Algorithms

Boston Globe reporters Aaron Pressman and Jon Chesto spotlight Liquid AI, a new startup founded by MIT researchers that is developing an AI system that relies on neural-network models that are “much simpler and require significantly less computer power to train and operate” than generative AI systems. “You need a fraction of the cost of developing generative AI, and the carbon footprint is much lower,” explains Liquid AI CEO Ramin Hasani, a research affiliate at CSAIL. “You get the same capabilities with a much smaller representation.”

Prof. Daniela Rus, director of CSAIL, and research affiliates Ramin Hasani, Mathias Lechner, and Alexander Amini have co-founded Liquid AI, a startup building a general-purpose AI system powered by a liquid neural network, reports Kyle Wiggers for TechCrunch. “Accountability and safety of large AI models is of paramount importance,” says Hasani. “Liquid AI offers more capital efficient, reliable, explainable and capable machine learning models for both domain-specific and generative AI applications." 

MIT researchers have “used an algorithm to sort through millions of genomes to find new, rare types of CRISPR systems that could eventually be adapted into genome-editing tools,” writes Sara Reardon for Nature. “We are just amazed at the diversity of CRISPR systems,” says Prof. Feng Zhang. “Doing this analysis kind of allows us to kill two birds with one stone: both study biology and also potentially find useful things.”

MIT CSAIL researchers have developed a new air safety system, called Air-Guardian, that is designed to serve as a “proactive co-pilot, enhancing safety during critical moments of flight,” reports Jace Dela Cruz for Tech Times. 

A new study by MIT researchers demonstrates how “machine-learning systems designed to spot someone breaking a policy rule—a dress code, for example—will be harsher or more lenient depending on minuscule-seeming differences in how humans annotated data that were used to train the system,” reports Ananya for Scientific American. “This is an important warning for a field where datasets are often used without close examination of labeling practices, and [it] underscores the need for caution in automated decision systems—particularly in contexts where compliance with societal rules is essential,” says Prof. Marzyeh Ghassemi.

Forbes reporter Rob Toews spotlights Prof. Daniela Rus, director of CSAIL, and research affiliate Ramin Hasani and their work with liquid neural networks. “The ‘liquid’ in the name refers to the fact that the model’s weights are probabilistic rather than constant, allowing them to vary fluidly depending on the inputs the model is exposed to,” writes Toews.

Prof. Yoon Kim speaks with Popular Science reporter Charlotte Hu about how large language models like ChatGPT operate. “You can think of [chatbots] as algorithms with little knobs on them,” says Kim. “These knobs basically learn on data that you see out in the wild,” allowing the software to create “probabilities over the entire English vocab.”

MIT researchers have developed a new tool called “PhotoGuard” that can help protect images from AI manipulation, reports Ross Cristantiello for Boston.com. The tool “is designed to make real images resistant to advanced models that can generate new images, such as DALL-E and Midjourney,” writes Cristantiello.

Researchers at MIT have developed “PhotoGuard,” a tool that can be used to protect images from AI manipulation, reports Catherine Thorbecke for CNN. The tool “puts an invisible ‘immunization’ over images that stops AI models from being able to manipulate the picture,” writes Thorbecke.

At CSAIL’s Imagination in Action event, Prof. Stefanie Jegelka’s presentation provided insight into “the failures and successes of neural networks and explored some crucial context that can help engineers and other human observers to focus in on how learning is happening,” reports research affiliate John Werner for Forbes.

Prof. Jacob Andreas explored the concept of language guided program synthesis at CSAIL’s Imagination in Action event, reports research affiliate John Werner for Forbes. “Language is a tool,” said Andreas during his talk. “Not just for training models, but actually interpreting them and sometimes improving them directly, again, in domains, not just involving languages (or) inputs, but also these kinds of visual domains as well.”

Prof. Daniela Rus, director of CSAIL, writes for Forbes about Prof. Dina Katabi’s work using insights from wireless systems to help glean information about patient health. “Incorporating continuous time data collection in healthcare using ambient WiFi detectable by machine learning promises an era where early and accurate diagnosis becomes the norm rather than the exception,” writes Rus.

Researchers from MIT and Massachusetts General Hospital have developed “Sybil,” an AI tool that can detect the risk of a patient developing lung cancer within six years, reports Mary Kekatos for ABC News. “Sybil was trained on low-dose chest computer tomography scans, which is recommended for those between ages 50 and 80 who either have a significant history of smoking or currently smoke,” explains Kekatos.

During her talk at CSAIL’s Imagination in Action event, Prof. Daniela Rus, director of CSAIL, explored the promise of using liquid neural networks “to solve some of AI’s notorious complexity problems,” writes research affiliate John Werner for Forbes. “Liquid networks are a new model for machine learning,” said Rus. “They're compact, interpretable and causal. And they have shown great promise in generalization under heavy distribution shifts.”

In an article for Forbes, research affiliate John Werner spotlights Prof. Dina Katabi and her work showcasing how AI can boost the capabilities of clinical data. “We are going to collect data, clinical data from patients continuously in their homes, track the symptoms, the evolution of those symptoms, and process this data with machine learning so that we can get insights before problems occur,” says Katabi.