Electrical Engineering & Computer Science (eecs)

Subterms

    More stories

    • 38 Shares199 Views

      in Data Management & Statistics

      Design’s new frontier

      by

      In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

      “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

      Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

      In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

      Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

      Computational design

      Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

      “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

      For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

      To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

      Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

      Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

      “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

      Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

      Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

      “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

      Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

      Personal fabrication

      Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

      “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

      Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

      First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

      To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

      “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

      Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

      Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

      “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

      Computational fluid dynamics and participatory design

      Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

      As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

      “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

      After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

      The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

      Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

      “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

      These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

      Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

      Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

      The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

      Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

      “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

      While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

      Designing intelligent systems

      When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

      Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

      “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

      For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

      To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

      “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

      Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

      “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

      To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

      In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

      “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

      Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

      Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

      “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

      Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

    • 75 Shares159 Views

      in Data Management & Statistics

      Theoretical breakthrough could boost data storage

      by

      A trio of researchers that includes William Kuszmaul — a computer science PhD student at MIT — has made a discovery that could lead to more efficient data storage and retrieval in computers.

      The team’s findings relate to so-called “linear-probing hash tables,” which were introduced in 1954 and are among the oldest, simplest, and fastest data structures available today. Data structures provide ways of organizing and storing data in computers, with hash tables being one of the most commonly utilized approaches. In a linear-probing hash table, the positions in which information can be stored lie along a linear array.

      Suppose, for instance, that a database is designed to store the Social Security numbers of 10,000 people, Kuszmaul suggests. “We take your Social Security number, x, and we’ll then compute the hash function of x, h(x), which gives you a random number between one and 10,000.” The next step is to take that random number, h(x), go to that position in the array, and put x, the Social Security number, into that spot.

      If there’s already something occupying that spot, Kuszmaul says, “you just move forward to the next free position and put it there. This is where the term ‘linear probing’ comes from, as you keep moving forward linearly until you find an open spot.” In order to later retrieve that Social Security number, x, you just go to the designated spot, h(x), and if it’s not there, you move forward until you either find x or come to a free position and conclude that x is not in your database.

      There’s a somewhat different protocol for deleting an item, such as a Social Security number. If you just left an empty spot in the hash table after deleting the information, that could cause confusion when you later tried to find something else, as the vacant spot might erroneously suggest that the item you’re looking for is nowhere to be found in the database. To avoid that problem, Kuszmaul explains, “you can go to the spot where the element was removed and put a little marker there called a ‘tombstone,’ which indicates there used to be an element here, but it’s gone now.”

      This general procedure has been followed for more than half-a-century. But in all that time, almost everyone using linear-probing hash tables has assumed that if you allow them to get too full, long stretches of occupied spots would run together to form “clusters.” As a result, the time it takes to find a free spot would go up dramatically — quadratically, in fact — taking so long as to be impractical. Consequently, people have been trained to operate hash tables at low capacity — a practice that can exact an economic toll by affecting the amount of hardware a company has to purchase and maintain.

      But this time-honored principle, which has long militated against high load factors, has been totally upended by the work of Kuszmaul and his colleagues, Michael Bender of Stony Brook University and Bradley Kuszmaul of Google. They found that for applications where the number of insertions and deletions stays about the same — and the amount of data added is roughly equal to that removed — linear-probing hash tables can operate at high storage capacities without sacrificing speed.

      In addition, the team has devised a new strategy, called “graveyard hashing,” which involves artificially increasing the number of tombstones placed in an array until they occupy about half the free spots. These tombstones then reserve spaces that can be used for future insertions.

      This approach, which runs contrary to what people have customarily been instructed to do, Kuszmaul says, “can lead to optimal performance in linear-probing hash tables.” Or, as he and his coauthors maintain in their paper, the “well-designed use of tombstones can completely change the … landscape of how linear probing behaves.”

      Kuszmaul wrote up these findings with Bender and Kuszmaul in a paper posted earlier this year that will be presented in February at the Foundations of Computer Science (FOCS) Symposium in Boulder, Colorado.

      Kuszmaul’s PhD thesis advisor, MIT computer science professor Charles E. Leiserson (who did not participate in this research), agrees with that assessment. “These new and surprising results overturn one of the oldest conventional wisdoms about hash table behavior,” Leiserson says. “The lessons will reverberate for years among theoreticians and practitioners alike.”

      As for translating their results into practice, Kuszmaul notes, “there are many considerations that go into building a hash table. Although we’ve advanced the story considerably from a theoretical standpoint, we’re just starting to explore the experimental side of things.” More

    • 75 Shares99 Views

      in Data Management & Statistics

      Avoiding shortcut solutions in artificial intelligence

      by

      If your Uber driver takes a shortcut, you might get to your destination faster. But if a machine learning model takes a shortcut, it might fail in unexpected ways.

      In machine learning, a shortcut solution occurs when the model relies on a simple characteristic of a dataset to make a decision, rather than learning the true essence of the data, which can lead to inaccurate predictions. For example, a model might learn to identify images of cows by focusing on the green grass that appears in the photos, rather than the more complex shapes and patterns of the cows.  

      A new study by researchers at MIT explores the problem of shortcuts in a popular machine-learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data in its decision-making.

      By removing the simpler characteristics the model is focusing on, the researchers force it to focus on more complex features of the data that it hadn’t been considering. Then, by asking the model to solve the same task two ways — once using those simpler features, and then also using the complex features it has now learned to identify — they reduce the tendency for shortcut solutions and boost the performance of the model.

      One potential application of this work is to enhance the effectiveness of machine learning models that are used to identify disease in medical images. Shortcut solutions in this context could lead to false diagnoses and have dangerous implications for patients.

      “It is still difficult to tell why deep networks make the decisions that they do, and in particular, which parts of the data these networks choose to focus upon when making a decision. If we can understand how shortcuts work in further detail, we can go even farther to answer some of the fundamental but very practical questions that are really important to people who are trying to deploy these networks,” says Joshua Robinson, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper.

      Robinson wrote the paper with his advisors, senior author Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) and a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems; and Stefanie Jegelka, the X-Consortium Career Development Associate Professor in EECS and a member of CSAIL and IDSS; as well as University of Pittsburgh assistant professor Kayhan Batmanghelich and PhD students Li Sun and Ke Yu. The research will be presented at the Conference on Neural Information Processing Systems in December. 

      The long road to understanding shortcuts

      The researchers focused their study on contrastive learning, which is a powerful form of self-supervised machine learning. In self-supervised machine learning, a model is trained using raw data that do not have label descriptions from humans. It can therefore be used successfully for a larger variety of data.

      A self-supervised learning model learns useful representations of data, which are used as inputs for different tasks, like image classification. But if the model takes shortcuts and fails to capture important information, these tasks won’t be able to use that information either.

      For example, if a self-supervised learning model is trained to classify pneumonia in X-rays from a number of hospitals, but it learns to make predictions based on a tag that identifies the hospital the scan came from (because some hospitals have more pneumonia cases than others), the model won’t perform well when it is given data from a new hospital.     

      For contrastive learning models, an encoder algorithm is trained to discriminate between pairs of similar inputs and pairs of dissimilar inputs. This process encodes rich and complex data, like images, in a way that the contrastive learning model can interpret.

      The researchers tested contrastive learning encoders with a series of images and found that, during this training procedure, they also fall prey to shortcut solutions. The encoders tend to focus on the simplest features of an image to decide which pairs of inputs are similar and which are dissimilar. Ideally, the encoder should focus on all the useful characteristics of the data when making a decision, Jegelka says.

      So, the team made it harder to tell the difference between the similar and dissimilar pairs, and found that this changes which features the encoder will look at to make a decision.

      “If you make the task of discriminating between similar and dissimilar items harder and harder, then your system is forced to learn more meaningful information in the data, because without learning that it cannot solve the task,” she says.

      But increasing this difficulty resulted in a tradeoff — the encoder got better at focusing on some features of the data but became worse at focusing on others. It almost seemed to forget the simpler features, Robinson says.

      To avoid this tradeoff, the researchers asked the encoder to discriminate between the pairs the same way it had originally, using the simpler features, and also after the researchers removed the information it had already learned. Solving the task both ways simultaneously caused the encoder to improve across all features.

      Their method, called implicit feature modification, adaptively modifies samples to remove the simpler features the encoder is using to discriminate between the pairs. The technique does not rely on human input, which is important because real-world data sets can have hundreds of different features that could combine in complex ways, Sra explains.

      From cars to COPD

      The researchers ran one test of this method using images of vehicles. They used implicit feature modification to adjust the color, orientation, and vehicle type to make it harder for the encoder to discriminate between similar and dissimilar pairs of images. The encoder improved its accuracy across all three features — texture, shape, and color — simultaneously.

      To see if the method would stand up to more complex data, the researchers also tested it with samples from a medical image database of chronic obstructive pulmonary disease (COPD). Again, the method led to simultaneous improvements across all features they evaluated.

      While this work takes some important steps forward in understanding the causes of shortcut solutions and working to solve them, the researchers say that continuing to refine these methods and applying them to other types of self-supervised learning will be key to future advancements.

      “This ties into some of the biggest questions about deep learning systems, like ‘Why do they fail?’ and ‘Can we know in advance the situations where your model will fail?’ There is still a lot farther to go if you want to understand shortcut learning in its full generality,” Robinson says.

      This research is supported by the National Science Foundation, National Institutes of Health, and the Pennsylvania Department of Health’s SAP SE Commonwealth Universal Research Enhancement (CURE) program. More

    • 50 Shares149 Views

      in Data Management & Statistics

      Taming the data deluge

      by

      An oncoming tsunami of data threatens to overwhelm huge data-rich research projects on such areas that range from the tiny neutrino to an exploding supernova, as well as the mysteries deep within the brain. 

      When LIGO picks up a gravitational-wave signal from a distant collision of black holes and neutron stars, a clock starts ticking for capturing the earliest possible light that may accompany them: time is of the essence in this race. Data collected from electrical sensors monitoring brain activity are outpacing computing capacity. Information from the Large Hadron Collider (LHC)’s smashed particle beams will soon exceed 1 petabit per second. 

      To tackle this approaching data bottleneck in real-time, a team of researchers from nine institutions led by the University of Washington, including MIT, has received $15 million in funding to establish the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute. From MIT, the research team includes Philip Harris, assistant professor of physics, who will serve as the deputy director of the A3D3 Institute; Song Han, assistant professor of electrical engineering and computer science, who will serve as the A3D3’s co-PI; and Erik Katsavounidis, senior research scientist with the MIT Kavli Institute for Astrophysics and Space Research.

      Infused with this five-year Harnessing the Data Revolution Big Idea grant, and jointly funded by the Office of Advanced Cyberinfrastructure, A3D3 will focus on three data-rich fields: multi-messenger astrophysics, high-energy particle physics, and brain imaging neuroscience. By enriching AI algorithms with new processors, A3D3 seeks to speed up AI algorithms for solving fundamental problems in collider physics, neutrino physics, astronomy, gravitational-wave physics, computer science, and neuroscience. 

      “I am very excited about the new Institute’s opportunities for research in nuclear and particle physics,” says Laboratory for Nuclear Science Director Boleslaw Wyslouch. “Modern particle detectors produce an enormous amount of data, and we are looking for extraordinarily rare signatures. The application of extremely fast processors to sift through these mountains of data will make a huge difference in what we will measure and discover.”

      The seeds of A3D3 were planted in 2017, when Harris and his colleagues at Fermilab and CERN decided to integrate real-time AI algorithms to process the incredible rates of data at the LHC. Through email correspondence with Han, Harris’ team built a compiler, HLS4ML, that could run an AI algorithm in nanoseconds.

      “Before the development of HLS4ML, the fastest processing that we knew of was roughly a millisecond per AI inference, maybe a little faster,” says Harris. “We realized all the AI algorithms were designed to solve much slower problems, such as image and voice recognition. To get to nanosecond inference timescales, we recognized we could make smaller algorithms and rely on custom implementations with Field Programmable Gate Array (FPGA) processors in an approach that was largely different from what others were doing.”

      A few months later, Harris presented their research at a physics faculty meeting, where Katsavounidis became intrigued. Over coffee in Building 7, they discussed combining Harris’ FPGA with Katsavounidis’s use of machine learning for finding gravitational waves. FPGAs and other new processor types, such as graphics processing units (GPUs), accelerate AI algorithms to more quickly analyze huge amounts of data.

      “I had worked with the first FPGAs that were out in the market in the early ’90s and have witnessed first-hand how they revolutionized front-end electronics and data acquisition in big high-energy physics experiments I was working on back then,” recalls Katsavounidis. “The ability to have them crunch gravitational-wave data has been in the back of my mind since joining LIGO over 20 years ago.”

      Two years ago they received their first grant, and the University of Washington’s Shih-Chieh Hsu joined in. The team initiated the Fast Machine Lab, published about 40 papers on the subject, built the group to about 50 researchers, and “launched a whole industry of how to explore a region of AI that has not been explored in the past,” says Harris. “We basically started this without any funding. We’ve been getting small grants for various projects over the years. A3D3 represents our first large grant to support this effort.”  

      “What makes A3D3 so special and suited to MIT is its exploration of a technical frontier, where AI is implemented not in high-level software, but rather in lower-level firmware, reconfiguring individual gates to address the scientific question at hand,” says Rob Simcoe, director of MIT Kavli Institute for Astrophysics and Space Research and the Francis Friedman Professor of Physics. “We are in an era where experiments generate torrents of data. The acceleration gained from tailoring reprogrammable, bespoke computers at the processor level can advance real-time analysis of these data to new levels of speed and sophistication.”

      The Huge Data from the Large Hadron Collider 

      With data rates already exceeding 500 terabits per second, the LHC processes more data than any other scientific instrument on earth. Its future aggregate data rates will soon exceed 1 petabit per second, the biggest data rate in the world. 

      “Through the use of AI, A3D3 aims to perform advanced analyses, such as anomaly detection, and particle reconstruction on all collisions happening 40 million times per second,” says Harris.

      The goal is to find within all of this data a way to identify the few collisions out of the 3.2 billion collisions per second that could reveal new forces, explain how dark matter is formed, and complete the picture of how fundamental forces interact with matter. Processing all of this information requires a customized computing system capable of interpreting the collider information within ultra-low latencies.  

      “The challenge of running this on all of the 100s of terabits per second in real-time is daunting and requires a complete overhaul of how we design and implement AI algorithms,” says Harris. “With large increases in the detector resolution leading to data rates that are even larger the challenge of finding the one collision, among many, will become even more daunting.” 

      The Brain and the Universe

      Thanks to advances in techniques such as medical imaging and electrical recordings from implanted electrodes, neuroscience is also gathering larger amounts of data on how the brain’s neural networks process responses to stimuli and perform motor information. A3D3 plans to develop and implement high-throughput and low-latency AI algorithms to process, organize, and analyze massive neural datasets in real time, to probe brain function in order to enable new experiments and therapies.   

      With Multi-Messenger Astrophysics (MMA), A3D3 aims to quickly identify astronomical events by efficiently processing data from gravitational waves, gamma-ray bursts, and neutrinos picked up by telescopes and detectors. 

      The A3D3 researchers also include a multi-disciplinary group of 15 other researchers, including project lead the University of Washington, along with Caltech, Duke University, Purdue University, UC San Diego, University of Illinois Urbana-Champaign, University of Minnesota, and the University of Wisconsin-Madison. It will include neutrinos research at Icecube and DUNE, and visible astronomy at Zwicky Transient Facility, and will organize deep-learning workshops and boot camps to train students and researchers on how to contribute to the framework and widen the use of fast AI strategies.

      “We have reached a point where detector network growth will be transformative, both in terms of event rates and in terms of astrophysical reach and ultimately, discoveries,” says Katsavounidis. “‘Fast’ and ‘efficient’ is the only way to fight the ‘faint’ and ‘fuzzy’ that is out there in the universe, and the path for getting the most out of our detectors. A3D3 on one hand is going to bring production-scale AI to gravitational-wave physics and multi-messenger astronomy; but on the other hand, we aspire to go beyond our immediate domains and become the go-to place across the country for applications of accelerated AI to data-driven disciplines.” More

    • 113 Shares179 Views

      in Data Management & Statistics

      Making machine learning more useful to high-stakes decision makers

      by

      The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation.

      But these models don’t do any good if the humans they are intended to help don’t understand or trust their outputs.

      Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening. In collaboration with a child welfare department in Colorado, the researchers studied how call screeners assess cases, with and without the help of machine learning predictions. Based on feedback from the call screeners, they designed a visual analytics tool that uses bar graphs to show how specific factors of a case contribute to the predicted risk that a child will be removed from their home within two years.

      The researchers found that screeners are more interested in seeing how each factor, like the child’s age, influences a prediction, rather than understanding the computational basis of how the model works. Their results also show that even a simple model can cause confusion if its features are not described with straightforward language.

      These findings could be applied to other high-risk fields where humans use machine learning models to help them make decisions, but lack data science experience, says senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and senior author of the paper.

      “Researchers who study explainable AI, they often try to dig deeper into the model itself to explain what the model did. But a big takeaway from this project is that these domain experts don’t necessarily want to learn what machine learning actually does. They are more interested in understanding why the model is making a different prediction than what their intuition is saying, or what factors it is using to make this prediction. They want information that helps them reconcile their agreements or disagreements with the model, or confirms their intuition,” he says.

      Co-authors include electrical engineering and computer science PhD student Alexandra Zytek, who is the lead author; postdoc Dongyu Liu; and Rhema Vaithianathan, professor of economics and director of the Center for Social Data Analytics at the Auckland University of Technology and professor of social data analytics at the University of Queensland. The research will be presented later this month at the IEEE Visualization Conference.

      Real-world research

      The researchers began the study more than two years ago by identifying seven factors that make a machine learning model less usable, including lack of trust in where predictions come from and disagreements between user opinions and the model’s output.

      With these factors in mind, Zytek and Liu flew to Colorado in the winter of 2019 to learn firsthand from call screeners in a child welfare department. This department is implementing a machine learning system developed by Vaithianathan that generates a risk score for each report, predicting the likelihood the child will be removed from their home. That risk score is based on more than 100 demographic and historic factors, such as the parents’ ages and past court involvements.

      “As you can imagine, just getting a number between one and 20 and being told to integrate this into your workflow can be a bit challenging,” Zytek says.

      They observed how teams of screeners process cases in about 10 minutes and spend most of that time discussing the risk factors associated with the case. That inspired the researchers to develop a case-specific details interface, which shows how each factor influenced the overall risk score using color-coded, horizontal bar graphs that indicate the magnitude of the contribution in a positive or negative direction.

      Based on observations and detailed interviews, the researchers built four additional interfaces that provide explanations of the model, including one that compares a current case to past cases with similar risk scores. Then they ran a series of user studies.

      The studies revealed that more than 90 percent of the screeners found the case-specific details interface to be useful, and it generally increased their trust in the model’s predictions. On the other hand, the screeners did not like the case comparison interface. While the researchers thought this interface would increase trust in the model, screeners were concerned it could lead to decisions based on past cases rather than the current report.   

      “The most interesting result to me was that, the features we showed them — the information that the model uses — had to be really interpretable to start. The model uses more than 100 different features in order to make its prediction, and a lot of those were a bit confusing,” Zytek says.

      Keeping the screeners in the loop throughout the iterative process helped the researchers make decisions about what elements to include in the machine learning explanation tool, called Sibyl.

      As they refined the Sibyl interfaces, the researchers were careful to consider how providing explanations could contribute to some cognitive biases, and even undermine screeners’ trust in the model.

      For instance, since explanations are based on averages in a database of child abuse and neglect cases, having three past abuse referrals may actually decrease the risk score of a child, since averages in this database may be far higher. A screener may see that explanation and decide not to trust the model, even though it is working correctly, Zytek explains. And because humans tend to put more emphasis on recent information, the order in which the factors are listed could also influence decisions.

      Improving interpretability

      Based on feedback from call screeners, the researchers are working to tweak the explanation model so the features that it uses are easier to explain.

      Moving forward, they plan to enhance the interfaces they’ve created based on additional feedback and then run a quantitative user study to track the effects on decision making with real cases. Once those evaluations are complete, they can prepare to deploy Sibyl, Zytek says.

      “It was especially valuable to be able to work so actively with these screeners. We got to really understand the problems they faced. While we saw some reservations on their part, what we saw more of was excitement about how useful these explanations were in certain cases. That was really rewarding,” she says.

      This work is supported, in part, by the National Science Foundation. More

    • 63 Shares159 Views

      in Data Management & Statistics

      Making data visualizations more accessible

      by

      In the early days of the Covid-19 pandemic, the Centers for Disease Control and Prevention produced a simple chart to illustrate how measures like mask wearing and social distancing could “flatten the curve” and reduce the peak of infections.

      The chart was amplified by news sites and shared on social media platforms, but it often lacked a corresponding text description to make it accessible for blind individuals who use a screen reader to navigate the web, shutting out many of the 253 million people worldwide who have visual disabilities.

      This alternative text is often missing from online charts, and even when it is included, it is frequently uninformative or even incorrect, according to qualitative data gathered by scientists at MIT.

      These researchers conducted a study with blind and sighted readers to determine which text is useful to include in a chart description, which text is not, and why. Ultimately, they found that captions for blind readers should focus on the overall trends and statistics in the chart, not its design elements or higher-level insights.

      They also created a conceptual model that can be used to evaluate a chart description, whether the text was generated automatically by software or manually by a human author. Their work could help journalists, academics, and communicators create descriptions that are more effective for blind individuals and guide researchers as they develop better tools to automatically generate captions.

      “Ninety-nine-point-nine percent of images on Twitter lack any kind of description — and that is not hyperbole, that is the actual statistic,” says Alan Lundgard, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper. “Having people manually author those descriptions seems to be difficult for a variety of reasons. Perhaps semiautonomous tools could help with that. But it is crucial to do this preliminary participatory design work to figure out what is the target for these tools, so we are not generating content that is either not useful to its intended audience or, in the worst case, erroneous.”

      Lundgard wrote the paper with senior author Arvind Satyanarayan, an assistant professor of computer science who leads the Visualization Group in CSAIL. The research will be presented at the Institute of Electrical and Electronics Engineers Visualization Conference in October.

      Evaluating visualizations

      To develop the conceptual model, the researchers planned to begin by studying graphs featured by popular online publications such as FiveThirtyEight and NYTimes.com, but they ran into a problem — those charts mostly lacked any textual descriptions. So instead, they collected descriptions for these charts from graduate students in an MIT data visualization class and through an online survey, then grouped the captions into four categories.

      Level 1 descriptions focus on the elements of the chart, such as its title, legend, and colors. Level 2 descriptions describe statistical content, like the minimum, maximum, or correlations. Level 3 descriptions cover perceptual interpretations of the data, like complex trends or clusters. Level 4 descriptions include subjective interpretations that go beyond the data and draw on the author’s knowledge.

      In a study with blind and sighted readers, the researchers presented visualizations with descriptions at different levels and asked participants to rate how useful they were. While both groups agreed that level 1 content on its own was not very helpful, sighted readers gave level 4 content the highest marks while blind readers ranked that content among the least useful.

      Survey results revealed that a majority of blind readers were emphatic that descriptions should not contain an author’s editorialization, but rather stick to straight facts about the data. On the other hand, most sighted readers preferred a description that told a story about the data.

      “For me, a surprising finding about the lack of utility for the highest-level content is that it ties very closely to feelings about agency and control as a disabled person. In our research, blind readers specifically didn’t want the descriptions to tell them what to think about the data. They want the data to be accessible in a way that allows them to interpret it for themselves, and they want to have the agency to do that interpretation,” Lundgard says.

      A more inclusive future

      This work could have implications as data scientists continue to develop and refine machine learning methods for autogenerating captions and alternative text.

      “We are not able to do it yet, but it is not inconceivable to imagine that in the future we would be able to automate the creation of some of this higher-level content and build models that target level 2 or level 3 in our framework. And now we know what the research questions are. If we want to produce these automated captions, what should those captions say? We are able to be a bit more directed in our future research because we have these four levels,” Satyanarayan says.

      In the future, the four-level framework could also help researchers develop machine learning models that can automatically suggest effective visualizations as part of the data analysis process, or models that can extract the most useful information from a chart.

      This research could also inform future work in Satyanarayan’s group that seeks to make interactive visualizations more accessible for blind readers who use a screen reader to access and interpret the information. 

      “The question of how to ensure that charts and graphs are accessible to screen reader users is both a socially important equity issue and a challenge that can advance the state-of-the-art in AI,” says Meredith Ringel Morris, director and principal scientist of the People + AI Research team at Google Research, who was not involved with this study. “By introducing a framework for conceptualizing natural language descriptions of information graphics that is grounded in end-user needs, this work helps ensure that future AI researchers will focus their efforts on problems aligned with end-users’ values.”

      Morris adds: “Rich natural-language descriptions of data graphics will not only expand access to critical information for people who are blind, but will also benefit a much wider audience as eyes-free interactions via smart speakers, chatbots, and other AI-powered agents become increasingly commonplace.”

      This research was supported by the National Science Foundation. More

    • 100 Shares99 Views

      in Data Management & Statistics

      Enabling AI-driven health advances without sacrificing patient privacy

      by

      There’s a lot of excitement at the intersection of artificial intelligence and health care. AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more.

      By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients — AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it’s difficult to verify that researchers are only using the data they need and deleting it after they’re done.

      Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner’s system. Health care organizations can control how their datasets are used, while researchers can protect the confidentiality of their models and search queries. Neither party needs to see the data or the model to collaborate.

      SAIL’s platform can also combine data from multiple sources, creating rich insights that fuel more effective algorithms.

      “You shouldn’t have to schmooze with hospital executives for five years before you can run your machine learning algorithm,” says SAIL co-founder and MIT Professor Manolis Kellis, who co-founded the company with CEO Anne Kim ’16, SM ’17. “Our goal is to help patients, to help machine learning scientists, and to create new therapeutics. We want new algorithms — the best algorithms — to be applied to the biggest possible data set.”

      SAIL has already partnered with hospitals and life science companies to unlock anonymized data for researchers. In the next year, the company hopes to be working with about half of the top 50 academic medical centers in the country.

      Unleashing AI’s full potential

      As an undergraduate at MIT studying computer science and molecular biology, Kim worked with researchers in the Computer Science and Artificial Intelligence Laboratory (CSAIL) to analyze data from clinical trials, gene association studies, hospital intensive care units, and more.

      “I realized there is something severely broken in data sharing, whether it was hospitals using hard drives, ancient file transfer protocol, or even sending stuff in the mail,” Kim says. “It was all just not well-tracked.”

      Kellis, who is also a member of the Broad Institute of MIT and Harvard, has spent years establishing partnerships with hospitals and consortia across a range of diseases including cancers, heart disease, schizophrenia, and obesity. He knew that smaller research teams would struggle to get access to the same data his lab was working with.

      In 2017, Kellis and Kim decided to commercialize technology they were developing to allow AI algorithms to run on encrypted data.

      In the summer of 2018, Kim participated in the delta v startup accelerator run by the Martin Trust Center for MIT Entrepreneurship. The founders also received support from the Sandbox Innovation Fund and the Venture Mentoring Service, and made various early connections through their MIT network.

      To participate in SAIL’s program, hospitals and other health care organizations make parts of their data available to researchers by setting up a node behind their firewall. SAIL then sends encrypted algorithms to the servers where the datasets reside in a process called federated learning. The algorithms crunch the data locally in each server and transmit the results back to a central model, which updates itself. No one — not the researchers, the data owners, or even SAIL —has access to the models or the datasets.

      The approach allows a much broader set of researchers to apply their models to large datasets. To further engage the research community, Kellis’ lab at MIT has begun holding competitions in which it gives access to datasets in areas like protein function and gene expression, and challenges researchers to predict results.

      “We invite machine learning researchers to come and train on last year’s data and predict this year’s data,” says Kellis. “If we see there’s a new type of algorithm that is performing best in these community-level assessments, people can adopt it locally at many different institutions and level the playing field. So, the only thing that matters is the quality of your algorithm rather than the power of your connections.”

      By enabling a large number of datasets to be anonymized into aggregate insights, SAIL’s technology also allows researchers to study rare diseases, in which small pools of relevant patient data are often spread out among many institutions. That has historically made the data difficult to apply AI models to.

      “We’re hoping that all of these datasets will eventually be open,” Kellis says. “We can cut across all the silos and enable a new era where every patient with every rare disorder across the entire world can come together in a single keystroke to analyze data.”

      Enabling the medicine of the future

      To work with large amounts of data around specific diseases, SAIL has increasingly sought to partner with patient associations and consortia of health care groups, including an international health care consulting company and the Kidney Cancer Association. The partnerships also align SAIL with patients, the group they’re most trying to help.

      Overall, the founders are happy to see SAIL solving problems they faced in their labs for researchers around the world.

      “The right place to solve this is not an academic project. The right place to solve this is in industry, where we can provide a platform not just for my lab but for any researcher,” Kellis says. “It’s about creating an ecosystem of academia, researchers, pharma, biotech, and hospital partners. I think it’s the blending all of these different areas that will make that vision of medicine of the future become a reality.” More

    • 138 Shares179 Views

      in Data Management & Statistics

      3 Questions: Kalyan Veeramachaneni on hurdles preventing fully automated machine learning

      by

      The proliferation of big data across domains, from banking to health care to environmental monitoring, has spurred increasing demand for machine learning tools that help organizations make decisions based on the data they gather.

      That growing industry demand has driven researchers to explore the possibilities of automated machine learning (AutoML), which seeks to automate the development of machine learning solutions in order to make them accessible for nonexperts, improve their efficiency, and accelerate machine learning research. For example, an AutoML system might enable doctors to use their expertise interpreting electroencephalography (EEG) results to build a model that can predict which patients are at higher risk for epilepsy — without requiring the doctors to have a background in data science.

      Yet, despite more than a decade of work, researchers have been unable to fully automate all steps in the machine learning development process. Even the most efficient commercial AutoML systems still require a prolonged back-and-forth between a domain expert, like a marketing manager or mechanical engineer, and a data scientist, making the process inefficient.

      Kalyan Veeramachaneni, a principal research scientist in the MIT Laboratory for Information and Decision Systems who has been studying AutoML since 2010, has co-authored a paper in the journal ACM Computing Surveys that details a seven-tiered schematic to evaluate AutoML tools based on their level of autonomy.

      A system at level zero has no automation and requires a data scientist to start from scratch and build models by hand, while a tool at level six is completely automated and can be easily and effectively used by a nonexpert. Most commercial systems fall somewhere in the middle.

      Veeramachaneni spoke with MIT News about the current state of AutoML, the hurdles that prevent truly automatic machine learning systems, and the road ahead for AutoML researchers.

      Q: How has automatic machine learning evolved over the past decade, and what is the current state of AutoML systems?

      A: In 2010, we started to see a shift, with enterprises wanting to invest in getting value out of their data beyond just business intelligence. So then came the question, maybe there are certain things in the development of machine learning-based solutions that we can automate? The first iteration of AutoML was to make our own jobs as data scientists more efficient. Can we take away the grunt work that we do on a day-to-day basis and automate that by using a software system? That area of research ran its course until about 2015, when we realized we still weren’t able to speed up this development process.

      Then another thread emerged. There are a lot of problems that could be solved with data, and they come from experts who know those problems, who live with them on a daily basis. These individuals have very little to do with machine learning or software engineering. How do we bring them into the fold? That is really the next frontier.

      There are three areas where these domain experts have strong input in a machine learning system. The first is defining the problem itself and then helping to formulate it as a prediction task to be solved by a machine learning model. Second, they know how the data have been collected, so they also know intuitively how to process that data. And then third, at the end, machine learning models only give you a very tiny part of a solution — they just give you a prediction. The output of a machine learning model is just one input to help a domain expert get to a decision or action.

      Q: What steps of the machine learning pipeline are the most difficult to automate, and why has automating them been so challenging?

      A: The problem-formulation part is extremely difficult to automate. For example, if I am a researcher who wants to get more government funding, and I have a lot of data about the content of the research proposals that I write and whether or not I receive funding, can machine learning help there? We don’t know yet. In problem formulation, I use my domain expertise to translate the problem into something that is more tangible to predict, and that requires somebody who knows the domain very well. And he or she also knows how to use that information post-prediction. That problem is refusing to be automated.

      There is one part of problem-formulation that could be automated. It turns out that we can look at the data and mathematically express several possible prediction tasks automatically. Then we can share those prediction tasks with the domain expert to see if any of them would help in the larger problem they are trying to tackle. Then once you pick the prediction task, there are a lot of intermediate steps you do, including feature engineering, modeling, etc., that are very mechanical steps and easy to automate.

      But defining the prediction tasks has typically been a collaborative effort between data scientists and domain experts because, unless you know the domain, you can’t translate the domain problem into a prediction task. And then sometimes domain experts don’t know what is meant by “prediction.” That leads to the major, significant back and forth in the process. If you automate that step, then machine learning penetration and the use of data to create meaningful predictions will increase tremendously.

      Then what happens after the machine learning model gives a prediction? We can automate the software and technology part of it, but at the end of the day, it is root cause analysis and human intuition and decision making. We can augment them with a lot of tools, but we can’t fully automate that.

      Q: What do you hope to achieve with the seven-tiered framework for evaluating AutoML systems that you outlined in your paper?

      A: My hope is that people start to recognize that some levels of automation have already been achieved and some still need to be tackled. In the research community, we tend to focus on what we are comfortable with. We have gotten used to automating certain steps, and then we just stick to it. Automating these other parts of the machine learning solution development is very important, and that is where the biggest bottlenecks remain.

      My second hope is that researchers will very clearly understand what domain expertise means. A lot of this AutoML work is still being conducted by academics, and the problem is that we often don’t do applied work. There is not a crystal-clear definition of what a domain expert is and in itself, “domain expert,” is a very nebulous phrase. What we mean by domain expert is the expert in the problem you are trying to solve with machine learning. And I am hoping that everyone unifies around that because that would make things so much clearer.

      I still believe that we are not able to build that many models for that many problems, but even for the ones that we are building, the majority of them are not getting deployed and used in day-to-day life. The output of machine learning is just going to be another data point, an augmented data point, in someone’s decision making. How they make those decisions, based on that input, how that will change their behavior, and how they will adapt their style of working, that is still a big, open question. Once we automate everything, that is what’s next.

      We have to determine what has to fundamentally change in the day-to-day workflow of someone giving loans at a bank, or an educator trying to decide whether he or she should change the assignments in an online class. How are they going to use machine learning’s outputs? We need to focus on the fundamental things we have to build out to make machine learning more usable. More