Leveraging language to understand machines
Master's students Irene Terpstra ’23 and Rujul Gandhi ’22 use language to design new integrated circuits and make it understandable to robots.
Human-computer interaction
Automated system teaches users when to collaborate with an AI assistant
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
New method uses crowdsourced feedback to help train robots
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.
Explained: Generative AI
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
To excel at engineering design, generative AI must learn to innovate, study finds
AI models that prioritize similarity falter when asked to design something completely new.
A method to interpret AI might not be so interpretable after all
Some researchers see formal specifications as a way for autonomous systems to "explain themselves" to humans. But a new study finds that we aren't understanding.
AI copilot enhances human precision for safer aviation
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
Is AI in the eye of the beholder?
Study shows users can be primed to believe certain things about an AI chatbot’s motives, which influences their interactions with the chatbot.
Meet the 2023-24 Accenture Fellows
The MIT and Accenture Convergence Initiative for Industry and Technology announces the 2023-24 graduate fellows.
Multi-AI collaboration helps reasoning and factual accuracy in large language models
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.