Last fall, MIT Campus Dining recruited a group of students to make short videos and share their experiences as student diners on Instagram. The MIT Dining Ambassadors program is an effort to get students talking about — and helping to improve — MIT’s food services and systems. 

One of the inaugural ambassadors, Michaela Brown, a biochemical engineering major from Kingston, Jamaica, sat down to discuss what she’s learning as an ambassador, how she has adapted to dining-hall life, the best things about her mom’s cooking, what it was like to experience American Thanksgiving for the first time, and more.

Q: How did you get involved in the Dining Ambassadors program? 

A: Last October, my friend got a job. So I was like, I need to get a job. When I read the description, I said, Wait, this involves food, and talking to people, and posting on Instagram? That’s literally what I do every day. And I wanted to do my part. 

The ambassadors program has clear goals: They want to encourage students to use the dining halls, and they want us to find genuine issues MIT can work on. I wanted to be a part of that. The food at MIT is OK, but everything can be better. And you can’t make things better in any circumstance without trying. Plus — getting paid to eat food and talk? That is good money.

Q: What did you eat growing up? 

A: I love Jamaican food. On Sunday, we do a big dinner. (Well, my mom would do a big dinner; sometimes I would wash the vegetables.) She would cook rice, peas, and vegetables with a sauce, and either fried chicken with sauce or stewed chicken. We would eat that food on Sunday, and then maybe Monday, too. We call it “Sunday-Monday” in Jamaica. 

During the week, we eat flour dumplings, boiled green bananas, and lots of plantains. Sometimes, when my mom is on a health kick, she will boil everything, but plantains are so much better when you fry them! Often, she will serve that with ackee. That’s our national food. And she will cook saltfish or mackerel mixed with coconut milk. She also makes things like corned beef or tuna. On Fridays, we usually go out. 

For special occasions, sometimes we do pork or oxtail. Or sometimes we have escovitch fish; I think you fry it and you steam it. And then we have sides like dumpling, or banana, or bami, which is fried flour. And usually we eat these with okra and pickled onions, and add a little spice with Scotch bonnet pepper. 

And curry chicken! If I am home and I smell the curry, I get so happy. I genuinely feel better about myself. If you’re buying food from a vendor, like fried chicken and rice, you would ask for curry gravy because it is very essential in Jamaican culture. 

Q: What was your first project for the ambassadors program?

A: I did a video about Thanksgiving. I was excited, because it would be my first American Thanksgiving. As a kid in Jamaica, I saw it on TV. I watched Nickelodeon. Also, we learned about it in school. But we didn’t do Thanksgiving in Jamaica. So I was excited.

In the video, I was trying to cater to students who don’t normally celebrate Thanksgiving and show them the experience from a fresh perspective. I brought my friends with me and we all ate together. And luckily everyone thought the food was good. I really wanted to show the food — the mashed potatoes, the turkey, the jelly, the ham, all those things — because I think New Vassar did it really well. I wanted to show that. 

There are a lot of international students at MIT. I didn’t know what MIT was like until I got here. I wanted to show that I came here and liked it. Even while I was missing home, I was being introduced to other cultures — like the one in America — and MIT was helping me appreciate it through food.

Also, I wanted to show the community — being with my friends, giving thanks for the people around me. I really enjoyed that, and I thought it went well. My mom loved it.

Q: What have you done since then? 

A: Usually, I just try to take pictures when I’m in a dining hall and post them on Instagram. You know — regular life.

The other major thing was the global Olympics. Each day over two weeks, they had a special theme at each of the dining halls — Latin American at New Vassar, East Asian at Simmons, African at McCormick, European at Next House, Indian at Massey, and North American and Caribbean at Baker. 

My favorite was Baker, because, well, I’m a little biased. And also, I love the burgers at Baker. 
I told my friends they had to come. A lot of the cooking staff in Baker are Haitian. They would know how food from Haiti and Jamaica should taste. I knew they wouldn’t mess it up. 

I interviewed a lot of students, including two Haitians and one of my Jamaican friends. I asked about the food, about how it compared to regular dining hall meals. They were really positive. I think they liked the change. 

Q: Do you like to cook? 

A: Not really. The summer before I came here, I was like, OK, I’m gonna learn something. 
And then I proceeded to spend the summer out with my friends, and volunteering. So I wasn’t really in the kitchen. My mom would call me to come help her, and when I stepped in the kitchen it was so hot! I was like, I can’t do this, and I went back to my room. 

So I’m not really a cook, even though I live in Burton-Connor. It’s a cook-for-yourself dorm, so it doesn’t have a dining hall. A few weeks ago, I tried to do burritos. I got the beef and the seasoning. It was actually really good! I’m looking forward to it again. It’s just really hard to find the time. 


Q: When you’re posting, who do you imagine is looking at it? 

A: My friends. And my mom. Honestly, I just try to make sure you can understand what I’m saying because sometimes my Jamaican patois comes out, and I talk too fast. I also think about how the people I’m interviewing want to be seen, because this is not their job. They don’t have to be on camera, or help me. I try to make the experience as fun as possible for them. 

Q: What have you learned doing this work?

A: Walking up to strangers and getting their permission to record them is really new to me. I have learned so much about people. The other day, I was looking at a job application and it asked: Are you comfortable talking to other people and being social? This job has prepared me for all that so well. 

It also prepared me for dealing with people who might not be open to talking. I have learned to be OK with that, just walking away and handling it well. This is a skill set that I have now, and I look forward to working more and doing more interviews. I feel like, you know, a YouTuber!

Q: What dining stories do you want to tell next? 

A: I’m not sure. Dining is different for different people. For me personally, sometimes eating is a time to get together with other people. But sometimes I go to the dining hall by myself. It’s very much a time for me to decompress. Sometimes I don’t even want anyone to sit with me. I’m just trying to be with myself, watch my show, or do the learning sequence I have due at 11 o’clock. Or I just watch my TikToks. 

Maybe I’ll do a day-in-my-life dining story next, and go for breakfast at a dining hall. I would have to wake up earlier, but I would do it. 

Corporations harvest and monetize ever-growing amounts of our personal data, such as our browsing history and physical location. One bitter fruit of this poisonous tree is known as “surveillance pricing”: corporations offer the same product to two different people at two different prices, based on scrutiny of these people’s respective personal data.

Surveillance pricing is bad for privacy, equity, and price transparency. So EFF supports a California bill, S.B. 2564, which would ban this creepy practice.

How Surveillance Pricing Works

In 2025, the Federal Trade Commission (FTC) published a report about the practices of six companies that provide surveillance pricing services to hundreds of other companies, including grocery stores and apparel retailers. The report found that surveillance pricing draws upon customers’ browsing history, physical location, and shopping transaction history. Customers’ data can come from the vendor itself, from its surveillance pricing service provider, or from third-party data brokers. Customers are sorted into groups based on their personal data, as is done for targeted ads. As a result of surveillance pricing, a business might offer two customers different prices for the same product, based for example on whether they are a new parent, or whether they live near a business’s competitor.

As former FTC Chair Lina Khan explained:

Initial staff findings show that retailers frequently use people’s personal information to set targeted, tailored prices for goods and services – from a person’s location and demographics, down to their mouse movements on a webpage.

Unfortunately, the current FTC chair closed the FTC’s portal for public comments regarding surveillance pricing. Fortunately, the California Attorney General has initiated its own investigation of this practice.

Researchers have identified many examples of surveillance pricing:

• The Princeton Review offered people who lived in some zip codes a higher price for test prep services, compared to people in other zip codes. As a result, Asians were twice as likely as non-Asians to be offered a higher price.
• In a year-long study of tens of millions of rides in Chicago, Uber and Lyft offered a higher price for trips that ended in neighborhoods with high non-white populations.
• Tindr offered older people (aged 30 to 49) higher prices for Tindr Plus, compared to younger people (aged 18-29).
• Orbitz offered people who used Apple computers a higher price for hotel rooms, compared to people who used other types of computers.
• Hotel booking sites offered people from San Francisco a higher price for hotel rooms, compared to people from other cities.
• Target offered a higher price to people physically located at the store, compared to people located elsewhere.
• Staples offered a higher price to customers who lived further from the company’s competitors, compared to customers who lived closer.

Why EFF Hates Surveillance Pricing

This practice is harmful in many ways. First, surveillance pricing invades our privacy.  Vendors offer us a price only after scrutinizing our personal data about what we’ve clicked online and where we’ve travelled offline. Moreover, surveillance pricing incentivizes all businesses to harvest as much of our personal data as possible. Some businesses will use it for their own surveillance pricing. Other businesses, which might not themselves use it this way, will sell it to data brokers, which in turn will sell it to others for use in surveillance pricing.

Second, surveillance pricing can disparately burden people of color and other vulnerable groups. For example, as described above, surveillance pricing led to Asian people paying more for test prep services, older people paying more for dating services, and people living in non-white neighborhoods paying more for a ride home.

Third, surveillance pricing is opaque. Many people don’t even know when they’ve been subjected to it. Those that do often cannot determine the unknown reasons for the price they’re offered. As a result, consumer advocates will be less able to publish meaningful price comparisons to help consumers make choices. And regulators will be less able to identify unlawful pricing practices.

Thus, EFF and many other groups object to surveillance pricing.

Its defenders sometimes argue that surveillance pricing benefits consumers because it can lead to lower prices. But while some consumers some of the time might get lower prices because of surveillance of their personal data, other consumers will get higher prices, as shown by the examples above. Some recent studies indicate there will be losers and winners based on factors like whether a consumer is willing or able to switch products. Who loses or wins also will turn on the accuracy of the underlying data – yet surveillance pricing is often based on false information.

In any event, both losers and winners of this price discrimination are harmed by surveillance. Privacy is a human right, not a property to be bought and sold on a market. For this reason, EFF has long opposed pay-for-privacy schemes, in which a company charges a higher price to a customer who refuses to submit to processing of their personal data. Thus, even if surveillance pricing sometimes leads to lower prices (and again, it often will not), we oppose it as just another way that corporations try to make customers pay for their privacy.

What the California Bill Would Do

The key term of California’s S.B. 2564 is short and sweet: “a retailer shall not engage in surveillance pricing.”

The banned practice is defined as: “[i] a customized price for a good for a specific consumer or group of consumers, [ii] based, in whole or in part, on personally identifiable information collected through electronic surveillance,” including if that information is “acquired from a third party.” In other words, “surveillance pricing” is a customized price based on personal information.

The bill has two enforcement methods. First, state and local government may bring enforcement actions, and seek all manner of remedies including monetary penalties. Second, individual consumers may bring their own enforcements lawsuits, and seek the remedies of an injunction and attorney fees. We are pleased the bill provides this private right of action, which is the most important method of enforcement (we’d be even more pleased if the private remedies included liquidated damages).

The bill has three exemptions where surveillance pricing is allowed:

• First, for price differences “based solely on costs associated with providing the good to different consumers.”
• Second, for a discount offered to a consumer who is taking steps to terminate a service.
• Third, for a discount, conspicuously posted on a retailer’s website, that is uniformly available based on (1) criteria anyone can meet, such as signing up for a mailing list, (2) membership in a broadly defined group, such as seniors, or (3) participation in a loyalty program.

The bill’s author is California Assembly Member Chris Ward. Its co-sponsors are Consumer Reports and TechEquity. Its supporters include Consumer Federation, EPIC, Kapor Center Advocacy, Oakland Privacy, Privacy Rights Clearinghouse, labor unions, and other groups. The bill has advanced through the California Assembly and has arrived for consideration in the California Senate.

Why EFF Supports the California Bill

Surveillance pricing is just one part of a much larger problem: corporations maximizing their profits by invading our privacy. The all-too-common business model is to systematically harvest, collate, and store as much of our personal data as possible, and then monetize it through use and sale.

EFF’s general approach to this problem is a strong regulatory framework that we call “privacy first.” For example, laws should require businesses to “minimize” their data processing, meaning they must not collect, store, use, or disclose our data unless doing so is strictly necessary to give us what we asked for. Likewise, laws should require businesses to get our voluntary and informed opt-in consent before processing our data, buttressed by legal bans on coercive pay-for-privacy schemes and manipulative “dark patterns.”

A.B. 2564 is just a specific application of the minimization rule. Nobody who uses a web browser or a mobile app expects that, as a result, their clicks and footsteps will be funneled into personal dossiers, and later used by downstream businesses to offer a higher or lower price.

A.B. 2564 is also a specific application of the “no pay-for-privacy” rule. At its best, surveillance pricing is a corporate offer of a lower price in exchange for a consumer’s submission to surveillance of their personal data. This scheme encourages all people to surrender their privacy in exchange for a lower price. This is especially coercive for people with lower incomes, and thus carries the risk of creating a society of privacy “haves” and “have nots.” And swept into this supposed “bargain” is the potential for higher surveillance-based prices based on false information or erroneous inferences.

Surveillance pricing is very similar to online behavioral advertising, a business practice that EFF urges governments to ban. Both practices incentivize all businesses to collect as much of our personal data as possible, in order to later monetize it. Both practices lead some businesses to collate and store our data into dossiers about us for later use. Both practices use these surveillance-based dossiers to manipulate and limit our economic choices, by altering the advertisements and prices we see online. In the words of the FTC report discussed above: “Existing and common techniques used for targeted advertising can also be used for other forms of targeting prices.”

Absent a specific ban on surveillance pricing, as in A.B. 2564, it would be very difficult to protect the public from the many harms it causes. Corporate price-setting is increasingly opaque, making it difficult for consumers and regulators to determine whether a particular company set a particular price for a particular consumer based on their data, and if so, the particular data that it used. As a result, it would be very difficult in this context to enforce general laws requiring minimization or consent. Moreover, many such laws exempt how a business processes the data it directly collected from its own customers; for example, the California Consumer Privacy Act’s limits on “cross-context behavioral advertising” do not apply to how a business uses personal data it collected on its own website. Yet many practitioners of surveillance pricing (like Tindr) rely on such data.

Finally, there is little to no risk that A.B. 2564 will have unintended consequences that hurt internet users’ speech or technological innovation. The bill does not address any particular type of technology. It does not limit any collection, retention, or disclosure of personal data. It limits only one very narrow and easily defined use of data: use to set a customized price. And it has three broad exemptions.

In sum, EFF is proud to join with other groups in support of California’s A.B. 2564. You can read our support letter here.

In his 1927 paper, “A law of comparative judgment,” the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice. 

Thurstone was a pioneer of “psychometrics” — a field built upon the premise that mental processes, which we cannot see, can nevertheless be measured and quantified. His 1927 paper laid the groundwork for what are now called random utility models, which provide a mathematical framework for describing human preferences — information that can be relied upon, in turn, to make predictions about various hypothetical situations.

Random utility models (RUMs) are so named because they assess the “utility,” or benefit, that can be obtained from a given choice — such as deciding which book to read first among the stack of novels you brought back from the library. “These models are inherently random,” explains Gabriele Farina, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and principal investigator at the Laboratory for Information and Decision Systems (LIDS), “because people are different. Everyone has their own preferences, and even those preferences can vary from time to time.” For example, someone who normally picks coffee over tea in the morning, and prefers tea after dinner, may, upon occasion, mix up that order entirely.

RUMs, to be sure, are frequently used within government and industry in situations of far greater consequence than the selection of a hot (or iced) beverage. The models routinely facilitate predictions regarding what people will elect to do in so-called counterfactual (“what-if”) scenarios such as: How will they get to work or school if a major thoroughfare is shut down for construction? What routes and modes of transport will they take? Or, if a city suddenly receives a windfall of $20 million, how should those funds be disbursed to maximize the common good?

Given that RUMs have been with us for almost 100 years, growing in sophistication over time, one might imagine that, at this stage, there would be little room for improvement. That, however, is not the case. 

A paper presented in April at the International Conference on Learning Representations in Rio de Janeiro, Brazil, uncovered basic facts that show there is much more to be gleaned from these models than had traditionally been supposed. The paper was authored by Yeshwanth Cherapanamjeri, a former MIT postdoc now based at Nanyang Technological University in Singapore; Farina, also core faculty in MIT’s Operations Research Center (ORC); Constantinos Daskalakis, the Avanessians Professor of Computer Science at MIT and a member of MIT's Computer Science and Artificial Intelligence Laboratory; and Sobhan Mohammadpour, an MIT PhD student in computer science based at LIDS and EECS.

The group’s findings stem, in part, from a deficiency in the way RUMs are commonly estimated in practice, which has persisted since the days of Thurstone. The data upon which the models are estimated have been largely drawn from so-called pairwise-comparisons: In a choice between items A and B — whether it pertains to movies on Netflix, competing products on Amazon.com, news stories posted on Google, and so forth — which one would you pick? One reason this approach has been so pervasive, explains Daskalakis, is that “assigning a precise numerical score, such as 4.37, to the benefit you get from a single item is very hard. Whereas comparing two things, and deciding which one you like better, is cognitively much easier to do.” But therein lies the rub, he adds. “With this way of assessing people’s preferences, looking at just two things at a time, it is impossible to find correlations between the numerous choices.”

The standard way of applying RUMs assumes that the utilities derived from A and B are independent, but they may, in fact, be linked, and that would be important to know. If someone campaigning for elective office finds out that a potential voter favors gun control, for instance, there is a reasonable chance that same person also favors government-sponsored child care. Similarly, a fan of independent movies might also be partial to foreign films, but less enthusiastic about Hollywood action blockbusters. “If a digital platform has a blind eye to the existence of such correlations, it will not be able to estimate preferences very accurately,” Daskalakis notes. “And if Netflix regularly shows you an assortment of movies you don’t care about, you might sign off and cancel your subscription.”

The MIT team proved that it is impossible to get information about correlations from two-way comparisons alone. Correlations can be discerned, however, when large numbers of people rate three alternatives in their order of preference. The same information can also be obtained from a combination of best-of-three and best-of-two choices. In practice, Mohammadpour explains, “you would get a bunch of people to rank three items. You could then utilize the method we developed for merging those individual results into one big model that can provide us with the big picture.”

Their research effort, according to Farina, is focused on the computational side of RUMs, devising algorithms that can extract preference information and figuring out how much data is needed to do so or, equivalently, how many experiments need to be run. The good news, he says, is that efficient algorithms are, indeed, possible for this purpose. The requisite number of experiments does not grow exponentially with the number of items in the catalog or database that’s under review.

“This paper provides a crucial breakthrough,” comments Emma Frejinger, a computer scientist at the University of Montreal. “It mathematically proves why traditional data collection fails and demonstrates that simply asking users for their best-of-three [choices] unlocks the ability to accurately train these powerful models. This finding provides a highly practical roadmap for collecting better data to drive more accurate optimizations.”

“Building utility models is going to remain a very active area,” Daskalakis insists. “Just as RUMs have been critical to the internet economy since the late 1990s, they are, and will remain to be, critical to the alignment of AI models going forward.” More importantly, he adds, “RUMs play a central role in the commercial viability and usefulness of large language models [LLMs].” During the training period, people are typically asked to rank the various candidate outputs of these LLMs, from which the models can gain a better sense as to the kind of text — in terms of tone, style, and content — that is preferred. 

Given that we’re constantly “besieged with a vast sea of options in so many different domains,” Daskalakis says, “you cannot possibly ask people to communicate all their personal preferences for all possible scenarios. So what you can do instead is build a model that predicts what people think about the different possible outcomes. And you have to keep improving and updating your model in an iterative process until, hopefully, you can make good predictions.”

What do EFF staffers Sarah Chen, Javier Morales, Caitlin Chin, Emma Rodriguez, and Mikko Kopponen have in common? 

For one thing, they don’t exist. 

For another, all have been quoted as EFF experts in articles published in the past two months on a site called News-USA Today, which describes itself as “an independent news publisher focused on clear, accurate, and useful journalism.” 

Uh… 

(Please don’t confuse this site with USA Today, in which real EFF experts are accurately quoted on a regular basis.) 

News-USA Today is hardly the only slagheap that’s hallucinating or fabricating EFF personnel and quotes; as we wrote last September, media companies large and small are using AI to generate news content because it’s cheaper than paying for journalists’ salaries, but that savings can come at the cost of the outlets’ reputations— assuming they care about reputation at all. 

But this many fake EFF sources in two months? That’s making a play for the championship title of bogus news content. 

News-USA Today’s site proclaims, “Our goal is simple: give readers the facts and the context they need to make informed decisions.” It then defines its mission:

• “Deliver timely, factual reporting grounded in verifiable sources and public documents.”
• “Make complex topics understandable without losing nuance or accuracy.”
• “Serve the public interest by surfacing stories that affect lives, institutions, and communities.”
• “Maintain a clear separation between news, analysis, opinion, and sponsored content.” 

Attempts to reach contacts listed on the site went unanswered. In fact, after we reached out to them, they published a story on June 9 with quotes from Electronic Frontier Foundation Executive Director Jared Cohen — who also doesn’t exist. 

As we noted last year, EFF is all about having our words spread far and wide. Per our copyright policy, any and all original material on the EFF website may be freely distributed at will under the Creative Commons Attribution 4.0 International License (CC-BY), unless otherwise noted.  

However, we don't want disreputable sites making up words (or false identities!) for us, whether or not they’re using AI. False quotations that misstate our positions damage the trust that the public and reputable media outlets have in us.  

The best thing a news consumer can do is invest a little time and energy to learn how to discern the real from the fake. It’s unfortunate that it's the public’s burden to put in this much effort, but while we're adjusting to new tools and a new normal, a little effort now can go a long way.   

As we’ve noted before in the context of election misinformation, the nonprofit journalism organization ProPublica has published a handy guide about how to tell if what you’re reading is accurate or “fake news,” as has FactCheck.org. 

One September day, it started to snow inside MIT’s Pierce Laboratory. 

Researchers depressurized a tank of liquid carbon dioxide (CO2), instantly freezing it and releasing solid flakes. These were blended into cement paste and pressed into discs roughly the size of a dime, each sealed with a thin layer of vegetable oil to keep water in and air out. The team trained lasers on each, observing for the first time the transient chemical reaction that might explain why CO2-injected cement paste gains its strength faster.

Injecting CO2 into cement products like concrete is one way to store it and keep it out of the atmosphere. The process has attracted commercial interest, with a growing number of companies offering CO2-injected concrete mixes. But until now, the underlying cement chemistry hadn't been directly visualized.

A new open-access paper in the Journal of the American Ceramic Society — led by Associate Professor Admir Masic and first-authored by graduate student Marcin Hajduczek, both of the MIT Concrete Sustainability Hub and MIT Department of Civil and Environmental Engineering — describes the chemical sequence that unfolds after CO2 meets fresh cement paste. Co-authors include MIT colleagues Santiago El Awad and Franz-Josef Ulm, alongside researchers from IIT Jodhpur and CarbonCure Technologies.

Previous studies had pieced together a story about CO2 injection’s chemical impacts from theory and indirect evidence; the key reactions simply moved too fast, and vanished too completely, for conventional techniques to catch them in the act. Raman confocal microscopy could — and it works on a simple principle: Illuminate a molecule with a laser, and the scattered light will reveal its identity. The light interacts with each material’s unique chemical bonds, shifting in energy to produce a distinct spectral “fingerprint.” Even the most fleeting and amorphous phases leave a readable trace.

“We’ve used Raman spectroscopy to better understand some of the most interesting materials in history, from the Dead Sea Scrolls to Ancient Roman concrete,” says Masic. “Cement paste may seem less glamorous in comparison, but pointing a laser at CO2-injected cement paste as it hardens allows us to visualize things that haven’t been seen before.”

What they saw, unfolding during 24 hours of continuous scanning, was a three-act chemical drama.

Act One: Capturing calcium

The moment that CO2 is added to the fresh cement paste, it goes to work. It dissolves into the pore solution and reacts with calcium released by the dissolving clinker, precipitating as various forms of calcium carbonate. Clinker is produced by heating limestone and aluminosilicate materials in a kiln, forming the primary ingredient ground into a fine powder to make cement. This happens within the first hour, temporarily slowing the normal hydration reaction, which requires calcium to proceed. 

In contrast, when CO2 is not present, the calcium released by the dissolving clinker remains available locally, supporting the gradual formation of the material’s binding phases as it sets.

Left without calcium, the silicates released by the clinker dissolve into the pore solution and precipitate far from their source, linking together into chains that form an interconnected silica gel network throughout the paste. This amorphous, fleeting gel sets the stage for what follows.

Act Two: The ghostly gel

Once the injected CO2 is fully mineralized — around four to five hours after mixing — normal hydration resumes. Calcium hydroxide begins to precipitate into the pore space, and when it does, it encounters the silica gel network waiting for it.

The reaction between the two phases begins immediately, producing calcium silicate hydrate (C-S-H), the compound that gives cement its binding ability. What makes this form of C-S-H distinct is where and how it forms: not clustered around clinker particles as in conventional hydration, but distributed throughout the entire matrix, wherever the silica gel had spread.

The CO2 had temporarily suppressed the paste’s alkalinity, and that lower pH was the only thing keeping the silica-gel intact. As hydration reasserts itself and produces standard hydration products, namely C-S-H and calcium hydroxide, the latter drives pH back up to typical levels in a self-reinforcing loop; the silica-gel reacts with calcium hydroxide through a so-called pozzolanic reaction. Within eight hours, the silica gel is almost entirely gone — the previously well-distributed gel network turns rapidly into additional C-S-H during this critical early window. 

“At first, the fleeting nature of the silica gel looked like a fluke in the Raman data. But it quickly became clear that its sudden disappearance was a consistent, undeniable feature of every CO2-injected sample,” says Hajduczek.

Act Three: A rewired matrix

With the silica gel consumed, the paste settles into conventional hydration, but what it leaves behind is measurably different. Because the new binder was distributed more evenly throughout the cement matrix, the resulting microstructure is stronger and more uniform at an early age. In the study, paste mixed with CO2 at 1 percent by cement weight achieved, on average, 13 percent higher compressive strength at 24 hours, compared to reference mixes.

“We’ve been injecting CO2 into cement products for years without fully understanding what it was doing inside. Now that we can see it and understand the underlying mechanism that leads to improved performance, we can start to control it. And there’s a lot of room to push,” says Masic.

The findings also refine a leading explanation for CO2-injected cement paste’s higher early age strength: the calcium carbonate crystals, previously suspected to seed C-S-H growth, turn out to be passive bystanders embedded in the silica gel template rather than reacting to form C-S-H. 

Where the chemistry goes next

Knowing the mechanism gives researchers a more specific set of questions to pursue. The silica gel template explains the distribution of the new C-S-H, but directly measuring its mechanical properties remains a next step.

On the practical side, dosage matters: Flood the system with too much CO2 and calcium gets locked into carbonate before the gel can form and react. If the paste used here forms abundant C-S-H, it could theoretically offset up to 40 percent of the carbon emissions from cement production, excluding emissions associated with the fossil fuels used in the process. In practice, however, the achievable offset is likely to be only a fraction of that value, although still potentially significant.

But even with these open questions, the ghostly gel has been caught. And now that researchers know what to look for, the chemistry that unfolds in those first eight hours is no longer invisible.

Last June during Pride, we launched a new initiative—LGBT Q&A—where we answered your most pressing queer-related digital rights questions on EFF’s Instagram and TikTok accounts. No question was too big or too small! You asked us things like what pictures to use on dating apps; how to remove your name from internet searches; why homophobic content doesn't get removed after you report it; and how to stay safe at Pride marches.

And this year, we’re doing it all again. 

Both online and offline, LGBTQ+ individuals and the fight for queer liberation are under threat; and the need for guidance and protection from prying eyes and oppressive structures is increasingly pertinent. This is particularly true for those of us who face consequences when intimate details around gender or sexual identities are revealed without consent. 

But we know that it can feel overwhelming to even start thinking about how you can protect yourself online in the face of these issues. That's why this Pride, we’re answering all your digital rights questions. 

How to submit your questions?

• If you would like to remain anonymous and away from social platforms, you can submit questions via this secure link. 
• Head to EFF’s Reddit or the r/LGBTQ subreddit and submit your questions underneath the posts. 
• Your questions can also be submitted under the linked posts on EFF’s Instagram and TikTok, as well as on our stories where you can submit questions directly. 
• If you prefer Mastodon and Bluesky, comment your questions under the linked posts. 

As always, we will not engage with comments that discriminate against marginalized groups, including the LGBTQ+ community.

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The surveillance company Leonardo wants more data:

A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers.

The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data...

An emerging field of research that can measure how much climate change has worsened individual disasters is under attack by friends of the fossil fuel industry. Billions of dollars are at stake.

Some progressive primary challengers to Democratic lawmakers are criticizing their opponents over the changes to the state’s climate law.

In a letter to state electricity officials, Gov. Greg Abbott asked for an outline of actions and recommendations by mid-July to help prevent a surge in residential electric costs.

The country's manufacturing investments in other countries fell short.

A top Democrat said Wednesday he wanted to see more movement on renewable energy approvals. A White House official didn’t appear willing to budge.

The modifications came in response to petitions from the oil and gas industry.

As Xavier Becerra and Steve Hilton prepare to face off, environmentalists lament the race that could have been.

A Prague-led coalition warned that Europe’s climate ambitions will become increasingly difficult to achieve if freight continues shifting from rail to road.

Any such move, which would increase costs for airlines, is likely to anger trading partners like the United States.

The effective closure of the Strait of Hormuz in early March has driven Asian buyers to look for alternative sources to replace disrupted Persian Gulf barrels.

Colombia is seeking to reverse decades of forest loss, much of it driven by the expansion of cattle ranching into previously forested areas.

When a friend buys you a cup of coffee, it’s likely that next time, you’ll return the gesture. This type of reciprocal generosity has been well-documented in behavioral economic studies.

However, anthropologists and other social scientists have known for decades that in the context of relationships where one person has more power, status, or influence, reciprocal generosity is usually not the norm. 

Researchers at MIT have now experimentally demonstrated, for the first time, that small changes to the relationship context can dramatically change people’s actions and expectations of reciprocal generosity. 

During interactions between people of different social status, people tend to expect that generosity will flow one way, and it can be either up or down. It may be that a professor always buys coffee for her students, or that a student always offers to help carry groceries for his resident advisor. Once the precedent is established, it is expected to continue.

One interpretation of the findings is that keeping track of whose turn it is to do a favor is the exception in social interactions, not the rule. That is, it is extra work that we do when we want to maintain equal relationships.

“In many intimate relationships, hierarchical relationships, or other kinds of role-based relationships, you don’t put in the work of trying to keep track of turns,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and associate dean of science at MIT. “Under this interpretation, we just follow precedent because following a precedent is easier. We all know what to expect, and we don’t have to keep track of what happened last time.”

Saxe is the senior author of the study, which appears in the journal Open Mind. MIT graduate student Alicia Chen is the paper’s lead author.

Changing expectations

Most experimental studies of generosity have been done in the context of behavioral economics and game theory. In such experiments, people are usually paired with a stranger and asked to play games that require coordination. Such studies have found that people tend to use turn-taking and reciprocity as their default strategies. These scenarios, however, are stripped from any social context that might exist between people in the real world.

Saxe and Chen wanted to see if they could measure the effects of social context by incorporating relationships into the type of experiments used to evaluate people’s expectations regarding generosity.

“Where generosity becomes hard and complicated is when it starts to occur in the context of existing relationships, because it changes the terms of the relationships,” Saxe says. “What’s expected of you is very different within a relationship than outside of one.”

To study these effects, the researchers designed experiments in which participants read stories about different types of interactions. In some of the scenarios, the subjects of the stories were described as having either symmetric or asymmetric relationships. In others, they were given specific social relationships such as aunt-niece or manager-employee.

Each story described interactions that might be seen in typical daily life, such as buying coffee for a co-worker or preparing a meal for one’s family. Participants were then asked to predict what would happen the next time the interaction occurred.

In all of these scenarios, the researchers found that people expected that generous acts would be reciprocated when they occurred between individuals in symmetric relationships such as friends, cousins, or co-workers of equal rank. However, their expectations changed for asymmetric relationships, where each person has a different social status. In those cases, people expected that any precedent that was set would continue in the future.

One possible explanation for this is that reciprocity is not the norm but an exception that only occurs in the interactions between equals or strangers, the researchers say. Many of our interactions are with people with whom we have asymmetric relationship, and to maintain those relationships, it’s simply easier to follow precedent.

“If there’s no need to keep track of our equal status, then in some ways it’s the default to fall back on following precedents,” Saxe says.

Maintaining relationships

The study showed that in asymmetric relationships, generosity could flow in either direction. Once that direction was established, it was expected to continue. For example, after an older brother bought concert tickets for a much younger brother, the study participants expected that the older brother would also buy the tickets for the next concert. 

“We found that when people know the relationship is asymmetric, they don’t expect reciprocity; they expect the same action to keep on going,” Chen says. “If the lower-rank person acts generously, people expect that to continue, and if the higher-rank person acts generously, people expect that to continue.”

Following precedents is not only easier, but keeping up these actions may help solidify and define existing relationships. For example, anthropologists have long known that gift-giving helps to construct and maintain social relationships. 

“Following a precedent can be a way of actively maintaining relationships and hierarchies, when the asymmetry of the exchange truly reflects the asymmetry of the relationship,” Saxe says. 

The researchers are now working on creating computational models that could be used to analyze different factors that people take into account when they’re considering whether someone might reciprocate a generous act. In addition to the factors examined in this study, others could include how much each person will benefit, what type of relationship they’re in, and culturally specific expectations of how people should act in different situations.

“One really powerful thing about these models is that we can build in existing theories, add things to the models, and then compare how much these extra factors, like considerations related to social relationships, matter in terms of explaining what people are doing,” Chen says. “This allows us to quantitatively compare the different theories to each other.”

The research was funded by the Simons Foundation Autism Research Initiative and the Patrick J. McGovern Foundation.

For remotely operated underwater vehicles, cloudy and turbulent waters are often a no-go. When vehicles settle on the seafloor or dig through a sandbed, they can kick up clouds of sediment that make it tough for onboard cameras to see through. Often, the only thing to do is to wait until the marine dust settles before a vehicle can safely proceed. 

But a new underwater mapping technique developed by engineers at MIT and the Woods Hole Oceanographic Institution (WHOI) may allow vehicles to see through murky, low-visibility waters. 

The method fuses visual images from optical cameras with acoustic data from sonar sensors. The combination enables a vehicle to quickly map the general shape of its surroundings using sonar, even in low-visibility waters. A vehicle can move toward certain shapes in the sonar-mapped environment, coming close enough for optical cameras to visually resolve specific objects in detail. 

The technique is akin to pairing a dolphin’s echolocation with a sea turtle’s close-range vision to see and navigate through murky water, in real-time. 

The researchers tested the method in tank experiments where they could control the water’s degree of visibility. Even in the cloudiest conditions, the system was able to see through the sediment to map the tank’s environment and visualize centimeter-scale details of objects in the tank. 

The team is further improving the technique, which they’ve named Sonar-MASt3R. They envision that the mapping method could safely guide underwater vehicles through murky environments for a range of applications, including scientific exploration, underwater construction and maintenance, and deep-sea recovery. 

“We hope that this work enables us to do more operations in those challenging, low-visibility environments, and helps provide more coverage in areas that are difficult to operate in today,” says Amy Phung, a graduate student in MIT’s Department of Aeronautics and Astronautics, who led the work. 

Phung presented a paper detailing Sonar-MASt3R this week at the IEEE International Conference on Robotics and Automation (ICRA). The paper’s co-author is Richard Camilli, senior scientist of applied ocean physics and engineering at WHOI. 

The best of both

To see underwater, scientists have generally taken an either/or approach, using either optical cameras or sonar sensors to guide the way. Optical cameras can provide detailed visual imagery of a scene, but only in waters that are relatively clear and well-lit. In contrast, sonar sensors perform just as well in clear and murky water; by emitting acoustic waves and measuring the time and angle at which they return, sonar sensors can determine the exact shape, distance, and depth of objects in the environment, though a sonar map lacks any visual detail. 

To get the best of both modes, scientists have looked to combine the two in a new approach known as “opti-acoustic fusion.” In a handful of prior works, research groups have merged sonar and optical data in mapping techniques that are mostly geared toward object recognition and reconstructing workplace environments. Most techniques require time to sync and process the data and therefore do not work in real-time, while only a few can map an environment in 3D. None have been applied to high-resolution mapping underwater in murky, turbid conditions. 

Phung, who is a student in the MIT-WHOI Joint Program, and Camilli, her advisor, aimed to develop an opti-acoustic fusion technique that would generate detailed 3D maps of underwater environments in real time and in low-visibility conditions. The team was motivated, in part, by challenges in safely recovering unexploded underwater mines.

“There can be old explosives in areas that make it unsafe for ships to be in, and the ability to get rid of those safely is best done by robotics,” Camilli says. “But a lot of these explosives are set in surf zone environments where visibility adds to the challenge of doing this safely. That’s one of many applications that our technique can be used for.”

Cloudy, with a chance of mapping

The new method, Sonar-MASt3R, builds on an existing technique, MASt3R, that was developed by researchers in France. MASt3R is an image matching algorithm that is trained to take in visual images of the same scene and quickly estimate the relative depth of each pixel in the scene. In this way, MASt3R can generate a 3D map of the environment in real-time, based on a camera’s 2D images. 

“The downside is that there is no sense of scale,” Phung says. “It will say ‘this pixel is five units closer than this pixel,’ but it can’t say whether that’s 5 meters or 5 feet.”

Luckily, sonar provides absolute measurements of scale. The timing of sonar reflections can be translated directly into a specific depth and distance of objects that the signals bounced off, as well as their shape and contour. 

In their new work, Phung and Camilli used sonar data to correct MASt3R’s scaling and generate precise 3D maps of underwater environments. Even in murky water, the method’s sonar-corrected map would enable a vehicle to know the precise location of objects, and therefore how far to safely move in for a closer inspection, which the vehicle could then do using conventional optical cameras.

The team tested Sonar-MASt3R in experiments with a tank that they filled with water, sediment, and a variety of objects such as a small boulder, a coffee mug, and a packing crate. Inside the tank, they also set up a robotic arm, onto which they mounted an underwater camera, and a sonar sensor. 

For each experimental run, they first carried out a sweep trajectory, in which the robotic arm slowly swept from one side of the tank to the other to capture sonar and visual data. With this first sweep, Sonar-MASt3R quickly creates a coarse sonar-based map of the shapes and contours of the tank and its objects. The coarse map is then used to record close-up camera images of the objects, which are used to improve the map resolution. A “keyframe” approach quickly compares each new image frame to the last keyframe. If a frame provides new information not contained in the last keyframe, the image is added as a new keyframe to the map. If it is similar, it is immediately discarded. In this way, the approach can quickly fill in the map with relevant visual detail, in real-time. 

The researchers tested their new approach underwater, testing eight different levels of turbidity, which they created by stirring up the tank’s sediment. Compared with other opti-acoustic fusion approaches, Sonar-MASt3R generated more accurate 3D maps and resolved smaller, centimeter-scale details, and in cloudier conditions. In the cloudiest condition, which the robotic arm’s cameras could not see through, its sonar sensors were able to generate a rough map of the tank’s hidden objects. This initial map enabled the arm to move safely through the murk and closer to specific objects, which its underwater camera could then visualize in more detail. 

“An analogy would be if you were to go into a china shop in the dark, and try to pick your way around to find a specific coffee mug without knocking things over,” Camilli offers. “This would allow you to do that.”

The team plans to test the approach in natural underwater conditions, where they suspect that the mapping task should be more straightforward. 

“In a tank, it’s like an echo chamber,” Camilli says. “It’s like trying to do this in a funhouse mirror setting where you get all these distortions and reverberations and ghost images that really complicates the processing. If you put it in the real world, it should be easier.”

Then, they say, Sonar-MASt3R could help scientists safely explore in cloudy, turbid, and murky underwater regions.

“The real value in this effort is so we can use this technology in mission scenarios that are untractable right now,” Phung says. “And there are plenty of untractable missions because we don’t have the observational or perception capabilities.”

This research was supported, in part, by NASA, and the National Science Foundation.