Jens Stoltenberg — now Norway's finance minister — urged the Trump administration to lift a stop-work order on the New York offshore wind project.

Images of the U.S. president pitching oil and gas agreements in the Middle East last week contrasted with China's effort to expand its clean energy commitments with Brazil.

Senate Democrats gave the EPA administrator an earful over Trump administration efforts to cancel spending.

Democrat Wes Moore cited the state's budget woes in vetoing a top priority of climate hawks.

The record comes as the EU has decided to delay anti-deforestation rules and pull back other environmental protections in a bid to boost economic competitiveness.

The quick melt caused much of the snow to change to vapor, which has robbed rivers, streams and reservoirs of runoff needed to replenish water supplies.

The U.S. Gelogical Survey calls amphibian declines "a global phenomenon" — one that's been underway in the U.S. since at least the 1960s.

The deal marks one of the earliest under the Paris Agreement to trade a carbon credit known as ITMOs — internationally transferred mitigation outcomes.

Humans naturally learn by making connections between sight and sound. For instance, we can watch someone playing the cello and recognize that the cellist’s movements are generating the music we hear.

A new approach developed by researchers from MIT and elsewhere improves an AI model’s ability to learn in this same fashion. This could be useful in applications such as journalism and film production, where the model could help with curating multimodal content through automatic video and audio retrieval.

In the longer term, this work could be used to improve a robot’s ability to understand real-world environments, where auditory and visual information are often closely connected.

Improving upon prior work from their group, the researchers created a method that helps machine-learning models align corresponding audio and visual data from video clips without the need for human labels.

They adjusted how their original model is trained so it learns a finer-grained correspondence between a particular video frame and the audio that occurs in that moment. The researchers also made some architectural tweaks that help the system balance two distinct learning objectives, which improves performance.

Taken together, these relatively simple improvements boost the accuracy of their approach in video retrieval tasks and in classifying the action in audiovisual scenes. For instance, the new method could automatically and precisely match the sound of a door slamming with the visual of it closing in a video clip.

“We are building AI systems that can process the world like humans do, in terms of having both audio and visual information coming in at once and being able to seamlessly process both modalities. Looking forward, if we can integrate this audio-visual technology into some of the tools we use on a daily basis, like large language models, it could open up a lot of new applications,” says Andrew Rouditchenko, an MIT graduate student and co-author of a paper on this research.

He is joined on the paper by lead author Edson Araujo, a graduate student at Goethe University in Germany; Yuan Gong, a former MIT postdoc; Saurabhchand Bhati, a current MIT postdoc; Samuel Thomas, Brian Kingsbury, and Leonid Karlinsky of IBM Research; Rogerio Feris, principal scientist and manager at the MIT-IBM Watson AI Lab; James Glass, senior research scientist and head of the Spoken Language Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Hilde Kuehne, professor of computer science at Goethe University and an affiliated professor at the MIT-IBM Watson AI Lab. The work will be presented at the Conference on Computer Vision and Pattern Recognition.

Syncing up

This work builds upon a machine-learning method the researchers developed a few years ago, which provided an efficient way to train a multimodal model to simultaneously process audio and visual data without the need for human labels.

The researchers feed this model, called CAV-MAE, unlabeled video clips and it encodes the visual and audio data separately into representations called tokens. Using the natural audio from the recording, the model automatically learns to map corresponding pairs of audio and visual tokens close together within its internal representation space.

They found that using two learning objectives balances the model’s learning process, which enables CAV-MAE to understand the corresponding audio and visual data while improving its ability to recover video clips that match user queries.

But CAV-MAE treats audio and visual samples as one unit, so a 10-second video clip and the sound of a door slamming are mapped together, even if that audio event happens in just one second of the video.

In their improved model, called CAV-MAE Sync, the researchers split the audio into smaller windows before the model computes its representations of the data, so it generates separate representations that correspond to each smaller window of audio.

During training, the model learns to associate one video frame with the audio that occurs during just that frame.

“By doing that, the model learns a finer-grained correspondence, which helps with performance later when we aggregate this information,” Araujo says.

They also incorporated architectural improvements that help the model balance its two learning objectives.

Adding “wiggle room”

The model incorporates a contrastive objective, where it learns to associate similar audio and visual data, and a reconstruction objective which aims to recover specific audio and visual data based on user queries.

In CAV-MAE Sync, the researchers introduced two new types of data representations, or tokens, to improve the model’s learning ability.

They include dedicated “global tokens” that help with the contrastive learning objective and dedicated “register tokens” that help the model focus on important details for the reconstruction objective.

“Essentially, we add a bit more wiggle room to the model so it can perform each of these two tasks, contrastive and reconstructive, a bit more independently. That benefitted overall performance,” Araujo adds.

While the researchers had some intuition these enhancements would improve the performance of CAV-MAE Sync, it took a careful combination of strategies to shift the model in the direction they wanted it to go.

“Because we have multiple modalities, we need a good model for both modalities by themselves, but we also need to get them to fuse together and collaborate,” Rouditchenko says.

In the end, their enhancements improved the model’s ability to retrieve videos based on an audio query and predict the class of an audio-visual scene, like a dog barking or an instrument playing.

Its results were more accurate than their prior work, and it also performed better than more complex, state-of-the-art methods that require larger amounts of training data.

“Sometimes, very simple ideas or little patterns you see in the data have big value when applied on top of a model you are working on,” Araujo says.

In the future, the researchers want to incorporate new models that generate better data representations into CAV-MAE Sync, which could improve performance. They also want to enable their system to handle text data, which would be an important step toward generating an audiovisual large language model.

This work is funded, in part, by the German Federal Ministry of Education and Research and the MIT-IBM Watson AI Lab.

On Dec. 21, 2022, just as peak holiday season travel was getting underway, Southwest Airlines went through a cascading series of failures in their scheduling, initially triggered by severe winter weather in the Denver area. But the problems spread through their network, and over the course of the next 10 days the crisis ended up stranding over 2 million passengers and causing losses of $750 million for the airline.

How did a localized weather system end up triggering such a widespread failure? Researchers at MIT have examined this widely reported failure as an example of cases where systems that work smoothly most of the time suddenly break down and cause a domino effect of failures. They have now developed a computational system for using the combination of sparse data about a rare failure event, in combination with much more extensive data on normal operations, to work backwards and try to pinpoint the root causes of the failure, and hopefully be able to find ways to adjust the systems to prevent such failures in the future.

The findings were presented at the International Conference on Learning Representations (ICLR), which was held in Singapore from April 24-28 by MIT doctoral student Charles Dawson, professor of aeronautics and astronautics Chuchu Fan, and colleagues from Harvard University and the University of Michigan.

“The motivation behind this work is that it’s really frustrating when we have to interact with these complicated systems, where it’s really hard to understand what’s going on behind the scenes that’s creating these issues or failures that we’re observing,” says Dawson.

The new work builds on previous research from Fan’s lab, where they looked at problems involving hypothetical failure prediction problems, she says, such as with groups of robots working together on a task, or complex systems such as the power grid, looking for ways to predict how such systems may fail. “The goal of this project,” Fan says, “was really to turn that into a diagnostic tool that we could use on real-world systems.”

The idea was to provide a way that someone could “give us data from a time when this real-world system had an issue or a failure,” Dawson says, “and we can try to diagnose the root causes, and provide a little bit of a look behind the curtain at this complexity.”

The intent is for the methods they developed “to work for a pretty general class of cyber-physical problems,” he says. These are problems in which “you have an automated decision-making component interacting with the messiness of the real world,” he explains. There are available tools for testing software systems that operate on their own, but the complexity arises when that software has to interact with physical entities going about their activities in a real physical setting, whether it be the scheduling of aircraft, the movements of autonomous vehicles, the interactions of a team of robots, or the control of the inputs and outputs on an electric grid. In such systems, what often happens, he says, is that “the software might make a decision that looks OK at first, but then it has all these domino, knock-on effects that make things messier and much more uncertain.”

One key difference, though, is that in systems like teams of robots, unlike the scheduling of airplanes, “we have access to a model in the robotics world,” says Fan, who is a principal investigator in MIT’s Laboratory for Information and Decision Systems (LIDS). “We do have some good understanding of the physics behind the robotics, and we do have ways of creating a model” that represents their activities with reasonable accuracy. But airline scheduling involves processes and systems that are proprietary business information, and so the researchers had to find ways to infer what was behind the decisions, using only the relatively sparse publicly available information, which essentially consisted of just the actual arrival and departure times of each plane.

“We have grabbed all this flight data, but there is this entire system of the scheduling system behind it, and we don’t know how the system is working,” Fan says. And the amount of data relating to the actual failure is just several day’s worth, compared to years of data on normal flight operations.

The impact of the weather events in Denver during the week of Southwest’s scheduling crisis clearly showed up in the flight data, just from the longer-than-normal turnaround times between landing and takeoff at the Denver airport. But the way that impact cascaded though the system was less obvious, and required more analysis. The key turned out to have to do with the concept of reserve aircraft.

Airlines typically keep some planes in reserve at various airports, so that if problems are found with one plane that is scheduled for a flight, another plane can be quickly substituted. Southwest uses only a single type of plane, so they are all interchangeable, making such substitutions easier. But most airlines operate on a hub-and-spoke system, with a few designated hub airports where most of those reserve aircraft may be kept, whereas Southwest does not use hubs, so their reserve planes are more scattered throughout their network. And the way those planes were deployed turned out to play a major role in the unfolding crisis.

“The challenge is that there’s no public data available in terms of where the aircraft are stationed throughout the Southwest network,” Dawson says. “What we’re able to find using our method is, by looking at the public data on arrivals, departures, and delays, we can use our method to back out what the hidden parameters of those aircraft reserves could have been, to explain the observations that we were seeing.”

What they found was that the way the reserves were deployed was a “leading indicator” of the problems that cascaded in a nationwide crisis. Some parts of the network that were affected directly by the weather were able to recover quickly and get back on schedule. “But when we looked at other areas in the network, we saw that these reserves were just not available, and things just kept getting worse.”

For example, the data showed that Denver’s reserves were rapidly dwindling because of the weather delays, but then “it also allowed us to trace this failure from Denver to Las Vegas,” he says. While there was no severe weather there, “our method was still showing us a steady decline in the number of aircraft that were able to serve flights out of Las Vegas.”

He says that “what we found was that there were these circulations of aircraft within the Southwest network, where an aircraft might start the day in California and then fly to Denver, and then end the day in Las Vegas.” What happened in the case of this storm was that the cycle got interrupted. As a result, “this one storm in Denver breaks the cycle, and suddenly the reserves in Las Vegas, which is not affected by the weather, start to deteriorate.”

In the end, Southwest was forced to take a drastic measure to resolve the problem: They had to do a “hard reset” of their entire system, canceling all flights and flying empty aircraft around the country to rebalance their reserves.

Working with experts in air transportation systems, the researchers developed a model of how the scheduling system is supposed to work. Then, “what our method does is, we’re essentially trying to run the model backwards.” Looking at the observed outcomes, the model allows them to work back to see what kinds of initial conditions could have produced those outcomes.

While the data on the actual failures were sparse, the extensive data on typical operations helped in teaching the computational model “what is feasible, what is possible, what’s the realm of physical possibility here,” Dawson says. “That gives us the domain knowledge to then say, in this extreme event, given the space of what’s possible, what’s the most likely explanation” for the failure.

This could lead to a real-time monitoring system, he says, where data on normal operations are constantly compared to the current data, and determining what the trend looks like. “Are we trending toward normal, or are we trending toward extreme events?” Seeing signs of impending issues could allow for preemptive measures, such as redeploying reserve aircraft in advance to areas of anticipated problems.

Work on developing such systems is ongoing in her lab, Fan says. In the meantime, they have produced an open-source tool for analyzing failure systems, called CalNF, which is available for anyone to use. Meanwhile Dawson, who earned his doctorate last year, is working as a postdoc to apply the methods developed in this work to understanding failures in power networks.

The research team also included Max Li from the University of Michigan and Van Tran from Harvard University. The work was supported by NASA, the Air Force Office of Scientific Research, and the MIT-DSTA program.

We’ve all felt the sting of guilt when fruit and vegetables go bad before we could eat them. Now, researchers from MIT and the Singapore-MIT Alliance for Research and Technology (SMART) have shown they can extend the shelf life of harvested plants by injecting them with melatonin using biodegradable microneedles.

That’s a big deal because the problem of food waste goes way beyond our salads. More than 30 percent of the world’s food is lost after it’s harvested — enough to feed more than 1 billion people. Refrigeration is the most common way to preserve foods, but it requires energy and infrastructure that many regions of the world can’t afford or lack access to.

The researchers believe their system could offer an alternative or complement to refrigeration. Central to their approach are patches of silk microneedles. The microneedles can get through the tough, waxy skin of plants without causing a stress response, and deliver precise amounts of melatonin into plants’ inner tissues.

“This is the first time that we’ve been able to apply these microneedles to extend the shelf life of a fresh-cut crop,” says Benedetto Marelli, the study’s senior author, associate professor of civil and environmental engineering at MIT, and the director of the Wild Cards mission of the MIT Climate Project. “We thought we could use this technology to deliver something that could regulate or control the plant’s post-harvest physiology. Eventually, we looked at hormones, and melatonin is already used by plants to regulate such functions. The food we waste could feed about 1.6 billion people. Even in the U.S., this approach could one day expand access to healthy foods.”

For the study, which appears today in Nano Letters, Marelli and researchers from SMART applied small patches of the microneedles containing melatonin to the base of the leafy vegetable pak choy. After application, the researchers found the melatonin was able to extend the vegetables’ shelf life by four days at room temperature and 10 days when refrigerated, which could allow more crops to reach consumers before they’re wasted.

“Post-harvest waste is a huge issue. This problem is extremely important in emerging markets around Africa and Southeast Asia, where many crops are produced but can't be maintained in the journey from farms to markets,” says Sarojam Rajani, co-senior author of the study and a senior principal investigator at the Temasek Life Sciences Laboratory in Singapore.

Plant destressors

For years, Marelli’s lab has been exploring the use of silk microneedles for things like delivering nutrients to crops and monitoring plant health. Microneedles made from silk fibroin protein are nontoxic and biodegradable, and Marelli’s previous work has described ways of manufacturing them at scale.

To test microneedle’s ability to extend the shelf life of food, the researchers wanted to study their ability to deliver a hormone known to affect the senescence process. Aside from helping humans sleep, melatonin is also a natural hormone in many plants that helps them regulate growth and aging.

“The dose of melatonin we’re delivering is so low that it’s fully metabolized by the crops, so it would not significantly increase the amount of melatonin normally present in the food; we would not ingest more melatonin than usual,” Marelli says. “We chose pak choy because it's a very important crop in Asia, and also because pak choy is very perishable.”

Pak choy is typically harvested by cutting the leafy plant from the root system, exposing the shoot base that provides easy access to vascular bundles which distribute water and nutrients to the rest of the plant. To begin their study, the researchers first used their microneedles to inject a fluorescent dye into the base to confirm that vasculature could spread the dye throughout the plant.

The researchers then compared the shelf life of regular pak choy plants and plants that had been sprayed with or dipped into melatonin, finding no difference.

With their baseline shelf life established, the researchers applied small patches of the melatonin-filled microneedles to the bottom of pak choy plants by hand. They then stored the treated plants, along with controls, in plastic boxes both at room temperature and under refrigeration.

The team evaluated the plants by monitoring their weight, visual appearance, and concentration of chlorophyll, a green pigment that decreases as plants age.

At room temperature, the leaves of the untreated control group began yellowing within two or three days. By the fourth day, the yellowing accelerated to the point that the plants likely could not be sold. Plants treated with the melatonin-loaded silk microneedles, in contrast, remained green on day five, and the yellowing process was significantly delayed. The weight loss and chlorophyll reduction of treated plants also slowed significantly at room temperature. Overall, the researchers estimated the microneedle-treated plants retained their saleable value until the eighth day.

“We clearly saw we could enhance the shelf life of pak choy without the cold chain,” Marelli says.

In refrigerated conditions of about 40 degrees Fahrenheit, plant yellowing was delayed by about five days on average, with treated plants remaining relatively green until day 25.

“Spectrophotometric analysis of the plants indicated the treated plants had higher antioxidant activity, while gene analysis showed the melatonin set off a protective chain reaction inside the plants, preserving chlorophyll and adjusting hormones to slow senescence,” says Monika Jangir, co-first author and former postdoc at the Temasek Life Sciences Laboratory.

“We studied melatonin’s effects and saw it improves the stress response of the plant after it’s been cut, so it’s basically decreasing the stress that plant’s experience, and that extends its shelf life,” says Yangyang Han, co-first author and research scientist at the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group at SMART.

Toward postharvest preservation

While the microneedles could make it possible to minimize waste when compared to other application methods like spraying or dipping crops, the researchers say more work is needed to deploy microneedles at scale. For instance, although the researchers applied the microneedle patches by hand in this experiment, the patches could be applied using tractors, autonomous drones, and other farming equipment in the future.

“For this to be widely adopted, we’d need to reach a performance versus cost threshold to justify its use,” Marelli explains. “This method would need to become cheap enough to be used by farmers regularly.”

Moving forward, the research team plans to study the effects of a variety of hormones on different crops using its microneedle delivery technology. The team believes the technique should work with all kinds of produce.

“We’re going to continue to analyze how we can increase the impact this can have on the value and quality of crops,” Marelli says. “For example, could this let us modulate the nutritional values of the crop, how it’s shaped, its texture, etc.? We're also going to continue looking into scaling up the technology so this can be used in the field.”

The work was supported by the Singapore-MIT Alliance for Research and Technology (SMART) and the National Research Foundation of Singapore.

I already knew about the declining response rate for polls and surveys. The percentage of AI bots that respond to surveys is also increasing.

Solutions are hard:

1. Make surveys less boring.
We need to move past bland, grid-filled surveys and start designing experiences people actually want to complete. That means mobile-first layouts, shorter runtimes, and maybe even a dash of storytelling. TikTok or dating app style surveys wouldn’t be a bad idea or is that just me being too much Gen Z?

2. Bot detection.
There’s a growing toolkit of ways to spot AI-generated responses—using things like response entropy, writing style patterns or even metadata like keystroke timing. Platforms should start integrating these detection tools more widely. Ideally, you introduce an element that only humans can do, e.g., you have to pick up your price somewhere in-person. Btw, note that these bots can easily be designed to find ways around the most common detection tactics such as Captcha’s, timed responses and postcode and IP recognition. Believe me, way less code than you suspect is needed to do this...

The adminstration's abrupt decision allowing Empire Wind 1 to proceed has officials wondering if New York Gov. Kathy Hochul cut a deal with the president.

Prices are expected to remain low for the state auction Wednesday, giving the state less money to cut pollution and address climate change.

A gradual build-out of charging stations can reduce the initial load on the electric grid and provide a haven for early adopters of green trucks.

A top-ranking official told the new FEMA Review Council that tasks take longer and divert from disaster response.

The EPA administrator told lawmakers that “multiple entities” have expressed interest in taking over the energy efficiency program for appliances.

Its auto plant in Marysville, Ohio, will be adapted to produce both electric vehicles and hybrids under the new plan.

Up to 12 percent of India's greenhouse gas emission come from steelmaking. That number could double in five years.

Officials are warning that the situation could get worse in the summer and urge residents to use water sparingly while showering, cleaning or washing dishes.