AI models are powerful, but are they biologically plausible?
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.
Training artificial neural networks with data from real brains can make computer vision more robust.
MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.
A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans.
Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.
These tunable proteins could be used to create new materials with specific mechanical properties, like toughness or flexibility.
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.