Autor: MK

More than a decade ago, Ted Adelson set out to create tactile sensors for robots that would give them a sense of touch. The result? A handheld imaging system powerful enough to visualize the raised print on a dollar bill. The technology was spun into GelSight, to answer an industry need for low-cost, high-resolution imaging.

An expert in both human and machine vision, Adelson was pleased to have created something useful. But he never lost sight of his original dream: to endow robots with a sense of touch. In a new Science Hub project with Amazon, he’s back on the case. He plans to build out the GelSight system with added capabilities to sense temperature and vibrations. A professor in MIT’s Department of Brain and Cognitive Sciences, Adelson recently sat down to talk about his work.

Q: What makes the human hand so hard to recreate in a robot?

A: A human finger has soft, sensitive skin, which deforms as it touches things. The question is how to get precise sensing when the sensing surface itself is constantly moving and changing during manipulation.

Q: You’re an expert on human and computer vision. How did touch grab your interest?

A: When my daughters were babies, I was amazed by how skillfully they used their fingers and hands to explore the world. I wanted to understand the way they were gathering information through their sense of touch. Being a vision researcher, I naturally looked for a way to do it with cameras.

Q: How does the GelSight robot finger work? What are its limitations?

A: A camera captures an image of the skin from inside, and a computer vision system calculates the skin’s 3D deformation. GelSight fingers offer excellent tactile acuity, far exceeding that of human fingers. However, the need for an inner optical system limits the sizes and shapes we can achieve today.

Q: How did you come up with the idea of giving a robot finger a sense of touch by, in effect, giving it sight?

A: A camera can tell you about the geometry of the surface it is viewing. By putting a tiny camera inside the finger, we can measure how the skin geometry is changing from point to point. This tells us about tactile properties like force, shape, and texture.

Q: How did your prior work on cameras figure in?

A: My prior research on the appearance of reflective materials helped me engineer the optical properties of the skin. We create a very thin matte membrane and light it with grazing illumination so all the details can be seen.

Q: Did you know there was a market for measuring 3D surfaces?

A: No. My postdoc Kimo Johnson posted a YouTube video showing GelSight’s capabilities about a decade ago. The video went viral, and we got a flood of email with interesting suggested applications. People have since used the technology for measuring the microtexture of shark skin, packed snow, and sanded surfaces. The FBI uses it in forensics to compare spent cartridge casings.

Q: What’s GelSight’s main application?  

A: Industrial inspection. For example, an inspector can press a GelSight sensor against a scratch or bump on an airplane fuselage to measure its exact size and shape in 3D. This application may seem quite different from the original inspiration of baby fingers, but it shows that tactile sensing can have many uses. As for robotics, tactile sensing is mainly a research topic right now, but we expect it to increasingly be useful in industrial robots.

Q: You’re now building in a way to measure temperature and vibrations. How do you do that with a camera? How else will you try to emulate human touch?

A: You can convert temperature to a visual signal that a camera can read by using liquid crystals, the molecules that make mood rings and forehead thermometers change color. For vibrations we will use microphones. We also want to extend the range of shapes a finger can have. Finally, we need to understand how to use the information coming from the finger to improve robotics.

Q: Why are we sensitive to temperature and vibrations, and why is that useful for robotics?

A: Identifying material properties is an important aspect of touch. Sensing temperature helps you tell whether something is metal or wood, and whether it is wet or dry. Vibrations can help you distinguish a slightly textured surface, like unvarnished wood, from a perfectly smooth surface, like wood with a glossy finish.

Q: What’s next?

A: Making a tactile sensor is the first step. Integrating it into a useful finger and hand comes next. Then you have to get the robot to use the hand to perform real-world tasks.

Q: Evolution gave us five fingers and two hands. Will robots have the same?

A: Different robots will have different kinds of hands, optimized for different situations. Big hands, small hands, hands with three fingers or six fingers, and hands we can’t even imagine today. Our goal is to provide the sensing capability, so that the robot can skillfully interact with the world.

Four researchers with MIT ties — Juncal Arbelaiz, Xiangkun (Elvis) Cao, Sandya Subramanian, and Hannah Zlotnick ’17 — have been honored with competitive Schmidt Science Fellowships.

Created in 2017, the fellows program aims to bring together the world’s brightest minds “to solve society’s toughest challenges.”

The four MIT-affiliated researchers are among 29 Schmidt Science Fellows from around the world who will receive postdoctoral support for either one or two years with an annual stipend of $100,000, along with individualized mentoring and participation in the program’s Global Meeting Series. The fellows will also have opportunities to engage with thought-leaders from science, business, policy, and society. According to the award announcement, the fellows are expected to pursue research that shifts from the focus of their PhDs, to help expand and enhance their futures as scientific leaders.

Juncal Arbelaiz is a PhD candidate in applied mathematics at MIT, who is completing her doctorate this summer. Her doctoral research at MIT is advised by Ali Jadbabaie, the JR East Professor of Engineering and head of the Department of Civil and Environmental Engineering; Anette Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of the School of Engineering; and Bassam Bamieh, professor of mechanical engineering and associate director of the Center for Control, Dynamical Systems, and Computation at the University of California at Santa Barbara. Arbelaiz’s research revolves around the design of optimal decentralized intelligence for spatially-distributed dynamical systems.

“I cannot think of a better way to start my independent scientific career. I feel very excited and grateful for this opportunity,” says Arbelaiz. With her fellowship, she will enlist systems biology to explore how the nervous system encodes and processes sensory information to address future safety-critical artificial intelligence applications. “The Schmidt Science Fellowship will provide me with a unique opportunity to work at the intersection of biological and machine intelligence for two years and will be a steppingstone towards my longer-term objective of becoming a researcher in bio-inspired machine intelligence,” she says.

Xiangkun (Elvis) Cao is currently a postdoc in the lab of T. Alan Hatton, the Ralph Landau Professor in Chemical Engineering, and an Impact Fellow at the MIT Climate and Sustainability Consortium. Cao received his PhD in mechanical engineering from Cornell University in 2021, during which he focused on microscopic precision in the simultaneous delivery of light and fluids by optofluidics, with advances relevant to health and sustainability applications. As a Schmidt Science Fellow, he plans to be co-advised by Hatton on carbon capture, and Ted Sargent, professor of chemistry at Northwestern University, on carbon utilization. Cao is passionate about integrated carbon capture and utilization (CCU) from molecular to process levels, machine learning to inspire smart CCU, and the nexus of technology, business, and policy for CCU.

“The Schmidt Science Fellowship provides the perfect opportunity for me to work across disciplines to study integrated carbon capture and utilization from molecular to process levels,” Cao explains. “My vision is that by integrating carbon capture and utilization, we can concurrently make scientific discoveries and unlock economic opportunities while mitigating global climate change. This way, we can turn our carbon liability into an asset.”

Sandya Subramanian, a 2021 PhD graduate of the Harvard-MIT Program in Health Sciences and Technology (HST) in the area of medical engineering and medical physics, is currently a postdoc at Stanford Data Science. She is focused on the topics of biomedical engineering, statistics, machine learning, neuroscience, and health care. Her research is on developing new technologies and methods to study the interactions between the brain, the autonomic nervous system, and the gut. “I’m extremely honored to receive the Schmidt Science Fellowship and to join the Schmidt community of leaders and scholars,” says Subramanian. “I’ve heard so much about the fellowship and the fact that it can open doors and give people confidence to pursue challenging or unique paths.”

According to Subramanian, the autonomic nervous system and its interactions with other body systems are poorly understood but thought to be involved in several disorders, such as functional gastrointestinal disorders, Parkinson’s disease, diabetes, migraines, and eating disorders. The goal of her research is to improve our ability to monitor and quantify these physiologic processes. “I’m really interested in understanding how we can use physiological monitoring technologies to inform clinical decision-making, especially around the autonomic nervous system, and I look forward to continuing the work that I’ve recently started at Stanford as Schmidt Science Fellow,” she says. “A huge thank you to all of the mentors, colleagues, friends, and leaders I had the pleasure of meeting and working with at HST and MIT; I couldn’t have done this without everything I learned there.”

Hannah Zlotnick ’17 attended MIT for her undergraduate studies, majoring in biological engineering with a minor in mechanical engineering. At MIT, Zlotnick was a student-athlete on the women’s varsity soccer team, a UROP student in Alan Grodzinsky’s laboratory, and a member of Pi Beta Phi. For her PhD, Zlotnick attended the University of Pennsylvania, and worked in Robert Mauck’s laboratory within the departments of Bioengineering and Orthopaedic Surgery.

Zlotnick’s PhD research focused on harnessing remote forces, such as magnetism or gravity, to enhance engineered cartilage and osteochondral repair both in vitro and in large animal models. Zlotnick now plans to pivot to the field of biofabrication to create tissue models of the knee joint to assess potential therapeutics for osteoarthritis. “I am humbled to be a part of the Schmidt Science Fellows community, and excited to venture into the field of biofabrication,” Zlotnick says. “Hopefully this work uncovers new therapies for patients with inflammatory joint diseases.“

The MIT Stephen A. Schwarzman College of Computing has named Costis Daskalakis as the inaugural holder of the Avanessians Professorship. His chair began on July 1.

Daskalakis is the first person appointed to this position generously endowed by Armen Avanessians ’81. Established in the MIT Schwarzman College of Computing, the new chair provides Daskalakis with additional support to pursue his research and develop his career.

“I’m delighted to recognize Costis for his scholarship and extraordinary achievements with this distinguished professorship,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

A professor in the MIT Department of Electrical Engineering and Computer Science, Daskalakis is a theoretical computer scientist who works at the interface of game theory, economics, probability theory, statistics, and machine learning. He has resolved long-standing open problems about the computational complexity of the Nash equilibrium, the mathematical structure and computational complexity of multi-item auctions, and the behavior of machine-learning methods such as the expectation-maximization algorithm. He has obtained computationally and statistically efficient methods for statistical hypothesis testing and learning in high-dimensional settings, as well as results characterizing the structure and concentration properties of high-dimensional distributions. His current work focuses on multi-agent learning, learning from biased and dependent data, causal inference, and econometrics.

A native of Greece, Daskalakis joined the MIT faculty in 2009. He is a member of the Computer Science and Artificial Intelligence Laboratory and is affiliated with the Laboratory for Information and Decision Systems and the Operations Research Center. He is also an investigator in the Foundations of Data Science Institute.

He has previously received such honors as the 2018 Nevanlinna Prize from the International Mathematical Union, the 2018 ACM Grace Murray Hopper Award, the Kalai Game Theory and Computer Science Prize from the Game Theory Society, and the 2008 ACM Doctoral Dissertation Award.

The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? Millions? Billions? Trillions? The answer: novemdecillion, or 1060. This gargantuan number prolongs the drug development process for fast-spreading diseases like Covid-19 because it is far beyond what existing drug design models can compute. To put it into perspective, the Milky Way has about 100 billion, or 1011, stars.

In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins. EquiBind is based on its predecessor, EquiDock, which specializes in binding two proteins using a technique developed by the late Octavian-Eugen Ganea, a recent MIT Computer Science and Artificial Intelligence Laboratory and Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) postdoc, who also co-authored the EquiBind paper.

Before drug development can even take place, drug researchers must find promising drug-like molecules that can bind or “dock” properly onto certain protein targets in a process known as drug discovery. After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning. If this happens to an essential protein of a bacterium, it can kill the bacterium, conferring protection to the human body.

However, the process of drug discovery can be costly both financially and computationally, with billions of dollars poured into the process and over a decade of development and testing before final approval from the Food and Drug Administration. What’s more, 90 percent of all drugs fail once they are tested in humans due to having no effects or too many side effects. One of the ways drug companies recoup the costs of these failures is by raising the prices of the drugs that are successful.

The current computational process for finding promising drug candidate molecules goes like this: most state-of-the-art computational models rely upon heavy candidate sampling coupled with methods like scoring, ranking, and fine-tuning to get the best “fit” between the ligand and the protein. 

Hannes Stärk, lead author of the paper and a first-year graduate student advised by Regina Barzilay and Tommi Jaakkola in the MIT Department of Electrical Engineering and Computer Science, likens typical ligand-to-protein binding methodologies to “trying to fit a key into a lock with a lot of keyholes.” Typical models time-consumingly score each “fit” before choosing the best one. In contrast, EquiBind directly predicts the precise key location in a single step without prior knowledge of the protein’s target pocket, which is known as “blind docking.”

Unlike most models that require several attempts to find a favorable position for the ligand in the protein, EquiBind already has built-in geometric reasoning that helps the model learn the underlying physics of molecules and successfully generalize to make better predictions when encountering new, unseen data.

The release of these findings quickly attracted the attention of industry professionals, including Pat Walters, the chief data officer for Relay Therapeutics. Walters suggested that the team try their model on an already existing drug and protein used for lung cancer, leukemia, and gastrointestinal tumors. Whereas most of the traditional docking methods failed to successfully bind the ligands that worked on those proteins, EquiBind succeeded.

“EquiBind provides a unique solution to the docking problem that incorporates both pose prediction and binding site identification,” Walters says. “This approach, which leverages information from thousands of publicly available crystal structures, has the potential to impact the field in new ways.”

“We were amazed that while all other methods got it completely wrong or only got one correct, EquiBind was able to put it into the correct pocket, so we were very happy to see the results for this,” Stärk says.

While EquiBind has received a great deal of feedback from industry professionals that has helped the team consider practical uses for the computational model, Stärk hopes to find different perspectives at the upcoming ICML in July.

“The feedback I’m most looking forward to is suggestions on how to further improve the model,” he says. “I want to discuss with those researchers … to tell them what I think can be the next steps and encourage them to go ahead and use the model for their own papers and for their own methods … we’ve had many researchers already reaching out and asking if we think the model could be useful for their problem.”

This work was funded, in part, by the Pharmaceutical Discovery and Synthesis consortium; the Jameel Clinic; the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program; the DARPA Accelerated Molecular Discovery program; the MIT-Takeda Fellowship; and the NSF Expeditions grant Collaborative Research: Understanding the World Through Code.

This work is dedicated to the memory of Octavian-Eugen Ganea, who made crucial contributions to geometric machine learning research and generously mentored many students — a brilliant scholar with a humble soul.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.

Often, an expert operator must use manual trial-and-error — possibly making thousands of prints — to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.

MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.

They used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer. Their system printed objects more accurately than all the other 3D printing controllers they compared it to.

The work avoids the prohibitively expensive process of printing thousands or millions of real objects to train the neural network. And it could enable engineers to more easily incorporate novel materials into their prints, which could help them develop objects with special electrical or chemical properties. It could also help technicians make adjustments to the printing process on-the-fly if material or environmental conditions change unexpectedly.

“This project is really the first demonstration of building a manufacturing system that uses machine learning to learn a complex control policy,” says senior author Wojciech Matusik, professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group (CDFG) within the Computer Science and Artificial Intelligence Laboratory (CSAIL). “If you have manufacturing machines that are more intelligent, they can adapt to the changing environment in the workplace in real-time, to improve the yields or the accuracy of the system. You can squeeze more out of the machine.”

The co-lead authors on the research are Mike Foshey, a mechanical engineer and project manager in the CDFG, and Michal Piovarci, a postdoc at the Institute of Science and Technology in Austria. MIT co-authors include Jie Xu, a graduate student in electrical engineering and computer science, and Timothy Erps, a former technical associate with the CDFG.

Picking parameters

Determining the ideal parameters of a digital manufacturing process can be one of the most expensive parts of the process because so much trial-and-error is required. And once a technician finds a combination that works well, those parameters are only ideal for one specific situation. She has little data on how the material will behave in other environments, on different hardware, or if a new batch exhibits different properties.

Using a machine-learning system is fraught with challenges, too. First, the researchers needed to measure what was happening on the printer in real-time.

To do this, they developed a machine-vision system using two cameras aimed at the nozzle of the 3D printer. The system shines light at material as it is deposited and, based on how much light passes through, calculates the material’s thickness.

“You can think of the vision system as a set of eyes watching the process in real-time,” Foshey says.

The controller would then process images it receives from the vision system and, based on any error it sees, adjust the feed rate and the direction of the printer.

But training a neural network-based controller to understand this manufacturing process is data-intensive, and would require making millions of prints. So, the researchers built a simulator instead.

Successful simulation

To train their controller, they used a process known as reinforcement learning in which the model learns through trial-and-error with a reward. The model was tasked with selecting printing parameters that would create a certain object in a simulated environment. After being shown the expected output, the model was rewarded when the parameters it chose minimized the error between its print and the expected outcome.

In this case, an “error” means the model either dispensed too much material, placing it in areas that should have been left open, or did not dispense enough, leaving open spots that should be filled in. As the model performed more simulated prints, it updated its control policy to maximize the reward, becoming more and more accurate.

However, the real world is messier than a simulation. In practice, conditions typically change due to slight variations or noise in the printing process. So the researchers created a numerical model that approximates noise from the 3D printer. They used this model to add noise to the simulation, which led to more realistic results.

“The interesting thing we found was that, by implementing this noise model, we were able to transfer the control policy that was purely trained in simulation onto hardware without training with any physical experimentation,” Foshey says. “We didn’t need to do any fine-tuning on the actual equipment afterwards.”

When they tested the controller, it printed objects more accurately than any other control method they evaluated. It performed especially well at infill printing, which is printing the interior of an object. Some other controllers deposited so much material that the printed object bulged up, but the researchers’ controller adjusted the printing path so the object stayed level.

Their control policy can even learn how materials spread after being deposited and adjust parameters accordingly.

“We were also able to design control policies that could control for different types of materials on-the-fly. So if you had a manufacturing process out in the field and you wanted to change the material, you wouldn’t have to revalidate the manufacturing process. You could just load the new material and the controller would automatically adjust,” Foshey says.

Now that they have shown the effectiveness of this technique for 3D printing, the researchers want to develop controllers for other manufacturing processes. They’d also like to see how the approach can be modified for scenarios where there are multiple layers of material, or multiple materials being printed at once. In addition, their approach assumed each material has a fixed viscosity (“syrupiness”), but a future iteration could use AI to recognize and adjust for viscosity in real-time.

Additional co-authors on this work include Vahid Babaei, who leads the Artificial Intelligence Aided Design and Manufacturing Group at the Max Planck Institute; Piotr Didyk, associate professor at the University of Lugano in Switzerland; Szymon Rusinkiewicz, the David M. Siegel ’83 Professor of computer science at Princeton University; and Bernd Bickel, professor at the Institute of Science and Technology in Austria.

The work was supported, in part, by the FWF Lise-Meitner program, a European Research Council starting grant, and the U.S. National Science Foundation.

Moore’s Law needs a hug. The days of stuffing transistors on little silicon computer chips are numbered, and their life rafts — hardware accelerators — come with a price. 

When programming an accelerator — a process where applications offload certain tasks to system hardware especially to accelerate that task — you have to build a whole new software support. Hardware accelerators can run certain tasks orders of magnitude faster than CPUs, but they cannot be used out of the box. Software needs to efficiently use accelerators’ instructions to make it compatible with the entire application system. This translates to a lot of engineering work that then would have to be maintained for a new chip that you’re compiling code to, with any programming language. 

Now, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a new programming language called “Exo” for writing high-performance code on hardware accelerators. Exo helps low-level performance engineers transform very simple programs that specify what they want to compute, into very complex programs that do the same thing as the specification, but much, much faster by using these special accelerator chips. Engineers, for example, can use Exo to turn a simple matrix multiplication into a more complex program, which runs orders of magnitude faster by using these special accelerators.

Unlike other programming languages and compilers, Exo is built around a concept called “Exocompilation.” “Traditionally, a lot of research has focused on automating the optimization process for the specific hardware,” says Yuka Ikarashi, a PhD student in electrical engineering and computer science and CSAIL affiliate who is a lead author on a new paper about Exo. “This is great for most programmers, but for performance engineers, the compiler gets in the way as often as it helps. Because the compiler’s optimizations are automatic, there’s no good way to fix it when it does the wrong thing and gives you 45 percent efficiency instead of 90 percent.”   

With Exocompilation, the performance engineer is back in the driver’s seat. Responsibility for choosing which optimizations to apply, when, and in what order is externalized from the compiler, back to the performance engineer. This way, they don’t have to waste time fighting the compiler on the one hand, or doing everything manually on the other.  At the same time, Exo takes responsibility for ensuring that all of these optimizations are correct. As a result, the performance engineer can spend their time improving performance, rather than debugging the complex, optimized code.

“Exo language is a compiler that’s parameterized over the hardware it targets; the same compiler can adapt to many different hardware accelerators,” says Adrian Sampson, assistant professor in the Department of Computer Science at Cornell University. “ Instead of writing a bunch of messy C++ code to compile for a new accelerator, Exo gives you an abstract, uniform way to write down the ’shape‘ of the hardware you want to target. Then you can reuse the existing Exo compiler to adapt to that new description instead of writing something entirely new from scratch. The potential impact of work like this is enormous: If hardware innovators can stop worrying about the cost of developing new compilers for every new hardware idea, they can try out and ship more ideas. The industry could break its dependence on legacy hardware that succeeds only because of ecosystem lock-in and despite its inefficiency.” 

The highest-performance computer chips made today, such as Google’s TPU, Apple’s Neural Engine, or NVIDIA’s Tensor Cores, power scientific computing and machine learning applications by accelerating something called “key sub-programs,” kernels, or high-performance computing (HPC) subroutines.  

Clunky jargon aside, the programs are essential. For example, something called Basic Linear Algebra Subroutines (BLAS) is a “library” or collection of such subroutines, which are dedicated to linear algebra computations, and enable many machine learning tasks like neural networks, weather forecasts, cloud computation, and drug discovery. (BLAS is so important that it won Jack Dongarra the Turing Award in 2021.) However, these new chips — which take hundreds of engineers to design — are only as good as these HPC software libraries allow.

Currently, though, this kind of performance optimization is still done by hand to ensure that every last cycle of computation on these chips gets used. HPC subroutines regularly run at 90 percent-plus of peak theoretical efficiency, and hardware engineers go to great lengths to add an extra five or 10 percent of speed to these theoretical peaks. So, if the software isn’t aggressively optimized, all of that hard work gets wasted — which is exactly what Exo helps avoid. 

Another key part of Exocompilation is that performance engineers can describe the new chips they want to optimize for, without having to modify the compiler. Traditionally, the definition of the hardware interface is maintained by the compiler developers, but with most of these new accelerator chips, the hardware interface is proprietary. Companies have to maintain their own copy (fork) of a whole traditional compiler, modified to support their particular chip. This requires hiring teams of compiler developers in addition to the performance engineers.

“In Exo, we instead externalize the definition of hardware-specific backends from the exocompiler. This gives us a better separation between Exo — which is an open-source project — and hardware-specific code — which is often proprietary. We’ve shown that we can use Exo to quickly write code that’s as performant as Intel’s hand-optimized Math Kernel Library. We’re actively working with engineers and researchers at several companies,” says Gilbert Bernstein, a postdoc at the University of California at Berkeley. 

The future of Exo entails exploring a more productive scheduling meta-language, and expanding its semantics to support parallel programming models to apply it to even more accelerators, including GPUs.

Ikarashi and Bernstein wrote the paper alongside Alex Reinking and Hasan Genc, both PhD students at UC Berkeley, and MIT Assistant Professor Jonathan Ragan-Kelley.

This work was partially supported by the Applications Driving Architectures center, one of six centers of JUMP, a Semiconductor Research Corporation program co-sponsored by the Defense Advanced Research Projects Agency. Ikarashi was supported by Funai Overseas Scholarship, Masason Foundation, and Great Educators Fellowship. The team presented the work at the ACM SIGPLAN Conference on Programming Language Design and Implementation 2022.

As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

“With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

“The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

“The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

“Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

Accelerating deep learning

Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

Surprising speed

PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

“The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

“The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

“Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

“The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

“Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

“This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

This research is funded, in part, by the MIT-IBM Watson AI Lab.

For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

“Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way,” says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM ’17, ScD ’21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

To predict or not to predict

Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input — it can make a prediction or abstain from a prediction if it doesn’t have enough confidence in its decision.

When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

“It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage,” says Shah.

Focus on fairness

The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

“We see that if we don’t impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected,” Sattigeri says.

The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

And they hope to improve the confidence estimates in selective regression to prevent situations where the model’s confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation.

About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer.

These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. 

As the field of machine learning has grown, artificial neural networks have grown along with it.

Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data. But because the models are so enormously complex, even the researchers who design them don’t fully understand how they work. This makes it hard to know whether they are working correctly.

For instance, maybe a model designed to help physicians diagnose patients correctly predicted that a skin lesion was cancerous, but it did so by focusing on an unrelated mark that happens to frequently occur when there is cancerous tissue in a photo, rather than on the cancerous tissue itself. This is known as a spurious correlation. The model gets the prediction right, but it does so for the wrong reason. In a real clinical setting where the mark does not appear on cancer-positive images, it could result in missed diagnoses.

With so much uncertainty swirling around these so-called “black-box” models, how can one unravel what’s going on inside the box?

This puzzle has led to a new and rapidly growing area of study in which researchers develop and test explanation methods (also called interpretability methods) that seek to shed some light on how black-box machine-learning models make predictions.

What are explanation methods?

At their most basic level, explanation methods are either global or local. A local explanation method focuses on explaining how the model made one specific prediction, while global explanations seek to describe the overall behavior of an entire model. This is often done by developing a separate, simpler (and hopefully understandable) model that mimics the larger, black-box model.

But because deep learning models work in fundamentally complex and nonlinear ways, developing an effective global explanation model is particularly challenging. This has led researchers to turn much of their recent focus onto local explanation methods instead, explains Yilun Zhou, a graduate student in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL) who studies models, algorithms, and evaluations in interpretable machine learning.

The most popular types of local explanation methods fall into three broad categories.

The first and most widely used type of explanation method is known as feature attribution. Feature attribution methods show which features were most important when the model made a specific decision.

Features are the input variables that are fed to a machine-learning model and used in its prediction. When the data are tabular, features are drawn from the columns in a dataset (they are transformed using a variety of techniques so the model can process the raw data). For image-processing tasks, on the other hand, every pixel in an image is a feature. If a model predicts that an X-ray image shows cancer, for instance, the feature attribution method would highlight the pixels in that specific X-ray that were most important for the model’s prediction.

Essentially, feature attribution methods show what the model pays the most attention to when it makes a prediction.

“Using this feature attribution explanation, you can check to see whether a spurious correlation is a concern. For instance, it will show if the pixels in a watermark are highlighted or if the pixels in an actual tumor are highlighted,” says Zhou.

A second type of explanation method is known as a counterfactual explanation. Given an input and a model’s prediction, these methods show how to change that input so it falls into another class. For instance, if a machine-learning model predicts that a borrower would be denied a loan, the counterfactual explanation shows what factors need to change so her loan application is accepted. Perhaps her credit score or income, both features used in the model’s prediction, need to be higher for her to be approved.

“The good thing about this explanation method is it tells you exactly how you need to change the input to flip the decision, which could have practical usage. For someone who is applying for a mortgage and didn’t get it, this explanation would tell them what they need to do to achieve their desired outcome,” he says.

The third category of explanation methods are known as sample importance explanations. Unlike the others, this method requires access to the data that were used to train the model.

A sample importance explanation will show which training sample a model relied on most when it made a specific prediction; ideally, this is the most similar sample to the input data. This type of explanation is particularly useful if one observes a seemingly irrational prediction. There may have been a data entry error that affected a particular sample that was used to train the model. With this knowledge, one could fix that sample and retrain the model to improve its accuracy.

How are explanation methods used?

One motivation for developing these explanations is to perform quality assurance and debug the model. With more understanding of how features impact a model’s decision, for instance, one could identify that a model is working incorrectly and intervene to fix the problem, or toss the model out and start over.

Another, more recent, area of research is exploring the use of machine-learning models to discover scientific patterns that humans haven’t uncovered before. For instance, a cancer diagnosing model that outperforms clinicians could be faulty, or it could actually be picking up on some hidden patterns in an X-ray image that represent an early pathological pathway for cancer that were either unknown to human doctors or thought to be irrelevant, Zhou says.

It’s still very early days for that area of research, however.

Words of warning

While explanation methods can sometimes be useful for machine-learning practitioners when they are trying to catch bugs in their models or understand the inner-workings of a system, end-users should proceed with caution when trying to use them in practice, says Marzyeh Ghassemi, an assistant professor and head of the Healthy ML Group in CSAIL.

As machine learning has been adopted in more disciplines, from health care to education, explanation methods are being used to help decision makers better understand a model’s predictions so they know when to trust the model and use its guidance in practice. But Ghassemi warns against using these methods in that way.

“We have found that explanations make people, both experts and nonexperts, overconfident in the ability or the advice of a specific recommendation system. I think it is very important for humans not to turn off that internal circuitry asking, ‘let me question the advice that I am
given,’” she says.

Scientists know explanations make people over-confident based on other recent work, she adds, citing some recent studies by Microsoft researchers.

Far from a silver bullet, explanation methods have their share of problems. For one, Ghassemi’s recent research has shown that explanation methods can perpetuate biases and lead to worse outcomes for people from disadvantaged groups.

Another pitfall of explanation methods is that it is often impossible to tell if the explanation method is correct in the first place. One would need to compare the explanations to the actual model, but since the user doesn’t know how the model works, this is circular logic, Zhou says.

He and other researchers are working on improving explanation methods so they are more faithful to the actual model’s predictions, but Zhou cautions that, even the best explanation should be taken with a grain of salt.

“In addition, people generally perceive these models to be human-like decision makers, and we are prone to overgeneralization. We need to calm people down and hold them back to really make sure that the generalized model understanding they build from these local explanations are balanced,” he adds.

Zhou’s most recent research seeks to do just that.

What’s next for machine-learning explanation methods?

Rather than focusing on providing explanations, Ghassemi argues that more effort needs to be done by the research community to study how information is presented to decision makers so they understand it, and more regulation needs to be put in place to ensure machine-learning models are used responsibly in practice. Better explanation methods alone aren’t the answer.

“I have been excited to see that there is a lot more recognition, even in industry, that we can’t just take this information and make a pretty dashboard and assume people will perform better with that. You need to have measurable improvements in action, and I’m hoping that leads to real guidelines about improving the way we display information in these deeply technical fields, like medicine,” she says.

And in addition to new work focused on improving explanations, Zhou expects to see more research related to explanation methods for specific use cases, such as model debugging, scientific discovery, fairness auditing, and safety assurance. By identifying fine-grained characteristics of explanation methods and the requirements of different use cases, researchers could establish a theory that would match explanations with specific scenarios, which could help overcome some of the pitfalls that come from using them in real-world scenarios.

Physicians often query a patient’s electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that even when a doctor has been trained to use an electronic health record (EHR), finding an answer to just one question can take, on average, more than eight minutes.

The more time physicians must spend navigating an oftentimes clunky EHR interface, the less time they have to interact with patients and provide treatment.

Researchers have begun developing machine-learning models that can streamline the process by automatically finding information physicians need in an EHR. However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions. Existing models struggle to generate authentic questions — those that would be asked by a human doctor — and are often unable to successfully find correct answers.

To overcome this data shortage, researchers at MIT partnered with medical experts to study the questions physicians ask when reviewing EHRs. Then, they built a publicly available dataset of more than 2,000 clinically relevant questions written by these medical experts.

When they used their dataset to train a machine-learning model to generate clinical questions, they found that the model asked high-quality and authentic questions, as compared to real questions from medical experts, more than 60 percent of the time.

With this dataset, they plan to generate vast numbers of authentic medical questions and then use those questions to train a machine-learning model which would help doctors find sought-after information in a patient’s record more efficiently.

“Two thousand questions may sound like a lot, but when you look at machine-learning models being trained nowadays, they have so much data, maybe billions of data points. When you train machine-learning models to work in health care settings, you have to be really creative because there is such a lack of data,” says lead author Eric Lehman, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

The senior author is Peter Szolovits, a professor in the Department of Electrical Engineering and Computer Science (EECS) who heads the Clinical Decision-Making Group in CSAIL and is also a member of the MIT-IBM Watson AI Lab. The research paper, a collaboration between co-authors at MIT, the MIT-IBM Watson AI Lab, IBM Research, and the doctors and medical experts who helped create questions and participated in the study, will be presented at the annual conference of the North American Chapter of the Association for Computational Linguistics.

“Realistic data is critical for training models that are relevant to the task yet difficult to find or create,” Szolovits says. “The value of this work is in carefully collecting questions asked by clinicians about patient cases, from which we are able to develop methods that use these data and general language models to ask further plausible questions.”

Data deficiency

The few large datasets of clinical questions the researchers were able to find had a host of issues, Lehman explains. Some were composed of medical questions asked by patients on web forums, which are a far cry from physician questions. Other datasets contained questions produced from templates, so they are mostly identical in structure, making many questions unrealistic.

“Collecting high-quality data is really important for doing machine-learning tasks, especially in a health care context, and we’ve shown that it can be done,” Lehman says.

To build their dataset, the MIT researchers worked with practicing physicians and medical students in their last year of training. They gave these medical experts more than 100 EHR discharge summaries and told them to read through a summary and ask any questions they might have. The researchers didn’t put any restrictions on question types or structures in an effort to gather natural questions. They also asked the medical experts to identify the “trigger text” in the EHR that led them to ask each question.

For instance, a medical expert might read a note in the EHR that says a patient’s past medical history is significant for prostate cancer and hypothyroidism. The trigger text “prostate cancer” could lead the expert to ask questions like “date of diagnosis?” or “any interventions done?”

They found that most questions focused on symptoms, treatments, or the patient’s test results. While these findings weren’t unexpected, quantifying the number of questions about each broad topic will help them build an effective dataset for use in a real, clinical setting, says Lehman.

Once they had compiled their dataset of questions and accompanying trigger text, they used it to train machine-learning models to ask new questions based on the trigger text.

Then the medical experts determined whether those questions were “good” using four metrics: understandability (Does the question make sense to a human physician?), triviality (Is the question too easily answerable from the trigger text?), medical relevance (Does it makes sense to ask this question based on the context?), and relevancy to the trigger (Is the trigger related to the question?).

Cause for concern

The researchers found that when a model was given trigger text, it was able to generate a good question 63 percent of the time, whereas a human physician would ask a good question 80 percent of the time.

They also trained models to recover answers to clinical questions using the publicly available datasets they had found at the outset of this project. Then they tested these trained models to see if they could find answers to “good” questions asked by human medical experts.

The models were only able to recover about 25 percent of answers to physician-generated questions.

“That result is really concerning. What people thought were good-performing models were, in practice, just awful because the evaluation questions they were testing on were not good to begin with,” Lehman says.

The team is now applying this work toward their initial goal: building a model that can automatically answer physicians’ questions in an EHR. For the next step, they will use their dataset to train a machine-learning model that can automatically generate thousands or millions of good clinical questions, which can then be used to train a new model for automatic question answering.

While there is still much work to do before that model could be a reality, Lehman is encouraged by the strong initial results the team demonstrated with this dataset.

This research was supported, in part, by the MIT-IBM Watson AI Lab. Additional co-authors include Leo Anthony Celi of the MIT Institute for Medical Engineering and Science; Preethi Raghavan and Jennifer J. Liang of the MIT-IBM Watson AI Lab; Dana Moukheiber of the University of Buffalo; Vladislav Lialin and Anna Rumshisky of the University of Massachusetts at Lowell; Katelyn Legaspi, Nicole Rose I. Alberto, Richard Raymund R. Ragasa, Corinna Victoria M. Puyat, Isabelle Rose I. Alberto, and Pia Gabrielle I. Alfonso of the University of the Philippines; Anne Janelle R. Sy and Patricia Therese S. Pile of the University of the East Ramon Magsaysay Memorial Medical Center; Marianne Taliño of the Ateneo de Manila University School of Medicine and Public Health; and Byron C. Wallace of Northeastern University.