5 天之前 · AI tool generates high-quality images faster than state-of-the-art approaches 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.

2025年2月28日 · AI model deciphers the code in proteins that tells them where to go Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

2024年10月1日 · Researchers from MIT and elsewhere created a system that enables users to have an online, text-based conversation with an AI-generated simulation of their potential future self.

2024年10月16日 · Diffusion Forcing, a method led by researchers at MIT CSAIL, can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.

2024年8月19日 · A theory of mind model developed by MIT CSAIL researchers represents communication in epistemic planning for human and AI agents. This AI assistant is risk-bounded, aligning beliefs about plans among teammates and intervening when necessary.

2024年7月23日 · MAIA is a multimodal agent for neural network interpretability tasks developed at MIT CSAIL. It uses a vision-language model as a backbone and equips it with tools for experimenting on other AI systems.

6 天之前 · AI model deciphers the code in proteins that tells them where to go Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

2024年6月21日 · MosaicML, co-founded by an MIT alumnus and a professor, made deep-learning models faster and more efficient. Its acquisition by Databricks broadened that mission.

2024年7月8日 · Researchers from MIT and elsewhere developed an easy-to-use tool that enables someone to perform complicated statistical analyses on tabular data using just a few keystrokes. Their method combines probabilistic AI models with the programming language SQL to provide faster and more accurate results than other methods.

2024年8月30日 · The Data Provenance Explorer could help AI practitioners build more effective models by enabling them to select training datasets that fit their model’s intended purpose. In the long run, this could improve the accuracy of AI models in real-world situations, such as those used to evaluate loan applications or respond to customer queries.