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State of Supply Chain Sustainability report reveals growing investor pressure, challenges with emissions tracking

The MIT Center for Transportation and Logistics (MIT CTL) and the Council of Supply Chain Management Professionals (CSCMP) have released the 2024 State of Supply Chain Sustainability report, marking the fifth edition of this influential research. The report highlights how supply chain sustainability practices have evolved over the past five years, assessing their global implementation and implications for industries, professionals, and the environment.

This year’s report is based on four years of comprehensive international surveys with responses from over 7,000 supply chain professionals representing more than 80 countries, coupled with insights from executive interviews. It explores how external pressures on firms, such as the growing investor demand and climate regulations, are driving sustainability initiatives. However, it also reveals persistent gaps between companies’ sustainability goals and the actual investments required to achieve them.

“Over the past five years, we have seen supply chains face unprecedented global challenges. While companies have made strides, our analysis shows that many are still struggling to align their sustainability ambitions with real progress, particularly when it comes to tackling Scope 3 emissions,” says Josué Velázquez Martínez, MIT CTL research scientist and lead investigator. “Scope 3 emissions, which account for the vast majority of a company’s carbon footprint, remain a major hurdle due to the complexity of tracking emissions from indirect supply chain activities. The margin of error of the most common approach to estimate emissions are drastic, which disincentivizes companies to make more sustainable choices at the expense of investing in green alternatives.”

Among the key findings:

  • Increased pressure from investors: Over five years, pressure from investors to improve supply chain sustainability has grown by 25 percent, making it the fastest-growing driver of sustainability efforts.
  • Lack of readiness for net-zero goals: Although 67 percent of firms surveyed do not have a net-zero goal in place, those that do are often unprepared to meet them, especially when it comes to measuring and reducing Scope 3 emissions.
  • Company response to sustainability efforts in times of crisis: Companies react to different types of crises differently in regards to staying on track with their sustainable goals, whether it is a network disruption like the Covid-19 pandemic or economic turbulence.
  • Challenges with Scope 3 emissions: Despite significant efforts, Scope 3 emissions — which can account for up to 75 percent of a company’s total emissions — continue to be the most difficult to track and manage, due to the complexity of supplier networks and inconsistent data-sharing practices.

Mark Baxa, president and CEO of CSCMP, emphasized the importance of collaboration: “Businesses and consumers alike are putting pressure on us to source and supply products to live up to their social and environmental standards. The State of Supply Chain Sustainability 2024 provides a thorough analysis of our current understanding, along with valuable insights on how to improve our Scope 3 emissions accounting to have a greater impact on lowering our emissions.”

The report also underscores the importance of technological innovations, such as machine learning, advanced data analytics, and standardization to improve the accuracy of emissions tracking and help firms make data-driven sustainability decisions.

The 2024 State of Supply Chain Sustainability can be accessed online or in PDF format at sustainable.mit.edu.

The MIT CTL is a world leader in supply chain management research and education, with over 50 years of expertise. The center’s work spans industry partnerships, cutting-edge research, and the advancement of sustainable supply chain practices. CSCMP is the leading global association for supply chain professionals. Established in 1963, CSCMP provides its members with education, research, and networking opportunities to advance the field of supply chain management.

Aligning economic and regulatory frameworks for today’s nuclear reactor technology

Liam Hines ’22 didn’t move to Sarasota, Florida, until high school, but he’s a Floridian through and through. He jokes that he’s even got a floral shirt, what he calls a “Florida formal,” for every occasion.

Which is why it broke his heart when toxic red algae used to devastate the Sunshine State’s coastline, including at his favorite beach, Caspersen. The outbreak made headline news during his high school years, with the blooms destroying marine wildlife and adversely impacting the state’s tourism-driven economy.

In Florida, Hines says, environmental awareness is pretty high because everyday citizens are being directly impacted by climate change. After all, it’s hard not to worry when beautiful white sand beaches are covered in dead fish. Ongoing concerns about the climate cemented Hines’ resolve to pick a career that would have a strong “positive environmental impact.” He chose nuclear, as he saw it as “a green, low-carbon-emissions energy source with a pretty straightforward path to implementation.”

Liam Hines: Ensuring that nuclear policy keeps up with nuclear technology.

Undergraduate studies at MIT

Knowing he wanted a career in the sciences, Hines applied and got accepted to MIT for undergraduate studies in fall 2018. An orientation program hosted by the Department of Nuclear Science and Engineering (NSE) sold him on the idea of pursuing the field. “The department is just a really tight-knit community, and that really appealed to me,” Hines says.

During his undergraduate years, Hines realized he needed a job to pay part of his bills. “Instead of answering calls at the dorm front desk or working in the dining halls, I decided I’m going to become a licensed nuclear operator onsite,” he says. “Reactor operations offer so much hands-on experience with real nuclear systems. It doesn’t hurt that it pays better.” Becoming a licensed nuclear reactor operator is hard work, however, involving a year-long training process studying maintenance, operations, and equipment oversight. A bonus: The job, supervising the MIT Nuclear Reactor Laboratory, taught him the fundamentals of nuclear physics and engineering.

Always interested in research, Hines got an early start by exploring the regulatory challenges of advanced fusion systems. There have been questions related to licensing requirements and the safety consequences of the onsite radionuclide inventory. Hines’ undergraduate research work involved studying precedent for such fusion facilities and comparing them to experimental facilities such as Princeton University’s Tokamak Fusion Test Reactor.

Doctoral focus on legal and regulatory frameworks

When scientists want to make technologies as safe as possible, they have to do two things in concert: First they evaluate the safety of the technology, and then make sure legal and regulatory structures take into account the evolution of these advanced technologies. Hines is taking such a two-pronged approach to his doctoral work on nuclear fission systems.

Under the guidance of Professor Koroush Shirvan, Hines is conducting systems modeling of various reactor cores that include graphite, and simulating operations under long time spans. He then studies radionuclide transport from low-level waste facilities — the consequences of offsite storage after 50 or 100 or even 10,000 years of storage. The work has to make sure to hit safety and engineering margins, but also tread a fine line. “You want to make sure you’re not over-engineering systems and adding undue cost, but also making sure to assess the unique hazards of these advanced technologies as accurately as possible,” Hines says.

On a parallel track, under Professor Haruko Wainwright’s advisement, Hines is applying the current science on radionuclide geochemistry to track radionuclide wastes and map their profile for hazards. One of the challenges fission reactors face is that existing low-level waste regulations were fine-tuned to old reactors. Regulations have not kept up: “Now that we have new technologies with new wastes, some of the hazards of the new waste are completely missed by existing standards,” Hines says. He is working to seal these gaps.

A philosophy-driven outlook

Hines is grateful for the dynamic learning environment at NSE. “A lot of the faculty have that go-getter attitude,” he points out, impressed by the entrepreneurial spirit on campus. “It’s made me confident to really tackle the things that I care about.”

An ethics class as an undergraduate made Hines realize there were discussions in class he could apply to the nuclear realm, especially when it came to teasing apart the implications of the technology — where the devices would be built and who they would serve. He eventually went on to double-major in NSE and philosophy.

The framework style of reading and reasoning involved in studying philosophy is particularly relevant in his current line of work, where he has to extract key points regarding nuclear regulatory issues. Much like philosophy discussions today that involve going over material that has been discussed for centuries and framing them through new perspectives, nuclear regulatory issues too need to take the long view.

“In philosophy, we have to insert ourselves into very large conversations. Similarly, in nuclear engineering, you have to understand how to take apart the discourse that’s most relevant to your research and frame it,” Hines says. This technique is especially necessary because most of the time the nuclear regulatory issues might seem like wading in the weeds of nitty-gritty technical matters, but they can have a huge impact on the public and public perception, Hines adds.

As for Florida, Hines visits every chance he can get. The red tide still surfaces but not as consistently as it once did. And since he started his job as a nuclear operator in his undergraduate days, Hines has progressed to senior reactor operator. This time around he gets to sign off on the checklists. “It’s much like when I was shift lead at Dunkin’ Donuts in high school,” Hines says, “everyone is kind of doing the same thing, but you get to be in charge for the afternoon.”

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AI pareidolia: Can machines spot faces in inanimate objects?

In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary’s image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000. But how much do we really understand about pareidolia, the phenomenon of seeing faces and patterns in objects when they aren’t really there? 

A new study from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) delves into this phenomenon, introducing an extensive, human-labeled dataset of 5,000 pareidolic images, far surpassing previous collections. Using this dataset, the team discovered several surprising results about the differences between human and machine perception, and how the ability to see faces in a slice of toast might have saved your distant relatives’ lives.

“Face pareidolia has long fascinated psychologists, but it’s been largely unexplored in the computer vision community,” says Mark Hamilton, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead researcher on the work. “We wanted to create a resource that could help us understand how both humans and AI systems process these illusory faces.”

So what did all of these fake faces reveal? For one, AI models don’t seem to recognize pareidolic faces like we do. Surprisingly, the team found that it wasn’t until they trained algorithms to recognize animal faces that they became significantly better at detecting pareidolic faces. This unexpected connection hints at a possible evolutionary link between our ability to spot animal faces — crucial for survival — and our tendency to see faces in inanimate objects. “A result like this seems to suggest that pareidolia might not arise from human social behavior, but from something deeper: like quickly spotting a lurking tiger, or identifying which way a deer is looking so our primordial ancestors could hunt,” says Hamilton.

A row of five photos of animal faces atop five photos of inanimate objects that look like faces

Another intriguing discovery is what the researchers call the “Goldilocks Zone of Pareidolia,” a class of images where pareidolia is most likely to occur. “There’s a specific range of visual complexity where both humans and machines are most likely to perceive faces in non-face objects,” William T. Freeman, MIT professor of electrical engineering and computer science and principal investigator of the project says. “Too simple, and there’s not enough detail to form a face. Too complex, and it becomes visual noise.”

To uncover this, the team developed an equation that models how people and algorithms detect illusory faces.  When analyzing this equation, they found a clear “pareidolic peak” where the likelihood of seeing faces is highest, corresponding to images that have “just the right amount” of complexity. This predicted “Goldilocks zone” was then validated in tests with both real human subjects and AI face detection systems.

3 photos of clouds above 3 photos of a fruit tart. The left photo of each is “Too Simple” to perceive a face; the middle photo is “Just Right,” and the last photo is “Too Complex"

This new dataset, “Faces in Things,” dwarfs those of previous studies that typically used only 20-30 stimuli. This scale allowed the researchers to explore how state-of-the-art face detection algorithms behaved after fine-tuning on pareidolic faces, showing that not only could these algorithms be edited to detect these faces, but that they could also act as a silicon stand-in for our own brain, allowing the team to ask and answer questions about the origins of pareidolic face detection that are impossible to ask in humans. 

To build this dataset, the team curated approximately 20,000 candidate images from the LAION-5B dataset, which were then meticulously labeled and judged by human annotators. This process involved drawing bounding boxes around perceived faces and answering detailed questions about each face, such as the perceived emotion, age, and whether the face was accidental or intentional. “Gathering and annotating thousands of images was a monumental task,” says Hamilton. “Much of the dataset owes its existence to my mom,” a retired banker, “who spent countless hours lovingly labeling images for our analysis.”

The study also has potential applications in improving face detection systems by reducing false positives, which could have implications for fields like self-driving cars, human-computer interaction, and robotics. The dataset and models could also help areas like product design, where understanding and controlling pareidolia could create better products. “Imagine being able to automatically tweak the design of a car or a child’s toy so it looks friendlier, or ensuring a medical device doesn’t inadvertently appear threatening,” says Hamilton.

“It’s fascinating how humans instinctively interpret inanimate objects with human-like traits. For instance, when you glance at an electrical socket, you might immediately envision it singing, and you can even imagine how it would ‘move its lips.’ Algorithms, however, don’t naturally recognize these cartoonish faces in the same way we do,” says Hamilton. “This raises intriguing questions: What accounts for this difference between human perception and algorithmic interpretation? Is pareidolia beneficial or detrimental? Why don’t algorithms experience this effect as we do? These questions sparked our investigation, as this classic psychological phenomenon in humans had not been thoroughly explored in algorithms.”

As the researchers prepare to share their dataset with the scientific community, they’re already looking ahead. Future work may involve training vision-language models to understand and describe pareidolic faces, potentially leading to AI systems that can engage with visual stimuli in more human-like ways.

“This is a delightful paper! It is fun to read and it makes me think. Hamilton et al. propose a tantalizing question: Why do we see faces in things?” says Pietro Perona, the Allen E. Puckett Professor of Electrical Engineering at Caltech, who was not involved in the work. “As they point out, learning from examples, including animal faces, goes only half-way to explaining the phenomenon. I bet that thinking about this question will teach us something important about how our visual system generalizes beyond the training it receives through life.”

Hamilton and Freeman’s co-authors include Simon Stent, staff research scientist at the Toyota Research Institute; Ruth Rosenholtz, principal research scientist in the Department of Brain and Cognitive Sciences, NVIDIA research scientist, and former CSAIL member; and CSAIL affiliates postdoc Vasha DuTell, Anne Harrington MEng ’23, and Research Scientist Jennifer Corbett. Their work was supported, in part, by the National Science Foundation and the CSAIL MEnTorEd Opportunities in Research (METEOR) Fellowship, while being sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator. The MIT SuperCloud and Lincoln Laboratory Supercomputing Center provided HPC resources for the researchers’ results.

This work is being presented this week at the European Conference on Computer Vision.

Helping robots zero in on the objects that matter

Imagine having to straighten up a messy kitchen, starting with a counter littered with sauce packets. If your goal is to wipe the counter clean, you might sweep up the packets as a group. If, however, you wanted to first pick out the mustard packets before throwing the rest away, you would sort more discriminately, by sauce type. And if, among the mustards, you had a hankering for Grey Poupon, finding this specific brand would entail a more careful search.

MIT engineers have developed a method that enables robots to make similarly intuitive, task-relevant decisions.

The team’s new approach, named Clio, enables a robot to identify the parts of a scene that matter, given the tasks at hand. With Clio, a robot takes in a list of tasks described in natural language and, based on those tasks, it then determines the level of granularity required to interpret its surroundings and “remember” only the parts of a scene that are relevant.

In real experiments ranging from a cluttered cubicle to a five-story building on MIT’s campus, the team used Clio to automatically segment a scene at different levels of granularity, based on a set of tasks specified in natural-language prompts such as “move rack of magazines” and “get first aid kit.”

The team also ran Clio in real-time on a quadruped robot. As the robot explored an office building, Clio identified and mapped only those parts of the scene that related to the robot’s tasks (such as retrieving a dog toy while ignoring piles of office supplies), allowing the robot to grasp the objects of interest.

Clio is named after the Greek muse of history, for its ability to identify and remember only the elements that matter for a given task. The researchers envision that Clio would be useful in many situations and environments in which a robot would have to quickly survey and make sense of its surroundings in the context of its given task.

“Search and rescue is the motivating application for this work, but Clio can also power domestic robots and robots working on a factory floor alongside humans,” says Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), and director of the MIT SPARK Laboratory. “It’s really about helping the robot understand the environment and what it has to remember in order to carry out its mission.”

The team details their results in a study appearing today in the journal Robotics and Automation Letters. Carlone’s co-authors include members of the SPARK Lab: Dominic Maggio, Yun Chang, Nathan Hughes, and Lukas Schmid; and members of MIT Lincoln Laboratory: Matthew Trang, Dan Griffith, Carlyn Dougherty, and Eric Cristofalo.

Open fields

Huge advances in the fields of computer vision and natural language processing have enabled robots to identify objects in their surroundings. But until recently, robots were only able to do so in “closed-set” scenarios, where they are programmed to work in a carefully curated and controlled environment, with a finite number of objects that the robot has been pretrained to recognize.

In recent years, researchers have taken a more “open” approach to enable robots to recognize objects in more realistic settings. In the field of open-set recognition, researchers have leveraged deep-learning tools to build neural networks that can process billions of images from the internet, along with each image’s associated text (such as a friend’s Facebook picture of a dog, captioned “Meet my new puppy!”).

From millions of image-text pairs, a neural network learns from, then identifies, those segments in a scene that are characteristic of certain terms, such as a dog. A robot can then apply that neural network to spot a dog in a totally new scene.

But a challenge still remains as to how to parse a scene in a useful way that is relevant for a particular task.

“Typical methods will pick some arbitrary, fixed level of granularity for determining how to fuse segments of a scene into what you can consider as one ‘object,’” Maggio says. “However, the granularity of what you call an ‘object’ is actually related to what the robot has to do. If that granularity is fixed without considering the tasks, then the robot may end up with a map that isn’t useful for its tasks.”

Information bottleneck

With Clio, the MIT team aimed to enable robots to interpret their surroundings with a level of granularity that can be automatically tuned to the tasks at hand.

For instance, given a task of moving a stack of books to a shelf, the robot should be able to  determine that the entire stack of books is the task-relevant object. Likewise, if the task were to move only the green book from the rest of the stack, the robot should distinguish the green book as a single target object and disregard the rest of the scene — including the other books in the stack.

The team’s approach combines state-of-the-art computer vision and large language models comprising neural networks that make connections among millions of open-source images and semantic text. They also incorporate mapping tools that automatically split an image into many small segments, which can be fed into the neural network to determine if certain segments are semantically similar. The researchers then leverage an idea from classic information theory called the “information bottleneck,” which they use to compress a number of image segments in a way that picks out and stores segments that are semantically most relevant to a given task.

“For example, say there is a pile of books in the scene and my task is just to get the green book. In that case we push all this information about the scene through this bottleneck and end up with a cluster of segments that represent the green book,” Maggio explains. “All the other segments that are not relevant just get grouped in a cluster which we can simply remove. And we’re left with an object at the right granularity that is needed to support my task.”

The researchers demonstrated Clio in different real-world environments.

“What we thought would be a really no-nonsense experiment would be to run Clio in my apartment, where I didn’t do any cleaning beforehand,” Maggio says.

The team drew up a list of natural-language tasks, such as “move pile of clothes” and then applied Clio to images of Maggio’s cluttered apartment. In these cases, Clio was able to quickly segment scenes of the apartment and feed the segments through the Information Bottleneck algorithm to identify those segments that made up the pile of clothes.

They also ran Clio on Boston Dynamic’s quadruped robot, Spot. They gave the robot a list of tasks to complete, and as the robot explored and mapped the inside of an office building, Clio ran in real-time on an on-board computer mounted to Spot, to pick out segments in the mapped scenes that visually relate to the given task. The method generated an overlaying map showing just the target objects, which the robot then used to approach the identified objects and physically complete the task.

“Running Clio in real-time was a big accomplishment for the team,” Maggio says. “A lot of prior work can take several hours to run.”

Going forward, the team plans to adapt Clio to be able to handle higher-level tasks and build upon recent advances in photorealistic visual scene representations.

“We’re still giving Clio tasks that are somewhat specific, like ‘find deck of cards,’” Maggio says. “For search and rescue, you need to give it more high-level tasks, like ‘find survivors,’ or ‘get power back on.’ So, we want to get to a more human-level understanding of how to accomplish more complex tasks.”

This research was supported, in part, by the U.S. National Science Foundation, the Swiss National Science Foundation, MIT Lincoln Laboratory, the U.S. Office of Naval Research, and the U.S. Army Research Lab Distributed and Collaborative Intelligent Systems and Technology Collaborative Research Alliance.