MIT scientists have developed tiny, soft-bodied robots that can be controlled with a weak magnet. The robots, formed from rubbery magnetic spirals, can be programmed to walk, crawl, swim -- all in response to a simple, easy-to-apply magnetic field. "This is the first time this has been done, to be able to control three-dimensional locomotion of robots with a one-dimensional magnetic field," says Professor Polina Anikeeva, whose team published an open-access paper on the magnetic robots in the journal Advanced Materials. "And because they are predominantly composed of polymer and polymers are soft, you don't need a very large magnetic field to activate them. It's actually a really tiny magnetic field that drives these robots," adds Anikeeva, who is a professor of materials science and engineering and brain and cognitive sciences at MIT, a McGovern Institute for Brain Research associate investigator, as well as the associate director of MIT's Research Laboratory of Electronics and director of MIT's K. Lisa Yang Brain-Body Center.

When artificial intelligence is tasked with visually identifying objects and faces, it assigns specific components of its network to face recognition -- just like the human brain. The human brain seems to care a lot about faces. It's dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency -- and neuroscientists at MIT's McGovern Institute for Brain Research have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out. The finding, reported on March 16, 2022, in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition.

The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion. Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language. But a new study from MIT neuroscientists suggests the underlying function of these models resembles the function of language-processing centers in the human brain. Computer models that perform well on other types of language tasks do not show this similarity to the human brain, offering evidence that the human brain may use next-word prediction to drive language processing.

Neuroscientists find the internal workings of next-word prediction models resemble those of language-processing centers in the brain. In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type. The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion.

Summary: Artificial intelligence sheds new light on how the brain processes language. Researchers report the human brain may use next word prediction to drive language processing. In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type. The most recent generation of predictive language models also appears to learn something about the underlying meaning of language.

In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type. The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion. Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language.

Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors. Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology's strategy so faithfully. "The algorithm we use has no resemblance to the actual process of evolution," says Guangyu Robert Yang, an associate investigator at MIT's McGovern Institute, who led the work as a postdoctoral fellow at Columbia University. The similarities between the artificial and biological systems suggest that the brain's olfactory network is optimally suited to its task.

In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it. In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing. Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.

Dealing with a global pandemic has taken a toll on the mental health of millions of people. A team of MIT and Harvard University researchers has shown that they can measure those effects by analyzing the language that people use to express their anxiety online. Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide. "We found that there were these natural clusters that emerged related to suicidality and loneliness, and the amount of posts in these clusters more than doubled during the pandemic as compared to the same months of the preceding year, which is a grave concern," says Daniel Low, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT and the lead author of the study.

MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision. The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain. Led by Kohitij Kar, a postdoc at the McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, the study looked at an area called the ventrolateral prefrontal cortex (vlPFC), which sends feedback signals to the inferior temporal (IT) cortex via a network of neurons. The main goal of this study was to test how the back-and-forth information processing of this circuitry -- that is, this recurrent neural network -- is essential to rapid object identification in primates. The current study, published in Neuron and available via open access, is a followup to prior work published by Kar and James DiCarlo, the Peter de Florez Professor of Neuroscience, the head of MIT's Department of Brain and Cognitive Sciences, and an investigator in the McGovern Institute and the Center for Brains, Minds, and Machines.