Artificial Intelligence

Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.
Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.
The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.
The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.
“We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.”
Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.
Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.
“The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.”
The following projects are the MIT recipients of the inaugural C3.ai DTI Awards: 
“Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions” — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science
“Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data” — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)
“Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic” — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics
“Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak” — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering
“Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections” — David Gifford, professor of biological engineering and of electrical engineering and computer science
“Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19” — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)
“Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic” — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

Topics: School of Engineering, MIT Schwarzman College of Computing, Civil and environmental engineering, Biological engineering, Electrical engineering and computer science (EECS), Covid-19, Pandemic, Artificial intelligence, Grants, Funding, IDSS, Machine learning, Technology and society, Medicine, Faculty More

For humans, it can be challenging to manipulate thin flexible objects like ropes, wires, or cables. But if these problems are hard for humans, they are nearly impossible for robots. As a cable slides between the fingers, its shape is constantly changing, and the robot’s fingers must be constantly sensing and adjusting the cable’s position and motion.
Standard approaches have used a series of slow and incremental deformations, as well as mechanical fixtures, to get the job done. Recently, a group of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) pursued the task from a different angle, in a manner that more closely mimics us humans. The team’s new system uses a pair of soft robotic grippers with high-resolution tactile sensors (and no added mechanical constraints) to successfully manipulate freely moving cables.

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One could imagine using a system like this for both industrial and household tasks, to one day enable robots to help us with things like tying knots, wire shaping, or even surgical suturing. 
The team’s first step was to build a novel two-fingered gripper. The opposing fingers are lightweight and quick moving, allowing nimble, real-time adjustments of force and position. On the tips of the fingers are vision-based “GelSight” sensors, built from soft rubber with embedded cameras. The gripper is mounted on a robot arm, which can move as part of the control system.
The team’s second step was to create a perception-and-control framework to allow cable manipulation. For perception, they used the GelSight sensors to estimate the pose of the cable between the fingers, and to measure the frictional forces as the cable slides. Two controllers run in parallel: one modulates grip strength, while the other adjusts the gripper pose to keep the cable within the gripper.
When mounted on the arm, the gripper could reliably follow a USB cable starting from a random grasp position. Then, in combination with a second gripper, the robot can move the cable “hand over hand” (as a human would) in order to find the end of the cable. It could also adapt to cables of different materials and thicknesses.
As a further demo of its prowess, the robot performed an action that humans routinely do when plugging earbuds into a cell phone. Starting with a free-floating earbud cable, the robot was able to slide the cable between its fingers, stop when it felt the plug touch its fingers, adjust the plug’s pose, and finally insert the plug into the jack. 
“Manipulating soft objects is so common in our daily lives, like cable manipulation, cloth folding, and string knotting,” says Yu She, MIT postdoc and lead author on a new paper about the system. “In many cases, we would like to have robots help humans do this kind of work, especially when the tasks are repetitive, dull, or unsafe.” 
String me along 
Cable following is challenging for two reasons. First, it requires controlling the “grasp force” (to enable smooth sliding), and the “grasp pose” (to prevent the cable from falling from the gripper’s fingers).  
This information is hard to capture from conventional vision systems during continuous manipulation, because it’s usually occluded, expensive to interpret, and sometimes inaccurate. 
What’s more, this information can’t be directly observed with just vision sensors, hence the team’s use of tactile sensors. The gripper’s joints are also flexible — protecting them from potential impact. 
The algorithms can also be generalized to different cables with various physical properties like material, stiffness, and diameter, and also to those at different speeds. 
When comparing different controllers applied to the team’s gripper, their control policy could retain the cable in hand for longer distances than three others. For example, the “open-loop” controller only followed 36 percent of the total length, the gripper easily lost the cable when it curved, and it needed many regrasps to finish the task. 
Looking ahead 
The team observed that it was difficult to pull the cable back when it reached the edge of the finger, because of the convex surface of the GelSight sensor. Therefore, they hope to improve the finger-sensor shape to enhance the overall performance. 
In the future, they plan to study more complex cable manipulation tasks such as cable routing and cable inserting through obstacles, and they want to eventually explore autonomous cable manipulation tasks in the auto industry.
Yu She wrote the paper alongside MIT PhD students Shaoxiong Wang, Siyuan Dong, and Neha Sunil; Alberto Rodriguez, MIT associate professor of mechanical engineering; and Edward Adelson, the John and Dorothy Wilson Professor in the MIT Department of Brain and Cognitive Sciences.  More

When schools around the world closed their doors due to the coronavirus pandemic, the team behind MIT App Inventor — a web-based, visual-programming environment that allows children to develop applications for smartphones and tablets — began thinking about how they could not only help keep children engaged and learning, but also empower them to create new tools to address the pandemic.

In April, the App Inventor team launched a new challenge that encourages children and adults around the world to build mobile technologies that could be used to help stem the spread of Covid-19, aid local communities, and provide moral support to people around the world.
“Many people, including kids, are locked down at home with little to do and with a sense of loss of control over their lives,” says Selim Tezel, a curriculum developer for MIT App Inventor. “We wanted to empower them to take action, be involved in a creative process, and do something good for their fellow citizens.”
Since the Coronavirus App Inventor Challenge launched this spring, there have been submissions from inventors ranging in age from 9 to 72 years and from coders around the globe, including New Zealand, the Democratic Republic of Congo, Italy, China, India, and Spain. While the App Inventor platform has historically been used in classrooms as an educational tool, Tezel and Hal Abelson, the Class of 1922 Professor in the Department of Electrical Engineering in Computer Science, explain that they have seen increased individual engagement with the platform during the pandemic, particularly on a global scale.
“The nice thing about App Inventor is that you’re learning about coding, but it also gives you something that you can actually do and a chance to contribute,” says Abelson. “It provides kids with an opportunity to say, ‘I’m not just learning, I’m doing a project, and it’s not only a project for me, it’s a project that can actually help other people.’ I think that can be very powerful.”
Winners are announced on a monthly basis and honor apps for creativity, design, and overall inventiveness. Challenge participants have addressed a wide variety of issues associated with the pandemic, from health and hygiene to mental health and education. For example, April’s Young Inventors of the Month, Bethany Chow and Ice Chow from Hong Kong, developed an app aimed at motivating users to stay healthy. Their app features a game that encourages players to adapt healthy habits by collecting points that they can use to defeat virtual viruses, as well as an optional location tracker function that can alert users if they have frequented a location that has a Covid-19 outbreak.
Akshaj Singhal, a 11-year-old from India, was selected as the June Inventor of the Month in the Young Inventors category, which includes children 12 years old and younger, for his app called Covid-19 Warrior. The app offers a host of features aimed at spreading awareness of Covid-19, including a game and quiz to test a user’s knowledge of the virus, as well as local daily Covid-19 news updates and information on how to make your own mask.
The challenge has attracted participants with varying levels of technical expertise, allowing aspiring coders a chance to hone and improve their skills. Prayanshi Garg, a 12-year-old from India, created her first app for the challenge, an educational quiz aimed at increasing awareness of Covid-19. Vansh Reshamwala, a 10-year-old from India, created an app that features a recording of his voice sharing information about ways to help prevent the spread of Covid-19 and thanking heroes for their efforts during the pandemic.
Participants have also been able to come together virtually to develop apps during a time when social interactions and team activities are limited. For example, three high school students from Singapore developed Maskeraid, an app that connects users in need of assistance with volunteers who are able to help with a variety of services.
“The ultimate goal is to engage our very creative App Inventor community of all ages and empower them during this time,” says Tezel. “We also see this time as an incredible opportunity to help people vastly improve their coding skills.  When one is confronted by a tangible challenge, one’s skills and versatility can grow to meet the challenge.”
The App Inventor team plans to continue hosting the challenge for so long as the pandemic is having a worldwide impact. Later this month, the App Inventor team will be hosting a virtual hackathon or worldwide “appathon,” an event that will encourage participants to create apps aimed at improving the global good.
“Our global App Inventor community never ceases to amaze us,” says Tezel. “We are delighted by how inventors of all ages have been rising to the challenge of the coronavirus, empowering themselves by putting their coding skills to good use for the well-being of their communities.”

Topics: Computer science and technology, Technology and society, STEM education, K-12 education, Apps, smartphones, Invention, Hackathon, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, Pandemic, Education, teaching and academics More

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