Infants possess a remarkable ability to rapidly learn and process visual inputs.

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Chimp infants are three times more likely to take risks than adults. Breakthroughs, discoveries, and DIY tips sent six days a week. Given the many similarities between humans and chimpanzees, one might assume that both species similarly engage in risky behavior within the same age range. However, according to a study recently published in the journal, it turns out that in chimps, it's the infants you have to watch out for. After studying videos of 119 wild chimpanzees, researchers found that chimpanzees' risky behavior peaks in their infancy, and then lessens as they get older.

Infants possess a remarkable ability to rapidly learn and process visual inputs. As an infant's mobility increases, so does the variety and dynamics of their visual inputs. Is this change in the properties of the visual inputs beneficial or even critical for the proper development of the visual system? To address this question, we used video recordings from infants wearing head-mounted cameras to train a variety of self-supervised learning models. Critically, we separated the infant data by age group and evaluated the importance of training with a curriculum aligned with developmental order. We found that initiating learning with the data from the youngest age group provided the strongest learning signal and led to the best learning outcomes in terms of downstream task performance. We then showed that the benefits of the data from the youngest age group are due to the slowness and simplicity of the visual experience. The results provide strong empirical evidence for the importance of the properties of the early infant experience and developmental progression in training. More broadly, our approach and findings take a noteworthy step towards reverse engineering the learning mechanisms in newborn brains using image-computable models from artificial intelligence.

Humans expertly navigate the world by building rich internal models founded on an intuitive understanding of physics. Meanwhile, despite training on vast quantities of internet video data, state-of-the-art deep learning models still fall short of human-level performance on intuitive physics benchmarks. This work investigates whether data distribution, rather than volume, is the key to learning these principles. We pretrain a Video Joint Embedding Predictive Architecture (V-JEPA) model on SAYCam, a developmentally realistic, egocentric video dataset partially capturing three children's everyday visual experiences. We find that training on this dataset, which represents 0.01% of the data volume used to train SOTA models, does not lead to significant performance improvements on the IntPhys2 benchmark. Our results suggest that merely training on a developmentally realistic dataset is insufficient for current architectures to learn representations that support intuitive physics. We conclude that varying visual data volume and distribution alone may not be sufficient for building systems with artificial intuitive physics.

Figuring out which objects or concepts words refer to is a central language learning challenge for young children. Most models of this process posit that children learn early object labels from co-occurrences of words and their referents that occur when someone around them talks about an object in the immediate physical environment. But how aligned in time are children's visual and linguistic experiences during everyday learning? To date, answers to this question have been limited by the need for labor-intensive manual annotations of vision-language co-occurrences. Here, we evaluate the use of contrastive language-image pretraining (CLIP) models to automatically characterize vision-language alignment in egocentric videos taken from the infant perspective in home environments. After validating CLIP alignment scores using human alignment judgments, we apply this metric to a large corpus of infant-perspective videos. We show that idealized aligned moments for learning (e.g., "look at the ball" with a ball present in the child's view) are relatively rare in children's everyday experiences compared to modern machine learning datasets, and highlight variability in alignment both within and across children. These findings suggest that infrequent alignment is a constraint for models describing early word learning and offer a new method for investigating children's multimodal environment.

Gaussian process priors that combine observational and interventional data.

We thank all the reviewers for their insightful and constructive comments, and will revise the paper accordingly. We designed our model to match objects based on general principles (e.g., We stress that ADEPT's training was not specific to the test dataset: there were no We will release the dataset along with all code, human data, and model evaluations upon publication. We chose to model them separately to avoid producing a constant surprise signal. Observing the unexpected enhances infants' learning and exploration. Over-representation of extreme events in decision making reflects rational use of cognitive resources.

Chimpanzees' brutal battle for territory leads to a baby boom A rival chimp can die in less than 15 minutes during these deadly territorial fights. New research led by UCLA and the University of Michigan has shown that chimp communities that kill their neighbors to gain territory also gain reproductive advantages. Breakthroughs, discoveries, and DIY tips sent every weekday. Uganda's Ngogo chimpanzees are well known for their "chimpanzee warfare." Primatologists have observed their brutal, lethal fights between 10 or more chimpanzees for decades, deciphering what leads to such violence.