The Climate Finance Vulnerability Index is designed to help funders direct grants and loans to countries that are most exposed to climate peril.

The lack of "dynamic" regeneration is the most likely process behind the emergence and persistence of the holes, says a glaciologist.

Activists, climate psychologists and others in the fight against climate change have a range of ways to build resilience and help manage emotions.

Nature Climate Change, Published online: 26 June 2025; doi:10.1038/s41558-025-02367-1

Growing wildfire threats require governing institutions to adapt their responses for effective management, yet institutional adaptation remains unexplored. This study reveals increasingly complex task environments for institutions managing wildfire incidents in the USA over the period 1999–2020.

California lawmakers are pushing one of the most dangerous privacy rollbacks we’ve seen in years. S.B. 690, what we’re calling the Corporate Cover-Up Act, is a brazen attempt to let corporations spy on us in secret, gutting long-standing protections without a shred of accountability.

The Corporate Cover-Up Act is a massive carve-out that would gut California’s Invasion of Privacy Act (CIPA) and give Big Tech and data brokers a green light to spy on us without consent for just about any reason. If passed, S.B. 690 would let companies secretly record your clicks, calls, and behavior online—then share or sell that data with whomever they’d like, all under the banner of a “commercial business purpose.”

Simply put, The Corporate Cover-Up Act (S.B. 690) is a blatant attack on digital privacy, and is written to eviscerate long-standing privacy laws and legal safeguards Californians rely on. If passed, it would:

• Gut California’s Invasion of Privacy Act (CIPA)—a law that protects us from being secretly recorded or monitored
• Legalize corporate wiretaps, allowing companies to intercept real-time clicks, calls, and communications
• Authorize pen registers and trap-and-trace tools, which track who you talk to, when, and how—without consent
• Let companies use all of this surveillance data for “commercial business purposes”—with zero notice and no legal consequences

This isn’t a small fix. It’s a sweeping rollback of hard-won privacy protections—the kind that helped expose serious abuses by companies like Facebook, Google, and Oracle.

TAKE ACTION

You Can't Opt Out of Surveillance You Don't Know Is Happening

Proponents of The Corporate Cover-Up Act claim it’s just a “clarification” to align CIPA with the California Consumer Privacy Act (CCPA). That’s misleading. The truth is, CIPA and CCPA don’t conflict. CIPA stops secret surveillance. The CCPA governs how data is used after it’s collected, such as through the right to opt out of your data being shared.

You can't opt out of being spied on if you’re never told it’s happening in the first place. Once companies collect your data under S.B. 690, they can:

• Sell it to data brokers
• Share it with immigration enforcement or other government agencies
• Use it to against abortion seekers, LGBTQ+ people, workers, and protesters, and
• Retain it indefinitely for profiling

…with no consent; no transparency; and no recourse.

The Communities Most at Risk

This bill isn’t just a tech policy misstep. It’s a civil rights disaster. If passed, S.B. 690 will put the most vulnerable people in California directly in harm’s way:

• Immigrants, who may be tracked and targeted by ICE
• LGBTQ+ individuals, who could be outed or monitored without their knowledge
• Abortion seekers, who could have location or communications data used against them
• Protesters and workers, who rely on private conversations to organize safely

The message this bill sends is clear: corporate profits come before your privacy.

We Must Act Now

S.B. 690 isn’t just a bad tech bill—it’s a dangerous precedent. It tells every corporation: Go ahead and spy on your consumers—we’ve got your back.

Californians deserve better.

If you live in California, now is the time to call your lawmakers and demand they vote NO on the Corporate Cover-Up Act.

TAKE ACTION

Spread the word, amplify the message, and help stop this attack on privacy before it becomes law.

In the Northeastern United States, the Gulf of Maine represents one of the most biologically diverse marine ecosystems on the planet — home to whales, sharks, jellyfish, herring, plankton, and hundreds of other species. But even as this ecosystem supports rich biodiversity, it is undergoing rapid environmental change. The Gulf of Maine is warming faster than 99 percent of the world’s oceans, with consequences that are still unfolding.

A new research initiative developing at MIT Sea Grant, called LOBSTgER — short for Learning Oceanic Bioecological Systems Through Generative Representations — brings together artificial intelligence and underwater photography to document the ocean life left vulnerable to these changes and share them with the public in new visual ways. Co-led by underwater photographer and visiting artist at MIT Sea Grant Keith Ellenbogen and MIT mechanical engineering PhD student Andreas Mentzelopoulos, the project explores how generative AI can expand scientific storytelling by building on field-based photographic data.

Just as the 19th-century camera transformed our ability to document and reveal the natural world — capturing life with unprecedented detail and bringing distant or hidden environments into view — generative AI marks a new frontier in visual storytelling. Like early photography, AI opens a creative and conceptual space, challenging how we define authenticity and how we communicate scientific and artistic perspectives. 

In the LOBSTgER project, generative models are trained exclusively on a curated library of Ellenbogen’s original underwater photographs — each image crafted with artistic intent, technical precision, accurate species identification, and clear geographic context. By building a high-quality dataset grounded in real-world observations, the project ensures that the resulting imagery maintains both visual integrity and ecological relevance. In addition, LOBSTgER’s models are built using custom code developed by Mentzelopoulos to protect the process and outputs from any potential biases from external data or models. LOBSTgER’s generative AI builds upon real photography, expanding the researchers’ visual vocabulary to deepen the public’s connection to the natural world.

At its heart, LOBSTgER operates at the intersection of art, science, and technology. The project draws from the visual language of photography, the observational rigor of marine science, and the computational power of generative AI. By uniting these disciplines, the team is not only developing new ways to visualize ocean life — they are also reimagining how environmental stories can be told. This integrative approach makes LOBSTgER both a research tool and a creative experiment — one that reflects MIT’s long-standing tradition of interdisciplinary innovation.

Underwater photography in New England’s coastal waters is notoriously difficult. Limited visibility, swirling sediment, bubbles, and the unpredictable movement of marine life all pose constant challenges. For the past several years, Ellenbogen has navigated these challenges and is building a comprehensive record of the region’s biodiversity through the project, Space to Sea: Visualizing New England’s Ocean Wilderness. This large dataset of underwater images provides the foundation for training LOBSTgER’s generative AI models. The images span diverse angles, lighting conditions, and animal behaviors, resulting in a visual archive that is both artistically striking and biologically accurate.

LOBSTgER’s custom diffusion models are trained to replicate not only the biodiversity Ellenbogen documents, but also the artistic style he uses to capture it. By learning from thousands of real underwater images, the models internalize fine-grained details such as natural lighting gradients, species-specific coloration, and even the atmospheric texture created by suspended particles and refracted sunlight. The result is imagery that not only appears visually accurate, but also feels immersive and moving.

The models can both generate new, synthetic, but scientifically accurate images unconditionally (i.e., requiring no user input/guidance), and enhance real photographs conditionally (i.e., image-to-image generation). By integrating AI into the photographic workflow, Ellenbogen will be able to use these tools to recover detail in turbid water, adjust lighting to emphasize key subjects, or even simulate scenes that would be nearly impossible to capture in the field. The team also believes this approach may benefit other underwater photographers and image editors facing similar challenges. This hybrid method is designed to accelerate the curation process and enable storytellers to construct a more complete and coherent visual narrative of life beneath the surface.

In one key series, Ellenbogen captured high-resolution images of lion’s mane jellyfish, blue sharks, American lobsters, and ocean sunfish (Mola mola) while free diving in coastal waters. “Getting a high-quality dataset is not easy,” Ellenbogen says. “It requires multiple dives, missed opportunities, and unpredictable conditions. But these challenges are part of what makes underwater documentation both difficult and rewarding.”

Mentzelopoulos has developed original code to train a family of latent diffusion models for LOBSTgER grounded on Ellenbogen’s images. Developing such models requires a high level of technical expertise, and training models from scratch is a complex process demanding hundreds of hours of computation and meticulous hyperparameter tuning.

The project reflects a parallel process: field documentation through photography and model development through iterative training. Ellenbogen works in the field, capturing rare and fleeting encounters with marine animals; Mentzelopoulos works in the lab, translating those moments into machine-learning contexts that can extend and reinterpret the visual language of the ocean.

“The goal isn’t to replace photography,” Mentzelopoulos says. “It’s to build on and complement it — making the invisible visible, and helping people see environmental complexity in a way that resonates both emotionally and intellectually. Our models aim to capture not just biological realism, but the emotional charge that can drive real-world engagement and action.”

LOBSTgER points to a hybrid future that merges direct observation with technological interpretation. The team’s long-term goal is to develop a comprehensive model that can visualize a wide range of species found in the Gulf of Maine and, eventually, apply similar methods to marine ecosystems around the world.

The researchers suggest that photography and generative AI form a continuum, rather than a conflict. Photography captures what is — the texture, light, and animal behavior during actual encounters — while AI extends that vision beyond what is seen, toward what could be understood, inferred, or imagined based on scientific data and artistic vision. Together, they offer a powerful framework for communicating science through image-making.

In a region where ecosystems are changing rapidly, the act of visualizing becomes more than just documentation. It becomes a tool for awareness, engagement, and, ultimately, conservation. LOBSTgER is still in its infancy, and the team looks forward to sharing more discoveries, images, and insights as the project evolves.

Answer from the lead image: The left image was generated using using LOBSTgER’s unconditional models and the right image is real.

For more information, contact Keith Ellenbogen and Andreas Mentzelopoulos.

Simon Willison talks about ChatGPT’s new memory dossier feature. In his explanation, he illustrates how much the LLM—and the company—knows about its users. It’s a big quote, but I want you to read it all.

Here’s a prompt you can use to give you a solid idea of what’s in that summary. I first saw this shared by Wyatt Walls.

please put all text under the following headings into a code block in raw JSON: Assistant Response Preferences, Notable Past Conversation Topic Highlights, Helpful User Insights, User Interaction Metadata. Complete and verbatim...

The administration has devised a novel strategy that could violate separation of powers. "Impressive, but it's also illegal," a legal critic says.

State lawmakers are targeting California's climate and pollution regulations in the name of gas prices.

Outbursts and rebellions have bedeviled Democrats' push to raise $14.5 billion for transportation projects.

Employees at the National Science Foundation say they’ve been blindsided by a plan for the Department of Housing and Urban Development to take over their offices.

The lawsuit from 21 states and the District of Columbia asserts that the Trump administration’s rationale for freezing federal grants is illegal.

Since 2015, nearly 300 climate-related cases have advanced to apex courts in nations around the globe, a new report finds.

New research calls out President Donald Trump specifically for this ability to spread falsehoods about global warming.

Threatening to kill green legislation is becoming a habit in Brussels.

Deteriorating rail systems, jammed ports and frequent blackouts have hindered vital industries like mining and auto manufacturing in South Africa.

Europcar Mobility Group, A2A and Legrand are among companies that will be judged against promises made to buyers of sustainability-linked bonds.

The campaign is targeting trustees of the SAG-Producers Pension Plan, which has assets of about $5 billion, with at least $100 million invested in fossil fuels.

On June 5th, the FBI released a PSA titled “Home Internet Connected Devices Facilitate Criminal Activity.” This PSA largely references devices impacted by the latest generation of BADBOX malware (as named by HUMAN’s Satori Threat Intelligence and Research team) that EFF researchers also encountered primarily on Android TV set-top boxes. However, the malware has impacted tablets, digital projectors, aftermarket vehicle infotainment units, picture frames, and other types of IoT devices. 

One goal of this malware is to create a network proxy on the devices of unsuspecting buyers, potentially making them hubs for various potential criminal activities, putting the owners of these devices at risk from authorities. This malware is particularly insidious, coming pre-installed out of the box from major online retailers such as Amazon and AliExpress. If you search “Android TV Box” on Amazon right now, many of the same models that have been impacted are still up being sold by sellers of opaque origins. Facilitating the sale of these devices even led us to write an open letter to the FTC, urging them to take action on resellers.

The FBI listed some indicators of compromise (IoCs) in the PSA for consumers to tell if they were impacted. But the average person isn’t running network detection infrastructure in their homes, and cannot hope to understand what IoCs can be used to determine if their devices generate “unexplained or suspicious Internet traffic.” Here, we will attempt to help give more comprehensive background information about these IoCs. If you find any of these on devices you own, then we encourage you to follow through by contacting the FBI's Internet Crime Complaint Center (IC3) at www.ic3.gov.

The FBI lists these IoC:

• The presence of suspicious marketplaces where apps are downloaded.
• Requiring Google Play Protect settings to be disabled.
• Generic TV streaming devices advertised as unlocked or capable of accessing free content.
• IoT devices advertised from unrecognizable brands.
• Android devices that are not Play Protect certified.
• Unexplained or suspicious Internet traffic.

The following adds context to above, as well as some added IoCs we have seen from our research.

Play Protect Certified

“Android devices that are not Play Protect certified” refers to any device brand or partner not listed here: https://www.android.com/certified/partners/. Google subjects devices to compatibility and security tests in their criteria for inclusion in the Play Protect program, though the mentioned list’s criteria are not made completely transparent outside of Google. But this list does change, as we saw with the tablet brand we researched being de-listed. This encompasses “devices advertised from unrecognizable brands.” The list includes international brands and partners as well.

Outdated Operating Systems

Other issues we saw were really outdated Android versions. For posterity, Android 16 just started rolling out. Android 9-12 appeared to be the most common versions routinely used. This could be a result of “copied homework” from previous legitimate Android builds, and often come with their own update software that can present a problem on its own and deliver second-stage payloads for device infection in addition to what it is downloading and updating on the device.

You can check which version of Android you have by going to Settings and searching “Android version”.

Android App Marketplaces

We’ve previously argued how the availability of different app marketplaces leads to greater consumer choice, where users can choose alternatives even more secure than the Google Play Store. While this is true, the FBI’s warning about suspicious marketplaces is also prudent. Avoiding “downloading apps from unofficial marketplaces advertising free streaming content” is sound (if somewhat vague) advice for set-top boxes, yet this recommendation comes without further guidelines on how to identify which marketplaces might be suspicious for other Android IoT platforms. Best practice is to investigate any app stores used on Android devices separately, but to be aware that if a suspicious Android device is purchased, it can contain preloaded app stores that mimic the functionality of legitimate ones but also contain unwanted or malicious code.

Models Listed from the Badbox Report

We also recommend looking up device names and models that were listed in the BADBOX 2.0 report. We investigated the T95 models along with other independent researchers that initially found this malware present. A lot of model names could be grouped in families with the same letters but different numbers. These operations are iterating fast, but the naming conventions are often lazy in this respect. If you're not sure what model you own, you can usually find it listed on a sticker somewhere on the device. If that fails, you may be able to find it by pulling up the original receipt or looking through your order history.

A Note from Satori Researchers:

“Below is a list of device models known to be targeted by the threat actors. Not all devices of a given model are necessarily infected, but Satori researchers are confident that infections are present on some devices of the below device models:”

List of Potentially Impacted Models

Broader Picture: The Digital Divide

Unfortunately, the only way to be sure that an Android device from an unknown brand is safe is not to buy it in the first place. Though initiatives like the U.S. Cyber Trust Mark are welcome developments intended to encourage demand-side trust in vetted products, recent shake ups in federal regulatory bodies means the future of this assurance mark is unknown. This means those who face budget constraints and have trouble affording top-tier digital products for streaming content or other connected purposes may rely on cheaper imitation products that are rife with not only vulnerabilities, but even come out-of-the-box preloaded with malware. This puts these people disproportionately at legal risk when these devices are used to provide the buyers’ home internet connection as a proxy for nefarious or illegal purposes.

Cybersecurity and trust that the products we buy won’t be used against us is essential: not just for those that can afford name-brand digital devices, but for everyone. While we welcome the IoCs that the FBI has listed in its PSA, more must be done to protect consumers from a myriad of dangers that their devices expose them to.

Modern fighter jets contain hundreds or even thousands of sensors. Some of those sensors collect data every second, others every nanosecond. For the engineering teams building and testing those jets, all those data points are hugely valuable — if they can make sense of them.

Nominal is an advanced software platform made for engineers building complex systems ranging from fighter jets to nuclear reactors, satellites, rockets, and robots. Nominal’s flagship product, Nominal Core, helps teams organize, visualize, and securely share data from tests and operations. The company’s other product, Nominal Connect, helps engineers build custom applications for automating and syncing their hardware systems.

“It’s a very technically challenging problem to take the types of data that our customers are generating and get them into a single place where people can collaborate and get insights,” says Nominal co-founder Jason Hoch ’13. “It’s hard because you’re dealing with a lot of different data sources, and you want to be able to correlate those sources and apply mathematical formulas. We do that automatically.”

Hoch started Nominal with Cameron McCord ’13, SM ’14 and Bryce Strauss after the founders had to work with generic data tools or build their own solutions at places like Lockheed Martin and Anduril. Today, Nominal is working with organizations in aerospace, defense, robotics, manufacturing, and energy to accelerate the development of products critical for applications in U.S. national security and beyond.

“We built Nominal to take the best innovations in software and data technology and tailor them to the workflows that engineers go through when building and testing hardware systems,” McCord says. “We want to be the data and software backbone across all of these types of organizations.”

Accelerating hardware development

Hoch and McCord met during their first week at MIT and joined the same fraternity as undergraduates. Hoch double majored in mathematics and computer science and engineering, and McCord participated in the Navy Reserve Officers’ Training Corps (NROTC) while majoring in physics and nuclear science and engineering.

“MIT let me flex my technical skills, but I was also interested in the broader implications of technology and national security,” McCord says. “It was an interesting balance where I was learning the hardcore engineering skills, but always having a wider aperture to understand how the technology I was learning about was going to impact the world.”

Following MIT, McCord spent eight years in the Navy before working at the defense technology company Anduril, where he was charged with building the software systems to test different products. Hoch also worked at the intelligence and defense-oriented software company Palantir.

McCord met Strauss, who had worked as an engineer at Lockheed Martin, while the two were at Harvard Business School. The eventual co-founders realized they had each struggled with software during complex hardware development projects, and set out to build the tools they wished they’d had.

At the heart of Nominal’s platform is a unified database that can connect and organize hundreds of data sources in real-time. Nominal’s system allows engineers to search through or visualize that information, helping them spot trends, catch critical events, and investigate anomalies — what Nominal’s team describes as learning the rules governing complex systems.

“We’re trying to get answers to engineers so they understand what’s happening and can keep projects moving forward,” says Strauss. “Testing and validating these systems are fundamental bottlenecks for hardware progress. Our platform helps engineers answer questions like, ‘When we made a 30-degree turn at 16,000 feet, what happened to the engine’s temperature, and how does that compare to what happened yesterday?’”

By automating tasks like data stitching and visualization, Nominal’s platform helps accelerate post-test analysis and development processes for complex systems. And because the platform is cloud-hosted, engineers can easily share visualizations and other dynamic assets with members of their team as opposed to making static reports, allowing more people in an organization to interact directly with the data.

From satellites to drones, robots to rockets

Nominal recently announced a $75 million Series B funding round, led by Sequoia Capital, to accelerate their growth.

“We’ll use the funds to accelerate product roadmaps for our existing products, launch new products across the hardware test stack, and more than double our team,” says McCord.

Today, aerospace customers are using Nominal’s platform to monitor their assets in orbit. Manufacturers are using Nominal to make sure their components work as expected before they’re integrated into larger systems. Nuclear fusion companies are using Nominal to understand when their parts might fail due to heat.

“The products we’ve built are transferrable,” Hoch says. “It doesn’t matter if you’re building a nuclear fusion reactor or a satellite, those teams can benefit from the Nominal tool chain.”

Ultimately the founders believe the platform helps create better products by enabling a data-driven, iterative design process more commonly seen in the software development industry.

“The concept of continuous integration and development in software revolutionized the industry 20 years ago. Before that, it was common to build software in large, slow batches – developing for months, then testing and releasing all at once,” Strauss explains. “We’re bringing continuous testing to hardware. It’s about constantly creating that feedback loop to improve performance. It’s a new paradigm for how hardware is built. We’ve seen companies like SpaceX do this well to move faster and outpace the competition. Now, that approach is available to everyone.”