Microsoft is warning of a scam involving online payroll systems. Criminals use social engineering to steal people’s credentials, and then divert direct deposits into accounts that they control. Sometimes they do other things to make it harder for the victim to realize what is happening.

I feel like this kind of thing is happening everywhere, with everything. As we move more of our personal and professional lives online, we enable criminals to subvert the very systems we rely on.

The move — revealed in emails and internal drafts — sidelined EPA's deep bench of career economists.

The budge office wanted to weaken curbs on cars' soot- and smog-forming air pollutants as it unraveled a key climate policy.

Negotiators at shipping talks in London were told both they and their countries could be punished unless they voted with the U.S.

An “executive branch machinery that defaulted to caution, process, and reactive strategies” undercut the ex-president’s massive energy and infrastructure programs, a report by his former staffers details.

At risk is the ability for researchers to forecast dangerous blooms weeks in advance.

The bill would use tolls and gas taxes to expand rail and bus service. Republicans criticized the deal for taking money from highways.

“Clearly, that does damage,” EU Climate Commissioner Wopke Hoekstra told Bloomberg.

Teresa Ribera told ministers reducing the EU’s 2040 target would be an “invitation to waste money.”

Government coalition program says EU Green Deal “is unsustainable in its current form.”

The recommendation, by the former prime minister's policy think tank, is Blair’s third intervention on green policy this year.

More than 70 aircraft are struck by lightning every day. If you happen to be flying when a strike occurs, chances are you won’t feel a thing, thanks to lightning protection measures that are embedded in key zones throughout the aircraft.

Lightning protection systems work well, largely because they are designed for planes with a “tube-and-wing” structure, a simple geometry common to most aircraft today. But future airplanes may not look and fly the same way. The aviation industry is exploring new designs, including blended-wing bodies and truss-braced wings, partly to reduce fuel and weight costs. But researchers don’t yet know how these unconventional designs might respond to lightning strikes.

MIT aerospace engineers are hoping to change that with a new physics-based approach that predicts how lightning would sweep across a plane with any design. The tool then generates a zoning map highlighting sections of an aircraft that would require various degrees of lightning protection, given how they are likely to experience a strike.

“People are starting to conceive aircraft that look very different from what we’re used to, and we can’t apply exactly what we know from historical data to these new configurations because they’re just too different,” says Carmen Guerra-Garcia, associate professor of aeronautics and astronautics (AeroAstro) at MIT. “Physics-based methods are universal. They’re agnostic to the type of geometry or vehicle. This is the path forward to be able to do this lightning zoning and protect future aircraft.”

She and her colleagues report their results in a study appearing this week in IEEE Access. The study’s first author is AeroAstro graduate student Nathanael Jenkins. Other co-authors include Louisa Michael and Benjamin Westin of Boeing Research and Technology.

First strike

When lightning strikes, it first attaches to a part of a plane — typically a sharp edge or extremity — and hangs on for up to a second. During this brief flash, the plane continues speeding through the air, causing the lightning current to “sweep” over parts of its surface, potentially changing in intensity and re-attaching at certain points where the intense current flow could damage vulnerable sections of an aircraft.

In previous work, Guerra-Garcia’s group developed a model to predict the parts of a plane where lightning is most likely to first connect. That work, led by graduate student Sam Austin, established a starting point for the team’s new work, which aims to predict how and where the lightning will then sweep over the plane’s surface. The team next converted their lightning sweep predictions into zoning maps to identify vulnerable regions requiring certain levels of protection.

A typical tube-and-wing plane is divided into three main zones, as classified by the aviation industry. Each zone has a clear description of the level of current it must withstand in order to be certified for flight. Parts of a plane that are more likely to be hit by lightning are generally classified as zone 1 and require more protection, which can include embedded metal foil in the skin of the airplane that conducts away a lightning current.

To date, an airplane’s lightning zones have been determined over many years of flight inspections after lightning strikes and fine-tuning of protection measures. Guerra-Garcia and her colleagues looked to develop a zoning approach based on physics, rather than historical flight data. Such a physics-based mapping could be applied to any shape of aircraft, such as unconventional and largely untested designs, to identify regions that really require reinforcement.

“Protecting aircraft from lightning is heavy,” Jenkins says. “Embedding copper mesh or foil throughout an aircraft is an added weight penalty. And if we had the greatest level of protection for every part of the plane’s surface, the plane would weigh far too much. So zoning is about trying to optimize the weight of the system while also having it be as safe as possible.”

In the zone

For their new approach, the team developed a model to predict the pattern of lightning sweep and the corresponding lightning protection zones, for a given airplane geometry. Starting with a specific airplane shape — in their case, a typical tube-and-wing structure — the researchers simulated the fluid dynamics, or how air would flow around a plane, given a certain speed, altitude, and pitch angle. They also incorporated their previous model that predicts the places where lightning is more likely to initially attach.

For each initial attachment point, the team simulated tens of thousands of potential lightning arcs, or angles from which the current strikes the plane. They then ran the model forward to predict how the tens of thousands of potential strikes would follow the air flow across the plane’s surface. These runs produced a statistical representation of where lightning, striking a specific point on a plane, is likely to flow and potentially cause damage. The team converted this statistical representation into a map of zones of varying vulnerability.

They validated the method on a conventional tube-and-wing structure, showing that the zoning maps generated by the physics-based approach were consistent with what the aviation industry has determined over decades of fine-tuning.

“We now have a physics-based tool that provides some metrics like the probability of lightning attachment and dwell time, which is how long an arc will linger at a specific point,” Guerra-Garcia explains. “We convert those physics metrics into zoning maps to show, if I’m in this red region, the lightning arc will stay for a long time, so that region needs to be heavily protected.”

The team is starting to apply the approach to new geometries, such as blended-wing designs and truss-braced structures. The researchers envision that the tool can help designers incorporate safe and efficient lightning-protection systems early on in the design process.

“Lightning is incredible and terrifying at the same time, and I have full confidence in flying on planes at the moment,” Jenkins says. “I want to have that same confidence in 20 years’ time. So, we need a new way to zone aircraft.”

“With physics-based methods like the ones developed with professor Guerra-Garcia’s group we have the opportunity to shape industry standards and as an industry rely on the underlying physics to develop guidelines for aircraft certification through simulation,” says co-author Louisa Michael of Boeing Technology Innovation. Currently, we are engaging with industrial committees to propose these methods to be included in Aerospace Recommended Practices.”

“Zoning unconventional aircraft is not an easy task,” adds co-author Ben Westin of Boeing Technology Innovation. “But these methods will allow us to confidently identify which threat levels each part of the aircraft needs to be protected against and certified for, and they give our design engineers a platform to do their best work to optimize aircraft design.”

Beyond airplanes, Guerra-Garcia is looking at ways to adapt the lightning protection model to other technologies, including wind turbines.

“About 60 percent of blade losses are due to lightning and will become worse as we move offshore because wind turbines will be even bigger and more susceptible to upward lightning,” she says. “They have many of the same challenges of a flowing gas environment. It’s more complex, and we will apply this same sort of methodology to this space.”

This research was funded, in part, by the Boeing Company.

Quantum computers have the potential to model new molecules and weather patterns better than any computer today. They may also one day accelerate artificial intelligence algorithms at a much lower energy footprint. But anyone interested in using quantum computers faces a steep learning curve that starts with getting access to quantum devices and then figuring out one of the many quantum software programs on the market.

Now qBraid, founded by Kanav Setia and Jason Necaise ’20, is providing a gateway to quantum computing with a platform that gives users access to the leading quantum devices and software. Users can log on to qBraid’s cloud-based interface and connect with quantum devices and other computing resources from leading companies like Nvidia, Microsoft, and IBM. In a few clicks, they can start coding or deploy cutting-edge software that works across devices.

“The mission is to take you from not knowing anything about quantum computing to running your first program on these amazing machines in less than 10 minutes,” Setia says. “We’re a one-stop platform that gives access to everything the quantum ecosystem has to offer. Our goal is to enable anyone — whether they’re enterprise customers, academics, or individual users — to build and ultimately deploy applications.”

Since its founding in June of 2020, qBraid has helped more than 20,000 people in more than 120 countries deploy code on quantum devices. That traction is ultimately helping to drive innovation in a nascent industry that’s expected to play a key role in our future.

“This lowers the barrier to entry for a lot of newcomers,” Setia says. “They can be up and running in a few minutes instead of a few weeks. That’s why we’ve gotten so much adoption around the world. We’re one of the most popular platforms for accessing quantum software and hardware.”

A quantum “software sandbox”

Setia met Necaise while the two interned at IBM. At the time, Necaise was an undergraduate at MIT majoring in physics, while Setia was at Dartmouth College. The two enjoyed working together, and Necaise said if Setia ever started a company, he’d be interested in joining.

A few months later, Setia decided to take him up on the offer. At Dartmouth, Setia had taken one of the first applied quantum computing classes, but students spent weeks struggling to install all the necessary software programs before they could even start coding.

“We hadn’t even gotten close to developing any useful algorithms,” Seita said. “The idea for qBraid was, ‘Why don’t we build a software sandbox in the cloud and give people an easy programming setup out of the box?’ Connection with the hardware would already be done.”

The founders received early support from the MIT Sandbox Innovation Fund and took part in the delta v summer startup accelerator run by the Martin Trust Center for MIT Entrepreneurship.

“Both programs provided us with very strong mentorship,” Setia says. “They give you frameworks on what a startup should look like, and they bring in some of the smartest people in the world to mentor you — people you’d never have access to otherwise.”

Necaise left the company in 2021. Setia, meanwhile, continued to find problems with quantum software outside of the classroom.

“This is a massive bottleneck,” Setia says. “I’d worked on several quantum software programs that pushed out updates or changes, and suddenly all hell broke loose on my codebase. I’d spend two to four weeks jostling with these updates that had almost nothing to do with the quantum algorithms I was working on.”

QBraid started as a platform with pre-installed software that let developers start writing code immediately. The company also added support for version-controlled quantum software so developers could build applications on top without worrying about changes. Over time, qBraid added connections to quantum computers and tools that lets quantum programs run across different devices.

“The pitch was you don’t need to manage a bunch of software or a whole bunch of cloud accounts,” Setia says. “We’re a single platform: the quantum cloud.”

QBraid also launched qBook, a learning platform that offers interactive courses in quantum computing.

“If you see a piece of code you like, you just click play and the code runs,” Setia says. “You can run a whole bunch of code, modify it on the fly, and you can understand how it works. It runs on laptops, iPads, and phones. A significant portion of our users are from developing countries, and they’re developing applications from their phones.”

Democratizing quantum computing

Today qBraid’s 20,000 users come from over 400 universities and 100 companies around the world. As qBraid’s user base has grown, the company went from integrating quantum computers onto their platform from the outside to creating a quantum operating system, qBraid-OS, that is currently being used by four leading quantum companies.

“We are productizing these quantum computers,” Setia explains. “Many quantum companies are realizing they want to focus their energy completely on the hardware, with us productizing their infrastructure. We’re like the operating system for quantum computers.”

People are using qBraid to build quantum applications in AI and machine learning, to discover new molecules or develop new drugs, and to develop applications in finance and cybersecurity. With every new use case, Setia says qBraid is democratizing quantum computing to create the quantum workforce that will continue to advance the field.

“[In 2018], an article in The New York Times said there were possibly less than 1,000 people in the world that could be called experts in quantum programming,” Setia says. “A lot of people want to access these cutting-edge machines, but they don’t have the right software backgrounds. They are just getting started and want to play with algorithms. QBraid gives those people an easy programming setup out of the box.”

Nature Climate Change, Published online: 04 November 2025; doi:10.1038/s41558-025-02468-x

Human greenhouse gas emissions are raising temperatures and sea levels, collapsing ice sheets and acidifying oceans. Now, research maps out the range of emissions pathways that can limit these changes.

Nature Climate Change, Published online: 04 November 2025; doi:10.1038/s41558-025-02460-5

This study explores pathways of emissions and mitigation compatible with four climate boundaries—planetary boundaries for the climate system. The results highlight the importance of peak emission timing, limitation of carbon budgets as a sole indicator and trade-offs between mitigation options.

Amazon Ring’s upcoming face recognition tool has the potential to violate the privacy rights of millions of people and could result in Amazon breaking state biometric privacy laws.

Ring plans to introduce a feature to its home surveillance cameras called “Familiar Faces,” to identify specific people who come into view of the camera. When turned on, the feature will scan the faces of all people who approach the camera to try and find a match with a list of pre-saved faces. This will include many people who have not consented to a face scan, including friends and family, political canvassers, postal workers, delivery drivers, children selling cookies, or maybe even some people passing on the sidewalk.

When turned on, the feature will scan the faces of all people who approach the camera.

Many biometric privacy laws across the country are clear: Companies need your affirmative consent before running face recognition on you. In at least one state, ordinary people with the help of attorneys can challenge Amazon’s data collection. Where not possible, state privacy regulators should step in.

Sen. Ed Markey (D-Mass.) has already called on Amazon to abandon its plans and sent the company a list of questions. Ring spokesperson Emma Daniels answered written questions posed by EFF, which can be viewed here.

What is Ring’s “Familiar Faces”?

Amazon describes “Familiar Faces” as a tool that “intelligently recognizes familiar people.” It says this tool will provide camera owners with “personalized context of who is detected, eliminating guesswork and making it effortless to find and review important moments involving specific familiar people.” Amazon plans to release the feature in December.

The feature will allow camera owners to tag particular people so Ring cameras can automatically recognize them in the future. In order for Amazon to recognize particular people, it will need to perform face recognition on every person that steps in front of the camera. Even if a camera owner does not tag a particular face, Amazon says it may retain that biometric information for up to six months. Amazon said it does not currently use the biometric data for “model training or algorithmic purposes.”

In order to biometrically identify you, a company typically will take your image and extract a faceprint by taking tiny measurements of your face and converting that into a series of numbers that is saved for later. When you step in front of a camera again, the company takes a new faceprint and compares it to a list of previous prints to find a match. Other forms of biometric tracking can be done with a scan of your fingertip, eyeball, or even your particular gait.

Amazon has told reporters that the feature will be off by default and that it would be unavailable in certain jurisdictions with the most active biometric privacy enforcement—including the states of Illinois and Texas, and the city of Portland, Oregon. The company would not promise that this feature will remain off by default in the future.

Why is This a Privacy Problem?

Your biometric data, such as your faceprint, are some of the most sensitive pieces of data that a company can collect. Associated risks include mass surveillance, data breach, and discrimination.

Today’s feature to recognize your friend at your front door can easily be repurposed tomorrow for mass surveillance. Ring’s close partnership with police amplifies that threat. For example, in a city dense with face recognition cameras, the entirety of a person’s movements could be tracked with the click of a button, or all people could be identified at a particular location. A recent and unrelated private-public partnership in New Orleans unfortunately shows that mass surveillance through face recognition is not some far flung concern.

Amazon has already announced a related tool called “search party” that can identify and track lost dogs using neighbors’ cameras. A tool like this could be repurposed for law enforcement to track people. At least for now, Amazon says it does not have the technical capability to comply with law enforcement demanding a list of all cameras in which a person has been identified. Though, it complies with other law enforcement demands.

In addition, data breaches are a perpetual concern with any data collection. Biometrics magnify that risk because your face cannot be reset, unlike a password or credit card number. Amazon says it processes and stores biometrics collected by Ring cameras on its own servers, and that it uses comprehensive security measure to protect the data.

Face recognition has also been shown to have higher error rates with certain groups—most prominently with dark-skinned women. Similar technology has also been used to make questionable guesses about a person’s emotions, age, and gender.

Will Ring’s “Familiar Faces” Violate State Biometric Laws?

Any Ring collection of biometric information in states that require opt-in consent poses huge legal risk for the company. Amazon already told reporters that the feature will not be available in Illinois and Texas—strongly suggesting its feature could not survive legal scrutiny there. The company said it is also avoiding Portland, Oregon, which has a biometric privacy law that similar companies have avoided.

Its “familiar faces” feature will necessarily require its cameras to collect a faceprint from of every person who comes into view of an enabled camera, to try and find a match. It is impossible for Amazon to obtain consent from everyone—especially people who do not own Ring cameras. It appears that Amazon will try to unload some consent requirements onto individual camera owners themselves. Amazon says it will provide in-app messages to customers, reminding them to comply with applicable laws. But Amazon—as a company itself collecting, processing, and storing this biometric data—could have its own consent obligations under numerous laws.

Lawsuits against similar features highlight Amazon’s legal risks. In Texas, Google paid $1.375 billion to settle a lawsuit that alleged, among other things, that Google’s Nest cameras "indiscriminately capture the face geometry of any Texan who happens to come into view, including non-users." In Illinois, Facebook paid $650 million and shut down its face recognition tools that automatically scanned Facebook photos—even the faces of non-Facebook users—in order to identify people to recommend tagging. Later, Meta paid another $1.4 billion to settle a similar suit in Texas.

Many states aside from Illinois and Texas now protect biometric data. While the state has never enforced its law, Washington in 2017 passed a biometric privacy law. In 2023, the state passed an ever stronger law that protects biometric privacy, which allows individuals to sue on their own behalf. And at least 16 states have recently passed comprehensive privacy laws that often require companies to obtain opt-in consent for the collection of sensitive data, which typically includes biometric data. For example, in Colorado, a company that jointly with others determines the purpose and means of processing biometric data must obtain consent. Maryland goes farther, and such companies are essentially prohibited from collecting or processing biometric data from bystanders.

Many of these comprehensive laws have numerous loopholes and can only be enforced by state regulators—a glaring weakness facilitated in part by Amazon lobbyists.

Nonetheless, Ring’s new feature provides regulators a clear opportunity to step up to investigate, protect people’s privacy, and test the strength of their laws.

With the rapid advancement of generative artificial intelligence, teachers and school leaders are looking for answers to complicated questions about successfully integrating technology into lessons, while also ensuring students actually learn what they’re trying to teach. 

Justin Reich, an associate professor in MIT’s Comparative Media Studies/Writing program, hopes a new guidebook published by the MIT Teaching Systems Lab can support K-12 educators as they determine what AI policies or guidelines to craft.

“Throughout my career, I’ve tried to be a person who researches education and technology and translates findings for people who work in the field,” says Reich. “When tricky things come along I try to jump in and be helpful.” 

“A Guide to AI in Schools: Perspectives for the Perplexed,” published this fall, was developed with the support of an expert advisory panel and other researchers. The project includes input from more than 100 students and teachers from around the United States, sharing their experiences teaching and learning with new generative AI tools. 

“We’re trying to advocate for an ethos of humility as we examine AI in schools,” Reich says. “We’re sharing some examples from educators about how they’re using AI in interesting ways, some of which might prove sturdy and some of which might prove faulty. And we won’t know which is which for a long time.”

Finding answers to AI and education questions

The guidebook attempts to help K-12 educators, students, school leaders, policymakers, and others collect and share information, experiences, and resources. AI’s arrival has left schools scrambling to respond to multiple challenges, like how to ensure academic integrity and maintain data privacy. 

Reich cautions that the guidebook is not meant to be prescriptive or definitive, but something that will help spark thought and discussion. 

“Writing a guidebook on generative AI in schools in 2025 is a little bit like writing a guidebook of aviation in 1905,” the guidebook’s authors note. “No one in 2025 can say how best to manage AI in schools.”

Schools are also struggling to measure how student learning loss looks in the age of AI. “How does bypassing productive thinking with AI look in practice?” Reich asks. “If we think teachers provide content and context to support learning and students no longer perform the exercises housing the content and providing the context, that’s a serious problem.”

Reich invites people directly impacted by AI to help develop solutions to the challenges its ubiquity presents. “It’s like observing a conversation in the teacher’s lounge and inviting students, parents, and other people to participate about how teachers think about AI,” he says, “what they are seeing in their classrooms, and what they’ve tried and how it went.”

The guidebook, in Reich’s view, is ultimately a collection of hypotheses expressed in interviews with teachers: well-informed, initial guesses about the paths that schools could follow in the years ahead. 

Producing educator resources in a podcast

In addition to the guidebook, the Teaching Systems Lab also recently produced “The Homework Machine,” a seven-part series from the Teachlab podcast that explores how AI is reshaping K-12 education. 

Reich produced the podcast in collaboration with journalist Jesse Dukes. Each episode tackles a specific area, asking important questions about challenges related to issues like AI adoption, poetry as a tool for student engagement, post-Covid learning loss, pedagogy, and book bans. The podcast allows Reich to share timely information about education-related updates and collaborate with people interested in helping further the work.

“The academic publishing cycle doesn’t lend itself to helping people with near-term challenges like those AI presents,” Reich says. “Peer review takes a long time, and the research produced isn’t always in a form that’s helpful to educators.” Schools and districts are grappling with AI in real time, bypassing time-tested quality control measures. 

The podcast can help reduce the time it takes to share, test, and evaluate AI-related solutions to new challenges, which could prove useful in creating training and resources.  

“We hope the podcast will spark thought and discussion, allowing people to draw from others’ experiences,” Reich says.

The podcast was also produced into an hour-long radio special, which was broadcast by public radio stations across the country.

“We’re fumbling around in the dark”

Reich is direct in his assessment of where we are with understanding AI and its impacts on education. “We’re fumbling around in the dark,” he says, recalling past attempts to quickly integrate new tech into classrooms. These failures, Reich suggests, highlight the importance of patience and humility as AI research continues. “AI bypassed normal procurement processes in education; it just showed up on kids’ phones,” he notes. 

“We’ve been really wrong about tech in the past,” Reich says. Despite districts’ spending on tools like smartboards, for example, research indicates there’s no evidence that they improve learning or outcomes. In a new article for article for The Conversation, he argues that early teacher guidance in areas like web literacy has produced bad advice that still exists in our educational system. “We taught students and educators not to trust Wikipedia,” he recalls, “and to search for website credibility markers, both of which turned out to be incorrect.” Reich wants to avoid a similar rush to judgment on AI, recommending that we avoid guessing at AI-enabled instructional strategies.

These challenges, coupled with potential and observed student impacts, significantly raise the stakes for schools and students’ families in the AI race. “Education technology always provokes teacher anxiety,” Reich notes, “but the breadth of AI-related concerns is much greater than in other tech-related areas.” 

The dawn of the AI age is different from how we’ve previously received tech into our classrooms, Reich says. AI wasn’t adopted like other tech. It simply arrived. It’s now upending educational models and, in some cases, complicating efforts to improve student outcomes.

Reich is quick to point out that there are no clear, definitive answers on effective AI implementation and use in classrooms; those answers don’t currently exist. Each of the resources Reich helped develop invite engagement from the audiences they target, aggregating valuable responses others might find useful.

“We can develop long-term solutions to schools’ AI challenges, but it will take time and work,” he says. “AI isn’t like learning to tie knots; we don’t know what AI is, or is going to be, yet.” 

Reich also recommends learning more about AI implementation from a variety of sources. “Decentralized pockets of learning can help us test ideas, search for themes, and collect evidence on what works,” he says. “We need to know if learning is actually better with AI.” 

While teachers don’t get to choose regarding AI’s existence, Reich believes it’s important that we solicit their input and involve students and other stakeholders to help develop solutions that improve learning and outcomes. 

“Let’s race to answers that are right, not first,” Reich says.

It’s not news that Apple and Google use their app stores to shape what apps you can and cannot have on many of your devices. What is new is more governments—including the U.S. government—using legal and extralegal tools to lean on these gatekeepers in order to assert that same control. And rather than resisting, the gatekeepers are making it easier than ever. 

Apple’s decision to take down the ICEBlock app at least partially in response to threats from the U.S. government—with Google rapidly and voluntarily following suit—was bad enough. But it pales in comparison with Google’s new program, set to launch worldwide next year, requiring developers to register with the company in order to have their apps installable on Android certified devices—including paying a fee and providing personal information backed by government-issued identification. Google claims the new program of “is an extra layer of security that deters bad actors and makes it harder for them to spread harm,” but the registration requirements are barely tied to app effectiveness or security. Why, one wonders, does Google need to see your driver’s license to evaluate whether your app is safe?  Why, one also wonders, does Google want to create a database of virtually every Android app developer in the world? 

Those communities are likely to drop out of developing for Android altogether, depriving all Android users of valuable tools. 

F-Droid, a free and open-source repository for Android apps, has been sounding the alarm. As they’ve explained in an open letter, Google’s central registration system will be devastating for the Android developer community. Many mobile apps are created, improved, and distributed by volunteers, researchers, and/or small teams with limited financial resources. Others are created by developers who do not use the name attached to any government-issued identification. Others may have good reason to fear handing over their personal information to Google, or any other third party. Those communities are likely to drop out of developing for Android altogether, depriving all Android users of valuable tools. 

Google’s promise that it’s “working on” a program for “students and hobbyists” that may have different requirements falls far short of what is necessary to alleviate these concerns. 

It’s more important than ever to support technologies which decentralize and democratize our shared digital commons. A centralized global registration system for Android will inevitably chill this work. 

The point here is not that all the apps are necessarily perfect or even safe. The point is that when you set up a gate, you invite authorities to use it to block things they don’t like. And when you build a database, you invite governments (and private parties) to try to get access to that database. If you build it, they will come.  

Imagine you have developed a virtual private network (VPN) and corresponding Android mobile app that helps dissidents, journalists, and ordinary humans avoid corporate and government surveillance. In some countries, distributing that app could invite legal threats and even prosecution. Developers in those areas should not have to trust that Google would not hand over their personal information in response to a government demand just because they want their app to be installable by all Android users. By the same token, technologists that work on Android apps for reporting ICE misdeeds should not have to worry that Google will hand over their personal information to, say, the U.S. Department of Homeland Security. 

It’s easy to see how a new registration requirement for developers could give Google a new lever for maintaining its app store monopoly

Our tech infrastructure’s substantial dependence on just a few platforms is already creating new opportunities for those platforms to be weaponized to serve all kinds of disturbing purposes, from policing to censorship. In this context, it’s more important than ever to support technologies which decentralize and democratize our shared digital commons. A centralized global registration system for Android will inevitably chill this work. 

Not coincidentally, the registration system Google announced would also help cement Google’s outsized competitive power, giving the company an additional window—if it needed one, given the company’s already massive surveillance capabilities—into what apps are being developed, by whom, and how they are being distributed. It’s more than ironic that Google’s announcement came at the same time the company is fighting a court order (in the Epic Games v. Google lawsuit) that will require it to stop punishing developers who distribute their apps through app stores that compete with Google’s own. It’s easy to see how a new registration requirement for developers, potentially enforced by technical measures on billions of Android certified mobile devices, could give Google a new lever for maintaining its app store monopoly.  

EFF has signed on to F-Droid’s open letter. If you care about taking back control of tech, you should too. 

A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.

To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay are developing computer vision algorithms that carefully monitor animal populations. A member of the lab of MIT Department of Electrical Engineering and Computer Science assistant professor and CSAIL principal investigator Sara Beery, Kay is currently working on tracking salmon in the Pacific Northwest, where they provide crucial nutrients to predators like birds and bears, while managing the population of prey, like bugs.

With all that wildlife data, though, researchers have lots of information to sort through and many AI models to choose from to analyze it all. Kay and his colleagues at CSAIL and the University of Massachusetts Amherst are developing AI methods that make this data-crunching process much more efficient, including a new approach called “consensus-driven active model selection” (or “CODA”) that helps conservationists choose which AI model to use. Their work was named a Highlight Paper at the International Conference on Computer Vision (ICCV) in October.

That research was supported, in part, by the National Science Foundation, Natural Sciences and Engineering Research Council of Canada, and Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Here, Kay discusses this project, among other conservation efforts.

Q: In your paper, you pose the question of which AI models will perform the best on a particular dataset. With as many as 1.9 million pre-trained models available in the HuggingFace Models repository alone, how does CODA help us address that challenge?

A: Until recently, using AI for data analysis has typically meant training your own model. This requires significant effort to collect and annotate a representative training dataset, as well as iteratively train and validate models. You also need a certain technical skill set to run and modify AI training code. The way people interact with AI is changing, though — in particular, there are now millions of publicly available pre-trained models that can perform a variety of predictive tasks very well. This potentially enables people to use AI to analyze their data without developing their own model, simply by downloading an existing model with the capabilities they need. But this poses a new challenge: Which model, of the millions available, should they use to analyze their data? 

Typically, answering this model selection question also requires you to spend a lot of time collecting and annotating a large dataset, albeit for testing models rather than training them. This is especially true for real applications where user needs are specific, data distributions are imbalanced and constantly changing, and model performance may be inconsistent across samples. Our goal with CODA was to substantially reduce this effort. We do this by making the data annotation process “active.” Instead of requiring users to bulk-annotate a large test dataset all at once, in active model selection we make the process interactive, guiding users to annotate the most informative data points in their raw data. This is remarkably effective, often requiring users to annotate as few as 25 examples to identify the best model from their set of candidates. 

We’re very excited about CODA offering a new perspective on how to best utilize human effort in the development and deployment of machine-learning (ML) systems. As AI models become more commonplace, our work emphasizes the value of focusing effort on robust evaluation pipelines, rather than solely on training.

Q: You applied the CODA method to classifying wildlife in images. Why did it perform so well, and what role can systems like this have in monitoring ecosystems in the future?

A: One key insight was that when considering a collection of candidate AI models, the consensus of all of their predictions is more informative than any individual model’s predictions. This can be seen as a sort of “wisdom of the crowd:” On average, pooling the votes of all models gives you a decent prior over what the labels of individual data points in your raw dataset should be. Our approach with CODA is based on estimating a “confusion matrix” for each AI model — given the true label for some data point is class X, what is the probability that an individual model predicts class X, Y, or Z? This creates informative dependencies between all of the candidate models, the categories you want to label, and the unlabeled points in your dataset.

Consider an example application where you are a wildlife ecologist who has just collected a dataset containing potentially hundreds of thousands of images from cameras deployed in the wild. You want to know what species are in these images, a time-consuming task that computer vision classifiers can help automate. You are trying to decide which species classification model to run on your data. If you have labeled 50 images of tigers so far, and some model has performed well on those 50 images, you can be pretty confident it will perform well on the remainder of the (currently unlabeled) images of tigers in your raw dataset as well. You also know that when that model predicts some image contains a tiger, it is likely to be correct, and therefore that any model that predicts a different label for that image is more likely to be wrong. You can use all these interdependencies to construct probabilistic estimates of each model’s confusion matrix, as well as a probability distribution over which model has the highest accuracy on the overall dataset. These design choices allow us to make more informed choices over which data points to label and ultimately are the reason why CODA performs model selection much more efficiently than past work.

There are also a lot of exciting possibilities for building on top of our work. We think there may be even better ways of constructing informative priors for model selection based on domain expertise — for instance, if it is already known that one model performs exceptionally well on some subset of classes or poorly on others. There are also opportunities to extend the framework to support more complex machine-learning tasks and more sophisticated probabilistic models of performance. We hope our work can provide inspiration and a starting point for other researchers to keep pushing the state of the art.

Q: You work in the Beerylab, led by Sara Beery, where researchers are combining the pattern-recognition capabilities of machine-learning algorithms with computer vision technology to monitor wildlife. What are some other ways your team is tracking and analyzing the natural world, beyond CODA?

A: The lab is a really exciting place to work, and new projects are emerging all the time. We have ongoing projects monitoring coral reefs with drones, re-identifying individual elephants over time, and fusing multi-modal Earth observation data from satellites and in-situ cameras, just to name a few. Broadly, we look at emerging technologies for biodiversity monitoring and try to understand where the data analysis bottlenecks are, and develop new computer vision and machine-learning approaches that address those problems in a widely applicable way. It’s an exciting way of approaching problems that sort of targets the “meta-questions” underlying particular data challenges we face. 

The computer vision algorithms I’ve worked on that count migrating salmon in underwater sonar video are examples of that work. We often deal with shifting data distributions, even as we try to construct the most diverse training datasets we can. We always encounter something new when we deploy a new camera, and this tends to degrade the performance of computer vision algorithms. This is one instance of a general problem in machine learning called domain adaptation, but when we tried to apply existing domain adaptation algorithms to our fisheries data we realized there were serious limitations in how existing algorithms were trained and evaluated. We were able to develop a new domain adaptation framework, published earlier this year in Transactions on Machine Learning Research, that addressed these limitations and led to advancements in fish counting, and even self-driving and spacecraft analysis.

One line of work that I’m particularly excited about is understanding how to better develop and analyze the performance of predictive ML algorithms in the context of what they are actually used for. Usually, the outputs from some computer vision algorithm — say, bounding boxes around animals in images — are not actually the thing that people care about, but rather a means to an end to answer a larger problem — say, what species live here, and how is that changing over time? We have been working on methods to analyze predictive performance in this context and reconsider the ways that we input human expertise into ML systems with this in mind. CODA was one example of this, where we showed that we could actually consider the ML models themselves as fixed and build a statistical framework to understand their performance very efficiently. We have been working recently on similar integrated analyses combining ML predictions with multi-stage prediction pipelines, as well as ecological statistical models. 

The natural world is changing at unprecedented rates and scales, and being able to quickly move from scientific hypotheses or management questions to data-driven answers is more important than ever for protecting ecosystems and the communities that depend on them. Advancements in AI can play an important role, but we need to think critically about the ways that we design, train, and evaluate algorithms in the context of these very real challenges.

By stimulating cancer cells to produce a molecule that activates a signaling pathway in nearby immune cells, MIT researchers have found a way to force tumors to trigger their own destruction.

Activating this signaling pathway, known as the cGAS-STING pathway, worked even better when combined with existing immunotherapy drugs known as checkpoint blockade inhibitors, in a study of mice. That dual treatment was successfully able to control tumor growth.

The researchers turned on the cGAS-STING pathway in immune cells using messenger RNA delivered to cancer cells. This approach may avoid the side effects of delivering large doses of a STING activator, and takes advantage of a natural process in the body. This could make it easier to develop a treatment for use in patients, the researchers say.

“Our approach harnesses the tumor’s own machinery to produce immune-stimulating molecules, creating a powerful antitumor response,” says Natalie Artzi, a principal research scientist at MIT’s Institute for Medical Engineering and Science, an associate professor of medicine at Harvard Medical School, a core faculty member at the Wyss Institute for Biologically Inspired Engineering at Harvard, and the senior author of the study.

“By increasing cGAS levels inside cancer cells, we can enhance delivery efficiency — compared to targeting the more scarce immune cells in the tumor microenvironment — and stimulate the natural production of cGAMP, which then activates immune cells locally,” she says. “This strategy not only strengthens antitumor immunity but also reduces the toxicity associated with direct STING agonist delivery, bringing us closer to safer and more effective cancer immunotherapies.”

Alexander Cryer, a visiting scholar at IMES, is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences.

Immune activation

STING (short for stimulator of interferon genes) is a protein that helps to trigger immune responses. When STING is activated, it turns on a pathway that initiates production of type one interferons, which are cytokines that stimulate immune cells.

Many research groups, including Artzi’s, have explored the possibility of artificially stimulating this pathway with molecules called STING agonists, which could help immune cells to recognize and attack tumor cells. This approach has worked well in animal models, but it has had limited success in clinical trials, in part because the required doses can cause harmful side effects.

While working on a project exploring new ways to deliver STING agonists, Cryer became intrigued when he learned from previous work that cancer cells can produce a STING activator known as cGAMP. The cells then secrete cGAMP, which can activate nearby immune cells.

“Part of my philosophy of science is that I really enjoy using endogenous processes that the body already has, and trying to utilize them in a slightly different context. Evolution has done all the hard work. We just need to figure out how push it in a different direction,” Cryer says. “Once I saw that cancer cells produce this molecule, I thought: Maybe there’s a way to take this process and supercharge it.”

Within cells, the production of cGAMP is catalyzed by an enzyme called cGAS. To get tumor cells to activate STING in immune cells, the researchers devised a way to deliver messenger RNA that encodes cGAS. When this enzyme detects double-stranded DNA in the cell body, which can be a sign of either infection or cancer-induced damage, it begins producing cGAMP.

“It just so happens that cancer cells, because they’re dividing so fast and not particularly accurately, tend to have more double-stranded DNA fragments than healthy cells,” Cryer says.

The tumor cells then release cGAMP into tumor microenvironment, where it can be taken up by neighboring immune cells and activate their STING pathway.

Targeting tumors

Using a mouse model of melanoma, the researchers evaluated their new strategy’s potential to kill cancer cells. They injected mRNA encoding cGAS, encapsulated in lipid nanoparticles, into tumors. One group of mice received this treatment alone, while another received a checkpoint blockade inhibitor, and a third received both treatments.

Given on their own, cGAS and the checkpoint inhibitor each significantly slowed tumor growth. However, the best results were seen in the mice that received both treatments. In that group, tumors were completely eradicated in 30 percent of the mice, while none of the tumors were fully eliminated in the groups that received just one treatment.

An analysis of the immune response showed that the mRNA treatment stimulated production of interferon as well as many other immune signaling molecules. A variety of immune cells, including macrophages and dendritic cells, were activated. These cells help to stimulate T cells, which can then destroy cancer cells.

The researchers were able to elicit these responses with just a small dose of cancer-cell-produced cGAMP, which could help to overcome one of the potential obstacles to using cGAMP on its own as therapy: Large doses are required to stimulate an immune response, and these doses can lead to widespread inflammation, tissue damage, and autoimmune reactions. When injected on its own, cGAMP tends to spread through the body and is rapidly cleared from the tumor, while in this study, the mRNA nanoparticles and cGAMP remained at the tumor site.

“The side effects of this class of molecule can be pretty severe, and one of the potential advantages of our approach is that you’re able to potentially subvert some toxicity that you might see if you’re giving the free molecules,” Cryer says.

The researchers now hope to work on adapting the delivery system so that it could be given as a systemic injection, rather than injecting it into the tumor. They also plan to test the mRNA therapy in combination with chemotherapy drugs or radiotherapy that damage DNA, which could make the therapy even more effective because there could be even more double-stranded DNA available to help activate the synthesis of cGAMP.