MIT Schwarzman College of Computing

Researchers from MIT and NVIDIA have developed a new approach that combines two types of generative AI models—an autoregressive model and a diffusion model—into a tool that leverages the strengths of each to rapidly generate high-quality images. Known as HART, their tool could have a wide range of applications, such as helping researchers train robots to complete complex real-world tasks and aiding designers in producing striking scenes for video games. https://bit.ly/MITHART

Created by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), a new programming language called “Exo 2” could enable high-performance coding that can compete with state-of-the-art libraries with a few hundred lines of code, instead of tens or hundreds of thousands. https://bit.ly/MIT-Exo2

The AI4Society Seminar Series features a roster of distinguished scholars exploring AI's impact on society, ethics, governance, and human-computer interaction. Join us on Tuesday, March 18?on the MIT campus as we welcome Northeastern University's Kathleen Creel for the first talk?in the series, "Ethics of Algorithmic Monoculture and Systemic Exclusion." Co-sponsored by the MIT Schwarzman College of Computing and MIT Laboratory for Information and Decision Systems (LIDS). https://lnkd.in/eFYT_Yx5

Hosted by the MIT Schwarzman College of Computing, the Expanding Horizons in Computing series explored four essential computing topics. Organized by MIT faculty, the series delved into deep learning, societal impacts, cryptography, security, and quantum technology, and offered a compelling look at the opportunities and challenges shaping the future of computing. Videos from the series are now out for your viewing pleasure! https://lnkd.in/egKcWJrv

MIT computer scientists developed an automated system that enhances AI efficiency by leveraging both sparsity and symmetry in deep learning data. This dual redundancy approach reduces computation, bandwidth, and memory needs, speeding up computations by nearly 30 times in some cases. With a user-friendly programming language, the system broadens access to AI optimization, benefiting machine learning developers, as well as scientists who are not experts in deep learning but want to improve the efficiency of AI algorithms they use to process data. The system could also have applications in scientific computing. https://bit.ly/SySTeC-Code

MIT researchers probed the inner workings of large language models to better understand how they process such diverse data and found evidence that they share some similarities with the human brain. https://lnkd.in/ecnyQTCY

A new method developed by MIT researchers could help scientists make better predictions in areas like weather forecasting, climate research, public health, and ecological management. The researchers devised a technique to assess prediction-validation methods and used it to prove that two classical methods can be substantively wrong on spatial problems. They then determined why these methods can fail and created a new method designed to handle the types of data used for spatial predictions. “Hopefully, this will lead to more reliable evaluations when people are coming up with new predictive methods and a better understanding of how well methods are performing,” says Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS), and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL). https://lnkd.in/e26sd5xw

"How do we make sure that a machine does what we want, and only what we want?" In the new course 6.C40/24.C40 (Ethics of Computing), co-taught by MIT EECS and MIT philosophy professors, students wrestle with this question and explore the moral dilemmas of the digital age. Developed through the Common Ground for Computing Education, an initiative of the MIT Schwarzman College of Computing, the course bridges philosophy and AI, with each instructor bringing their discipline's lens for examining the broader implications of today's ethical issues. https://lnkd.in/eFmY_5fm

In the seven months since Kaiming He joined the MIT Schwarzman College of Computing as the Douglas Ross (1954) Career Development Professor of Software Technology in the Department of Electrical Engineering and Computer Science, He says he is experiencing something that in his opinion is “very rare in human scientific history” —?a lowering of the walls that expands across different scientific disciplines. “There is no way I could ever understand high-energy physics, chemistry, or the frontier of biology research, but now we are seeing something that can help us to break these walls,” He?says, “and that is the creation of a common language that has been found in AI.” https://lnkd.in/dMeDqmhu

MIT senior Audrey Lorvo, a computer science, economics, and data science major (Course 6-14), is researching AI safety, which seeks to ensure increasingly intelligent AI models are reliable and can benefit humanity. “We need to both ensure humans reap AI’s benefits and that we don’t lose control of the technology,” she says. An MIT Schwarzman College of Computing?Social and Ethical Responsibilities of Computing (SERC) scholar, Lorvo looks closely at how AI might automate AI research and development processes and practices. A member of the?Big Data research group, she’s investigating the social and economic implications associated with AI’s potential to accelerate research on itself and how to effectively communicate these ideas and potential impacts to general audiences including legislators, strategic advisors, and others. https://lnkd.in/dvejvTt4