Artificial intelligence | MIT News | Massachusetts Institute of …
3 天之前 · Artificial intelligence meets “blisk” in new DARPA-funded collaboration. Collaborative multi-university team will pursue new AI-enhanced design tools and high-throughput testing methods for next-generation turbomachinery. October 8, 2024. Read full story.
AI simulation gives people a glimpse of their potential ... - MIT News
2024年10月1日 · The AI system uses this information to create what the researchers call “future self memories” which provide a backstory the model pulls from when interacting with the user. For instance, the chatbot could talk about the highlights of someone’s future career or answer questions about how the user overcame a particular challenge.
Explained: Generative AI | MIT News | Massachusetts Institute of …
2023年11月9日 · Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on. “When it comes to the actual machinery underlying generative AI and other types ...
Combining next-token prediction and video diffusion in ... - MIT …
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.
Massachusetts Institute of Technology - MIT News
2024年10月17日 · Prof. Daron Acemoglu speaks with Greg Rosalsky of NPR’s Planet Money about a recent survey that claims "almost 40% of Americans, ages 18 to 64, have used generative AI." "My concern with their numbers is that it does not distinguish fundamentally productive uses of generative AI from occasional/frivolous uses," says Acemoglu.
New hardware offers faster computation for artificial ... - MIT News
2022年7月28日 · MIT researchers created protonic programmable resistors — building blocks of analog deep learning systems — that can process data 1 million times faster than synapses in the human brain. These ultrafast, low-energy resistors could enable analog deep learning systems that can train new and more powerful neural networks rapidly, which could be used for areas like self-driving cars, fraud ...
MIT researchers advance automated interpretability in AI models
2024年7月23日 · They developed “MAIA” (Multimodal Automated Interpretability Agent), a system that automates a variety of neural network interpretability tasks using a vision-language model backbone equipped with tools for experimenting on other AI systems. “Our goal is to create an AI researcher that can conduct interpretability experiments autonomously.
Researchers enhance peripheral vision in AI models - MIT News
2024年3月8日 · Unlike humans, AI does not have peripheral vision. Equipping computer vision models with this ability could help them detect approaching hazards more effectively or predict whether a human driver would notice an oncoming object.
Massachusetts Institute of Technology - MIT News
2024年9月19日 · A fast and flexible approach to help doctors annotate medical scans. “ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities. September 9, 2024. Read full story.
AI assistant monitors teamwork to promote effective ... - MIT News
2024年8月19日 · AI to the rescue Even with a sophisticated plan, both human and robotic agents will encounter confusion and even make mistakes if their roles are unclear. This plight looms especially large in search-and-rescue missions, where the objective may be to locate someone in danger despite limited time and a vast area to scan.