unlike the vast, unfiltered data used to train large language models.
- This curated data, structured by developing motor skills and interactions, facilitates efficient learning.
Early Computer Vision Challenges
- Mike Frank initially tried using computer vision to analyze head-mounted camera data, but the technology was insufficient.
- Early computer vision algorithms, trained on standard photos, struggled with the unique angles and occlusions present in the baby's view.