Study could lead to LLMs that are better at complex reasoning
Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.
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.
Chemists could use this quick computational method to design more efficient reactions that yield useful compounds, from fuels to pharmaceuticals.
Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.
A new method from the MIT-IBM Watson AI Lab helps large language models to steer their own responses toward safer, more ethical, value-aligned outputs.
A new method lets users ask, in plain language, for a new molecule with certain properties, and receive a detailed description of how to synthesize it.
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.