Posts in category: MIT

With various physical distancing guidelines in place throughout the world as a means to curb the spread of Covid-19, the internet has experienced a dramatic spike in overall traffic. MIT Professor Tom Leighton is chief executive officer and co-founder of Akamai Technologies, a global content delivery network, cybersecurity, and cloud service company that provides web and internet security services. At MIT he specializes in applied mathematics in the Department of Mathematics and is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The Department of Mathematics Communications spoke to Leighton about his company’s response to the world’s increased reliance on the internet during the Covid-19 pandemic.

Q: How is the pandemic changing the way people use the internet?

A: The internet has become our lifeline as we face the challenges of working remotely, distance learning, and sheltering in place. Everything has moved online: religious services, movie premieres, commerce of all kinds, and even gatherings of friends for a cup of coffee. We’ve already been doing many of these things online for years — the big difference now is that we are suddenly only doing them online.

When we’ve emerged from the pandemic, it seems quite possible that our usage of the internet for nearly every facet of our lives will have increased permanently. Many more people may be working remotely even when offices reopen; the shift to virtual meetings may become the norm even when we can travel again; a much greater share of commerce may be conducted online even when we can return to shopping malls; and our usage of social media and video streaming could well be greater than ever before, even when it’s OK to meet others in person.

Q: How much more use is the internet seeing as a result of the pandemic?

A: Akamai operates a globally distributed intelligent edge platform with more than 270,000 servers in 4,000 locations across 137 countries. From our vantage point, we can see that global internet traffic increased by about 30 percent during the past month. That’s about 10 times normal, and it means we’ve seen an entire year’s worth of growth in internet traffic in just the past few weeks. And that’s without any live sports streaming, like the usual March Madness college basketball tournament in the United States.

Just a few weeks ago, we set a new peak record of traffic on the Akamai edge platform of 167 terabits per second. That’s more than double the peak we saw one year before. These are truly unprecedented times. The internet is being used at a scale that the world has never experienced.

Q: Can the internet keep up with the surge in traffic?

A: The answer is yes, but with many more caveats now.

Around the world, some regulators, major carriers, and content providers are taking steps to reduce load during peak traffic times in an effort to avert online gridlock. For example, European regulators have asked telecom providers and streaming platforms to switch to standard definition video during periods of peak demand. And Akamai is working with leading companies such as Microsoft and Sony to deliver software updates for e-gaming at off-peak traffic times. The typical software update uses as much traffic as about 30,000 web pages, so this makes a big difference when it comes to managing congestion.

In addition, Akamai’s intelligent edge network architecture is designed to mitigate and minimize network congestion. Because we’ve deployed our infrastructure deep into carrier networks, we can help those networks avoid overload by diverting traffic away from areas experiencing high levels of congestion.

Overall, we fully expect to maintain the integrity and reliability of website and mobile application delivery, as well as security services, for all of our customers during this time. In particular, Akamai customers across sectors such as government, health care, financial services, commerce, manufacturing, and business services should not experience any change in the performance of their services. We will continue working with governments, network operators, and our customers to minimize stress on the system. At the same time, we’ll do our best to make sure that everyone who is relying on the internet for their work, studies, news, and entertainment continues to have a high-quality, positive experience.

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Developing drugs to combat Covid-19 is a global priority, requiring communities to come together to fight the spread of infection. At MIT, researchers with backgrounds in machine learning and life sciences are collaborating, sharing datasets and tools to develop machine learning methods that can identify novel cures for Covid-19.

This research is an extension of a community effort launched earlier this year. In February, before the Institute de-densified as a result of the pandemic, the first-ever AI Powered Drug Discovery and Manufacturing Conference, conceived and hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health, drew attendees including pharmaceutical industry researchers, government regulators, venture capitalists, and pioneering drug researchers. More than 180 health care companies and 29 universities developing new artificial intelligence methods used in pharmaceuticals got involved, making the conference a singular event designed to lift the mask and reveal what goes on in the process of drug discovery.

As secretive as Silicon Valley seems, computer science and engineering students typically know what a job looks like when aspiring to join companies like Facebook or Tesla. But the global head of research and development for Janssen — the innovative pharmaceutical company owned by Johnson & Johnson — said it’s often much harder for students to grasp how their work fits into drug discovery.

“That’s a problem at the moment,” Mathai Mammen says, after addressing attendees, including MIT graduate students and postdocs, who gathered in the Samberg Conference Center in part to get a glimpse behind the scenes of companies currently working on bold ideas blending artificial intelligence with health care. Mathai, who is a graduate of the Harvard-MIT Program in Health Sciences and Technology and whose work at Theravance has brought to market five new medicines and many more on their way, is here to be part of the answer to that problem. “What the industry needs to do, is talk to students and postdocs about the sorts of interesting scientific and medical problems whose solutions can directly and profoundly benefit the health of people everywhere” he says.

“The conference brought together research communities that rarely overlap at technical conferences,” says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, Jameel Clinic faculty co-lead, and one of the conference organizers. “This blend enables us to better understand open problems and opportunities in the intersection. The exciting piece for MIT students, especially for computer science and engineering students, is to see where the industry is moving and to understand how they can contribute to this changing industry, which will happen when they graduate.”

Over two days, conference attendees snapped photographs through a packed schedule of research presentations, technical sessions, and expert panels, covering everything from discovering new therapeutic molecules with machine learning to funding AI research. Carefully curated, the conference provided a roadmap of bold tech ideas at work in health care now and traced the path to show how those tech solutions get implemented.

At the conference, Barzilay and Jim Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and Jameel Clinic faculty co-lead, presented research from a study published in Cell where they used machine learning to help identify a new drug that can target antibiotic-resistant bacteria. Together with MIT researchers Tommi Jaakkola, Kevin Yang, Kyle Swanson, and the first author Jonathan Stokes, they demonstrated how blending their backgrounds can yield potential answers to combat the growing antibiotic resistance crisis.

Collins saw the conference as an opportunity to inspire interest in antibiotic research, hoping to get the top young minds involved in battling resistance to antibiotics built up over decades of overuse and misuse, an urgent predicament in medicine that computer science students might not understand their role in solving. “I think we should take advantage of the innovation ecosystem at MIT and the fact that there are many experts here at MIT who are willing to step outside their comfort zone and get engaged in a new problem,” Collins says. “Certainly in this case, the development and discovery of novel antibiotics, is critically needed around the globe.”

AIDM showed the power of collaboration, inviting experts from major health-care companies and relevant organizations like Merck, Bayer, Darpa, Google, Pfizer, Novartis, Amgen, the U.S. Food and Drug Administration, and Janssen. Reaching capacity for conference attendees, it also showed people are ready to pull together to get on the same page. “I think the time is right and I think the place is right,” Collins says. “I think MIT is well-positioned to be a national, if not an international leader in this space, given the excitement and engagement of our students and our position in Kendall Square.”

A biotech hub for decades, Kendall Square has come a long way since big data came to Cambridge, Massachusetts, forever changing life science companies based here. AIDM kicked off with Institute Professor and Professor of Biology Phillip Sharp walking attendees through a brief history of AI in health care in the area. He was perhaps the person at the conference most excited for others to see the potential, as through his long career, he’s watched firsthand the history of innovation that led to this conference.

“The bigger picture, which this conference is a major part of, is this bringing together of the life science — biologists and chemists with machine learning and artificial intelligence — it’s the future of life science,” Sharp says. “It’s clear. It will reshape how we talk about our science, how we think about solving problems, how we deal with the other parts of the process of taking insights to benefit society.”

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Albert Einstein famously postulated that “the only real valuable thing is intuition,” arguably one of the most important keys to understanding intention and communication. 

But intuitiveness is hard to teach — especially to a machine. Looking to improve this, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a method that dials us closer to more seamless human-robot collaboration. The system, called “Conduct-A-Bot,” uses human muscle signals from wearable sensors to pilot a robot’s movement. 

“We envision a world in which machines help people with cognitive and physical work, and to do so, they adapt to people rather than the other way around,” says Professor Daniela Rus, director of CSAIL, deputy dean of research for the MIT Stephen A. Schwarzman College of Computing, and co-author on a paper about the system. 

To enable seamless teamwork between people and machines, electromyography and motion sensors are worn on the biceps, triceps, and forearms to measure muscle signals and movement. Algorithms then process the signals to detect gestures in real time, without any offline calibration or per-user training data. The system uses just two or three wearable sensors, and nothing in the environment — largely reducing the barrier to casual users interacting with robots.

While Conduct-A-Bot could potentially be used for various scenarios, including navigating menus on electronic devices or supervising autonomous robots, for this research the team used a Parrot Bebop 2 drone, although any commercial drone could be used.

By detecting actions like rotational gestures, clenched fists, tensed arms, and activated forearms, Conduct-A-Bot can move the drone left, right, up, down, and forward, as well as allow it to rotate and stop. 

If you gestured toward the right to your friend, they could likely interpret that they should move in that direction. Similarly, if you waved your hand to the left, for example, the drone would follow suit and make a left turn. 

In tests, the drone correctly responded to 82 percent of over 1,500 human gestures when it was remotely controlled to fly through hoops. The system also correctly identified approximately 94 percent of cued gestures when the drone was not being controlled.

“Understanding our gestures could help robots interpret more of the nonverbal cues that we naturally use in everyday life,” says Joseph DelPreto, lead author on the new paper. “This type of system could help make interacting with a robot more similar to interacting with another person, and make it easier for someone to start using robots without prior experience or external sensors.” 

This type of system could eventually target a range of applications for human-robot collaboration, including remote exploration, assistive personal robots, or manufacturing tasks like delivering objects or lifting materials. 

These intelligent tools are also consistent with social distancing — and could potentially open up a realm of future contactless work. For example, you can imagine machines being controlled by humans to safely clean a hospital room, or drop off medications, while letting us humans stay a safe distance.

Muscle signals can often provide information about states that are hard to observe from vision, such as joint stiffness or fatigue.    

For example, if you watch a video of someone holding a large box, you might have difficulty guessing how much effort or force was needed — and a machine would also have difficulty gauging that from vision alone. Using muscle sensors opens up possibilities to estimate not only motion, but also the force and torque required to execute that physical trajectory.

For the gesture vocabulary currently used to control the robot, the movements were detected as follows: 

• stiffening the upper arm to stop the robot (similar to briefly cringing when seeing something going wrong): biceps and triceps muscle signals;

• waving the hand left/right and up/down to move the robot sideways or vertically: forearm muscle signals (with the forearm accelerometer indicating hand orientation);

• fist clenching to move the robot forward: forearm muscle signals; and

• rotating clockwise/counterclockwise to turn the robot: forearm gyroscope.

Machine learning classifiers detected the gestures using the wearable sensors. Unsupervised classifiers processed the muscle and motion data and clustered it in real time to learn how to separate gestures from other motions. A neural network also predicted wrist flexion or extension from forearm muscle signals.  

The system essentially calibrates itself to each person’s signals while they’re making gestures that control the robot, making it faster and easier for casual users to start interacting with robots.

In the future, the team hopes to expand the tests to include more subjects. And while the movements for Conduct-A-Bot cover common gestures for robot motion, the researchers want to extend the vocabulary to include more continuous or user-defined gestures. Eventually, the hope is to have the robots learn from these interactions to better understand the tasks and provide more predictive assistance or increase their autonomy. 

“This system moves one step closer to letting us work seamlessly with robots so they can become more effective and intelligent tools for everyday tasks,” says DelPreto. “As such collaborations continue to become more accessible and pervasive, the possibilities for synergistic benefit continue to deepen.” 

DelPreto and Rus presented the paper virtually earlier this month at the ACM/IEEE International Conference on Human Robot Interaction.

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Six MIT faculty members are among more than 250 leaders from academia, business, public affairs, the humanities, and the arts elected to the American Academy of Arts and Sciences, the academy announced Thursday.

One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

Those elected from MIT this year are:

• Robert C. Armstrong, Chevron Professor in Chemical Engineering;

• Dave L. Donaldson, professor of economics;

• Catherine L. Drennan, professor of biology and chemistry;

• Ronitt Rubinfeld, professor of electrical engineering and computer science;

• Joshua B. Tenenbaum, professor of brain and cognitive sciences; and

• Craig Steven Wilder, Barton L. Weller Professor of History.

“The members of the class of 2020 have excelled in laboratories and lecture halls, they have amazed on concert stages and in surgical suites, and they have led in board rooms and courtrooms,” said academy President David W. Oxtoby. “With today’s election announcement, these new members are united by a place in history and by an opportunity to shape the future through the academy’s work to advance the public good.”

Since its founding in 1780, the academy has elected leading thinkers from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 250 Nobel and Pulitzer Prize winners.

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As cases of Covid-19 continue to climb in parts of the United States, the number of people seeking treatment is threatening to overwhelm many hospitals, forcing some facilities to ration their care and reserve ventilators, hospital beds, and other limited medical resources for the sickest patients. 

Having a handle on local hospitals’ capacity and resource availability could help balance the load of Covid-19 patients requiring hospitalization across a region, for instance allowing an EMT to send a patient to a facility where they are more likely to be treated quickly. But many states lack real-time data on their current capacity to treat Covid-19 patients. 

A group of researchers in MIT’s Computer Science and Intelligence Laboratory (CSAIL), working with the MIT spinoff Mobi Systems, are aiming to help level demand across the entire health care network by providing real-time updates of hospital resources, which they hope will help patients, EMTs, and physicians quickly decide which facility is best equipped to handle a new patient at any given time. 

The team has developed a web app which is now publicly accessible at: https://Covid19hospitalstatus.com. The interface allows users such as patients, nurses, and doctors to report a hospital’s current status in a number of metrics, from the average wait time (something that a patient may get a sense for as they spend time in a waiting room), to the number of ventilators and ICU beds, which doctors and nurses may be able to approximate.

EMTS can use the app as a map, zooming in by state, county, or city to quickly gauge hospital capacity, and decide which nearby hospitals have available beds where they can send a patient requiring hospitalization. The app can also generate a list of hospitals, prioritized by availability, time of travel, and most recently updated data. 

“We want to flatten the Covid curve by physical distancing over the course of months,” says MIT graduate Anna Jaffe ’07, CEO of Mobi Systems. “But there’s another curve to flatten, which is this real-time challenge of getting the right patient to the right hospital, in the right moment, to level the load on hospitals and health care workers.”

“Do something”

As the pandemic began to unfold around the world, Jaffe was intrigued by the results of a short hackathon that one Mobi member, Julius Pätzold, recently attended in Germany. The weekend challenge, sponsored by the German government, included a problem to match supply and demand, for instance in a hospital facing a surge in patient visits. 

His team mapped the German hospital infrastructure, including the status of individual hospitals’ capacity, then simulated dispatching patients to hospitals according to a hospital’s capacity, its relative location to a patient, and a patient’s medical needs. The real-time maps developed over this short time suggested such tools would have a positive impact on a patient’s quality of care, specifically in decreasing death rates.

“That intersected with my feeling that I think everyone wants to do something around Covid-19 in response to the current crisis, and not just be cooped up in our respective homes,” says Jaffe, whose company, Mobi Systems, develops tools for large-scale network optimization problems surrounding mobility and hospitality. 

Mobi originally grew out of CSAIL’s Model-based Embedded Robotic Systems group, led by MIT Professor Brian Williams, whose work involves developing autonomous planning tools to help individuals make complex, real-time decisions in the face of uncertainty and risk. 

Jaffe reached out to Williams to help develop a web-based reporting tool for hospitals, to similarly help patients and medical professionals make critical, real-time decisions of where best to send a patient, based on resource availability. 

“Our question was, how can the resources statewide or nationwide be used most effectively, in order to keep the most people healthy,” Williams says. “And for the individual, which hospital will meet their needs, and how do they get there. That’s the exercise we’re tackling here.”

Crowd power

The team’s app is heavily dependent on crowdsourced data, and the willingness of patients and medical professionals to report on various metrics, from a hospital’s current wait time to the approximate number of ICU beds and ventilators available. 

“The reporting options right now are very specific,” Jaffe says. “But what we really want to know is, can your hospital accept a patient right now?” 

A user can enter their role — patient, nurse, or physician — then report on, for instance, a hospital’s average wait time. With a sliding scale, they can rate their confidence in their report before submitting it. 

But what if those users are reporting false or inaccurate data, whether intentionally or not? 

Williams says in order to guard against such uncertainty, the team takes a probabilistic approach. For instance, the app assumes that one user’s reporting of a hospital’s status is one of low confidence, which is initially not weighed heavily in the overall estimation for that metric. They can then incorporate this one data point into all the other reports they’ve received for that metric. If most of those reports have also been rated with low confidence, but report the same result, that estimate, such as of wait time, is automatically weighed more heavily, and therefore rated at a higher confidence overall.  

Additionally, he says if the app receives reports from more trusted sources — for instance, if hospitals make in-house, aggregated data available to the app — those sources would “swamp out” or take higher priority over low-confidence reports of the same metric. 

The team is testing the app with just such a trustworthy dataset, from the state of Pennsylvania, which for the last several years has had a system in place for hospitals to report resource availability, that is updated at least twice a day. The team has used data from the last week to track Covid-19 visits across the state’s hospital system.

“In this data, you can see that not all hospitals are overrun — there are clear differences in availability,” says MIT graduate Peng Yu ’SM 13, ’PhD 17, chief technology officer at Mobi, highlighting the potential for distributing patients across a region’s hospitals, to balance resources across a hospital network. 

However, most states lack such aggregated, updated information. In most other states, for instance, EMTs either have a handful of default facilities where they typically send patients, or they have to call around to surrounding hospitals to check availability. 

“It’s really about word of mouth — who do you know, and who do you call up,” says Williams, whose nephew is an EMT who has worked in regions with varying decision-making practices. “We’re trying to aggregate that information, to make these recommendations much faster.

The team is now reaching out to thousands of medical professionals to test-drive the reporting tool, in hopes of boosting the crowdsourcing component for the app, which is now available on any internet-enabled device. To address the pandemic, the team believes that data need to be made available at a faster rate than the virus’ spread. Their hope is that states will follow in Pennsylvania’s footsteps and, for instance, mandate that hospitals report resource data, and provide reporting tools such as the new app to doctors and EMTs. 

“This project is very much for the people, by the people, and will be kept open and free,” Williams says.  

“Unfortunately, it doesn’t feel like this is a flash pandemic,” Jaffe says. “Even in a recovery period, hospitals will have to resume normal care, concurrently with treating Covid-19 over time. Our app may help load balance in that way as well, so hospitals can more effectively predict how many floors they need to quarantine for Covid-19, so that the rest of the hospital can go back to things like having families around a mother giving birth. We aim to really understand how to bring things back to a more normal operational status, while still handling the crisis.

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Marija Ilic — a senior research scientist at the Laboratory for Information and Decision Systems, affiliate of the MIT Institute for Data, Systems, and Society, senior staff in MIT Lincoln Laboratory’s Energy Systems Group, and Carnegie Mellon University professor emerita — is a researcher on a mission: making electric energy systems future-ready.

Since the earliest days of streetcars and public utilities, electric power systems have had a fairly standard structure: for a given area, a few large generation plants produce and distribute electricity to customers. It is a one-directional structure, with the energy plants being the only source of power for many end users.

Today, however, electricity can be generated from many and varied sources — and move through the system in multiple directions. An electric power system may include stands of huge turbines capturing wild ocean winds, for instance. There might be solar farms of a hundred megawatts or more, or houses with solar panels on their roofs that some days make more electricity than occupants need, some days much less. And there are electric cars, their batteries hoarding stored energy overnight. Users may draw electricity from one source or another, or feed it back into the system, all at the same time. Add to that the trend toward open electricity markets, where end users like households can pick and choose the electricity services they buy depending on their needs. How should systems operators integrate all these while keeping the grid stable and ensuring power gets to where it is needed?

To explore this question, Ilic has developed a new way to model complex power systems.

Electric power systems, even traditional ones, are complex and heterogeneous to begin with. They cover wide geographical areas and have legal and political barriers to contend with, such as state borders and energy policies. In addition, all electric power systems have inherent physical limitations. For instance, power does not flow in a set path in an electric grid, but rather along all possible paths connecting supply to demand. To maintain grid stability and quality of service, then, the system must control for the impact of interconnections: a change in supply and demand at one point in a system changes supply and demand for the other points in the system. This means there is much more complexity to manage as new sources of energy (more interconnections) with sometimes unpredictable supply (such as wind or solar power) come into play. Ultimately, however, to maintain stability and quality of service, and to balance supply and demand within the system, it comes down to a relatively simple concept: the power consumed and the rate at which it is consumed (plus whatever is lost along the way), must always equal the power produced and the rate at which it is produced.

Using this simpler concept to manage the complexities and limitations of electric power systems, Ilic is taking a non-traditional approach: She models the systems using information about energy, power, and ramp rate (the rate at which power can increase over time) for each part of the system — distributing decision-making calculations into smaller operational chunks. Doing this streamlines the model but retains information about the system’s physical and temporal structure. “That’s the minimal information you need to exchange. It’s simple and technology-agnostic, but we don’t teach systems that way.”

She believes regulatory organizations such as the Federal Energy Regulatory Commission and North American Energy Reliability Corporation should have standard protocols for such information exchanges, just as internet protocols govern how data is exchanged on the internet. “If you were to [use a standard set of] specifications like: what is your capacity, how much does it vary over time, how much energy do you need and within what power range — the system operator could integrate different sources in a much simpler way than we are doing now.” 

Another important aspect of Ilic’s work is that her models lend themselves to controlling the system with a layer of sensor and communications technologies. This uses a framework she developed called Dynamic Monitoring and Decision Systems framework, or DyMonDS. The data-enabled decision-making concept has been tested using real data from Portugal’s Azores Islands, and since applied to real-world challenges. After so many years it appears that her new modeling approach fittingly supports DyMonDS design, including systematic use of many theoretical concepts used by the LIDS community in their research.

One such challenge included work on Puerto Rico’s power grid. Ilic was the technical lead on a Lincoln Laboratory project on designing future architectures and software to make Puerto Rico’s electric power grid more resilient without adding much more production capacity or cost. Typically, a power grid’s generation capacity is scheduled in a simple, brute-force way, based on weather forecasts and the hottest and coldest days of the year, that doesn’t respond sensitively to real-time needs. Making such a system more resilient would mean spending a lot more on generation and transmission and distribution capacity, whereas a more dynamic system that integrates distributed microgrids could tame the cost, Ilic says: “What we are trying to do is to have systematic frameworks for embedding intelligence into small microgrids serving communities, and having them interact with large-scale power grids. People are realizing that you can make many small microgrids to serve communities rather than relying only on large scale electrical power generation.”

Although this is one of Ilic’s most recent projects, her work on DyMonDS can be traced back four decades, to when she was a student at the University of Belgrade in the former country of Yugoslavia, which sent her to the United States to learn how to use computers to prevent blackouts.

She ended up at Washington University in St. Louis, Missouri, studying with applied mathematician John Zaborszky, a legend in the field who was originally chief engineer of Budapest’s municipal power system before moving to the United States. (“The legend goes that in the morning he would teach courses, and in the afternoon he would go and operate Hungarian power system protection by hand.”) Under Zaborszky, a systems and control expert, Ilic learned to think in abstract terms as well as in terms of physical power systems and technologies. She became fascinated by the question of how to model, simulate, monitor, and control power systems — and that’s where she’s been ever since. (Although, she admits as she uncoils to her full height from behind her desk, her first love was actually playing basketball.)

Ilic first arrived at MIT in 1987 to work with the late professor Fred Schweppe on connecting electricity technologies with electricity markets. She stayed on as a senior research scientist until 2002, when she moved to Carnegie Mellon University (CMU) to lead the multidisciplinary Electric Energy Systems Group there. In 2018, after her consulting work for Lincoln Lab ramped up, she retired from CMU to move back to the familiar environs of Cambridge, Massachusetts. CMU’s loss has been MIT’s gain: In fall 2019, Ilic taught a course in modeling, simulation, and control of electric energy systems, applying her work on streamlined models that use pared-down information.

Addressing the evolving needs of electric power systems has not been a “hot” topic, historically. Traditional power systems are often seen by the academic community as legacy technology with no fundamentally new developments. And yet when new software and systems are developed to help integrate distributed energy generation and storage, commercial systems operators regard them as untested and disruptive. “I’ve always been a bit on the sidelines from mainstream power and electrical engineering because I’m interested in some of these things,” she remarks.

However, Ilic’s work is becoming increasingly urgent. Much of today’s power system is physically very old and will need to be retired and replaced over the next decade. This presents an opportunity for innovation: the next generation of electric energy systems could be built to integrate renewable and distributed energy resources at scale — addressing the pressing challenge of climate change and making way for further progress.

“That’s why I’m still working, even though I should be retired.” She smiles. “It supports the evolution of the system to something better.”

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Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. 

Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. That’s equivalent to nearly five times the lifetime emissions of the average U.S. car, including its manufacturing.

This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. 

MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved — in some cases, down to low triple digits. 

The researchers’ system, which they call a once-for-all network, trains one large neural network comprising many pretrained subnetworks of different sizes that can be tailored to diverse hardware platforms without retraining. This dramatically reduces the energy usually required to train each specialized neural network for new platforms — which can include billions of internet of things (IoT) devices. Using the system to train a computer-vision model, they estimated that the process required roughly 1/1,300 the carbon emissions compared to today’s state-of-the-art neural architecture search approaches, while reducing the inference time by 1.5-2.6 times. 

“The aim is smaller, greener neural networks,” says Song Han, an assistant professor in the Department of Electrical Engineering and Computer Science. “Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.”

The work was carried out on Satori, an efficient computing cluster donated to MIT by IBM that is capable of performing 2 quadrillion calculations per second. The paper is being presented next week at the International Conference on Learning Representations. Joining Han on the paper are four undergraduate and graduate students from EECS, MIT-IBM Watson AI Lab, and Shanghai Jiao Tong University. 

Creating a “once-for-all” network

The researchers built the system on a recent AI advance called AutoML (for automatic machine learning), which eliminates manual network design. Neural networks automatically search massive design spaces for network architectures tailored, for instance, to specific hardware platforms. But there’s still a training efficiency issue: Each model has to be selected then trained from scratch for its platform architecture. 

“How do we train all those networks efficiently for such a broad spectrum of devices — from a $10 IoT device to a $600 smartphone? Given the diversity of IoT devices, the computation cost of neural architecture search will explode,” Han says.   

The researchers invented an AutoML system that trains only a single, large “once-for-all” (OFA) network that serves as a “mother” network, nesting an extremely high number of subnetworks that are sparsely activated from the mother network. OFA shares all its learned weights with all subnetworks — meaning they come essentially pretrained. Thus, each subnetwork can operate independently at inference time without retraining. 

The team trained an OFA convolutional neural network (CNN) — commonly used for image-processing tasks — with versatile architectural configurations, including different numbers of layers and “neurons,” diverse filter sizes, and diverse input image resolutions. Given a specific platform, the system uses the OFA as the search space to find the best subnetwork based on the accuracy and latency tradeoffs that correlate to the platform’s power and speed limits. For an IoT device, for instance, the system will find a smaller subnetwork. For smartphones, it will select larger subnetworks, but with different structures depending on individual battery lifetimes and computation resources. OFA decouples model training and architecture search, and spreads the one-time training cost across many inference hardware platforms and resource constraints. 

This relies on a “progressive shrinking” algorithm that efficiently trains the OFA network to support all of the subnetworks simultaneously. It starts with training the full network with the maximum size, then progressively shrinks the sizes of the network to include smaller subnetworks. Smaller subnetworks are trained with the help of large subnetworks to grow together. In the end, all of the subnetworks with different sizes are supported, allowing fast specialization based on the platform’s power and speed limits. It supports many hardware devices with zero training cost when adding a new device.
 
In total, one OFA, the researchers found, can comprise more than 10 quintillion — that’s a 1 followed by 19 zeroes — architectural settings, covering probably all platforms ever needed. But training the OFA and searching it ends up being far more efficient than spending hours training each neural network per platform. Moreover, OFA does not compromise accuracy or inference efficiency. Instead, it provides state-of-the-art ImageNet accuracy on mobile devices. And, compared with state-of-the-art industry-leading CNN models , the researchers say OFA provides 1.5-2.6 times speedup, with superior accuracy. 
    
“That’s a breakthrough technology,” Han says. “If we want to run powerful AI on consumer devices, we have to figure out how to shrink AI down to size.”

“The model is really compact. I am very excited to see OFA can keep pushing the boundary of efficient deep learning on edge devices,” says Chuang Gan, a researcher at the MIT-IBM Watson AI Lab and co-author of the paper.

“If rapid progress in AI is to continue, we need to reduce its environmental impact,” says John Cohn, an IBM fellow and member of the MIT-IBM Watson AI Lab. “The upside of developing methods to make AI models smaller and more efficient is that the models may also perform better.”

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Housed at TED and supported by leading social impact advisor The Bridgespan Group, The Audacious Project is a collaborative funding initiative that’s catalyzing social impact on a grand scale by convening funders and social entrepreneurs, with the goal of supporting bold solutions to the world’s most urgent challenges.

Among this year’s carefully selected change-makers is Jim Collins and a team at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), including co-principal investigator Regina Barzilay. The funding provided through The Audacious Project will support the response to the antibiotic resistance crisis through the development of new classes of antibiotics to protect patients against some of the world’s deadliest bacterial pathogens.

“The work of Jim Collins and his colleagues is more relevant now than ever before,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are grateful for the commitment from The Audacious Project and its contributors, to both support and foster the research around AI and drug discovery, and to join our efforts in the School of Engineering to realize the potential global impact of this incredible work.” 

Collins’ and Barzilay’s Antibiotics-AI Project seeks to produce the first new classes of antibiotics society has seen in three decades, by calling in an interdisciplinary team of world-class bioengineers, microbiologists, computer scientists, and chemists.

Collins is the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and the Department of Biological Engineering, faculty co-lead of Jameel Clinic, faculty lead of the MIT-Takeda Program, and a member of the Harvard-MIT Health Sciences and Technology faculty. He is also a core founding faculty member of the Wyss Institute for Biologically Inspired Engineering at Harvard University and an Institute member of the Broad Institute of MIT and Harvard.

Barzilay is the Delta Electronics Professor in MIT’s Department of Electrical Engineering and Computer Science, faculty co-lead of Jameel Clinic, and a member of the Computer Science and Artificial Intelligence Laboratory at MIT.

Earlier this year, Collins and Barzilay along with Tommi Jaakkola, Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, and postdoc Jonathan Stokes were part of a research team that successfully used a deep-learning model to identify a new antibiotic. Over the next seven years, The Audacious Project’s commitment will support Collins and Barzilay as they continue to use the same process to rapidly explore over a billion molecules to identify and design novel antibiotics.

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Consider the days after a hurricane strikes. Trees and debris are blocking roads, bridges are destroyed, and sections of roadway are washed out. Emergency managers soon face a bevy of questions: How can supplies get delivered to certain areas? What’s the best route for evacuating survivors? Which roads are too damaged to remain open?

Without concrete data on the state of the road network, emergency managers often have to base their answers on incomplete information. The Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory hopes to use its airborne lidar platform, paired with artificial intelligence (AI) algorithms, to fill this information gap.  

“For a truly large-scale catastrophe, understanding the state of the transportation system as early as possible is critical,” says Chad Council, a researcher in the group. “With our particular approach, you can determine road viability, do optimal routing, and also get quantified road damage. You fly it, you run it, you’ve got everything.”

Since the 2017 hurricane season, the team has been flying its advanced lidar platform over stricken cities and towns. Lidar works by pulsing photons down over an area and measuring the time it takes for each photon to bounce back to the sensor. These time-of-arrival data points paint a 3D “point cloud” map of the landscape — every road, tree, and building — to within about a foot of accuracy.

To date, they’ve mapped huge swaths of the Carolinas, Florida, Texas, and all of Puerto Rico. In the immediate aftermath of hurricanes in those areas, the team manually sifted through the data to help the Federal Emergency Management Agency (FEMA) find and quantify damage to roads, among other tasks. The team’s focus now is on developing AI algorithms that can automate these processes and find ways to route around damage.

What’s the road status?

Information about the road network after a disaster comes to emergency managers in a “mosaic of different information streams,” Council says, namely satellite images, aerial photographs taken by the Civil Air Patrol, and crowdsourcing from vetted sources.

“These various efforts for acquiring data are important because every situation is different. There might be cases when crowdsourcing is fastest, and it’s good to have redundancy. But when you consider the scale of disasters like Hurricane Maria on Puerto Rico, these various streams can be overwhelming, incomplete, and difficult to coalesce,” he says.

During these times, lidar can act as an all-seeing eye, providing a big-picture map of an area and also granular details on road features. The laboratory’s platform is especially advanced because it uses Geiger-mode lidar, which is sensitive to a single photon. As such, its sensor can collect each of the millions of photons that trickle through openings in foliage as the system is flown overhead. This foliage can then be filtered out of the lidar map, revealing roads that would otherwise be hidden from aerial view.

To provide the status of the road network, the lidar map is first run through a neural network. This neural network is trained to find and extract the roads, and to determine their widths. Then, AI algorithms search these roads and flag anomalies that indicate the roads are impassable. For example, a cluster of lidar points extending up and across a road is likely a downed tree. A sudden drop in the elevation is likely a hole or washed out area in a road.

The extracted road network, with its flagged anomalies, is then merged with an OpenStreetMap of the area (an open-access map similar to Google Maps). Emergency managers can use this system to plan routes, or in other cases to identify isolated communities — those that are cut off from the road network. The system will show them the most efficient route between two specified locations, finding detours around impassable roads. Users can also specify how important it is to stay on the road; on the basis of that input, the system provides routes through parking lots or fields.  

This process, from extracting roads to finding damage to planning routes, can be applied to the data at the scale of a single neighborhood or across an entire city.

How fast and how accurate?

To gain an idea of how fast this system works, consider that in a recent test, the team flew the lidar platform, processed the data, and got AI-based analytics in 36 hours. That sortie covered an area of 250 square miles, an area about the size of Chicago, Illinois.

But accuracy is equally as important as speed. “As we incorporate AI techniques into decision support, we’re developing metrics to characterize an algorithm’s performance,” Council says.

For finding roads, the algorithm determines if a point in the lidar point cloud is “road” or “not road.” The team ran a performance evaluation of the algorithm against 50,000 square meters of suburban data, and the resulting ROC curve indicated that the current algorithm provided an 87 percent true positive rate (that is, correctly labeled a point as “road”), with a 20 percent false positive rate (that is, labeling a point as “road” that may not be road). The false positives are typically areas that geometrically look like a road but aren’t.

“Because we have another data source for identifying the general location of roads, OpenStreetMaps, these false positives can be excluded, resulting in a highly accurate 3D point cloud representation of the road network,” says Dieter Schuldt, who has been leading the algorithm-testing efforts.

For the algorithm that detects road damage, the team is in the process of further aggregating ground truth data to evaluate its performance. In the meantime, preliminary results have been promising. Their damage-finding algorithm recently flagged for review a potentially blocked road in Bedford, Massachusetts, which appeared to be a hole measuring 10 meters wide by 7 meters long by 1 meter deep. The town’s public works department and a site visit confirmed that construction blocked the road.

“We actually didn’t go in expecting that this particular sortie would capture examples of blocked roads, and it was an interesting find,” says Bhavani Ananthabhotla, a contributor to this work. “With additional ground truth annotations, we hope to not only evaluate and improve performance, but also to better tailor future models to regional emergency management needs, including informing route planning and repair cost estimation.”

The team is continuing to test, train, and tweak their algorithms to improve accuracy. Their hope is that these techniques may soon be deployed to help answer important questions during disaster recovery.

“We picture lidar as a 3D scaffold that other data can be draped over and that can be trusted,” Council says. “The more trust, the more likely an emergency manager, and a community in general, will use it to make the best decisions they can.”

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This week the White House announced that MIT Professor Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), has been selected to serve on the President’s Council of Advisors on Science and Technology (PCAST).

The council provides advice to the White House on topics critical to U.S. security and the economy, including policy recommendations on the future of work, American leadership in science and technology, and the support of U.S. research and development. 

PCAST operates under the aegis of the White House Office of Science and Technology Policy (OSTP), which was established in law in 1976. However, the council has existed more informally going back to Franklin Roosevelt’s Science Advisory Board in 1933.

“I’m grateful to be able to add my perspective as a computer scientist to this group at a time when so many issues involving AI and other aspects of computing raise important scientific and policy questions for the nation and the world,” says Rus.
 
Rus is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and the deputy dean of research for the MIT Stephen A. Schwarzman College of Computing. Her research in robotics, artificial intelligence, and data science focuses primarily on developing the science and engineering of autonomy, with the long-term objective of enabling a future where machines are integrated into daily life to support both cognitive and physical tasks. The applications of her work are broad and include transportation, manufacturing, medicine, and urban planning. 
 
More than a dozen MIT faculty and alumni have served on PCAST during past presidential administrations. These include former MIT president Charles Vest; Institute Professors Phillip Sharp and John Deutch; Ernest Moniz, professor of physics and former U.S. Secretary of Energy; and Eric Lander, director of the Broad Institute of MIT and Harvard and professor of biology, who co-chaired PCAST during the Obama administration. Previous councils have offered advice on topics ranging from data privacy and nanotechnology to job training and STEM education.

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