MIT Computer Science

• Biology-based brain model matches animals in learning, enables new discovery

  New “biomimetic” model of brain circuits and function at multiple scales produced naturalistic dynamics and learning, and even identified curious behavior by some neurons.

• Why it’s critical to move beyond overly aggregated machine-learning metrics

  New research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.

• A new way to “paint with light” to create radiant, color-changing items

  “MorphoChrome,” developed at MIT, pairs software with a handheld device to make everyday objects iridescent.

• Helping companies with physical operations around the world run more intelligently

  Founded by two MIT alumni, Samsara’s platform gives companies a central hub to learn from their workers, equipment, and other infrastructure.

• Generative AI tool helps 3D print personal items that sustain daily use

  “MechStyle” allows users to personalize 3D models, while ensuring they’re physically viable after fabrication, producing unique personal items and assistive technology.

• 3 Questions: How AI could optimize the power grid

  While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.

• Decoding the Arctic to predict winter weather

  With the help of AI, MIT Research Scientist Judah Cohen is reshaping subseasonal forecasting, with the goal of extending the lead time for predicting impactful weather.

• MIT scientists investigate memorization risk in the age of clinical AI

  New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.

• One pull of a string is all it takes to deploy these complex structures

  A new method could enable users to design portable medical devices, like a splint, that can be rapidly converted from flat panels to a 3D object without any tools.

• MIT in the media: 2025 in review

  MIT community members made headlines with key research advances and their efforts to tackle pressing challenges.

• “Wait, we have the tech skills to build that”

  From robotics to apps like “NerdXing,” senior Julianna Schneider is building technologies to solve problems in her community.

• Guided learning lets “untrainable” neural networks realize their potential

  CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.

• A new way to increase the capabilities of large language models

  MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.

• A “scientific sandbox” lets researchers explore the evolution of vision systems

  The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.

• “Robot, make me a chair”

  An AI-driven system lets users design and build simple, multicomponent objects by describing them with words.

• Enabling small language models to solve complex reasoning tasks

  The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.

• New MIT program to train military leaders for the AI age

  The new certificate program will equip naval officers with skills needed to solve the military’s hardest problems.

• New method improves the reliability of statistical estimations

  The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.

• New materials could boost the energy efficiency of microelectronics

  By stacking multiple active components based on new materials on the back end of a computer chip, this new approach reduces the amount of energy wasted during computation.

• MIT researchers “speak objects into existence” using AI and robotics

  The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.

• A smarter way for large language models to think about hard problems

  This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.

• New control system teaches soft robots the art of staying safe

  MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.

• Researchers discover a shortcoming that makes LLMs less reliable

  Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.

• MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

  BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

• The cost of thinking

  MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.

• Bigger datasets aren’t always better

  MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.

• Understanding the nuances of human-like intelligence

  Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

• Leading quantum at an inflection point

  The MIT Quantum Initiative is taking shape, leveraging quantum breakthroughs to drive the future of scientific and technological progress.

• Charting the future of AI, from safer answers to faster thinking

  MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.

• MIT researchers propose a new model for legible, modular software

  The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.