Natural language boosts LLM performance in coding, planning, and robotics
Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
Brain and cognitive sciences
Mapping the brain pathways of visual memorability
For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.
A crossroads for computing at MIT
The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.
A new computational technique could make it easier to engineer useful proteins
MIT researchers plan to search for proteins that could be used to measure electrical activity in the brain.
Researchers enhance peripheral vision in AI models
By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.
Complex, unfamiliar sentences make the brain’s language network work harder
A new study finds that language regions in the left hemisphere light up when reading uncommon sentences, while straightforward sentences elicit little response.
Deep neural networks show promise as models of human hearing
Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.
Search algorithm reveals nearly 200 new kinds of CRISPR systems
By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.
Using AI to optimize for rapid neural imaging
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
The brain may learn about the world the same way some computational models do
Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.