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Margaret Hamilton has died
Margaret H. Hamilton, a profoundly influential computer scientist best known for leading the software engineering team at MIT’s Instrumentation Lab during NASA’s Apollo program, died at age 90.
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2
LLMs use a surprisingly simple mechanism to retrieve some stored knowledge
Researchers find large language models use a simple mechanism to retrieve stored knowledge when they respond to a user prompt. These mechanisms can be leveraged to see what the model knows about different subjects and possibly to correct false information it has stored.
3
To study how chips work, MIT researchers built their own operating system
The new "Fractal" operating system kernel, developed at MIT, gives researchers a clearer look at how computer chips work internally. It was used to study the branch predictors inside Apple's M1 processor, where it revealed a potential vulnerability to major speculative attacks.
4
To the brain, reading computer code is not the same as reading language
MIT neuroscientists have found reading computer code does not rely on the regions of the brain involved in language processing. Instead, it activates the “multiple demand network,” which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.
5
Arvind has died
Arvind, a longtime MIT professor and leading scholar in computer architecture and programming languages, has died at 77. He transformed research in computer architectures and parallel computing, enabling faster and more efficient computation.
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Python-based compiler achieves orders-of-magnitude speedups
Codon is a new tool that compiles Python code to run it more efficiently and effectively while allowing for customization and adaptation to various domains.
7
How an MIT research project became the Julia programming language
Julia, the programming language that originated as a research project at MIT, has gained a loyal following among scientists, engineers, mathematicians, and others. The free and open-source language counts more than 1 million users, including people at thousands of companies and universities around the world.
8
New algorithm unlocks high-resolution insights for computer vision
MIT CSAIL researchers introduce FeatUp, a model-agnostic framework designed to significantly enhance the spatial resolution of deep learning features for improved performance in computer vision tasks such as semantic segmentation, depth prediction, and object detection.
9
A new programming language for high-performance computers
Researchers at MIT CSAIL developed a new programming language specifically for high-performance computing. With the prototype, says PhD student Amanda Liu, “speed and correctness do not have to compete ... they can go together, hand-in-hand.”
10
Automatic bug-repair system fixes 10 times as many errors as its predecessors
MIT researchers have developed a machine-learning system that can comb through repairs to open-source computer programs and learn their general properties, in order to produce new repairs for a different set of programs.
11
New debugging method found undetected security flaws in popular web apps
By exploiting some peculiarities of the popular Web programming framework Ruby on Rails, MIT researchers have developed a system that can quickly comb through tens of thousands of lines of application code to find security flaws.
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High-performance computing, with much less code
The “Exo 2” language allows programmers to write “schedules” that explicitly control how the compiler generates code. This allows performance engineers to transform simple programs into complex programs that do the same thing as the specification, but faster.
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Discovering which parts of the brain are engaged when coding
Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) found that the Multiple Demand and Language brain systems encode specific code properties and uniquely align with machine-learned representations of code.