MIT’s election observatory tracks how AI chatbots answer political questions
A research dashboard examines changing responses, with implications for the way AI information is evaluated.
The systems, decisions and human questions shaping the AI economy.
A research dashboard examines changing responses, with implications for the way AI information is evaluated.
A research award highlights the value of connecting machine-learning predictions with experimental evidence.
The university’s announcement spans research questions about AI’s capabilities and its effects on people.
A reported memory-checking system points to a practical robotics challenge: knowing when a stored description may be wrong.
A practical framework for examining the evidence, dependencies and operating responsibilities behind an AI purchase.
A useful experiment defines the conditions for expansion, revision and closure before enthusiasm turns into dependency.
A good report can sharpen a decision. It cannot remove the need to understand definitions, methods and uncertainty.
A focused task can reward a focused system, especially when latency, cost and maintenance matter as much as breadth.
Trustworthy principles become useful when they are translated into everyday decisions about data, responsibility and deployment.
Applications that read external text need a clear distinction between information to inspect and instructions they are allowed to follow.
Putting a person in the loop helps only when that person has the information, time and authority to intervene.
Electricity demand is part of the AI story, but useful decisions need a closer look at workload, location and system design.