In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...
AI systems are "trained" using massive datasets, and the quality of this data determines the model's performance. AI can ...
This article discusses legal issues surrounding the harms caused by artificial intelligence, and where liability may lie if a neural network is not functioning correctly in the real world.Artificial ...
Qiskit and Q# are major quantum programming languages from IBM and Microsoft, respectively, used for creating and testing ...
Machine-learning-informed simulations of physical phenomena ranging from drifting bands (left), resonant ripples (center) and ...
Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular ...
An integrated framework combining first-principles calculations and machine learning was developed to predict gas-sensing performance. Key descriptors such as adsorption energy, adsorption distance, ...
How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a ...
In a comprehensive new study, economists have redefined the boundaries of predictive modeling in economics, blending traditional macroeconomic analysis with advanced machine learning principles. The ...
The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...
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