On July 3, as a heat wave swept the region, New York State’s grid imported 52 gigawatt-hours of electricity from Canada—enough to meet about 9% of its total electricity demand that day.

Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens called the Champlain Hudson Power Express (CHPE). It opened in May and is officially the longest underground transmission line in North America.

An underground power line might not sound all that exciting, but this could be a big deal for the state’s grid planning, and for emissions. It could provide up to 20% of New York City’s electricity demand, largely with abundant hydropower from Quebec.

One wrinkle: The line has been down for most of this month, and some experts are concerned about how drought will affect the power supply feeding it. Let’s look at how the CHPE transmission line could help shape the future of our grid, and what barriers it needs to overcome to make a difference.

Planning for the CHPE (which is charmingly pronounced “chippy”) started 15 years ago, with the permitting process formally beginning in March 2010. The vision was to build infrastructure to better connect Quebec and southern New York.

Over 99% of Quebec’s electricity comes from renewable sources; most demand is met with hydropower, though the province’s wind capacity is growing quickly. New York has some hydropower of its own, as well as nuclear and wind, but the state still relies on fossil fuels for most of its energy generation.

Transmission Developers, a company owned by the alternative asset management firm Blackstone, and Hydro-Québec, the province’s manager of generation and transmission, partnered to build CHPE. Construction began in late 2022 and wrapped up earlier this year. The total cost for the privately funded project turned out to be  $6 billion.

The construction of this line was a feat. It’s made up of a bundle of two high-voltage direct-current power cables, each measuring roughly five inches across. Developers buried the bundle underground or underwater across the length of New York State. Much of the line was laid at the bottom of the Hudson River, requiring special boats that shot water jets deep into the sediment to create trenches for the cable.

Connecting grids together can help accelerate the transition away from fossil fuels. The ability to move electricity to where it’s needed could also help limit the amount of new capacity we need to build. Research has shown that interconnection can help cut emissions and lower system costs.

But CHPE is off to a slow start and has seen two outages so far. The first, on July 1, was reportedly caused by a trip at a converter on the Canadian side of the border. The second outage began on July 4, and the power line is still down as of the morning of July 22.

Some experts say this isn’t unusual for a new infrastructure project. Other power lines have seen similar startup challenges, and the equipment hasn’t really been fully tested until it’s in operation, Normand Mousseau, a physics professor at Université de Montréal, told the Gazette.

Officials traced the issue to a damaged section of cable on the US side of the border, and the company that manufactured the line sent experts to investigate the cause, according to reporting from RTO Insider, a trade publication. 

The damaged portion of the cable has been removed and replaced, says Lynn St-Laurent, a spokesperson for Hydro-Québec. “It is currently estimated that the remaining work, including necessary post-repair testing, will be completed by the weekend.”

Similar woes have afflicted the New England Clean Energy Connect line, which opened in January, stretching 145 miles from Quebec to Maine. That project has also seen outages, and very little additional energy has flowed into the Northeast.

The good news for New York is that the grid wasn’t relying on CHPE yet. “Our planning studies did not assume CHPE would be available this summer, and that was one reason the grid performed reliably during the heat wave earlier this month,” Kevin Lanahan, a spokesperson for the New York Independent System Operator, the state’s grid management company, said in a statement. “A core principle of reliability planning is not relying on any single project.” 

The idea is that eventually, states and regions will be able to rely—at least in part—on these projects, so there is pressure to get them working smoothly: Building massive transmission lines is a major long-term investment. In future years, as the equipment gets stress-tested and utilities begin to feel more confident in the projects’ reliability, they could play a bigger role on the grid.

One thing to keep an eye on moving forward is the condition of Quebec’s hydropower fleet: The region has seen intense drought for the past three years, eating into the water reserves used to generate electricity. That could mean there won’t always be abundant hydropower to ship across the border—even if the transmission lines are able to carry it. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Guardio Labs has disclosed a critical vulnerability chain in the Adobe Acrobat Chrome extension that could allow a malicious website to hijack and exfiltrate rendered WhatsApp Web data from affected users. This vulnerability is tracked as CVE-2026-48294 and has impacted Adobe Acrobat extension version 26.5.2. The extension is installed across approximately 329 million browsers. Adobe […]

The post Critical Adobe Acrobat Chrome Extension Flaw “HermeticReader” Lets Hackers Hijack WhatsApp Chats of 300M+ Users appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

Samsung’s Galaxy S26 FE and Galaxy Tab S12 appeared in a Google app list, hinting at software prep before a possible late-summer launch.

The post Samsung Galaxy S26 FE and Galaxy Tab S12 Surface in Google App Ahead of Launch appeared first on TechRepublic.

Samsung’s Galaxy S26 FE and Galaxy Tab S12 appeared in a Google app list, hinting at software prep before a possible late-summer launch.

The post Samsung Galaxy S26 FE and Galaxy Tab S12 Surface in Google App Ahead of Launch appeared first on TechRepublic.

An attacker only needed to convince the targeted user to visit a malicious website to exfiltrate WhatsApp messages and contacts.

The post Flaw in Adobe Extension With 300M Installs Enabled WhatsApp Data Theft appeared first on SecurityWeek.

When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. The telescope will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography.

It will allow astronomers to take the first pictures of planets orbiting other stars that are similar to those in our solar system. Ultimately, it could pave the way for a future mission that could snap the first photos of Earth-like worlds.

“I hope it’s remembered for it being that critical stepping stone for … finding Earth 2.0,” says Brandon Creager, the instrument’s lead mechanical engineer at NASA’s Jet Propulsion Laboratory (JPL).

Named after Nancy Grace Roman, NASA’s first chief of astronomy, this new telescope will carry a roughly 300-megapixel wide-field camera that will enable it to capture images about 100 times larger than the Hubble Space Telescope’s widest exposures at a similar resolution.

These capabilities will help astronomers unpack the mysterious identities of dark matter and dark energy—and to detect around 100,000 new exoplanets, planets outside our solar system, whose presence can be inferred from the way they distort the starlight of more distant stars. Javier Viaña, a research scientist at Harvard who has had two projects selected for Roman’s highly competitive first year of observing, compares the leap to moving from “interviewing a handful of people” to “conducting a global census.”

Another camera will use the coronagraph, blocking out a star’s light as it observes one stellar system at a time. The instrument will allow astronomers an unprecedented look at the space around stars, enabling them to see smaller, dimmer, and more close-in exoplanets. “It’s giving us the ability to see planets that we haven’t been able to physically see before,” says Creager.

The anatomy of a vanishing trick

Coronagraphs in space aren’t new. But earlier incarnations, such as those currently aboard Hubble and the James Webb Space Telescope, use a stationary system to block a star’s blinding light. The approach does help, but it’s a bit like putting your thumb over a flashlight while searching a dark room for a firefly. Though the bulb vanishes, stray glare can still escape and overwhelm the light of the insect. Inside a telescope, that glare can come from light leaking around the edges of machinery or from minuscule imperfections in mirrors and coatings that can scatter starlight into speckles. All this can hide, or even impersonate, a planet.

Roman’s coronagraph, however, will attempt something completely unseen in space telescopes until this year: Before each observation, it will measure that leftover light and try to suppress it, a technique known as active wavefront control.

The telescope is able to do this because it contains two deformable mirrors. Each has a 48-by-48 checkerboard of actuators (tiny pistons) beneath a thin, deformable sheet of glass. Applying a small amount of voltage makes the actuators contract and tug their patches of mirror slightly backward, like thousands of microscopic fingers delicately sculpting a surface.

The effect is very subtle: Each patch of mirror can deform by up to 0.5 micrometers, or about one-fourth the size of an E. coli bacterium, and in increments as small as approximately 10 picometers. That’s about a tenth the diameter of a hydrogen atom, says Ilya Poberezhskiy, the instrument’s project systems engineer at JPL.

The actuators allow the mirrors to create an “active wavefront,” where each component is moved to the perfect position to cancel out incoming waves of unwanted light—a bit like a pair of noise-canceling headphones, but for light instead of sound. The “canceled-out” light creates a “doughnut-shaped region around the star where we suppress starlight and where we’re hoping to see exoplanets,” says Poberezhskiy.

Compared with current space-based coronagraphs, the system is expected to improve sensitivity to exoplanets against the glare of their host stars by a factor of up to 1,000, revealing planets that would have been far too faint to detect before.

Like Hubble and JWST, Roman also uses masks, patterned plates placed in the path of the light that are designed to block the photons that run into them. One tool in Roman’s mask arsenal is “silicon grass,” a thicket of microscopic spikes on some masks that can be used in certain configurations to absorb photons so they don’t bounce around the telescope and accidentally reach a detector.

Light entering the forest bounces deeper and deeper between the blades and gets trapped instead of reflecting back toward the camera. “Once the light gets into there, it never gets out,” Poberezhskiy says. The mirrors and masks form a succession of gates and hedges to guide as much of the preserved planetary light as possible toward the final detector.

Alien Jupiters

This elaborate setup could open a new chapter in the direct imaging of exoplanets. Nearly all exoplanets photographed so far are oversize youngsters that are nothing like the residents of our solar system: several times the mass of Jupiter, still glowing with the heat left over from their birth, and orbiting tens or hundreds of times farther from their star than the Earth is from the sun. This is because they are relatively easy to see. Their size, warmth, and distance from their parent star makes them shine brightly in infrared light, far away from the worst of the stellar glare.

Roman, however, could directly image a true Jupiter analogue—a planet similar to Jupiter in mass and circling a sunlike star a few times farther out than Earth is from our sun. Unlike the hot Jupiters we can see now, this one would be a much more mature gas giant like ours, primarily reflecting its parent star’s light after billions of years of cooling instead of heavily emitting its own.

Astronomers have been able to infer the existence of such planets from the gravitational wobble they impart to the star. Roman instead will collect starlight reflected from the planet itself. “We’re not looking at the star. We’re not looking at the effect of the planet on the star,” says Meredith MacGregor, a professor of astronomy at Johns Hopkins who has also secured an observing program. “We are actually looking at the planet, and that is super powerful.”

Once this instrument becomes available, it will become the scientists’ turn to do their jobs. “I’m honestly a little terrified about how we’re all going to deal with it, because I think it’s just so much data,” MacGregor says. “I think people will legitimately still be working on Roman data for decades.”

But don’t expect to see a 4K photo of an alien Jupiter in the coming months. Roman will not be able to resolve such a planet into a solid globe—at best, it will likely resemble a smattering of pixels. Still, that will be enough, MacGregor says, as Roman can then use the coronagraph to get information on the various wavelengths of light from the planet, which can tell astronomers about its atmospheric chemistry.

“You’re taking something that’s a point of light and turning it into an actual world,” she says, “because if you know that about its atmosphere, now you know something about the surface of the planet and the possibility of life being on that planet, right? So that’s a big step.”

During its first observations, scientists and engineers will see whether they can hold a star at the very center of the coronagraph’s masks, shape the mirrors, “dig” the dark doughnut (as Poberezhskiy describes it), and then maintain everything as the spacecraft moves through space and actively changes temperature.

The results will inform NASA’s proposed Habitable Worlds Observatory, the daydream of many an exoplanet astronomer, which will in theory be able to separate the light of an Earthlike planet from that of a sunlike star, over 10 billion times brighter.

Creager, who has worked on the instrument since 2018, is proud of the achievement: “Not too many people get to say, ‘I built something and it’s taking a picture of a planet that’s at a star that’s 50 light-years away or 100 light-years away.’” He imagines the moment he and his team will be able to look at the first image as it arrives: “Yes, we did that.” While the planet may show up only as a tiny dot, Roman’s achievement will be the darkness engineered around it.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until March, branded Anthropic’s models as “lobotomized” and “woke.” Emil Michael, a top Pentagon official, called OpenAI’s new head of strategic futures a “supreme village idiot.”

It began because no one can agree on what to do about Kimi, a free, open source model that Chinese AI company Moonshot launched last week. It appears to rival the intelligence of models from OpenAI and Anthropic, which are very much not free. 

Kimi and other Chinese models like it pose a real problem for Trump. And they’re dividing the top AI strategists in his orbit into factions. Every time a new smart, free model from China like Kimi gets released, US companies see less reason to fork out money to access models from Anthropic or OpenAI. Given that enthusiasm for these and other AI companies is driving an outsized share of economic growth, China’s AI models create both economic and political problems for the president. They are “a threat for an administration that really doesn’t want more economic bad news,” Anton Leicht, a fellow at the Carnegie Endowment, wrote on X. They’ve already rattled US stocks. 

What is Trump to do? First, consider that this is all happening just a week after New York imposed the country’s first state ban on new data centers. There is growing distrust of AI companies, and I imagine a not-insignificant share of Americans would have little sympathy for OpenAI or Anthropic as they fend off cheaper competitors, and would say it’s not the government’s job to protect their interests.

On this point, they’d see a sliver of agreement (and really just a sliver) with David Sacks, who on July 19 criticized top AI companies that “want the government to eliminate their open source competition.” He has also argued that Chinese AI models have become popular because they come with fewer restrictions on how people can use them (putting aside the built-in state censorship). 

Sacks, however, is out of a job. He no longer has a formal role advising Trump, and his position that more open AI is better has been largely replaced in the administration by one that sees a larger role for government intervention. The thinking behind this view is that because AI models have gotten strong enough to pose threats to national security, the government must control how they’re used. 

This position has fueled the new White House review process that aims to vet AI models’ security before they’re released. Dean Ball, a former Trump AI advisor who now works for OpenAI, criticized it over the weekend as a “de facto licensing regime for frontier AI.” Ball predicted Trump may solve his Chinese open source problem with a bit of soft power, perhaps by making US companies afraid to use models like Kimi. That drew a response from Michael, who, with Secretary of Defense Pete Hegseth, has been the agency’s main liaison with AI companies. Michael called Ball the AI industry’s “supreme village idiot,” bristling at the suggestion that the government would quietly strong-arm companies rather than, as Michael put it, go through “the democratic process not some Deep State scheme.”

Left out of the conversation has been how a model like Kimi got so good in the first place. For much of the Biden administration and even the beginning of Trump’s second administration, keeping China from getting top chips was a priority. Those export controls have loosened—Trump made the controversial decision to allow Nvidia to sell more chips to China, in exchange for the US government taking a cut—and the government has alleged that some chip smuggling has taken place. But China nonetheless has limited computing power, and it’s not clear what chips the company behind Kimi used to train the model. 

It’s possible that the process involved some distillation, a practice in which AI models are trained on the outputs of existing AI models. OpenAI and Anthropic have long complained that Chinese AI companies do this, and they have requested government help to put a stop to it. In April, they got it, when the Trump administration announced a series of efforts to curb the practice.  

But Kimi is out there and free, and it is nearly as good as the Anthropic model the US government deemed so powerful that it was briefly shut down because it threatened national security. The weekend’s sparring suggests many in Trump’s orbit see that as a wake-up call. But nobody can agree on what for.

The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases. 

Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a psychology study that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki. 

In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job.

The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors. Newer models with higher reasoning capabilities, such as OpenAI’s o3 and DeepSeek’s R1, showed stronger biases.

The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study’s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI’s reasoning model o3 scoring 1.83, close to the maximum possible.

That’s because LLMs “really are eager to create generalizations from limited data,” says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. “That’s literally a lot of what they’re optimized for.”

Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better—a phenomenon psychologists call the “exploration-exploitation dilemma.” It’s like choosing between a new restaurant and your reliable favorite. 

Because LLMs are trained on math, coding, and science problems—tasks that reward generalizing from just a few examples—they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. When LLMs rush to generalize in social settings, “that’s when things tend to go wrong,” says Liu. OpenAI and Anthropic did not respond to requests for comment.

The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can “over-index on the same kinds of behaviors it’s experienced before” and form biases, she says.

Simply having chatbots remember less isn’t a fix, though, because users want chatbots to remember what they say. “We still are trying to figure out just the right amount that isn’t too much or too little,” says Wang.

Telling the model to be fair didn’t change its behavior much. “Either it can’t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,” says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that “incorporate desirable social values in order to make the large language model act in socially desirable ways,” says Liu.

The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again. 

To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they’d made successful hires, a model screening résumés in the real world doesn’t get an instant report card. Companies can take a long time to find out whether a new hire is any good, if they ever do.

As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. “These novel biases—they’re sort of ever present,” says Liu.