Author: MIT News

To the brain, reading computer code is not the same as reading language

Anne Trafton |

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.

However, although reading computer code activates the multiple demand network, it appears to rely more on different parts of the network than math or logic problems do, suggesting that coding does not precisely replicate the cognitive demands of mathematics either.

“Understanding computer code seems to be its own thing. It’s not the same as language, and it’s not the same as math and logic,” says Anna Ivanova, an MIT graduate student and the lead author of the study.

Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience and a member of the McGovern Institute for Brain Research, is the senior author of the paper, which appears today in eLife. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Tufts University were also involved in the study.

Language and cognition

McGovern Investivator Ev Fedorenko in the Martinos Imaging Center at MIT. Photo: Caitlin Cunningham

A major focus of Fedorenko’s research is the relationship between language and other cognitive functions. In particular, she has been studying the question of whether other functions rely on the brain’s language network, which includes Broca’s area and other regions in the left hemisphere of the brain. In previous work, her lab has shown that music and math do not appear to activate this language network.

“Here, we were interested in exploring the relationship between language and computer programming, partially because computer programming is such a new invention that we know that there couldn’t be any hardwired mechanisms that make us good programmers,” Ivanova says.

There are two schools of thought regarding how the brain learns to code, she says. One holds that in order to be good at programming, you must be good at math. The other suggests that because of the parallels between coding and language, language skills might be more relevant. To shed light on this issue, the researchers set out to study whether brain activity patterns while reading computer code would overlap with language-related brain activity.

The two programming languages that the researchers focused on in this study are known for their readability — Python and ScratchJr, a visual programming language designed for children age 5 and older. The subjects in the study were all young adults proficient in the language they were being tested on. While the programmers lay in a functional magnetic resonance (fMRI) scanner, the researchers showed them snippets of code and asked them to predict what action the code would produce.

The researchers saw little to no response to code in the language regions of the brain. Instead, they found that the coding task mainly activated the so-called multiple demand network. This network, whose activity is spread throughout the frontal and parietal lobes of the brain, is typically recruited for tasks that require holding many pieces of information in mind at once, and is responsible for our ability to perform a wide variety of mental tasks.

“It does pretty much anything that’s cognitively challenging, that makes you think hard,” says Ivanova, who was also named one of the McGovern Institute’s rising stars in neuroscience.

Previous studies have shown that math and logic problems seem to rely mainly on the multiple demand regions in the left hemisphere, while tasks that involve spatial navigation activate the right hemisphere more than the left. The MIT team found that reading computer code appears to activate both the left and right sides of the multiple demand network, and ScratchJr activated the right side slightly more than the left. This finding goes against the hypothesis that math and coding rely on the same brain mechanisms.

Effects of experience

The researchers say that while they didn’t identify any regions that appear to be exclusively devoted to programming, such specialized brain activity might develop in people who have much more coding experience.

“It’s possible that if you take people who are professional programmers, who have spent 30 or 40 years coding in a particular language, you may start seeing some specialization, or some crystallization of parts of the multiple demand system,” Fedorenko says. “In people who are familiar with coding and can efficiently do these tasks, but have had relatively limited experience, it just doesn’t seem like you see any specialization yet.”

In a companion paper appearing in the same issue of eLife, a team of researchers from Johns Hopkins University also reported that solving code problems activates the multiple demand network rather than the language regions.

The findings suggest there isn’t a definitive answer to whether coding should be taught as a math-based skill or a language-based skill. In part, that’s because learning to program may draw on both language and multiple demand systems, even if — once learned — programming doesn’t rely on the language regions, the researchers say.

“There have been claims from both camps — it has to be together with math, it has to be together with language,” Ivanova says. “But it looks like computer science educators will have to develop their own approaches for teaching code most effectively.”

The research was funded by the National Science Foundation, the Department of the Brain and Cognitive Sciences at MIT, and the McGovern Institute for Brain Research.

Neuroscientists find a way to improve object-recognition models

Anne Trafton |

Computer vision models known as convolutional neural networks can be trained to recognize objects nearly as accurately as humans do. However, these models have one significant flaw: Very small changes to an image, which would be nearly imperceptible to a human viewer, can trick them into making egregious errors such as classifying a cat as a tree.

A team of neuroscientists from MIT, Harvard University, and IBM have developed a way to alleviate this vulnerability, by adding to these models a new layer that is designed to mimic the earliest stage of the brain’s visual processing system. In a new study, they showed that this layer greatly improved the models’ robustness against this type of mistake.

A grid showing the visualization of many common image corruption types. First row, original image, followed by the noise corruptions; second row, blur corruptions; third row, weather corruptions; fourth row, digital corruptions.
Credits: Courtesy of the researchers.

“Just by making the models more similar to the brain’s primary visual cortex, in this single stage of processing, we see quite significant improvements in robustness across many different types of perturbations and corruptions,” says Tiago Marques, an MIT postdoc and one of the lead authors of the study.

Convolutional neural networks are often used in artificial intelligence applications such as self-driving cars, automated assembly lines, and medical diagnostics. Harvard graduate student Joel Dapello, who is also a lead author of the study, adds that “implementing our new approach could potentially make these systems less prone to error and more aligned with human vision.”

“Good scientific hypotheses of how the brain’s visual system works should, by definition, match the brain in both its internal neural patterns and its remarkable robustness. This study shows that achieving those scientific gains directly leads to engineering and application gains,” says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, an investigator in the Center for Brains, Minds, and Machines and the McGovern Institute for Brain Research, and the senior author of the study.

The study, which is being presented at the NeurIPS conference this month, is also co-authored by MIT graduate student Martin Schrimpf, MIT visiting student Franziska Geiger, and MIT-IBM Watson AI Lab Director David Cox.

Mimicking the brain

Recognizing objects is one of the visual system’s primary functions. In just a small fraction of a second, visual information flows through the ventral visual stream to the brain’s inferior temporal cortex, where neurons contain information needed to classify objects. At each stage in the ventral stream, the brain performs different types of processing. The very first stage in the ventral stream, V1, is one of the most well-characterized parts of the brain and contains neurons that respond to simple visual features such as edges.

“It’s thought that V1 detects local edges or contours of objects, and textures, and does some type of segmentation of the images at a very small scale. Then that information is later used to identify the shape and texture of objects downstream,” Marques says. “The visual system is built in this hierarchical way, where in early stages neurons respond to local features such as small, elongated edges.”

For many years, researchers have been trying to build computer models that can identify objects as well as the human visual system. Today’s leading computer vision systems are already loosely guided by our current knowledge of the brain’s visual processing. However, neuroscientists still don’t know enough about how the entire ventral visual stream is connected to build a model that precisely mimics it, so they borrow techniques from the field of machine learning to train convolutional neural networks on a specific set of tasks. Using this process, a model can learn to identify objects after being trained on millions of images.

Many of these convolutional networks perform very well, but in most cases, researchers don’t know exactly how the network is solving the object-recognition task. In 2013, researchers from DiCarlo’s lab showed that some of these neural networks could not only accurately identify objects, but they could also predict how neurons in the primate brain would respond to the same objects much better than existing alternative models. However, these neural networks are still not able to perfectly predict responses along the ventral visual stream, particularly at the earliest stages of object recognition, such as V1.

These models are also vulnerable to so-called “adversarial attacks.” This means that small changes to an image, such as changing the colors of a few pixels, can lead the model to completely confuse an object for something different — a type of mistake that a human viewer would not make.

A comparison of adversarial images with different perturbation strengths.
Credits: Courtesy of the researchers.

As a first step in their study, the researchers analyzed the performance of 30 of these models and found that models whose internal responses better matched the brain’s V1 responses were also less vulnerable to adversarial attacks. That is, having a more brain-like V1 seemed to make the model more robust. To further test and take advantage of that idea, the researchers decided to create their own model of V1, based on existing neuroscientific models, and place it at the front of convolutional neural networks that had already been developed to perform object recognition.

When the researchers added their V1 layer, which is also implemented as a convolutional neural network, to three of these models, they found that these models became about four times more resistant to making mistakes on images perturbed by adversarial attacks. The models were also less vulnerable to misidentifying objects that were blurred or distorted due to other corruptions.

“Adversarial attacks are a big, open problem for the practical deployment of deep neural networks. The fact that adding neuroscience-inspired elements can improve robustness substantially suggests that there is still a lot that AI can learn from neuroscience, and vice versa,” Cox says.

Better defense

Currently, the best defense against adversarial attacks is a computationally expensive process of training models to recognize the altered images. One advantage of the new V1-based model is that it doesn’t require any additional training. It is also better able to handle a wide range of distortions, beyond adversarial attacks.

The researchers are now trying to identify the key features of their V1 model that allows it to do a better job resisting adversarial attacks, which could help them to make future models even more robust. It could also help them learn more about how the human brain is able to recognize objects.

“One big advantage of the model is that we can map components of the model to particular neuronal populations in the brain,” Dapello says. “We can use this as a tool for novel neuroscientific discoveries, and also continue developing this model to improve its performance under this challenging task.”

The research was funded by the PhRMA Foundation Postdoctoral Fellowship in Informatics, the Semiconductor Research Corporation, DARPA, the MIT Shoemaker Fellowship, the U.S. Office of Naval Research, the Simons Foundation, and the MIT-IBM Watson AI Lab.

How humans use objects in novel ways to solve problems

Anne Trafton |

Human beings are naturally creative tool users. When we need to drive in a nail but don’t have a hammer, we easily realize that we can use a heavy, flat object like a rock in its place. When our table is shaky, we quickly find that we can put a stack of paper under the table leg to stabilize it. But while these actions seem so natural to us, they are believed to be a hallmark of great intelligence — only a few other species use objects in novel ways to solve their problems, and none can do so as flexibly as people. What provides us with these powerful capabilities for using objects in this way?

In a new paper published in the Proceedings of the National Academy of Sciences describing work conducted at MIT’s Center for Brains, Minds and Machines, researchers Kelsey Allen, Kevin Smith, and Joshua Tenenbaum study the cognitive components that underlie this sort of improvised tool use. They designed a novel task, the Virtual Tools game, that taps into tool-use abilities: People must select one object from a set of “tools” that they can place in a two-dimensional, computerized scene to accomplish a goal, such as getting a ball into a certain container. Solving the puzzles in this game requires reasoning about a number of physical principles, including launching, blocking, or supporting objects.

The team hypothesized that there are three capabilities that people rely on to solve these puzzles: a prior belief that guides people’s actions toward those that will make a difference in the scene, the ability to imagine the effect of their actions, and a mechanism to quickly update their beliefs about what actions are likely to provide a solution. They built a model that instantiated these principles, called the “Sample, Simulate, Update,” or “SSUP,” model, and had it play the same game as people. They found that SSUP solved each puzzle at similar rates and in similar ways as people did. On the other hand, a popular deep learning model that could play Atari games well but did not have the same object and physical structures was unable to generalize its knowledge to puzzles it was not directly trained on.

This research provides a new framework for studying and formalizing the cognition that supports human tool use. The team hopes to extend this framework to not just study tool use, but also how people can create innovative new tools for new problems, and how humans transmit this information to build from simple physical tools to complex objects like computers or airplanes that are now part of our daily lives.

Kelsey Allen, a PhD student in the Computational Cognitive Science Lab at MIT, is excited about how the Virtual Tools game might support other cognitive scientists interested in tool use: “There is just so much more to explore in this domain. We have already started collaborating with researchers across multiple different institutions on projects ranging from studying what it means for games to be fun, to studying how embodiment affects disembodied physical reasoning. I hope that others in the cognitive science community will use the game as a tool to better understand how physical models interact with decision-making and planning.”

Joshua Tenenbaum, professor of computational cognitive science at MIT, sees this work as a step toward understanding not only an important aspect of human cognition and culture, but also how to build more human-like forms of intelligence in machines. “Artificial Intelligence researchers have been very excited about the potential for reinforcement learning (RL) algorithms to learn from trial-and-error experience, as humans do, but the real trial-and-error learning that humans benefit from unfolds over just a handful of trials — not millions or billions of experiences, as in today’s RL systems,” Tenenbaum says. “The Virtual Tools game allows us to study this very rapid and much more natural form of trial-and-error learning in humans, and the fact that the SSUP model is able to capture the fast learning dynamics we see in humans suggests it may also point the way towards new AI approaches to RL that can learn from their successes, their failures, and their near misses as quickly and as flexibly as people do.”

A hunger for social contact

Anne Trafton |

Since the coronavirus pandemic began in the spring, many people have only seen their close friends and loved ones during video calls, if at all. A new study from MIT finds that the longings we feel during this kind of social isolation share a neural basis with the food cravings we feel when hungry.

The researchers found that after one day of total isolation, the sight of people having fun together activates the same brain region that lights up when someone who hasn’t eaten all day sees a picture of a plate of cheesy pasta.

“People who are forced to be isolated crave social interactions similarly to the way a hungry person craves food.”

“Our finding fits the intuitive idea that positive social interactions are a basic human need, and acute loneliness is an aversive state that motivates people to repair what is lacking, similar to hunger,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

The research team collected the data for this study in 2018 and 2019, long before the coronavirus pandemic and resulting lockdowns. Their new findings, described today in Nature Neuroscience, are part of a larger research program focusing on how social stress affects people’s behavior and motivation.

Former MIT postdoc Livia Tomova, who is now a research associate at Cambridge University, is the lead author of the paper. Other authors include Kimberly Wang, a McGovern Institute research associate; Todd Thompson, a McGovern Institute scientist; Atsushi Takahashi, assistant director of the Martinos Imaging Center; Gillian Matthews, a research scientist at the Salk Institute for Biological Studies; and Kay Tye, a professor at the Salk Institute.

Social craving

The new study was partly inspired by a recent paper from Tye, a former member of MIT’s Picower Institute for Learning and Memory. In that 2016 study, she and Matthews, then an MIT postdoc, identified a cluster of neurons in the brains of mice that represent feelings of loneliness and generate a drive for social interaction following isolation. Studies in humans have shown that being deprived of social contact can lead to emotional distress, but the neurological basis of these feelings is not well-known.

“We wanted to see if we could experimentally induce a certain kind of social stress, where we would have control over what the social stress was,” Saxe says. “It’s a stronger intervention of social isolation than anyone had tried before.”

To create that isolation environment, the researchers enlisted healthy volunteers, who were mainly college students, and confined them to a windowless room on MIT’s campus for 10 hours. They were not allowed to use their phones, but the room did have a computer that they could use to contact the researchers if necessary.

“There were a whole bunch of interventions we used to make sure that it would really feel strange and different and isolated,” Saxe says. “They had to let us know when they were going to the bathroom so we could make sure it was empty. We delivered food to the door and then texted them when it was there so they could go get it. They really were not allowed to see people.”

After the 10-hour isolation ended, each participant was scanned in an MRI machine. This posed additional challenges, as the researchers wanted to avoid any social contact during the scanning. Before the isolation period began, each subject was trained on how to get into the machine, so that they could do it by themselves, without any help from the researcher.

“Normally, getting somebody into an MRI machine is actually a really social process. We engage in all kinds of social interactions to make sure people understand what we’re asking them, that they feel safe, that they know we’re there,” Saxe says. “In this case, the subjects had to do it all by themselves, while the researcher, who was gowned and masked, just stood silently by and watched.”

Each of the 40 participants also underwent 10 hours of fasting, on a different day. After the 10-hour period of isolation or fasting, the participants were scanned while looking at images of food, images of people interacting, and neutral images such as flowers. The researchers focused on a part of the brain called the substantia nigra, a tiny structure located in the midbrain, which has previously been linked with hunger cravings and drug cravings. The substantia nigra is also believed to share evolutionary origins with a brain region in mice called the dorsal raphe nucleus, which is the area that Tye’s lab showed was active following social isolation in their 2016 study.

The researchers hypothesized that when socially isolated subjects saw photos of people enjoying social interactions, the “craving signal” in their substantia nigra would be similar to the signal produced when they saw pictures of food after fasting. This was indeed the case. Furthermore, the amount of activation in the substantia nigra was correlated with how strongly the patients rated their feelings of craving either food or social interaction.

Degrees of loneliness

The researchers also found that people’s responses to isolation varied depending on their normal levels of loneliness. People who reported feeling chronically isolated months before the study was done showed weaker cravings for social interaction after the 10-hour isolation period than people who reported a richer social life.

“For people who reported that their lives were really full of satisfying social interactions, this intervention had a bigger effect on their brains and on their self-reports,” Saxe says.

The researchers also looked at activation patterns in other parts of the brain, including the striatum and the cortex, and found that hunger and isolation each activated distinct areas of those regions. That suggests that those areas are more specialized to respond to different types of longings, while the substantia nigra produces a more general signal representing a variety of cravings.

Now that the researchers have established that they can observe the effects of social isolation on brain activity, Saxe says they can now try to answer many additional questions. Those questions include how social isolation affect people’s behavior, whether virtual social contacts such as video calls help to alleviate cravings for social interaction, and how isolation affects different age groups.

The researchers also hope to study whether the brain responses that they saw in this study could be used to predict how the same participants responded to being isolated during the lockdowns imposed during the early stages of the coronavirus pandemic.

The research was funded by a SFARI Explorer Grant from the Simons Foundation, a MINT grant from the McGovern Institute, the National Institutes of Health, including an NIH Pioneer Award, a Max Kade Foundation Fellowship, and an Erwin Schroedinger Fellowship from the Austrian Science Fund.

Imaging method reveals a “symphony of cellular activities”

Anne Trafton |

Within a single cell, thousands of molecules, such as proteins, ions, and other signaling molecules, work together to perform all kinds of functions — absorbing nutrients, storing memories, and differentiating into specific tissues, among many others.

Deciphering these molecules, and all of their interactions, is a monumental task. Over the past 20 years, scientists have developed fluorescent reporters they can use to read out the dynamics of individual molecules within cells. However, typically only one or two such signals can be observed at a time, because a microscope cannot distinguish between many fluorescent colors.

MIT researchers have now developed a way to image up to five different molecule types at a time, by measuring each signal from random, distinct locations throughout a cell.

This approach could allow scientists to learn much more about the complex signaling networks that control most cell functions, says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology and a professor of biological engineering, media arts and sciences, and brain and cognitive sciences at MIT.

“There are thousands of molecules encoded by the genome, and they’re interacting in ways that we don’t understand. Only by watching them at the same time can we understand their relationships,” says Boyden, who is also a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research.

In a new study, Boyden and his colleagues used this technique to identify two populations of neurons that respond to calcium signals in different ways, which may influence how they encode long-term memories, the researchers say.

Boyden is the senior author of the study, which appears today in Cell. The paper’s lead authors are MIT postdoc Changyang Linghu and graduate student Shannon Johnson.

Fluorescent clusters

Shannon Johnson is a graduate fellow in the fellow in the Yang-Tan Center for Molecular Therapeutics.

To make molecular activity visible within a cell, scientists typically create reporters by fusing a protein that senses a target molecule to a protein that glows. “This is similar to how a smoke detector will sense smoke and then flash a light,” says Johnson, who is also a fellow in the Yang-Tan Center for Molecular Therapeutics. The most commonly used glowing protein is green fluorescent protein (GFP), which is based on a molecule originally found in a fluorescent jellyfish.

“Typically a biologist can see one or two colors at the same time on a microscope, and many of the reporters out there are green, because they’re based on the green fluorescent protein,” Boyden says. “What has been lacking until now is the ability to see more than a couple of these signals at once.”

“Just like listening to the sound of a single instrument from an orchestra is far from enough to fully appreciate a symphony,” Linghu says, “by enabling observations of multiple cellular signals at the same time, our technology will help us understand the ‘symphony’ of cellular activities.”

To boost the number of signals they could see, the researchers set out to identify signals by location instead of by color. They modified existing reporters to cause them to accumulate in clusters at different locations within a cell. They did this by adding two small peptides to each reporter, which helped the reporters form distinct clusters within cells.

“It’s like having reporter X be tethered to a LEGO brick, and reporter Z tethered to a K’NEX piece — only LEGO bricks will snap to other LEGO bricks, causing only reporter X to be clustered with more of reporter X,” Johnson says.

Changyang Linghu is the J. Douglas Tan Postdoctoral Fellow in the Hock E. Tan and K. Lisa Yang Center for Autism Research.

With this technique, each cell ends up with hundreds of clusters of fluorescent reporters. After measuring the activity of each cluster under a microscope, based on the changing fluorescence, the researchers can identify which molecule was being measured in each cluster by preserving the cell and staining for peptide tags that are unique to each reporter.  The peptide tags are invisible in the live cell, but they can be stained and seen after the live imaging is done. This allows the researchers to distinguish signals for different molecules even though they may all be fluorescing the same color in the live cell.

Using this approach, the researchers showed that they could see five different molecular signals in a single cell. To demonstrate the potential usefulness of this strategy, they measured the activities of three molecules in parallel — calcium, cyclic AMP, and protein kinase A (PKA). These molecules form a signaling network that is involved with many different cellular functions throughout the body. In neurons, it plays an important role in translating a short-term input (from upstream neurons) into long-term changes such as strengthening the connections between neurons — a process that is necessary for learning and forming new memories.

Applying this imaging technique to pyramidal neurons in the hippocampus, the researchers identified two novel subpopulations with different calcium signaling dynamics. One population showed slow calcium responses. In the other population, neurons had faster calcium responses. The latter population had larger PKA responses. The researchers believe this heightened response may help sustain long-lasting changes in the neurons.

Imaging signaling networks

The researchers now plan to try this approach in living animals so they can study how signaling network activities relate to behavior, and also to expand it to other types of cells, such as immune cells. This technique could also be useful for comparing signaling network patterns between cells from healthy and diseased tissue.

In this paper, the researchers showed they could record five different molecular signals at once, and by modifying their existing strategy, they believe they could get up to 16. With additional work, that number could reach into the hundreds, they say.

“That really might help crack open some of these tough questions about how the parts of a cell work together,” Boyden says. “One might imagine an era when we can watch everything going on in a living cell, or at least the part involved with learning, or with disease, or with the treatment of a disease.”

The research was funded by the Friends of the McGovern Institute Fellowship; the J. Douglas Tan Fellowship; Lisa Yang; the Yang-Tan Center for Molecular Therapeutics; John Doerr; the Open Philanthropy Project; the HHMI-Simons Faculty Scholars Program; the Human Frontier Science Program; the U.S. Army Research Laboratory; the MIT Media Lab; the Picower Institute Innovation Fund; the National Institutes of Health, including an NIH Director’s Pioneer Award; and the National Science Foundation.

Using machine learning to track the pandemic’s impact on mental health

Anne Trafton |

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.

The analysis also revealed varying impacts on people who already suffer from different types of mental illness. The findings could help psychiatrists, or potentially moderators of the Reddit forums that were studied, to better identify and help people whose mental health is suffering, the researchers say.

“When the mental health needs of so many in our society are inadequately met, even at baseline, we wanted to bring attention to the ways that many people are suffering during this time, in order to amplify and inform the allocation of resources to support them,” says Laurie Rumker, a graduate student in the Bioinformatics and Integrative Genomics PhD Program at Harvard and one of the authors of the study.

Satrajit Ghosh, a principal research scientist at MIT’s McGovern Institute for Brain Research, is the senior author of the study, which appears in the Journal of Internet Medical Research. Other authors of the paper include Tanya Talkar, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT; John Torous, director of the digital psychiatry division at Beth Israel Deaconess Medical Center; and Guillermo Cecchi, a principal research staff member at the IBM Thomas J. Watson Research Center.

A wave of anxiety

The new study grew out of the MIT class 6.897/HST.956 (Machine Learning for Healthcare), in MIT’s Department of Electrical Engineering and Computer Science. Low, Rumker, and Talkar, who were all taking the course last spring, had done some previous research on using machine learning to detect mental health disorders based on how people speak and what they say. After the Covid-19 pandemic began, they decided to focus their class project on analyzing Reddit forums devoted to different types of mental illness.

“When Covid hit, we were all curious whether it was affecting certain communities more than others,” Low says. “Reddit gives us the opportunity to look at all these subreddits that are specialized support groups. It’s a really unique opportunity to see how these different communities were affected differently as the wave was happening, in real-time.”

The researchers analyzed posts from 15 subreddit groups devoted to a variety of mental illnesses, including schizophrenia, depression, and bipolar disorder. They also included a handful of groups devoted to topics not specifically related to mental health, such as personal finance, fitness, and parenting.

Using several types of natural language processing algorithms, the researchers measured the frequency of words associated with topics such as anxiety, death, isolation, and substance abuse, and grouped posts together based on similarities in the language used. These approaches allowed the researchers to identify similarities between each group’s posts after the onset of the pandemic, as well as distinctive differences between groups.

The researchers found that while people in most of the support groups began posting about Covid-19 in March, the group devoted to health anxiety started much earlier, in January. However, as the pandemic progressed, the other mental health groups began to closely resemble the health anxiety group, in terms of the language that was most often used. At the same time, the group devoted to personal finance showed the most negative semantic change from January to April 2020, and significantly increased the use of words related to economic stress and negative sentiment.

They also discovered that the mental health groups affected the most negatively early in the pandemic were those related to ADHD and eating disorders. The researchers hypothesize that without their usual social support systems in place, due to lockdowns, people suffering from those disorders found it much more difficult to manage their conditions. In those groups, the researchers found posts about hyperfocusing on the news and relapsing back into anorexia-type behaviors since meals were not being monitored by others due to quarantine.

Using another algorithm, the researchers grouped posts into clusters such as loneliness or substance use, and then tracked how those groups changed as the pandemic progressed. Posts related to suicide more than doubled from pre-pandemic levels, and the groups that became significantly associated with the suicidality cluster during the pandemic were the support groups for borderline personality disorder and post-traumatic stress disorder.

The researchers also found the introduction of new topics specifically seeking mental health help or social interaction. “The topics within these subreddit support groups were shifting a bit, as people were trying to adapt to a new life and focus on how they can go about getting more help if needed,” Talkar says.

While the authors emphasize that they cannot implicate the pandemic as the sole cause of the observed linguistic changes, they note that there was much more significant change during the period from January to April in 2020 than in the same months in 2019 and 2018, indicating the changes cannot be explained by normal annual trends.

Mental health resources

This type of analysis could help mental health care providers identify segments of the population that are most vulnerable to declines in mental health caused by not only the Covid-19 pandemic but other mental health stressors such as controversial elections or natural disasters, the researchers say.

Additionally, if applied to Reddit or other social media posts in real-time, this analysis could be used to offer users additional resources, such as guidance to a different support group, information on how to find mental health treatment, or the number for a suicide hotline.

“Reddit is a very valuable source of support for a lot of people who are suffering from mental health challenges, many of whom may not have formal access to other kinds of mental health support, so there are implications of this work for ways that support within Reddit could be provided,” Rumker says.

The researchers now plan to apply this approach to study whether posts on Reddit and other social media sites can be used to detect mental health disorders. One current project involves screening posts in a social media site for veterans for suicide risk and post-traumatic stress disorder.

The research was funded by the National Institutes of Health and the McGovern Institute.

Study helps explain why motivation to learn declines with age

Anne Trafton |

As people age, they often lose their motivation to learn new things or engage in everyday activities. In a study of mice, MIT neuroscientists have now identified a brain circuit that is critical for maintaining this kind of motivation.

This circuit is particularly important for learning to make decisions that require evaluating the cost and reward that come with a particular action. The researchers showed that they could boost older mice’s motivation to engage in this type of learning by reactivating this circuit, and they could also decrease motivation by suppressing the circuit.

“As we age, it’s harder to have a get-up-and-go attitude toward things,” says Ann Graybiel, an Institute Professor at MIT and member of the McGovern Institute for Brain Research. “This get-up-and-go, or engagement, is important for our social well-being and for learning — it’s tough to learn if you aren’t attending and engaged.”

Graybiel is the senior author of the study, which appears today in Cell. The paper’s lead authors are Alexander Friedman, a former MIT research scientist who is now an assistant professor at the University of Texas at El Paso, and Emily Hueske, an MIT research scientist.

Evaluating cost and benefit

The striatum is part of the basal ganglia — a collection of brain centers linked to habit formation, control of voluntary movement, emotion, and addiction. For several decades, Graybiel’s lab has been studying clusters of cells called striosomes, which are distributed throughout the striatum. Graybiel discovered striosomes many years ago, but their function had remained mysterious, in part because they are so small and deep within the brain that it is difficult to image them with functional magnetic resonance imaging (fMRI).

In recent years, Friedman, Graybiel, and colleagues including MIT research fellow Ken-ichi Amemori have discovered that striosomes play an important role in a type of decision-making known as approach-avoidance conflict. These decisions involve choosing whether to take the good with the bad — or to avoid both — when given options that have both positive and negative elements. An example of this kind of decision is having to choose whether to take a job that pays more but forces a move away from family and friends. Such decisions often provoke great anxiety.

In a related study, Graybiel’s lab found that striosomes connect to cells of the substantia nigra, one of the brain’s major dopamine-producing centers. These studies led the researchers to hypothesize that striosomes may be acting as a gatekeeper that absorbs sensory and emotional information coming from the cortex and integrates it to produce a decision on how to act. These actions can then be invigorated by the dopamine-producing cells.

The researchers later discovered that chronic stress has a major impact on this circuit and on this kind of emotional decision-making. In a 2017 study performed in rats and mice, they showed that stressed animals were far more likely to choose high-risk, high-payoff options, but that they could block this effect by manipulating the circuit.

In the new Cell study, the researchers set out to investigate what happens in striosomes as mice learn how to make these kinds of decisions. To do that, they measured and analyzed the activity of striosomes as mice learned to choose between positive and negative outcomes.

During the experiments, the mice heard two different tones, one of which was accompanied by a reward (sugar water), and another that was paired with a mildly aversive stimulus (bright light). The mice gradually learned that if they licked a spout more when they heard the first tone, they would get more of the sugar water, and if they licked less during the second, the light would not be as bright.

Learning to perform this kind of task requires assigning value to each cost and each reward. The researchers found that as the mice learned the task, striosomes showed higher activity than other parts of the striatum, and that this activity correlated with the mice’s behavioral responses to both of the tones. This suggests that striosomes could be critical for assigning subjective value to a particular outcome.

“In order to survive, in order to do whatever you are doing, you constantly need to be able to learn. You need to learn what is good for you, and what is bad for you,” Friedman says.

“A person, or this case a mouse, may value a reward so highly that the risk of experiencing a possible cost is overwhelmed, while another may wish to avoid the cost to the exclusion of all rewards. And these may result in reward-driven learning in some and cost-driven learning in others,” Hueske says.

The researchers found that inhibitory neurons that relay signals from the prefrontal cortex help striosomes to enhance their signal-to-noise ratio, which helps to generate the strong signals that are seen when the mice evaluate a high-cost or high-reward option.

Loss of motivation

Next, the researchers found that in older mice (between 13 and 21 months, roughly equivalent to people in their 60s and older), the mice’s engagement in learning this type of cost-benefit analysis went down. At the same time, their striosomal activity declined compared to that of younger mice. The researchers found a similar loss of motivation in a mouse model of Huntington’s disease, a neurodegenerative disorder that affects the striatum and its striosomes.

When the researchers used genetically targeted drugs to boost activity in the striosomes, they found that the mice became more engaged in performance of the task. Conversely, suppressing striosomal activity led to disengagement.

In addition to normal age-related decline, many mental health disorders can skew the ability to evaluate the costs and rewards of an action, from anxiety and depression to conditions such as PTSD. For example, a depressed person may undervalue potentially rewarding experiences, while someone suffering from addiction may overvalue drugs but undervalue things like their job or their family.

The researchers are now working on possible drug treatments that could stimulate this circuit, and they suggest that training patients to enhance activity in this circuit through biofeedback could offer another potential way to improve their cost-benefit evaluations.

“If you could pinpoint a mechanism which is underlying the subjective evaluation of reward and cost, and use a modern technique that could manipulate it, either psychiatrically or with biofeedback, patients may be able to activate their circuits correctly,” Friedman says.

The research was funded by the CHDI Foundation, the Saks Kavanaugh Foundation, the National Institutes of Health, the Nancy Lurie Marks Family Foundation, the Bachmann-Strauss Dystonia and Parkinson’s Foundation, the William N. and Bernice E. Bumpus Foundation, the Simons Center for the Social Brain, the Kristin R. Pressman and Jessica J. Pourian ’13 Fund, Michael Stiefel, and Robert Buxton.

Rapid test for Covid-19 shows improved sensitivity

by Anne Trafton |

Since the start of the Covid-19 pandemic, researchers at MIT and the Broad Institute of MIT and Harvard, along with their collaborators at the University of Washington, Fred Hutchinson Cancer Research Center, Brigham and Women’s Hospital, and the Ragon Institute, have been working on a CRISPR-based diagnostic for Covid-19 that can produce results in 30 minutes to an hour, with similar accuracy as the standard PCR diagnostics now used.

The new test, known as STOPCovid, is still in the research stage but, in principle, could be made cheaply enough that people could test themselves every day. In a study appearing today in the New England Journal of Medicine, the researchers showed that on a set of patient samples, their test detected 93 percent of the positive cases as determined by PCR tests for Covid-19.

“We need rapid testing to become part of the fabric of this situation so that people can test themselves every day, which will slow down outbreak,” says Omar Abudayyeh, an MIT McGovern Fellow working on the diagnostic.

Abudayyah is one of the senior authors of the study, along with Jonathan Gootenberg, a McGovern Fellow, and Feng Zhang, a core member of the Broad Institute, investigator at the MIT McGovern Institute and Howard Hughes Medical Institute, and the James and Patricia Poitras ’63 Professor of Neuroscience at MIT. The first authors of the paper are MIT biological engineering graduate students Julia Joung and Alim Ladha in the Zhang lab.

A streamlined test

Zhang’s laboratory began collaborating with the Abudayyeh and Gootenberg laboratory to work on the Covid-19 diagnostic soon after the SARS-CoV-2 outbreak began. They focused on making an assay, called STOPCovid, that was simple to carry out and did not require any specialized laboratory equipment. Such a test, they hoped, would be amenable to future use in point-of-care settings, such as doctors’ offices, pharmacies, nursing homes, and schools.

“We developed STOPCovid so that everything could be done in a single step,” Joung says. “A single step means the test can be potentially performed by nonexperts outside of laboratory settings.”

In the new version of STOPCovid reported today, the researchers incorporated a process to concentrate the viral genetic material in a patient sample by adding magnetic beads that attract RNA, eliminating the need for expensive purification kits that are time-intensive and can be in short supply due to high demand. This concentration step boosted the test’s sensitivity so that it now approaches that of PCR.

“Once we got the viral genomes onto the beads, we found that that could get us to very high levels of sensitivity,” Gootenberg says.

Working with collaborators Keith Jerome at Fred Hutchinson Cancer Research Center and Alex Greninger at the University of Washington, the researchers tested STOPCovid on 402 patient samples — 202 positive and 200 negative — and found that the new test detected 93 percent of the positive cases as determined by the standard CDC PCR test.

“Seeing STOPCovid working on actual patient samples was really gratifying,” Ladha says.

They also showed, working with Ann Woolley and Deb Hung at Brigham and Women’s Hospital, that the STOPCovid test works on samples taken using the less invasive anterior nares swab. They are now testing it with saliva samples, which could make at-home tests even easier to perform. The researchers are continuing to develop the test with the hope of delivering it to end users to help fight the COVID-19 pandemic.

“The goal is to make this test easy to use and sensitive, so that we can tell whether or not someone is carrying the virus as early as possible,” Zhang says.

The research was funded by the National Institutes of Health, the Swiss National Science Foundation, the Patrick J. McGovern Foundation, the McGovern Institute for Brain Research, the Massachusetts Consortium on Pathogen Readiness Evergrande Covid-19 Response Fund, the Mathers Foundation, the Howard Hughes Medical Institute, the Open Philanthropy Project, J. and P. Poitras, and R. Metcalfe.

 

FULL PAPER AT NEJM

School of Science appoints 12 faculty members to named professorships

Anne Trafton |

The School of Science has awarded chaired appointments to 12 faculty members. These faculty, who are members of the departments of Biology; Brain and Cognitive Sciences; Chemistry; Earth, Atmospheric and Planetary Sciences; and Physics, receive additional support to pursue their research and develop their careers.

Kristin Bergmann, an assistant professor in the Department of Earth, Atmospheric and Planetary Sciences, has been named a D. Reid Weedon, Jr. ’41 Career Development Professor. This is a three-year professorship. Bergmann’s research integrates across sedimentology and stratigraphy, geochemistry, and geobiology to reveal aspects of Earth’s ancient environments. She aims to better constrain Earth’s climate record and carbon cycle during the evolution of early eukaryotes, including animals. Most of her efforts involve reconstructing the details of carbonate rocks, which store much of Earth’s carbon, and thus, are an important component of Earth’s climate system over long timescales.

Joseph Checkelscky is an associate professor in the Department of Physics and has been named a Mitsui Career Development Professor in Contemporary Technology, an appointment he will hold until 2023. His research in quantum materials relies on experimental methods at the intersection of physics, chemistry, and nanoscience. This work is aimed toward synthesizing new crystalline systems that manifest their quantum nature on a macroscopic scale. He aims to realize and study these crystalline systems, which can then serve as platforms for next-generation quantum sensors, quantum communication, and quantum computers.

Mircea Dincă, appointed a W. M. Keck Professor of Energy, is a professor in the Department of Chemistry. This appointment has a five-year term. The topic of Dincă’s research falls largely under the umbrella of energy storage and conversion. His interest in applied energy usage involves creating new organic and inorganic materials that can improve the efficiency of energy collection, storage, and generation while decreasing environmental impacts. Recently, he has developed materials for efficient air-conditioning units and been collaborating with Automobili Lamborghini on electric vehicle design.

Matthew Evans has been appointed to a five-year Mathworks Physics Professorship. Evans, a professor in the Department of Physics, focuses on the instruments used to detect gravitational waves. A member of MIT’s Laser Interferometer Gravitational-Wave Observatory (LIGO) research group, he engineers ways to fine-tune the detection capabilities of the massive ground-based facilities that are being used to identify collisions between black holes and stars in deep space. By removing thermal and quantum limitations, he can increase the sensitivity of the device’s measurements and, thus, its scope of exploration. Evans is also a member of the MIT Kavli Institute for Astrophysics and Space Research.

Evelina Fedorenko is an associate professor in the Department of Brain and Cognitive Sciences and has been named a Frederick A. (1971) and Carole J. Middleton Career Development Professor of Neuroscience. Studying how the brain processes language, Fedorenko uses behavioral studies, brain imaging, neurosurgical recording and stimulation, and computational modelling to better grasp language comprehension and production. In her efforts to elucidate how and what parts of the brain support language processing, she evaluates both typical and atypical brains. Fedorenko is also a member of the McGovern Institute for Brain Research.

Ankur Jain is an assistant professor in the Department of Biology and now a Thomas D. and Virginia W. Cabot Career Development Professor. He will hold this career development appointment for a term of three years. Jain studies how cells organize their contents. Within a cell, there are numerous compartments that form due to weak interactions between biomolecules and exist without an enclosing membrane. By analyzing the biochemistry and biophysics of these compartments, Jain deduces the principles of cellular organization and its dysfunction in human disease. Jain is also a member of the Whitehead Institute for Biomedical Research.

Pulin Li, an assistant professor in the Department of Biology and the Eugene Bell Career Development Professor of Tissue Engineering for the next three years, explores genetic circuitry in building and maintain a tissue. In particular, she investigates how communication circuitry between individual cells can extrapolate into multicellular behavior using both natural and synthetically generated tissues, for which she combines the fields of synthetic and systems biology, biophysics, and bioengineering. A stronger understanding of genetic circuitry could allow for progress in medicine involving embryonic development and tissue engineering. Li is a member of the Whitehead Institute for Biomedical Research.

Elizabeth Nolan, appointed an Ivan R. Cottrell Professor of Immunology, investigates innate immunity and infectious disease. The Department of Chemistry professor, who will hold this chaired professorship for five years, combines experimental chemistry and microbiology to learn about human immune responses to, and interactions with, microbial pathogens. This research includes elucidating the fight between host and pathogen for essential metal nutrients and the functions of host-defense peptides and proteins during infection. With this knowledge, Nolan contributes to fundamental understanding of the host’s ability to combat microbial infection, which may provide new strategies to treat infectious disease.

Leigh “Wiki” Royden is now a Cecil and Ida Green Professor of Geology and Geophysics. The five-year appointment supports her research on the large-scale dynamics and tectonics of the Earth as a professor in the Department of Earth, Atmospheric and Planetary Sciences. Fundamental to geoscience, the tectonics of regional and global systems are closely linked, particularly through the subduction of the plates into the mantle. Royden’s research adds to our understanding a of the structure and dynamics of the crust and the upper portion of the mantle through observation, theory and modeling. This progress has profound implications for global natural events, like mountain building and continental break-up.

Phiala Shanahan has been appointed a Class of 1957 Career Development Professor for three years. Shanahan is an assistant professor in the Department of Physics, where she specializes in theoretical and nuclear physics. Shanahan’s research uses supercomputers to provide insight into the structure of protons and nuclei in terms of their quark and gluon constituents. Her work also informs searches for new physics beyond the current Standard Model, such dark matter. She is a member of the MIT Center for Theoretical Physics.

Xiao Wang, an assistant professor, has also been named a new Thomas D. and Virginia W. Cabot Professor. In the Department of Chemistry, Wang designs and produces novel methods and tools for analyzing the brain. Integrating chemistry, biophysics, and genomics, her work provides higher-resolution imaging and sampling to explain how the brain functions across molecular to system-wide scales. Wang is also a core member of the Broad Institute of MIT and Harvard.

Bin Zhang has been appointed a Pfizer Inc-Gerald Laubach Career Development Professor for a three-year term. Zhang, an assistant professor in the Department of Chemistry, hopes to connect the framework of the human genome sequence with its various functions on various time and spatial scales. By developing theoretical and computational approaches to categorize information about dynamics, organization, and complexity of the genome, he aims to build a quantitative, predictive modelling tool. This tool could even produce 3D representations of details happening at a microscopic level within the body.

National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions

by Laboratory for Nuclear Science |

The U.S. National Science Foundation (NSF) announced today an investment of more than $100 million to establish five artificial intelligence (AI) institutes, each receiving roughly $20 million over five years. One of these, the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), will be led by MIT’s Laboratory for Nuclear Science (LNS) and become the intellectual home of more than 25 physics and AI senior researchers at MIT and Harvard, Northeastern, and Tufts universities.

By merging research in physics and AI, the IAIFI seeks to tackle some of the most challenging problems in physics, including precision calculations of the structure of matter, gravitational-wave detection of merging black holes, and the extraction of new physical laws from noisy data.

“The goal of the IAIFI is to develop the next generation of AI technologies, based on the transformative idea that artificial intelligence can directly incorporate physics intelligence,” says Jesse Thaler, an associate professor of physics at MIT, LNS researcher, and IAIFI director.  “By fusing the ‘deep learning’ revolution with the time-tested strategies of ‘deep thinking’ in physics, we aim to gain a deeper understanding of our universe and of the principles underlying intelligence.”

IAIFI researchers say their approach will enable making groundbreaking physics discoveries, and advance AI more generally, through the development of novel AI approaches that incorporate first principles from fundamental physics.

“Invoking the simple principle of translational symmetry — which in nature gives rise to conservation of momentum — led to dramatic improvements in image recognition,” says Mike Williams, an associate professor of physics at MIT, LNS researcher, and IAIFI deputy director. “We believe incorporating more complex physics principles will revolutionize how AI is used to study fundamental interactions, while simultaneously advancing the foundations of AI.”

In addition, a core element of the IAIFI mission is to transfer their technologies to the broader AI community.

“Recognizing the critical role of AI, NSF is investing in collaborative research and education hubs, such as the NSF IAIFI anchored at MIT, which will bring together academia, industry, and government to unearth profound discoveries and develop new capabilities,” says NSF Director Sethuraman Panchanathan. “Just as prior NSF investments enabled the breakthroughs that have given rise to today’s AI revolution, the awards being announced today will drive discovery and innovation that will sustain American leadership and competitiveness in AI for decades to come.”

Research in AI and fundamental interactions

Fundamental interactions are described by two pillars of modern physics: at short distances by the Standard Model of particle physics, and at long distances by the Lambda Cold Dark Matter model of Big Bang cosmology. Both models are based on physical first principles such as causality and space-time symmetries.  An abundance of experimental evidence supports these theories, but also exposes where they are incomplete, most pressingly that the Standard Model does not explain the nature of dark matter, which plays an essential role in cosmology.

AI has the potential to help answer these questions and others in physics.

For many physics problems, the governing equations that encode the fundamental physical laws are known. However, undertaking key calculations within these frameworks, as is essential to test our understanding of the universe and guide physics discovery, can be computationally demanding or even intractable. IAIFI researchers are developing AI for such first-principles theory studies, which naturally require AI approaches that rigorously encode physics knowledge.

“My group is developing new provably exact algorithms for theoretical nuclear physics,” says Phiala Shanahan, an assistant professor of physics and LNS researcher at MIT. “Our first-principles approach turns out to have applications in other areas of science and even in robotics, leading to exciting collaborations with industry partners.”

Incorporating physics principles into AI could also have a major impact on many experimental applications, such as designing AI methods that are more easily verifiable. IAIFI researchers are working to enhance the scientific potential of various facilities, including the Large Hadron Collider (LHC) and the Laser Interferometer Gravity Wave Observatory (LIGO).

“Gravitational-wave detectors are among the most sensitive instruments on Earth, but the computational systems used to operate them are mostly based on technology from the previous century,” says Principal Research Scientist Lisa Barsotti of the MIT Kavli Institute for Astrophysics and Space Research. “We have only begun to scratch the surface of what can be done with AI; just enough to see that the IAIFI will be a game-changer.”

The unique features of these physics applications also offer compelling research opportunities in AI more broadly. For example, physics-informed architectures and hardware development could lead to advances in the speed of AI algorithms, and work in statistical physics is providing a theoretical foundation for understanding AI dynamics.

“Physics has inspired many time-tested ideas in machine learning: maximizing entropy, Boltzmann machines, and variational inference, to name a few,” says Pulkit Agrawal, an assistant professor of electrical engineering and computer science at MIT, and researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We believe that close interaction between physics and AI researchers will be the catalyst that leads to the next generation of machine learning algorithms.”

Cultivating early-career talent

AI technologies are advancing rapidly, making it both important and challenging to train junior researchers at the intersection of physics and AI. The IAIFI aims to recruit and train a talented and diverse group of early-career researchers, including at the postdoc level through its IAIFI Fellows Program.

“By offering our fellows their choice of research problems, and the chance to focus on cutting-edge challenges in physics and AI, we will prepare many talented young scientists to become future leaders in both academia and industry,” says MIT professor of physics Marin Soljacic of the Research Laboratory of Electronics (RLE).

IAIFI researchers hope these fellows will spark interdisciplinary and multi-investigator collaborations, generate new ideas and approaches, translate physics challenges beyond their native domains, and help develop a common language across disciplines. Applications for the inaugural IAIFI fellows are due in mid-October.

Another related effort spearheaded by Thaler, Williams, and Alexander Rakhlin, an associate professor of brain and cognitive science at MIT and researcher in the Institute for Data, Systems, and Society (IDSS), is the development of a new interdisciplinary PhD program in physics, statistics, and data science, a collaborative effort between the Department of Physics and the Statistics and Data Science Center.

“Statistics and data science are among the foundational pillars of AI. Physics joining the interdisciplinary doctoral program will bring forth new ideas and areas of exploration, while fostering a new generation of leaders at the intersection of physics, statistics, and AI,” says Rakhlin.

Education, outreach, and partnerships 

The IAIFI aims to cultivate “human intelligence” by promoting education and outreach. For example, IAIFI members will contribute to establishing a MicroMasters degree program at MIT for students from non-traditional backgrounds.

“We will increase the number of students in both physics and AI from underrepresented groups by providing fellowships for the MicroMasters program,” says Isaac Chuang, professor of physics and electrical engineering, senior associate dean for digital learning, and RLE researcher at MIT. “We also plan on working with undergraduate MIT Summer Research Program students, to introduce them to the tools of physics and AI research that they might not have access to at their home institutions.”

The IAIFI plans to expand its impact via numerous outreach efforts, including a K-12 program in which students are given data from the LHC and LIGO and tasked with rediscovering the Higgs boson and gravitational waves.

“After confirming these recent Nobel Prizes, we can ask the students to find tiny artificial signals embedded in the data using AI and fundamental physics principles,” says assistant professor of physics Phil Harris, an LNS researcher at MIT. “With projects like this, we hope to disseminate knowledge about — and enthusiasm for — physics, AI, and their intersection.”

In addition, the IAIFI will collaborate with industry and government to advance the frontiers of both AI and physics, as well as societal sectors that stand to benefit from AI innovation. IAIFI members already have many active collaborations with industry partners, including DeepMind, Microsoft Research, and Amazon.

“We will tackle two of the greatest mysteries of science: how our universe works and how intelligence works,” says MIT professor of physics Max Tegmark, an MIT Kavli Institute researcher. “Our key strategy is to link them, using physics to improve AI and AI to improve physics. We’re delighted that the NSF is investing the vital seed funding needed to launch this exciting effort.”

Building new connections at MIT and beyond

Leveraging MIT’s culture of collaboration, the IAIFI aims to generate new connections and to strengthen existing ones across MIT and beyond.

Of the 27 current IAIFI senior investigators, 16 are at MIT and members of the LNS, RLE, MIT Kavli Institute, CSAIL, and IDSS. In addition, IAIFI investigators are members of related NSF-supported efforts at MIT, such as the Center for Brains, Minds, and Machines within the McGovern Institute for Brain Research and the MIT-Harvard Center for Ultracold Atoms.

“We expect a lot of creative synergies as we bring physics and computer science together to study AI,” says Bill Freeman, the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science and researcher in CSAIL. “I’m excited to work with my physics colleagues on topics that bridge these fields.”

More broadly, the IAIFI aims to make Cambridge, Massachusetts, and the surrounding Boston area a hub for collaborative efforts to advance both physics and AI.

“As we teach in 8.01 and 8.02, part of what makes physics so powerful is that it provides a universal language that can be applied to a wide range of scientific problems,” says Thaler. “Through the IAIFI, we will create a common language that transcends the intellectual borders between physics and AI to facilitate groundbreaking discoveries.”