Many companies invest heavily in hiring talent to create the high-performance library code that underpins modern artificial intelligence systems. NVIDIA, for instance, developed some of the most advanced high-performance computing (HPC) libraries, creating a competitive moat that has proven difficult for others to breach.
A computation has two main constraints: the amount of memory a computation requires and how long it takes to do that calculation. If a task requires a certain number of steps, at worst the computer will need to access its memory for each one, meaning it'll require the same number of memory slots.
While early language models could only process text, contemporary large language models now perform highly diverse tasks on different types of data. For instance, LLMs can understand many languages, generate computer code, solve math problems, or answer questions about images and audio.
Proteins are the workhorses that keep our cells running, and there are many thousands of types of proteins in our cells, each performing a specialized function. Researchers have long known that the structure of a protein determines what it can do.
This week the National Academy of Engineering (NAE) elected Tomás Lozano-Pérez, MIT School of Engineering Professor in Teaching Excellence and CSAIL principal investigator, as a member for his work in robot motion planning and molecular design.
During a meeting of class 6.C40/24.C40 (Ethics of Computing), Professor Armando Solar-Lezama poses the same impossible question to his students that he often asks himself in the research he leads with the Computer Assisted Programming Group at MIT:
Imagine you’re a chef with a highly sought-after recipe. You write your top-secret instructions in a journal to ensure you remember them, but its location within the book is evident from the folds and tears on the edges of that often-referenced page.
Sara Beery came to MIT as an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) eager to focus on ecological challenges. She has fashioned her research career around the opportunity to apply her expertise in computer vision, machine learning, and data science to tackle real-world issues in conservation and sustainability. Beery was drawn to the Institute’s commitment to “computing for the planet,” and set out to bring her methods to global-scale environmental and biodiversity monitoring.
