Personalization features can make LLMs more agreeable
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Read MoreArchitecture students bring new forms of human-machine interaction into the kitchen.
Read MoreNew research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
Read MoreThe speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.
Read MoreLarge language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
Read MoreThe coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.
Read MoreTo reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.
Read MoreOptimized for generative AI, TX-GAIN is driving innovation in biodefense, materials discovery, cybersecurity, and other areas of research and development.
Read MoreFour new professors join the Department of Architecture and MIT Media Lab.
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