This CoR takes a unified approach to cover the full range of research areas required for success in artificial intelligence, including hardware, foundations, software systems, and applications.
The researchers anticipate breakthroughs that can only be made by working across traditional subfield boundaries.
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Graph Neural Networks (GNNs) are a powerful framework revolutionizing graph representation learning, but our understanding of their representational properties is limited. This project aims to explore the theoretical foundations of learning with graphs and relations in AI via the GNN architecture.
Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady-state (the throughput) is important for both compiler designers and performance engineers.
However, building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error-prone, and must be performed from scratch for each processor generation.
Ithemal is the first tool that learns to predict the throughput of a set of instructions. It does so more accurately than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, Ithemal has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA).
Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.
EQ-Radio can infer a person’s emotions using wireless signals. It transmits an RF signal and analyzes its reflections off a person’s body to recognize his emotional state (happy, sad, etc.).
Our goal is to develop methods that can "explain" the behavior of complex machine learning models, without restricting their power. We seek explanations that are simple, robust and grounded in statistical analysis of the model's behavior.
We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.
To obtain scalable Bayesian inference methods, we develop algorithms to create compact “summaries” of large quantities of data. We can then quickly run standard inference algorithms on these summaries without needing to look at the whole dataset.
Our research aims to scale hard-to-parallelize applications through new programming models and multicore architectures. Our goal is to enable
programmers to write efficient and scalable parallel programs as easily as they
write sequential programs today.
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
MIT CSAIL and the Technical University of Munich researchers have proposed a new approach to self-supervised learning that addresses the limitations of relying on pre-defined data augmentations, and instead learning a general representation that can adapt to different transformations by paying attention to context.
In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.
ACM SIGPLAN recently honored Armando Solar Lezama, MIT EECS professor and CSAIL Associate Director and Chief Operating Officer for his impactful work in program synthesis.
An interdisciplinary team of researchers thinks health AI could benefit from some of the aviation industry’s long history of hard-won lessons that have created one of the safest activities today.
“I believe by using AI, whether generative or otherwise, we have the opportunity to find the language of humanity,” media artist Refik Anadol told an MIT audience.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
MIT professor and CSAIL member Dina Katabi earns SIGCOMM Lifetime Achievement Award for her innovative contributions to both the Internet and wireless networks.
MIT researchers develop "FrameDiff," a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.
An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.
CSAIL researchers collaborated on the first neurosymbolic programming summer school at the California Institute of Technology, held from July 11 to 13.
Explanation methods that help users determine whether to trust machine-learning model predictions can be less accurate for disadvantaged subgroups, a new study finds.
With the vast growth of next-generation sequencing data, it’s hard to remember that in 1869 Friedrich Miescher isolated DNA for the first time using cells from nearby hospital bandages. Computational genomics has now ushered in a new era of precision medicine, helping find clinically relevant mutations, potential diagnostics for asthma, and precision-based, personalized medicine.
Last month, three MIT materials scientists and their colleagues published a paper describing a new artificial-intelligence system that can pore through scientific papers and extract “recipes” for producing particular types of materials.
This week it was announced that MIT professor and CSAIL principal investigator Regina Barzilay was named an Association for the Advancement of Artificial Intelligence (AAAI) Fellow for her research contributions to the field of artificial intelligence (AI).
Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they’re still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries.
When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it. There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.
As many a relationship book can tell you, understanding someone else’s emotions can be a difficult task. Facial expressions aren’t always reliable: a smile can conceal frustration, while a poker face might mask a winning hand.But what if technology could tell us how someone is really feeling?Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “EQ-Radio,” a device that can detect a person’s emotions using wireless signals.
