Examining the cognitive abilities of the AI language model, GPT-3, researchers found the algorithm can keep up and compete with humans in some areas but falls behind in others due to a lack of real-world experience and interactions.
Artificial intelligence was able to determine different behavioral phenotypes at different stages in the development of epilepsy in mice.
Using a combination of machine learning and neuroimaging data, researchers revealed a neural basis for aesthetic appreciation.
Researchers have created an accurate, easily interpretable new algorithm for predicting mild cognitive impairment and dementia in older adults.
When it comes to detecting what motivates a person's actions, infants outperform current artificial intelligence algorithms. The findings highlight fundamental differences between computation and human cognition, pointing to shortcomings in current machine learning and identifies where improvements are needed for AI to fully replicate human behavior.
While there is clear potential to use ChatGPT in a clinical setting, researchers say the AI algorithm may not yet be a reliable way of replacing the family doctor, especially when it comes to making effective decisions about prescribing antibiotics for infections.
Researchers are turning to artificial intelligence to find novel drugs that can block kappa opioid receptors with the hope to alleviate opioid addiction.
Tracking hippocampal neurons in mice as they watched a movie revealed novel ways to improve artificial intelligence and track neurological disorders associated with memory and learning deficits.
Study sheds light on why some people with autism experience aversion to certain smells and the neural mechanisms that underlie olfactory processing in those with ASD.
Researchers explain how deep neural networks are able to learn complex physics.
Artificial neural networks based on human brain dynamics can outperform current deep learning models in learning capabilities.
A new software framework incorporates dendritic properties and mechanisms into large-scale neural network models.
