Toward a code-breaking quantum computer
Building on a landmark algorithm, researchers propose a way to make a smaller and more noise-tolerant quantum factoring circuit for cryptography.
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Building on a landmark algorithm, researchers propose a way to make a smaller and more noise-tolerant quantum factoring circuit for cryptography.
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.
In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.
This new tool offers an easier way for people to analyze complex tabular data.
The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.
Smaller than a coin, this optical device could enable rapid prototyping on the go.
Research sheds light on the properties of novel materials that could be used in electronics operating in extremely hot environments.
Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
The advance offers a way to characterize a fundamental resource needed for quantum computing.
Researchers create a curious machine-learning model that finds a wider variety of prompts for training a chatbot to avoid hateful or harmful output.
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
The new approach “nudges” existing climate simulations closer to future reality.
The sustainable and cost-saving structure could dissipate more than 95 percent of incoming wave energy using a small fraction of the material normally needed.
The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.