A new generative AI approach to predicting chemical reactions
System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
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System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
This new approach could lead to enhanced AI models for drug and materials discovery.
Language models follow changing situations using clever arithmetic, instead of sequential tracking. By controlling when these approaches are used, engineers could improve the systems’ capabilities.
A team of researchers has mapped the challenges of AI in software development, and outlined a research agenda to move the field forward.
Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.
Developed to analyze new semiconductors, the system could streamline the development of more powerful solar panels.
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
By performing deep learning at the speed of light, this chip could give edge devices new capabilities for real-time data analysis.
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.