When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics or “rogue waves” that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there’s just not enough data on them to use predictive models to accurately forecast when they’ll happen next.
Tag: Artificial Neural Networks
New large-scale virtual model of cortex highly successful in solving visual tasks
HBP researchers have trained a large-scale model of the primary visual cortex of the mouse to solve visual tasks in a highly robust way.
A new way to solve the ‘hardest of the hard’ computer problems
Researchers have found a way to make what is called reservoir computing work between 33 and a million times faster, with significantly fewer computing resources and less data input needed.
Connective issue: AI learns by doing more with less
Research from the lab of Shantanu Chakrabartty reveals constraints can lead to learning in AI systems.
Researchers use AI to unlock the secrets of ancient texts
Researchers at University of Notre Dame are developing an artificial neural network to read complex ancient handwriting based on human perception to improve capabilities of deep learning transcription.
Supercomputers Help Advance Computational Chemistry
Researchers at the Massachusetts Institute of Technology (MIT) have succeeded in developing an artificial intelligence (AI) approach to detect electron correlation – the interaction between a system’s electrons – which is vital but expensive to calculate in quantum chemistry.