A robotic swimmer that mimics the movement of octopuses could help researchers better monitor aquatic environments remotely and in real time.
Can machine learning help in solid-state materials synthesis?
While AI has proven itself to be extremely capable in discerning optimal chemical structures, synthetic feasibility remains a challenge.
DeepMind solves 50-year-old challenge in predicting protein folding
Using their deep-learning program, AlphaFold, researchers predict the 3D structure of proteins using only their linear amino acid sequence, revolutionizing computational biology as we know it.
Machine learning methods provide new insights into organic-inorganic interfaces
Simulations at Graz University of Technology refute earlier theories on long-range charge transfer between organic and inorganic materials.
Online learning in a pandemic: A perspective
With a lack of motivation and determination, can this switch to online learning be beneficial for students? Three perspectives provide insight into learning during a pandemic.
Machine learning scopes out previously “invisible” microplastics
A new approach combines 3D coherent imaging with machine learning to detect microscale microplastics in filtered water samples.
Improving Image Recognition to Accelerate Machine Learning
By combining two types of memory arrays, researchers can accelerate image recognition for more efficient machine learning.
Predicting Rechargeable Battery Performance with Machine Learning
A new machine learning algorithm that can predict battery performance and failure beyond the expert level.
Machine Learning for Bioinformatics and Neuroimaging
Machine Learning (ML) is a well‐known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience.
AI is transforming climate forecasts for melting sea ice
Climate scientists leverage AI to enhance predictions of Arctic sea ice loss, using deep learning to address the urgency of climate change.