Artificial intelligence | MIT News | Massachusetts Institute of …
2024年10月29日 · How AI is improving simulations with smarter sampling techniques. MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy. October 2, 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.
Massachusetts Institute of Technology - MIT News
2024年10月30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.
Explained: Generative AI | MIT News | Massachusetts Institute of …
2023年11月9日 · Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data. For instance, such models are trained, using millions of examples, to predict whether a certain X-ray shows signs of a tumor or if a particular borrower is ...
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.
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.
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 …
MIT launches Working Group on Generative AI and the Work of
2024年3月28日 · The generative AI wave has elicited excitement, anxiety, and plenty of speculation about what's to come, but no clear answers to these core questions. To discover how generative AI can lead to better jobs, MIT is convening a working group on Generative AI and the Work of the Future. The working group is kicking off with 25 companies and ...
Massachusetts Institute of Technology - MIT News
2024年9月24日 · AI assistant monitors teamwork to promote effective collaboration. An AI team coordinator aligns agents’ beliefs about how to achieve a task, intervening when necessary to potentially help with tasks in search and rescue, hospitals, and video games. August 19, 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.