Researchers at MIT, Harvard, and the University of Washington have shunned traditional reinforcement learning approaches, using crowdsourced feedback to teach artificial intelligence (AI) new skills instead. Traditional methods to teach AI tasks often required a reward function, which was updated and managed by a human expert. This limited scalability and was often time-consuming, particularly if…
During the kickoff event of MIT’s Generative AI Week, the “Generative AI: Shaping the Future” symposium, Rodney Brooks, co-founder of iRobot, cautioned attendees about the dangers of overestimating the capabilities of generative AI technology. Brooks, also a professor emeritus at MIT and former director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), warned that…
MIT researchers have developed an algorithm called FeatUp that enables computer vision algorithms to capture both high-level details and fine-grained minutiae of a scene simultaneously. Modern computer vision algorithms, like human beings, can only recall the broad details of a scene while the more nuanced specifics are often lost. To understand an image, they break…
Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health, have developed a new search algorithm to find enzymes of interest in vast microbial sequence databases. This algorithm, called Fast Locality-Sensitive Hashing-based clustering (FLSHclust), discovered…
Researchers at MIT, Harvard, and the National Institutes of Health have utilized a new search algorithm to identify 188 different types of rare CRISPR systems in bacterial genomes. This data holds potential to advance genome-editing technology, enabling more precise treatments and diagnostics.
The algorithm, developed in the lab of prominent CRISPR researcher, Professor Feng Zhang uses…
Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health have developed a new algorithm that can sift through massive amounts of genomic data to identify unique CRISPR systems. Known as Fast Locality-Sensitive Hashing-based clustering…
Audio deepfakes, although often associated with unethical practices, have potential uses that can benefit society, suggests postdoc Nauman Dawalatabad in a Q&A with MIT News. He highlights the need for technology that protects sensitive information held within speech patterns, such as age, gender, and health conditions, stating that obscuring the speaker's identity in audio deepfakes…
Researchers at MIT have developed an image dataset that simulates peripheral vision for use in training machine learning (ML) models, an area where artificial intelligence (AI) notably diverges from human ability. Humans leverage less-detailed peripheral vision to detect shapes and items outside their direct line of sight, an ability AI lacks. Incorporating aspects of peripheral…
Machine learning models are widely used today in smart devices like smartphones, with diverse applications like autocorrecting keyboards or improved chatbot responses. However, fine-tuning these models requires considerable computational resources and transfers of data to and from cloud servers – which can pose both energy and security issues. The team of researchers from MIT and…
Microbial sequence databases hold a vast array of information about enzymes and other molecules that could be utilized in biotechnology applications. However, the sheer size of these databases has made it challenging to efficiently search for specific enzymes of interest.
Researchers from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and…