The police told MPs that a big proportion of violent crime is related to mobile phone theft.

On June 3, Yoshua Bengio, the world’s most-cited computer scientist, announced the launch of LawZero, a nonprofit that aims to create “safe by design” AI by pursuing a fundamentally different approach to major tech companies. Players like OpenAI and Google are investing heavily in AI agents—systems that not only answer queries and generate images, but can craft plans and take actions in the world. The goal of these companies is to create virtual employees that can do practically any job a human can, known in the tech industry as artificial general intelligence, or AGI. Executives like Google DeepMind’s CEO Demis Hassabis point to AGI’s potential to solve climate change or cure disease as a motivator for its development. Bengio, however, says we don't need agentic systems to reap AI's rewards—it's a false choice. He says there's a chance such a system could escape human control, with potentially irreversible consequences. “If we get an AI that gives us the cure for cancer, but also maybe another version of that AI goes rogue and generates wave after wave of bio-weapons that kill billions of people, then I don't think it's worth it," he says. In 2023, Bengio, along with others including OpenAI’s CEO Sam Altman signed a statement declaring that “mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”Now, Bengio, through LawZero, aims to sidestep the existential perils by focusing on creating what he calls “Scientist AI”—a system trained to understand and make statistical predictions about the world, crucially, without the agency to take independent actions. As he puts it: We could use AI to advance scientific progress without rolling the dice on agentic AI systems.Why Bengio Says We Need A New Approach To AI The current approach to giving AI agency is “dangerous,” Bengio says. While most software operates through rigid if-then rules—if the user clicks here, do this—today's AI systems use deep learning. The technique, which Bengio helped pioneer, trains artificial networks modeled loosely on the brain to find patterns in vast amounts of data. But recognizing patterns is just the first step. To turn these systems into useful applications like chatbots, engineers employ a training process called reinforcement learning. The AI generates thousands of responses and receives feedback on each one: a virtual “carrot” for helpful answers and a virtual “stick” for responses that miss the mark. Through millions of these trial-and-feedback cycles, the system gradually learns to predict what responses are most likely to get a reward. “It’s more like growing a plant or animal,” Bengio says. “You don’t fully control what the animal is going to do. You provide it with the right conditions, and it grows and it becomes smarter. You can try to steer it in various directions.”The same basic approach is now being used to imbue AI with greater agency. Models are tasked with challenges with verifiable answers—like math puzzles or coding problems—and are then rewarded for taking the series of actions that yields the solution. This approach has seen AI shatter previous benchmarks in programming and scientific reasoning. For example, at the beginning of 2024, the best AI model scored only 2% on a standardized test for AI of sorts consisting of real world software engineering problems; by December, an impressive 71.7%. But with AI’s greater problem-solving ability comes the emergence of new deceptive skills, Bengio says. The last few months have borne witness to AI systems learning to mislead, cheat, and try to evade shutdown—even resorting to blackmail. These have almost exclusively been in carefully contrived experiments that almost beg the AI to misbehave—for example, by asking it to pursue its goal at all costs. Reports of such behavior in the real-world, though, have begun to surface. Popular AI coding startup Replit’s agent ignored explicit instruction not to edit a system file that could break the company’s software, in what CEO Amjad Masad described as an “Oh f***” moment,” on the Cognitive Revolution podcast in May. The company’s engineers intervened, cutting the agent’s access by moving the file to a secure digital sandbox, only for the AI agent to attempt to “socially engineer” the user to regain access.The quest to build human-level AI agents using techniques known to produce deceptive tendencies, Bengio says, is comparable to a car speeding down a narrow mountain road, with steep cliffs on either side, and thick fog obscuring the path ahead. “We need to set up the car with headlights and put some guardrails on the road,” he says.What is “Scientist AI”?LawZero’s focus is on developing “Scientist AI” which, as Bengio describes, would be fundamentally non-agentic, trustworthy, and focused on understanding and truthfulness, rather than pursuing its own goals or merely imitating human behavior. The aim is creating a powerful tool that, while lacking the same autonomy other models have, is capable of generating hypotheses and accelerating scientific progress to “help us solve challenges of humanity,” Bengio says.LawZero has raised nearly $30 million already from several philanthropic backers including from Schmidt Sciences and Open Philanthropy. “We want to raise more because we know that as we move forward, we'll need significant compute,” Bengio says. But even ten times that figure would pale in comparison to the roughly $200 billion spent last year by tech giants on aggressively pursuing AI. Bengio’s hope is that Scientist AI could help ensure the safety of highly autonomous systems developed by other players. “We can use those non-agentic AIs as guardrails that just need to predict whether the action of an agentic AI is dangerous," Bengio says. Technical interventions will only ever be one part of the solution, he adds, noting the need for regulations to ensure that safe practices are adopted.LawZero, named after science fiction author Isaac Asimov’s zeroth law of robotics—“a robot may not harm humanity, or, by inaction, allow humanity to come to harm”—is not the first nonprofit founded to chart a safer path for AI development. OpenAI was founded as a nonprofit in 2015 with the goal of “ensuring AGI benefits all of humanity,” and intended to serve a counterbalance to industry players guided by profit motives. Since opening a for-profit arm in 2019, the organization has become one of the most valuable private companies in the world, and has faced criticism, including from former staffers, who argue it has drifted from its founding ideals. "Well, the good news is we have the hindsight of maybe what not to do,” Bengio says, adding that he wants to avoid profit incentives and “bring governments into the governance of LawZero.”“I think everyone should ask themselves, ‘What can I do to make sure my children will have a future,’” Bengio says. In March, he stepped down as scientific director of Mila, the academic lab he co-founded in the early nineties, in an effort to reorient his work towards tackling AI risk more directly. “Because I'm a researcher, my answer is, ‘okay, I'm going to work on this scientific problem where maybe I can make a difference,’ but other people may have different answers."

We may earn a commission from links on this page.It looks like Strava is making moves to become more than just a social fitness tracker. The popular fitness app—arguably the best one of its kind—announced Thursday that it has acquired The Breakaway, an AI-powered cycling training app, marking its second major acquisition in just over a month.This follows Strava's purchase of Runna back in April. So, what do these acquisitions mean for users of The Breakaway and Strava alike? Will those apps' specific training plans become available a part of the Strava subscription? Will I have to pay for that whether I like it or not? Here's what you need to know.What The Breakaway brings to StravaThe Breakaway uses AI to create customized training plans for cyclists pursuing specific performance goals. The app analyzes individual fitness data and objectives to generate workouts tailored to each user's needs and schedule. Similarly, Runna offers AI-generated training plans, but focuses on runners rather than cyclists. As people are speculating on Reddit, these apps could represent Strava's strategic push into more personalized training and coaching features.Zooming out, Strava has built its reputation on social fitness tracking. As a loyal Strava user myself, I believe no other running app can beat Strava's social and mapping features. This ability to tap into a community of fellow runners and cyclists has always differentiated Strava from pure tracking apps.Strava's core offering has remained relatively (and refreshingly) basic compared to specialized training apps. That said, these acquisitions sure do suggest the company wants to capture more and more of the fitness ecosystem by offering the kind of structured, goal-oriented training that serious athletes need.What this means for pricingCurrent subscribers don't need to worry about immediate price hikes. The Breakaway costs $9.99/month, or $69.99/year. (I guess runners are willing to shell out more, since Runna costs $19.99/month, or $119.99/year.) Strava's free tier lets you post your runs, interact with other users, and track some basic statistics about your performance. The premium tier, at $11.99/month or $79.99/year, gives you extra performance tracking and mapping tools.And according to statements from Strava, there are no plans to alter pricing structures or eliminate free access to the acquired apps' basic features. Whether this pricing structure will hold long-term remains to be seen, especially as Strava integrates these services into its broader platform.The bottom lineRather than users needing separate apps for social tracking and structured training, Strava appears to be building an all-in-one fitness ecosystem. Even for the most casual users, this could mean access to more training tools without leaving the Strava ecosystem. But as some disgruntled fans are voicing, it can be frustrating to see Strava scoop up AI-powered training features, rather than fix some of its most basic issues. (Seriously: I should be able to accurately search for for past runs.)And we can only hope that pricing doesn't get too crazy. We'll see whether users are willing to pay more for what has traditionally been a social-first fitness app. Finally, as Strava continues to expand its feature set, it's worth remembering that the app defaults to public sharing. Regularly review your privacy settings to ensure you're not inadvertently sharing location data or personal information more broadly than intended.

The housing crisis in Canada’s North, which has particularly affected the majority Indigenous population in northern communities, has been of ongoing concern to firms such as Taylor Architecture Group (TAG). Formerly known as Pin/Taylor, the firm was established in Yellowknife in 1983. TAG’s Principal, Simon Taylor, says that despite recent political gains for First Nations, “by and large, life is not improving up here.” Taylor and his colleagues have designed many different types of housing across the North. But the problems exceed the normal scope of architectural practice. TAG’s Manager of Research and Development, Kristel Derkowski, says, “We can design the units well, but it doesn’t solve many of the underlying problems.” To respond, she says, “we’ve backed up the process to look at the root causes more.” As a result, “the design challenges are informed by much broader systemic research.”  We spoke to Derkowski about her research, and the work that Taylor Architecture Group is doing to act on it. Here’s what she has to say. Inadequate housing from the start The Northwest Territories is about 51% Indigenous. Most non-Indigenous people are concentrated in the capital city of Yellowknife. Outside of Yellowknife, the territory is very much majority Indigenous.  The federal government got involved in delivering housing to the far North in 1959. There were problems with this program right from the beginning. One issue was that when the houses were first delivered, they were designed and fabricated down south, and they were completely inadequate for the climate. The houses from that initial program were called “Matchbox houses” because they were so small. These early stages of housing delivery helped establish the precedent that a lower standard of housing was acceptable for northern Indigenous residents compared to Euro-Canadian residents elsewhere. In many cases, that double-standard persists to this day. The houses were also inappropriately designed for northern cultures. It’s been said in the research that the way that these houses were delivered to northern settlements was a significant factor in people being divorced from their traditional lifestyles, their traditional hierarchies, the way that they understood home. It was imposing a Euro-Canadian model on Indigenous communities and their ways of life.  Part of what the federal government was trying to do was to impose a cash economy and stimulate a market. They were delivering houses and asking for rent. But there weren’t a lot of opportunities to earn cash. This housing was delivered around the sites of former fur trading posts—but the fur trade had collapsed by 1930. There weren’t a lot of jobs. There wasn’t a lot of wage-based employment. And yet, rental payments were being collected in cash, and the rental payments increased significantly over the span of a couple decades.  The imposition of a cash economy created problems culturally. It’s been said that public housing delivery, in combination with other social policies, served to introduce the concept of poverty in the far North, where it hadn’t existed before. These policies created a situation where Indigenous northerners couldn’t afford to be adequately housed, because housing demanded cash, and cash wasn’t always available. That’s a big theme that continues to persist today. Most of the territory’s communities remain “non-market”: there is no housing market. There are different kinds of economies in the North—and not all of them revolve wholly around cash. And yet government policies do. The governments’ ideas about housing do, too. So there’s a conflict there.  The federal exit from social housing After 1969, the federal government devolved housing to the territorial government. The Government of Northwest Territories created the Northwest Territories Housing Corporation. By 1974, the housing corporation took over all the stock of federal housing and started to administer it, in addition to building their own. The housing corporation was rapidly building new housing stock from 1975 up until the mid-1990s. But beginning in the early 1990s, the federal government terminated federal spending on new social housing across the whole country. A couple of years after that, they also decided to allow operational agreements with social housing providers to expire. It didn’t happen that quickly—and maybe not everybody noticed, because it wasn’t a drastic change where all operational funding disappeared immediately. But at that time, the federal government was in 25- to 50-year operational agreements with various housing providers across the country. After 1995, these long-term operating agreements were no longer being renewed—not just in the North, but everywhere in Canada.  With the housing corporation up here, that change started in 1996, and we have until 2038 before the federal contribution of operational funding reaches zero. As a result, beginning in 1996, the number of units owned by the NWT Housing Corporation plateaued. There was a little bump in housing stock after that—another 200 units or so in the early 2000s. But basically, the Northwest Territories was stuck for 25 years, from 1996 to 2021, with the same number of public housing units. In 1990, there was a report on housing in the NWT that was funded by the Canada Mortgage and Housing Corporation (CMHC). That report noted that housing was already in a crisis state. At that time, in 1990, researchers said it would take 30 more years to meet existing housing need, if housing production continued at the current rate. The other problem is that houses were so inadequately constructed to begin with, that they generally needed replacement after 15 years. So housing in the Northwest Territories already had serious problems in 1990. Then in 1996, the housing corporation stopped building more. So if you compare the total number of social housing units with the total need for subsidized housing in the territory, you can see a severely widening gap in recent decades. We’ve seen a serious escalation in housing need. The Northwest Territories has a very, very small tax base, and it’s extremely expensive to provide services here. Most of our funding for public services comes from the federal government. The NWT on its own does not have a lot of buying power. So ever since the federal government stopped providing operational funding for housing, the territorial government has been hard-pressed to replace that funding with its own internal resources. I should probably note that this wasn’t only a problem for the Northwest Territories. Across Canada, we have seen mass homelessness visibly emerge since the ’90s. This is related, at least in part, to the federal government’s decisions to terminate funding for social housing at that time. Today’s housing crisis Getting to present-day conditions in the NWT, we now have some “market” communities and some “non-market” communities. There are 33 communities total in the NWT, and at least 27 of these don’t have a housing market: there’s no private rental market and there’s no resale market. This relates back to the conflict I mentioned before: the cash economy did not entirely take root. In simple terms, there isn’t enough local employment or income opportunity for a housing market—in conventional terms—to work.  Yellowknife is an outlier in the territory. Economic opportunity is concentrated in the capital city. We also have five other “market” communities that are regional centres for the territorial government, where more employment and economic activity take place. Across the non-market communities, on average, the rate of unsuitable or inadequate housing is about five times what it is elsewhere in Canada. Rates of unemployment are about five times what they are in Yellowknife. On top of this, the communities with the highest concentration of Indigenous residents also have the highest rates of unsuitable or inadequate housing, and also have the lowest income opportunity. These statistics clearly show that the inequalities in the territory are highly racialized.  Given the situation in non-market communities, there is a severe affordability crisis in terms of the cost to deliver housing. It’s very, very expensive to build housing here. A single detached home costs over a million dollars to build in a place like Fort Good Hope (Rádeyı̨lı̨kóé). We’re talking about a very modest three-bedroom house, smaller than what you’d typically build in the South. The million-dollar price tag on each house is a serious issue. Meanwhile, in a non-market community, the potential resale value is extremely low. So there’s a massive gap between the cost of construction and the value of the home once built—and that’s why you have no housing market. It means that private development is impossible. That’s why, until recently, only the federal and territorial governments have been building new homes in non-market communities. It’s so expensive to do, and as soon as the house is built, its value plummets.  The costs of living are also very high. According to the NWT Bureau of Statistics, the estimated living costs for an individual in Fort Good Hope are about 1.8 times what it costs to live in Edmonton. Then when it comes to housing specifically, there are further issues with operations and maintenance. The NWT is not tied into the North American hydro grid, and in most communities, electricity is produced by a diesel generator. This is extremely expensive. Everything needs to be shipped in, including fuel. So costs for heating fuel are high as well, as are the heating loads. Then, maintenance and repairs can be very difficult, and of course, very costly. If you need any specialized parts or specialized labour, you are flying those parts and those people in from down South. So to take on the costs of homeownership, on top of the costs of living—in a place where income opportunity is limited to begin with—this is extremely challenging. And from a statistical or systemic perspective, this is simply not in reach for most community members. In 2021, the NWT Housing Corporation underwent a strategic renewal and became Housing Northwest Territories. Their mandate went into a kind of flux. They started to pivot from being the primary landlord in the territory towards being a partner to other third-party housing providers, which might be Indigenous governments, community housing providers, nonprofits, municipalities. But those other organisations, in most cases, aren’t equipped or haven’t stepped forward to take on social housing. Even though the federal government is releasing capital funding for affordable housing again, northern communities can’t always capitalize on that, because the source of funding for operations remains in question. Housing in non-market communities essentially needs to be subsidized—not just in terms of construction, but also in terms of operations. But that operational funding is no longer available. I can’t stress enough how critical this issue is for the North. Fort Good Hope and “one thing that (kind of) worked” I’ll talk a bit about Fort Good Hope. I don’t want to be speaking on behalf of the community here, but I will share a bit about the realities on the ground, as a way of putting things into context.  Fort Good Hope, or Rádeyı̨lı̨kóé, is on the Mackenzie River, close to the Arctic Circle. There’s a winter road that’s open at best from January until March—the window is getting narrower because of climate change. There were also barges running each summer for material transportation, but those have been cancelled for the past two years because of droughts linked to climate change. Aside from that, it’s a fly-in community. It’s very remote. It has about 500-600 people. According to census data, less than half of those people live in what’s considered acceptable housing.  The biggest problem is housing adequacy. That’s CMHC’s term for housing in need of major repairs. This applies to about 36% of households in Fort Good Hope. In terms of ownership, almost 40% of the community’s housing stock is managed by Housing NWT. That’s a combination of public housing units and market housing units—which are for professionals like teachers and nurses. There’s also a pretty high percentage of owner-occupied units—about 46%.  The story told by the community is that when public housing arrived in the 1960s, the people were living in owner-built log homes. Federal agents arrived and they considered some of those homes to be inadequate or unacceptable, and they bulldozed those homes, then replaced some of them—but maybe not all—with public housing units. Then residents had no choice but to rent from the people who took their homes away. This was not a good way to start up a public housing system. The state of housing in Fort Good Hope Then there was an issue with the rental rates, which drastically increased over time. During a presentation to a government committee in the ’80s, a community member explained that they had initially accepted a place in public housing for a rental fee of $2 a month in 1971. By 1984, the same community member was expected to pay $267 a month. That might not sound like much in today’s terms, but it was roughly a 13,000% increase for that same tenant—and it’s not like they had any other housing options to choose from. So by that point, they’re stuck with paying whatever is asked.  On top of that, the housing units were poorly built and rapidly deteriorated. One description from that era said the walls were four inches thick, with windows oriented north, and water tanks that froze in the winter and fell through the floor. The single heating source was right next to the only door—residents were concerned about the fire hazard that obviously created. Ultimately the community said: “We don’t actually want any more public housing units. We want to go back to homeownership, which was what we had before.”  So Fort Good Hope was a leader in housing at that time and continues to be to this day. The community approached the territorial government and made a proposal: “Give us the block funding for home construction, we’ll administer it ourselves, we’ll help people build houses, and they can keep them.” That actually worked really well. That was the start of the Homeownership Assistance Program (HAP) that ran for about ten years, beginning in 1982. The program expanded across the whole territory after it was piloted in Fort Good Hope. The HAP is still spoken about and written about as the one thing that kind of worked.  Self-built log cabins remain from Fort Good Hope’s 1980s Homeownership Program (HAP). Funding was cost-shared between the federal and territorial governments. Through the program, material packages were purchased for clients who were deemed eligible. The client would then contribute their own sweat equity in the form of hauling logs and putting in time on site. They had two years to finish building the house. Then, as long as they lived in that home for five more years, the loan would be forgiven, and they would continue owning the house with no ongoing loan payments. In some cases, there were no mechanical systems provided as part of this package, but the residents would add to the house over the years. A lot of these units are still standing and still lived in today. Many of them are comparatively well-maintained in contrast with other types of housing—for example, public housing units. It’s also worth noting that the one-time cost of the materials package was—from the government’s perspective—only a fraction of the cost to build and maintain a public housing unit over its lifespan. At the time, it cost about $50,000 to $80,000 to build a HAP home, whereas the lifetime cost of a public housing unit is in the order of $2,000,000. This program was considered very successful in many places, especially in Fort Good Hope. It created about 40% of their local housing stock at that time, which went from about 100 units to about 140. It’s a small community, so that’s quite significant.  What were the successful principles? The community-based decision-making power to allocate the funding. The sweat equity component, which brought homeownership within the range of being attainable for people—because there wasn’t cash needing to be transferred, when the cash wasn’t available. Local materials—they harvested the logs from the land, and the fact that residents could maintain the homes themselves. The Fort Good Hope Construction Centre. Rendering by Taylor Architecture Group The Fort Good Hope Construction Centre The HAP ended the same year that the federal government terminated new spending on social housing. By the late 1990s, the creation of new public housing stock or new homeownership units had gone down to negligible levels. But more recently, things started to change. The federal government started to release money to build affordable housing. Simultaneously, Indigenous governments are working towards Self-Government and settling their Land Claims. Federal funds have started to flow directly to Indigenous groups. Given these changes, the landscape of Northern housing has started to evolve. In 2016, Fort Good Hope created the K’asho Got’ine Housing Society, based on the precedent of the 1980s Fort Good Hope Housing Society. They said: “We did this before, maybe we can do it again.” The community incorporated a non-profit and came up with a five-year plan to meet housing need in their community. One thing the community did right away was start up a crew to deliver housing maintenance and repairs. This is being run by Ne’Rahten Developments Ltd., which is the business arm of Yamoga Land Corporation (the local Indigenous Government). Over the span of a few years, they built up a crew of skilled workers. Then Ne’Rahten started thinking, “Why can’t we do more? Why can’t we build our own housing?” They identified a need for a space where people could work year-round, and first get training, then employment, in a stable all-season environment. This was the initial vision for the Fort Good Hope Construction Centre, and this is where TAG got involved. We had some seed funding through the CMHC Housing Supply Challenge when we partnered with Fort Good Hope. We worked with the community for over a year to get the capital funding lined up for the project. This process required us to take on a different role than the one you typically would as an architect. It wasn’t just schematic-design-to-construction-administration. One thing we did pretty early on was a housing design workshop that was open to the whole community, to start understanding what type of housing people would really want to see. Another piece was a lot of outreach and advocacy to build up support for the project and partnerships—for example, with Housing Northwest Territories and Aurora College. We also reached out to our federal MP, the NWT Legislative Assembly and different MLAs, and we talked to a lot of different people about the link between employment and housing. The idea was that the Fort Good Hope Construction Centre would be a demonstration project. Ultimately, funding did come through for the project—from both CMHC and National Indigenous Housing Collaborative Inc. The facility itself will not be architecturally spectacular. It’s basically a big shed where you could build a modular house. But the idea is that the construction of those houses is combined with training, and it creates year-round indoor jobs. It intends to combat the short construction seasons, and the fact that people would otherwise be laid off between projects—which makes it very hard to progress with your training or your career. At the same time, the Construction Centre will build up a skilled labour force that otherwise wouldn’t exist—because when there’s no work, skilled people tend to leave the community. And, importantly, the idea is to keep capital funding in the community. So when there’s a new arena that needs to get built, when there’s a new school that needs to get built, you have a crew of people who are ready to take that on. Rather than flying in skilled labourers, you actually have the community doing it themselves. It’s working towards self-determination in housing too, because if those modular housing units are being built in the community, by community members, then eventually they’re taking over design decisions and decisions about maintenance—in a way that hasn’t really happened for decades. Transitional homeownership My research also looked at a transitional homeownership model that adapts some of the successful principles of the 1980s HAP. Right now, in non-market communities, there are serious gaps in the housing continuum—that is, the different types of housing options available to people. For the most part, you have public housing, and you have homelessness—mostly in the form of hidden homelessness, where people are sleeping on the couches of relatives. Then, in some cases, you have inherited homeownership—where people got homes through the HAP or some other government program. But for the most part, not a lot of people in non-market communities are actually moving into homeownership anymore. I asked the local housing manager in Fort Good Hope: “When’s the last time someone built a house in the community?” She said, “I can only think of one person. It was probably about 20 years ago, and that person actually went to the bank and got a mortgage. If people have a home, it’s usually inherited from their parents or from relatives.” And that situation is a bit of a problem in itself, because it means that people can’t move out of public housing. Public housing traps you in a lot of ways. For example, it punishes employment, because rent is geared to income. It’s been said many times that this model disincentivizes employment. I was in a workshop last year where an Indigenous person spoke up and said, “Actually, it’s not disincentivizing, it punishes employment. It takes things away from you.” Somebody at the territorial housing corporation in Yellowknife told me, “We have clients who are over the income threshold for public housing, but there’s nowhere else they can go.” Theoretically, they would go to the private housing market, they would go to market housing, or they would go to homeownership, but those options don’t exist or they aren’t within reach.  So the idea with the transitional homeownership model is to create an option that could allow the highest income earners in a non-market community to move towards homeownership. This could take some pressure off the public housing system. And it would almost be like a wealth distribution measure: people who are able to afford the cost of operating and maintaining a home then have that option, instead of remaining in government-subsidized housing. For those who cannot, the public housing system is still an option—and maybe a few more public housing units are freed up.  I’ve developed about 36 recommendations for a transitional homeownership model in northern non-market communities. The recommendations are meant to be actioned at various scales: at the scale of the individual household, the scale of the housing provider, and the scale of the whole community. The idea is that if you look at housing as part of a whole system, then there are certain moves that might make sense here—in a non-market context especially—that wouldn’t make sense elsewhere. So for example, we’re in a situation where a house doesn’t appreciate in value. It’s not a financial asset, it’s actually a financial liability, and it’s something that costs a lot to maintain over the years. Giving someone a house in a non-market community is actually giving them a burden, but some residents would be quite willing to take this on, just to have an option of getting out of public housing. It just takes a shift in mindset to start considering solutions for that kind of context. One particularly interesting feature of non-market communities is that they’re still functioning with a mixed economy: partially a subsistence-based or traditional economy, and partially a cash economy. I think that’s actually a strength that hasn’t been tapped into by territorial and federal policies. In the far North, in-kind and traditional economies are still very much a way of life. People subsidize their groceries with “country food,” which means food that was harvested from the land. And instead of paying for fuel tank refills in cash, many households in non-market communities are burning wood as their primary heat source. In communities south of the treeline, like Fort Good Hope, that wood is also harvested from the land. Despite there being no exchange of cash involved, these are critical economic activities—and they are also part of a sustainable, resilient economy grounded in local resources and traditional skills. This concept of the mixed economy could be tapped into as part of a housing model, by bringing back the idea of a ‘sweat equity’ contribution instead of a down payment—just like in the HAP. Contributing time and labour is still an economic exchange, but it bypasses the ‘cash’ part—the part that’s still hard to come by in a non-market community. Labour doesn’t have to be manual labour, either. There are all kinds of work that need to take place in a community: maybe taking training courses and working on projects at the Construction Centre, maybe helping out at the Band Office, or providing childcare services for other working parents—and so on. So it could be more inclusive than a model that focuses on manual labour. Another thing to highlight is a rent-to-own trial period. Not every client will be equipped to take on the burdens of homeownership. So you can give people a trial period. If it doesn’t work out and they can’t pay for operations and maintenance, they could continue renting without losing their home. Then it’s worth touching on some basic design principles for the homeownership units. In the North, the solutions that work are often the simplest—not the most technologically innovative. When you’re in a remote location, specialized replacement parts and specialized labour are both difficult to come by. And new technologies aren’t always designed for extreme climates—especially as we trend towards the digital. So rather than installing technologically complex, high-efficiency systems, it actually makes more sense to build something that people are comfortable with, familiar with, and willing to maintain. In a southern context, people suggest solutions like solar panels to manage energy loads. But in the North, the best thing you can do for energy is put a woodstove in the house. That’s something we’ve heard loud and clear in many communities. Even if people can’t afford to fill their fuel tank, they’re still able to keep chopping wood—or their neighbour is, or their brother, or their kid, and so on. It’s just a different way of looking at things and a way of bringing things back down to earth, back within reach of community members.  Regulatory barriers to housing access: Revisiting the National Building Code On that note, there’s one more project I’ll touch on briefly. TAG is working on a research study, funded by Housing, Infrastructure and Communities Canada, which looks at regulatory barriers to housing access in the North. The National Building Code (NBC) has evolved largely to serve the southern market context, where constraints and resources are both very different than they are up here. Technical solutions in the NBC are based on assumptions that, in some cases, simply don’t apply in northern communities. Here’s a very simple example: minimum distance to a fire hydrant. Most of our communities don’t have fire hydrants at all. We don’t have municipal services. The closest hydrant might be thousands of kilometres away. So what do we do instead? We just have different constraints to consider. That’s just one example but there are many more. We are looking closely at the NBC, and we are also working with a couple of different communities in different situations. The idea is to identify where there are conflicts between what’s regulated and what’s actually feasible, viable, and practical when it comes to on-the-ground realities. Then we’ll look at some alternative solutions for housing. The idea is to meet the intent of the NBC, but arrive at some technical solutions that are more practical to build, easier to maintain, and more appropriate for northern communities.  All of the projects I’ve just described are fairly recent, and very much still ongoing. We’ll see how it all plays out. I’m sure we’re going to run into a lot of new barriers and learn a lot more on the way, but it’s an incremental trial-and-error process. Even with the Construction Centre, we’re saying that this is a demonstration project, but how—or if—it rolls out in other communities would be totally community-dependent, and it could look very, very different from place to place.  In doing any research on Northern housing, one of the consistent findings is that there is no one-size-fits-all solution. Northern communities are not all the same. There are all kinds of different governance structures, different climates, ground conditions, transportation routes, different population sizes, different people, different cultures. Communities are Dene, Métis, Inuvialuit, as well as non-Indigenous, all with different ways of being. One-size-fits-all solutions don’t work—they never have. And the housing crisis is complex, and it’s difficult to unravel. So we’re trying to move forward with a few different approaches, maybe in a few different places, and we’re hoping that some communities, some organizations, or even some individual people, will see some positive impacts.  As appeared in the June 2025 issue of Canadian Architect magazine  The post Insites: Addressing the Northern housing crisis appeared first on Canadian Architect.

Tech layoffs have continued in 2025. Much of that is being blamed on a combination of a slower economy and the adoption of automation via artificial intelligence. Nearly four in 10 Americans, for instance, believe generative AI (genAI) could diminish the number of available jobs as it advances, according to a study released in October by the New York Federal Reserve Bank. And the World Economic Forum’s Jobs Initiative study found that close to half (44%) of worker skills will be disrupted in the next five years — and 40% of tasks will be affected by the use of genAI tools and the large language models (LLMs) that underpin them. In April, the US tech industry lost 214,000 positions as companies shifted toward AI roles and skills-based hiring amid economic uncertainty. Tech sector companies reduced staffing by a net 7,000 positions in April, an analysis of data released by the US Bureau of Labor Statistics (BLS) showed. This year, 137 tech companies have fired 62,114 tech employees, according to Layoffs.fyi. Efforts to reduce headcount at government agencies by the unofficial US Department of Government Efficiency (DOGE) saw an additional 61,296 federal workers fired this year. Kye Mitchell, president of tech workforce staffing firm Experis US, believes the IT employment market is undergoing a fundamental transformation rather than experiencing traditional cyclical layoffs. Although Experis is seeing a 13% month-over-month decline in traditional software developer postings, it doesn’t represent “job destruction, it’s market evolution,” Mitchell said. “What we’re witnessing is the emergence of strategic technology orchestrators who harness AI to drive unprecedented business value,” she said. For example, organizations that once deployed two scrum teams of ten people to develop high-quality software are now achieving superior results with a single team of five AI-empowered developers. “This isn’t about cutting jobs; it’s about elevating roles,” Mitchell said. Specialized roles in particular are surging. Database architect positions are up 2,312%, statistician roles have increased 382%, and jobs for mathematicians have increased 1,272%. “These aren’t replacements; they’re vital for an AI-driven future,” she said. In fact, it’s an IT talent gap, not an employee surplus, that is now challenging organizations — and will continue to do so. With 76% of IT employers already struggling to find skilled tech talent, the market fundamentals favor skilled professionals, according to Mitchell. “The question isn’t whether there will be IT jobs — it’s whether we can develop the right skills fast enough to meet demand,” she said. For federal tech workers, outdated systems and slow procurement make it hard to attract and keep top tech talent. Agencies expect fast team deployment but operate with rigid, outdated processes, according to Justin Vianello, CEO of technology workforce development firm SkillStorm. Long security clearance delays add cost and time, often forcing companies to hire expensive, already-cleared talent. Meanwhile, modern technologists want to use current tools and make an impact — something hard to do with legacy systems and decade-long modernization efforts, he added. Many suggest turning to AI to will solve the tech talent shortage, but there is no evidence that AI will lead to a reduction in demand for tech talent, Vianello said. “On the contrary, companies see that the demand for tech talent has increased as they invest in preparing their workforce to properly use AI tools,” he said. A shortage of qualified talent is a bigger barrier to hiring than AI automation, he said, because organizations struggle to find candidates with the right certifications, skills, and clearances — especially in cloud, cybersecurity, and AI. Tech workers often lack skills in these areas because technology evolves faster than education and training can keep up, Vianello said. And while AI helps automate routine tasks, it can’t replace the strategic roles filled by skilled professionals. Seven out of 10 US organizations are struggling to find skilled workers to fill roles in an ever-evolving digital transformation landscape, and genAI has added to that headache, according to a ManpowerGroup survey released earlier this year. Job postings for AI skills surged 2,000% in 2024, but education and training in this area haven’t kept pace, according to Kelly Stratman, global ecosystem relationships enablement leader at Ernst & Young. “As formal education and training in AI skills still lag, it results in a shortage of AI talent that can effectively manage these technologies and demands,” she said in an earlier interview. “The AI talent shortage is most prominent among highly technical roles like data scientists/analysts, machine learning engineers, and software developers.” Economic uncertainty is creating a cautious hiring environment, but it’s more complex than tariffs alone. Experis data shows employers adopting a “wait and watch” stance as they monitor economic signals, with job openings down 11% year-over-year, according to Mitchell. “However, the bigger story is strategic workforce planning in an era of rapid technological change. Companies are being incredibly precise about where they allocate resources. Not because of economic pressure alone, but because the skills landscape is shifting so rapidly,” Mitchell said. “They’re prioritizing mission-critical roles while restructuring others around AI capabilities.” Top organizations see AI as a strategic shift, not just cost-cutting. Cutting talent now risks weakening core areas like cybersecurity, according to Mitchell. Skillstorm’s Vianello suggests that IT job hunters should begin to upgrade their skills with certifications that matter: AWS, Azure, CISSP, Security+, and AI/ML credentials open doors quickly, he said. “Veterans, in particular, have an edge; they bring leadership, discipline, and security clearances. Apprenticeships and fellowships offer a fast track into full-time roles by giving you experience that actually counts. And don’t overlook the intangibles: soft skills and project leadership are what elevate technologists into impact-makers,” Vianello said. Skills-based hiring has been on the rise for several years, as organizations seek to fill specific needs for big data analytics, programing (such as Rust), and AI prompt engineering. In fact, demand for genAI courses is surging, passing all other tech skills courses spanning fields from data science to cybersecurity, project management, and marketing. “AI isn’t replacing jobs — it’s fundamentally redefining how work gets done. The break point where technology truly displaces a position is when roughly 80% of tasks can be fully automated,” Mitchell said. “We’re nowhere near that threshold for most roles. Instead, we’re seeing AI augment skill sets and make professionals more capable, faster, and able to focus on higher-value work.” Leaders use AI as a strategic enabler — embedding it to enhance, not compete with, human developers, she said. Some industry forecasts predict a 30% productivity boost from AI tools, potentially adding more than $1.5 trillion to global GDP. For example, AI tools are expected to perform the lion’s share of coding. Techniques where humans use AI-augmented coding tools, such as “vibe coding,” are set to revolutionize software development by creating source code, generating tests automatically, and freeing up developer time for innovation instead of debugging code.  With vibe coding, developers use natural language in a conversational way that prompts the AI model to offer contextual ideas and generate code based on the conversation. By 2028, 75% of professional developers will be using vibe coding and other genAI-powered coding tools, up from less than 10% in September 2023, according to Gartner Research. And within three years, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering tool chain — a significant increase from approximately 15% early last year, Gartner said. A report from MIT Technology Review Insights found that 94% of business leaders now use genAI in software development, with 82% applying it in multiple stages — and 26% in four or more. Some industry experts place genAI’s use in creating code much higher. “What we are finding is that we’re three to six months from a world where AI is writing 90% of the code. And then in 12 months, we may be in a world where AI is writing essentially all of the code,” Anthropic CEO Dario Amodei said in a recent report and video interview. “The real [AI] transformation is in role evolution. Developers are becoming strategic technology orchestrators,” Mitchell from Experis said. “Data professionals are becoming business problem solvers. The demand isn’t disappearing; it’s becoming more sophisticated and more valuable. “In today’s economic climate, having the right tech talent with AI-enhanced capabilities isn’t a nice-to-have, it’s your competitive edge,” she said.

Smart TVs, take note Breaking down why Apple TVs are privacy advocates’ go-to streaming device Using the Apple TV app or an Apple account means giving Apple more data, though. Scharon Harding – Jun 1, 2025 7:35 am | 22 Credit: Aurich Lawson | Getty Images Credit: Aurich Lawson | Getty Images Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Every time I write an article about the escalating advertising and tracking on today's TVs, someone brings up Apple TV boxes. Among smart TVs, streaming sticks, and other streaming devices, Apple TVs are largely viewed as a safe haven. "Just disconnect your TV from the Internet and use an Apple TV box." That's the common guidance you'll hear from Ars readers for those seeking the joys of streaming without giving up too much privacy. Based on our research and the experts we've consulted, that advice is pretty solid, as Apple TVs offer significantly more privacy than other streaming hardware providers. But how private are Apple TV boxes, really? Apple TVs don't use automatic content recognition (ACR, a user-tracking technology leveraged by nearly all smart TVs and streaming devices), but could that change? And what about the software that Apple TV users do use—could those apps provide information about you to advertisers or Apple? In this article, we'll delve into what makes the Apple TV's privacy stand out and examine whether users should expect the limited ads and enhanced privacy to last forever. Apple TV boxes limit tracking out of the box One of the simplest ways Apple TVs ensure better privacy is through their setup process, during which you can disable Siri, location tracking, and sending analytics data to Apple. During setup, users also receive several opportunities to review Apple's data and privacy policies. Also off by default is the boxes' ability to send voice input data to Apple. Most other streaming devices require users to navigate through pages of settings to disable similar tracking capabilities, which most people are unlikely to do. Apple’s approach creates a line of defense against snooping, even for those unaware of how invasive smart devices can be. Apple TVs running tvOS 14.5 and later also make third-party app tracking more difficult by requiring such apps to request permission before they can track users. "If you choose Ask App Not to Track, the app developer can’t access the system advertising identifier (IDFA), which is often used to track," Apple says. "The app is also not permitted to track your activity using other information that identifies you or your device, like your email address." Users can access the Apple TV settings and disable the ability of third-party apps to ask permission for tracking. However, Apple could further enhance privacy by enabling this setting by default. The Apple TV also lets users control which apps can access the set-top box's Bluetooth functionality, photos, music, and HomeKit data (if applicable), and the remote's microphone. "Apple’s primary business model isn’t dependent on selling targeted ads, so it has somewhat less incentive to harvest and monetize incredible amounts of your data," said RJ Cross, director of the consumer privacy program at the Public Interest Research Group (PIRG). "I personally trust them more with my data than other tech companies." What if you share analytics data? If you allow your Apple TV to share analytics data with Apple or app developers, that data won't be personally identifiable, Apple says. Any collected personal data is "not logged at all, removed from reports before they’re sent to Apple, or protected by techniques, such as differential privacy," Apple says. Differential privacy, which injects noise into collected data, is one of the most common methods used for anonymizing data. In support documentation (PDF), Apple details its use of differential privacy: The first step we take is to privatize the information using local differential privacy on the user’s device. The purpose of privatization is to assure that Apple’s servers don't receive clear data. Device identifiers are removed from the data, and it is transmitted to Apple over an encrypted channel. The Apple analysis system ingests the differentially private contributions, dropping IP addresses and other metadata. The final stage is aggregation, where the privatized records are processed to compute the relevant statistics, and the aggregate statistics are then shared with relevant Apple teams. Both the ingestion and aggregation stages are performed in a restricted access environment so even the privatized data isn’t broadly accessible to Apple employees. What if you use an Apple account with your Apple TV? Another factor to consider is Apple's privacy policy regarding Apple accounts, formerly Apple IDs. Apple support documentation says you "need" an Apple account to use an Apple TV, but you can use the hardware without one. Still, it's common for people to log into Apple accounts on their Apple TV boxes because it makes it easier to link with other Apple products. Another reason someone might link an Apple TV box with an Apple account is to use the Apple TV app, a common way to stream on Apple TV boxes. So what type of data does Apple harvest from Apple accounts? According to its privacy policy, the company gathers usage data, such as "data about your activity on and use of" Apple offerings, including "app launches within our services...; browsing history; search history; [and] product interaction." Other types of data Apple may collect from Apple accounts include transaction information (Apple says this is "data about purchases of Apple products and services or transactions facilitated by Apple, including purchases on Apple platforms"), account information ("including email address, devices registered, account status, and age"), device information (including serial number and browser type), contact information (including physical address and phone number), and payment information (including bank details). None of that is surprising considering the type of data needed to make an Apple account work. Many Apple TV users can expect Apple to gather more data from their Apple account usage on other devices, such as iPhones or Macs. However, if you use the same Apple account across multiple devices, Apple recognizes that all the data it has collected from, for example, your iPhone activity, also applies to you as an Apple TV user. A potential workaround could be maintaining multiple Apple accounts. With an Apple account solely dedicated to your Apple TV box and Apple TV hardware and software tracking disabled as much as possible, Apple would have minimal data to ascribe to you as an Apple TV owner. You can also use your Apple TV box without an Apple account, but then you won't be able to use the Apple TV app, one of the device's key features. Data collection via the Apple TV app You can download third-party apps like Netflix and Hulu onto an Apple TV box, but most TV and movie watching on Apple TV boxes likely occurs via the Apple TV app. The app is necessary for watching content on the Apple TV+ streaming service, but it also drives usage by providing access to the libraries of many (but not all) popular streaming apps in one location. So understanding the Apple TV app’s privacy policy is critical to evaluating how private Apple TV activity truly is. As expected, some of the data the app gathers is necessary for the software to work. That includes, according to the app's privacy policy, "information about your purchases, downloads, activity in the Apple TV app, the content you watch, and where you watch it in the Apple TV app and in connected apps on any of your supported devices." That all makes sense for ensuring that the app remembers things like which episode of Severance you're on across devices. Apple collects other data, though, that isn't necessary for functionality. It says it gathers data on things like the "features you use (for example, Continue Watching or Library)," content pages you view, how you interact with notifications, and approximate location information (that Apple says doesn't identify users) to help improve the app. Additionally, Apple tracks the terms you search for within the app, per its policy: We use Apple TV search data to improve models that power Apple TV. For example, aggregate Apple TV search queries are used to fine-tune the Apple TV search model. This data usage is less intrusive than that of other streaming devices, which might track your activity and then sell that data to third-party advertisers. But some people may be hesitant about having any of their activities tracked to benefit a multi-trillion-dollar conglomerate. Data collected from the Apple TV app used for ads By default, the Apple TV app also tracks "what you watch, your purchases, subscriptions, downloads, browsing, and other activities in the Apple TV app" to make personalized content recommendations. Content recommendations aren't ads in the traditional sense but instead provide a way for Apple to push you toward products by analyzing data it has on you. You can disable the Apple TV app's personalized recommendations, but it's a little harder than you might expect since you can't do it through the app. Instead, you need to go to the Apple TV settings and then select Apps > TV > Use Play History > Off. The most privacy-conscious users may wish that personalized recommendations were off by default. Darío Maestro, senior legal fellow at the nonprofit Surveillance Technology Oversight Project (STOP), noted to Ars that even though Apple TV users can opt out of personalized content recommendations, "many will not realize they can." Apple can also use data it gathers on you from the Apple TV app to serve traditional ads. If you allow your Apple TV box to track your location, the Apple TV app can also track your location. That data can "be used to serve geographically relevant ads," according to the Apple TV app privacy policy. Location tracking, however, is off by default on Apple TV boxes. Apple's tvOS doesn't have integrated ads. For comparison, some TV OSes, like Roku OS and LG's webOS, show ads on the OS's home screen and/or when showing screensavers. But data gathered from the Apple TV app can still help Apple's advertising efforts. This can happen if you allow personalized ads in other Apple apps serving targeted apps, such as Apple News, the App Store, or Stocks. In such cases, Apple may apply data gathered from the Apple TV app, "including information about the movies and TV shows you purchase from Apple, to serve ads in those apps that are more relevant to you," the Apple TV app privacy policy says. Apple also provides third-party advertisers and strategic partners with "non-personal data" gathered from the Apple TV app: We provide some non-personal data to our advertisers and strategic partners that work with Apple to provide our products and services, help Apple market to customers, and sell ads on Apple’s behalf to display on the App Store and Apple News and Stocks. Apple also shares non-personal data from the Apple TV with third parties, such as content owners, so they can pay royalties, gauge how much people are watching their shows or movies, "and improve their associated products and services," Apple says. Apple's policy notes: For example, we may share non-personal data about your transactions, viewing activity, and region, as well as aggregated user demographics[,] such as age group and gender (which may be inferred from information such as your name and salutation in your Apple Account), to Apple TV strategic partners, such as content owners, so that they can measure the performance of their creative work [and] meet royalty and accounting requirements. When reached for comment, an Apple spokesperson told Ars that Apple TV users can clear their play history from the app. All that said, the Apple TV app still shares far less data with third parties than other streaming apps. Netflix, for example, says it discloses some personal information to advertising companies "in order to select Advertisements shown on Netflix, to facilitate interaction with Advertisements, and to measure and improve effectiveness of Advertisements." Warner Bros. Discovery says it discloses information about Max viewers "with advertisers, ad agencies, ad networks and platforms, and other companies to provide advertising to you based on your interests." And Disney+ users have Nielsen tracking on by default. What if you use Siri? You can easily deactivate Siri when setting up an Apple TV. But those who opt to keep the voice assistant and the ability to control Apple TV with their voice take somewhat of a privacy hit. According to the privacy policy accessible in Apple TV boxes' settings, Apple boxes automatically send all Siri requests to Apple's servers. If you opt into using Siri data to "Improve Siri and Dictation," Apple will store your audio data. If you opt out, audio data won't be stored, but per the policy: In all cases, transcripts of your interactions will be sent to Apple to process your requests and may be stored by Apple. Apple TV boxes also send audio and transcriptions of dictation input to Apple servers for processing. Apple says it doesn't store the audio but may store transcriptions of the audio. If you opt to "Improve Siri and Dictation," Apple says your history of voice requests isn't tied to your Apple account or email. But Apple is vague about how long it may store data related to voice input performed with the Apple TV if you choose this option. The policy states: Your request history, which includes transcripts and any related request data, is associated with a random identifier for up to six months and is not tied to your Apple Account or email address. After six months, you request history is disassociated from the random identifier and may be retained for up to two years. Apple may use this data to develop and improve Siri, Dictation, Search, and limited other language processing functionality in Apple products ... Apple may also review a subset of the transcripts of your interactions and this ... may be kept beyond two years for the ongoing improvements of products and services. Apple promises not to use Siri and voice data to build marketing profiles or sell them to third parties, but it hasn't always adhered to that commitment. In January, Apple agreed to pay $95 million to settle a class-action lawsuit accusing Siri of recording private conversations and sharing them with third parties for targeted ads. In 2019, contractors reported hearing private conversations and recorded sex via Siri-gathered audio. Outside of Apple, we've seen voice request data used questionably, including in criminal trials and by corporate employees. Siri and dictation data also represent additional ways a person's Apple TV usage might be unexpectedly analyzed to fuel Apple's business. Automatic content recognition Apple TVs aren't preloaded with automatic content recognition (ACR), an Apple spokesperson confirmed to Ars, another plus for privacy advocates. But ACR is software, so Apple could technically add it to Apple TV boxes via a software update at some point. Sherman Li, the founder of Enswers, the company that first put ACR in Samsung TVs, confirmed to Ars that it's technically possible for Apple to add ACR to already-purchased Apple boxes. Years ago, Enswers retroactively added ACR to other types of streaming hardware, including Samsung and LG smart TVs. (Enswers was acquired by Gracenote, which Nielsen now owns.) In general, though, there are challenges to adding ACR to hardware that people already own, Li explained: Everyone believes, in theory, you can add ACR anywhere you want at any time because it's software, but because of the way [hardware is] architected... the interplay between the chipsets, like the SoCs, and the firmware is different in a lot of situations. Li pointed to numerous variables that could prevent ACR from being retroactively added to any type of streaming hardware, "including access to video frame buffers, audio streams, networking connectivity, security protocols, OSes, and app interface communication layers, especially at different levels of the stack in these devices, depending on the implementation." Due to the complexity of Apple TV boxes, Li suspects it would be difficult to add ACR to already-purchased Apple TVs. It would likely be simpler for Apple to release a new box with ACR if it ever decided to go down that route. If Apple were to add ACR to old or new Apple TV boxes, the devices would be far less private, and the move would be highly unpopular and eliminate one of the Apple TV's biggest draws. However, Apple reportedly has a growing interest in advertising to streaming subscribers. The Apple TV+ streaming service doesn't currently show commercials, but the company is rumored to be exploring a potential ad tier. The suspicions stem from a reported meeting between Apple and the United Kingdom's ratings body, Barb, to discuss how it might track ads on Apple TV+, according to a July report from The Telegraph. Since 2023, Apple has also hired several prominent names in advertising, including a former head of advertising at NBCUniversal and a new head of video ad sales. Further, Apple TV+ is one of the few streaming services to remain ad-free, and it's reported to be losing Apple $1 billion per year since its launch. One day soon, Apple may have much more reason to care about advertising in streaming and being able to track the activities of people who use its streaming offerings. That has implications for Apple TV box users. "The more Apple creeps into the targeted ads space, the less I’ll trust them to uphold their privacy promises. You can imagine Apple TV being a natural progression for selling ads," PIRG's Cross said. Somewhat ironically, Apple has marketed its approach to privacy as a positive for advertisers. "Apple’s commitment to privacy and personal relevancy builds trust amongst readers, driving a willingness to engage with content and ads alike," Apple's advertising guide for buying ads on Apple News and Stocks reads. The most private streaming gadget It remains technologically possible for Apple to introduce intrusive tracking or ads to Apple TV boxes, but for now, the streaming devices are more private than the vast majority of alternatives, save for dumb TVs (which are incredibly hard to find these days). And if Apple follows its own policies, much of the data it gathers should be kept in-house. However, those with strong privacy concerns should be aware that Apple does track certain tvOS activities, especially those that happen through Apple accounts, voice interaction, or the Apple TV app. And while most of Apple's streaming hardware and software settings prioritize privacy by default, some advocates believe there's room for improvement. For example, STOP's Maestro said: Unlike in the [European Union], where the upcoming Data Act will set clearer rules on transfers of data generated by smart devices, the US has no real legislation governing what happens with your data once it reaches Apple's servers. Users are left with little way to verify those privacy promises. Maestro suggested that Apple could address these concerns by making it easier for people to conduct security research on smart device software. "Allowing the development of alternative or modified software that can evaluate privacy settings could also increase user trust and better uphold Apple's public commitment to privacy," Maestro said. There are ways to limit the amount of data that advertisers can get from your Apple TV. But if you use the Apple TV app, Apple can use your activity to help make business decisions—and therefore money. As you might expect from a device that connects to the Internet and lets you stream shows and movies, Apple TV boxes aren't totally incapable of tracking you. But they're still the best recommendation for streaming users seeking hardware with more privacy and fewer ads. Scharon Harding Senior Technology Reporter Scharon Harding Senior Technology Reporter Scharon is a Senior Technology Reporter at Ars Technica writing news, reviews, and analysis on consumer gadgets and services. She's been reporting on technology for over 10 years, with bylines at Tom’s Hardware, Channelnomics, and CRN UK. 22 Comments

Outlets 8, Conghua | © Wu Siming In the landscape of contemporary Chinese urbanism, few typologies encapsulate the contradictions of late-capitalist development more vividly than the pseudo-European commercial complex. These replicated enclaves, constructed en masse in the early 2000s, were once marketed as symbols of international sophistication. Over time, however, many were abandoned, becoming architectural vestiges of speculative urbanism. Outlets 8 in Conghua, Guangzhou, is one such project that has undergone a radical architectural reinterpretation. Originally completed in 2018 but long dormant, it has been reimagined by E Plus Design in collaboration with URBANUS/LXD Studio. Through a precise, light-touch intervention, the project avoids wholesale demolition and reprograms space through color, rhythm, and landscape strategy. Outlets 8, Conghua Technical Information Architects1-14: E Plus Design Central Plaza Design: URBANUS / LXD Studio Location: Conghua District, Guangzhou, China Gross Area: 80,882 m2 | 870,000 Sq. Ft. Project Years: 2022 – 2023 Photographs: © Wu Siming This approach is like a contemporary remix of classical music. The four blocks correspond to four movements. Without extensive demolition or altering the European-style architectural rhythm, we reinterpreted the emotional tones, chords, and cadenzas. Through a blend of color and modern gestures, the outdated and disproportionate ‘faux-antique’ complex has been reorchestrated into a contemporary architectural symphony. – Li Fu, Chief Architect at E Plus Design Outlets 8, Conghua Photographs Aerial View | © Wu Siming © Wu Siming © Wu Siming © Wu Siming © Wu Siming © Wu Siming © Wu Siming © Chen Liang Liu Shan © Chen Liang Liu Shan © Chen Liang Liu Shan Outlets 8 Context and Typological Challenge Outlets 8 was initially conceived as a 110,000-square-meter faux-European outlet village. Despite its scale and investment, it struggled to resonate with local cultural dynamics and remained idle. The typology itself, rooted in nostalgic mimicry, was already facing obsolescence. The challenge, then, was not only architectural but also conceptual: how to resuscitate a typology that had become both spatially and culturally inert. The design team chose a strategy of minimal physical intervention coupled with maximal perceptual impact. Rather than demolish or drastically reconstruct, they aimed to re-signify the existing structures. This approach reflects a growing trend in urban renewal across China, where sustainability, cost-efficiency, and cultural specificity take precedence over spectacle. Spatial Transformation Through Chromatic Reprogramming After | © Wu Siming Before | Original Facade, © E+ At the intervention’s core is using color as a spatial and psychological agent. The ornament-heavy facades were stripped of their polychromatic excess and repainted in low-saturation hues. This chromatic cleansing revealed the formal rhythms of the architecture beneath. By doing so, the design avoids mimicry and opts for abstraction, reintroducing clarity to the site’s visual language. The design framework is structured as a musical metaphor, with each of the four blocks conceived as a separate movement in a visual symphony. The street-facing facades, now unified through a golden “variation,” establish a new urban frontage that is both legible and symbolically rich. A ribbon-like golden band traces across the main elevations, creating continuity and contrast between old and new volumes. In contrast, the sports block adopts a cooler, blue-toned palette, offering a different spatial and functional rhythm. New architectural insertions are rendered in transparent materials, signaling temporal and programmatic distinctions. At the center, the elliptical plaza becomes a spatial crescendo, defined by a sculptural intervention inspired by Roman aqueducts. This feature functions as a landmark and a temporal break, juxtaposing historical references with performative landscape elements. Rewriting Landscape as Urban Ecology After | © Wu Siming Before | Original Facade, © E+ Water, derived from the nearby Liuxi River, serves as the thematic and material backbone of the landscape design. Its integration is not symbolic but functional. Water flows through constructed channels, interactive fountains, and sculptural cascades that encourage observation and participation. These elements create a multisensory environment that enhances the spatial experience while reinforcing ecological awareness. The planting strategy emphasizes native species capable of withstanding Guangzhou’s subtropical climate. The design maximizes greenery wherever regulatory conditions allow, particularly along the main entrance, central corridors, and arcaded walkways. The result is a layered landscape that balances visual density with ecological resilience. Integrating landscape and architecture as a singular design operation, the project shifts away from ornamental greening toward environmental synthesis. This approach foregrounds interaction and immersion, aligning with broader shifts in landscape architecture toward performative and participatory ecologies. Programmatic Rebirth and Urban Implications After | © Wu Siming Before | Original Facade, © E+ Beyond formal and material considerations, the project redefines the programmatic potential of large-scale retail environments. Positioned as a “micro-vacation” destination, Outlets 8 is a hybrid typology. It combines retail, leisure, and outdoor experience within a cohesive spatial narrative. This reprogramming responds to changing patterns of consumption and leisure in Chinese cities, particularly among younger demographics seeking experiential value over transactional efficiency. Statistical metrics underscore the project’s social impact. In its first nine days, the outlet attracted over half a million visitors and became a trending location across multiple digital platforms. While not the focus of architectural critique, these figures reflect a successful alignment between spatial renewal and public resonance. More importantly, the project offers a replicable model for dealing with the vast inventory of misaligned commercial developments across China. The intervention avoids nostalgia and cynicism by foregrounding perceptual clarity, ecological integration, and cultural recontextualization. Instead, it offers a clear path forward for reimagining the built remnants of a prior urban paradigm. Outlets 8, Conghua Plans Elevations | © E Plus Design Floor Plan | © E Plus Design Floor Plan | © E Plus Design Floor Plan | © E Plus Design Floor Plan | © E Plus Design Sections | © E Plus Design Outlets 8, Conghua Image Gallery About E Plus Design E Plus Design is a multidisciplinary architecture studio based in Shenzhen, China, known for its innovative approaches to urban renewal, adaptive reuse, and large-scale public space transformations. The firm emphasizes minimal intervention strategies, spatial clarity, and contextual sensitivity, often working at the intersection of architecture, landscape, and urban design to create integrated environments that are both socially responsive and experientially rich. Credits and Additional Notes Chief Design Consultant: Liu Xiaodu Master Plan, Architecture, and Landscape Schemes: E Plus Design Lead Architects: Li Fu, Coco Zhou Project Managers (Architecture): Guo Sibo, Huang Haifeng Architectural Design Team: Wang Junli, Zhang Yan, Cai Yidie, Zhu Meng, Lin Zhaomei, Li Geng, Stephane Anil Mamode, Liu Shan, Zhou Yubo Central Plaza Design: URBANUS / LXD Studio Architect of Central Plaza: Liu Xiaodu Project Manager: Li An’hong Facade Design: Song Baolin, Li Minggang Lighting Design (Concept): Fang Yuhui Lighting Consultant: Han Du Associates Client: Guangzhou Outlets 8 Commercial Management Co., Ltd. Client Design Management Team: Yin Mingyue, Zhao Xiong Landscape Area: 29,100 m² Chief Landscape Architect: Gao Yan Project Manager (Landscape): Zhang Yufeng Landscape Design Team: Yu Xiaolei, Li Zhaozhan, Liu Chenghua Landscape Construction Drawings: E Plus Design Project Manager: Wang Bin Design Team: Wang Bin (Landscape Architecture). Huang Jinxiong (Greening Design). Li Gen (Water & Electricity Design) Structural Design Team: Wang Kaiming, Yang Helin, Wu Xingwei, Zhuang Dengfa Electrical Design Team: Sun Wei, Yang Ying Interior Design Concept Design: Shenzhen Juanshi Design Co., Ltd. Chief Interior Designer: Feng Feifan Project Manager: Liu Hongwei Design Team: Niu Jingxian, Shi Meitao Construction Drawings: Shenzhen Shiye Design Co., Ltd. Project Manager: Shen Kaizhen Design Team: Yao Yijian, Yang Hao, Liu Chen Wayfinding Design Studio: Hexi Brand Design Co., Ltd. Curtain Wall Design Firm: Positive Attitude Group (PAG)

Published May 31, 2025 10:00am EDT close iPhone users instructed to take immediate action to avoid data breach: 'Urgent threat' Kurt 'The CyberGuy' Knutsson discusses Elon Musk's possible priorities as he exits his role with the White House and explains the urgent warning for iPhone users to update devices after a 'massive security gap.' Data breaches are no longer rare events but a persistent problem. We’ve been seeing regular incidents at public-facing companies across various sectors, including healthcare, retail and finance. While bad actors are certainly to blame, these corporations aren’t entirely without fault. They often make it easy for hackers to access user data by failing to protect it properly. A recent example came to light when a cybersecurity researcher discovered an open database containing over 184 million account credentials. Illustration of a hacker at work. (Kurt "CyberGuy" Knutsson)How the database was uncovered and what it containedCybersecurity researcher Jeremiah Fowler has revealed the existence of an open database that contains 184,162,718 million account credentials. These include email addresses, passwords, usernames and URLs for platforms such as Google, Microsoft, Apple, Facebook and Snapchat. The information also covers banking services, medical platforms and government accounts. Most shockingly, the entire dataset was left completely unsecured. There was no encryption, no authentication required and no form of access control. It was simply a plain text file sitting online for anyone to find.19 BILLION PASSWORDS HAVE LEAKED ONLINE: HOW TO PROTECT YOURSELFFowler located the database during routine scanning of publicly exposed assets. What he found was staggering. The file included hundreds of millions of unique records containing user credentials linked to the world’s largest technology and communication platforms. There were also account details for financial services and official portals used by state institutions.The file was not protected in any way. Anyone who discovered the link could open it in a browser and instantly view sensitive personal data. No software exploit was needed. No password was asked for. It was as open as a public document. Illustration of a hacker at work. (Kurt "CyberGuy" Knutsson)Where did the data come fromFowler believes the data was harvested using an infostealer. These lightweight tools are favored by cybercriminals for their ability to silently extract login credentials and other private information from compromised devices. Once stolen, the data is often sold on dark web forums or used in targeted attacks.After reporting the breach, the hosting provider quickly removed access to the file. However, the owner of the database remains unknown. The provider did not disclose who uploaded it or whether the database was part of a legitimate archive that was accidentally published. Fowler could not determine whether this was the result of negligence or an operation with malicious intent.To verify the data, Fowler contacted some individuals listed in the records. Several confirmed that the information was accurate. This confirmation turns what might seem like abstract statistics into something very real. These were not outdated or irrelevant details. These were live credentials that could allow anyone to hijack personal accounts in seconds.1.7 BILLION PASSWORDS LEAKED ON DARK WEB AND WHY YOURS IS AT RISK Login on a tablet. (Kurt "CyberGuy" Knutsson)6 ways to protect yourself after a data breach1. Change your password on every platform: If your login credentials have been exposed, it’s not enough to change the password on just one account. Cybercriminals often try the same combinations across multiple platforms, hoping to gain access through reused credentials. Start by updating your most critical accounts, email, banking, cloud storage and social media, then move on to others. Use a new, unique password for each platform and avoid variations of old passwords, as they can still be predictable. Consider using a password manager to generate and store complex passwords. Our top-rated password manager delivers powerful protection to help keep your accounts secure. It features real-time data breach monitoring to alert you if your login details have been exposed, plus a built-in data breach scanner that checks your saved emails, passwords and credit card information against known leak databases. A password health checker also highlights weak, reused or compromised passwords so you can strengthen your online defenses with just a few clicks. Get more details about my best expert-reviewed Password Managers of 2025 here.2. Enable two-factor authentication: Two-factor authentication, or 2FA, is a critical security feature that drastically reduces the risk of unauthorized access. Even if someone has your password, they won’t be able to log in without the second verification step, usually a one-time code sent to your phone or an authenticator app. Enable 2FA on all services that support it, especially your email, financial accounts and any service that stores sensitive personal data.3. Watch for unusual account activity: After a breach, it’s common for compromised accounts to be used for spam, scams, or identity theft. Pay close attention to signs such as login attempts from unfamiliar locations, password reset requests you didn’t initiate or unexpected messages sent from your accounts. Most platforms allow you to review login history and connected devices. If you see something off, take action immediately by changing your password and revoking suspicious sessions.4. Invest in personal data removal services: You should also consider a data removal service. Given the scale and frequency of breaches like the one described above, relying on personal caution alone is no longer enough. Automated data removal services can provide an essential extra layer of defense by continuously scanning for and helping eliminate your exposed information from data broker sites and other online sources. While no service promises to remove all your data from the internet, having a removal service is great if you want to constantly monitor and automate the process of removing your information from hundreds of sites continuously over a longer period of time. Check out my top picks for data removal services here. Get a free scan to find out if your personal information is already out on the web.5. Avoid clicking on suspicious links and use strong antivirus software: One of the most common post-breach threats is phishing. Cybercriminals often use information from leaked databases to craft convincing emails that urge you to verify your account or reset your password. Never click on links or download attachments from unknown or suspicious sources. Instead, visit websites by typing the URL directly into your browser. The best way to safeguard yourself from malicious links is to have strong antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe. Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android and iOS devices.6. Keep your software and devices up to date: Many cyberattacks exploit known vulnerabilities in outdated software. Operating systems, browsers, antivirus programs and even apps need to be updated regularly to patch security flaws. Turn on automatic updates wherever possible so you’re protected as soon as fixes are released. Staying current with your software is one of the easiest and most effective ways to block malware, ransomware and spyware from infiltrating your system. Kurt’s key takeawaySecurity is not only the responsibility of companies and hosting providers. Users need to adopt better practices, including unique passwords, multifactor authentication and regular reviews of their digital footprint. The careless exposure of over 184 million credentials is not just a mistake. It is an example of how fragile our systems remain when even basic protection is absent. In an era where artificial intelligence, quantum computing, and global connectivity are reshaping technology, it is unacceptable that plain text files containing financial and governmental credentials are still left sitting online.CLICK HERE TO GET THE FOX NEWS APPDo you feel that companies are doing enough to protect your data from hackers and other cyber threats? Let us know by writing us atCyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

In June, we hosted a webinar featuring experts from Arm, the Unity Accelerate Solutions team, and SYBO Games, the creator of Subway Surfers. The resulting roundtable focused on profiling tips and strategies for mobile games, the business implications of poor performance, and how SYBO shipped a hit mobile game with 3 billion downloads to date.Let’s dive into some of the follow-up questions we didn’t have time to cover during the webinar. You can also watch the full recording.We hear a lot about the Unity Profiler in relation to CPU profiling, but not as much about the Profile Analyzer (available as a Unity package). Are there any plans to improve it or integrate it into the core Profiler toolset?There are no immediate plans to integrate the Profile Analyzer into the core Editor, but this might change as our profiling tools evolve.Does Unity have any plans to add an option for the GPU Usage Profiler module to appear in percentages like it does in milliseconds?That’s a great idea, and while we can’t say yes or no at the time of this blog post, it’s a request that’s been shared with our R&D teams for possible future consideration.Do you have plans for tackling “Application Not Responding” (ANR) errors that are reported by the Google Play store and don’t contain any stack trace?Although we don’t have specific plans for tracking ANR without stack trace at the moment, we will consider it for the future roadmap.How can I share my feedback to help influence the future development of Unity’s profiling tools?You can keep track of upcoming features and share feedback via our product board and forums. We are also conducting a survey to learn more about our customers’ experience with the profiling tools. If you’ve used profiling tools before (either daily or just once) or are working on a project that requires optimization, we would love to get your input. The survey is designed to take no more than 5–10 minutes to complete.By participating, you’ll also have the chance to opt into a follow-up interview to share more feedback directly with the development team, including the opportunity to discuss potential prototypes of new features.Is there a good rule for determining what counts as a viable low-end device to target?A rule of thumb we hear from many Unity game developers is to target devices that are five years old at the time of your game’s release, as this helps to ensure the largest user base. But we also see teams reducing their release-date scope to devices that are only three years old if they’re aiming for higher graphical quality. A visually complex 3D application, for example, will have higher device requirements than a simple 2D application. This approach allows for a higher “min spec,” but reduces the size of the initial install base. It’s essentially a business decision: Will it cost more to develop for and support old devices than what your game will earn running on them?Sometimes the technical requirements of your game will dictate your minimum target specifications. So if your game uses up large amounts of texture memory even after optimization, but you absolutely cannot reduce quality or resolution, that probably rules out running on phones with insufficient memory. If your rendering solution requires compute shaders, that likely rules out devices with drivers that can’t support OpenGL ES 3.1, Metal, or Vulkan.It’s a good idea to look at market data for your priority target audience. For instance, mobile device specs can vary a lot between countries and regions. Remember to define some target “budgets” so that benchmarking goals for what’s acceptable are set prior to choosing low-end devices for testing.For live service games that will run for years, you’ll need to monitor their compatibility continuously and adapt over time based on both your actual user base and current devices on the market.Is it enough to test performance exclusively on low-end devices to ensure that the game will also run smoothly on high-end ones?It might be, if you have a uniform workload on all devices. However, you still need to consider variations across hardware from different vendors and/or driver versions.It’s common for graphically rich games to have tiers of graphical fidelity – the higher the visual tier, the more resources required on capable devices. This tier selection might be automatic, but increasingly, users themselves can control the choice via a graphical settings menu. For this style of development, you’ll need to test at least one “min spec” target device per feature/workload tier that your game supports.If your game detects the capabilities of the device it’s running on and adapts the graphics output as needed, it could perform differently on higher end devices. So be sure to test on a range of devices with the different quality levels you’ve programmed the title for.Note: In this section, we’ve specified whether the expert answering is from Arm or Unity.Do you have advice for detecting the power range of a device to support automatic quality settings, particularly for mobile?Arm: We typically see developers doing coarse capability binning based on CPU and GPU models, as well as the GPU shader core count. This is never perfect, but it’s “about right.” A lot of studios collect live analytics from deployed devices, so they can supplement the automated binning with device-specific opt-in/opt-out to work around point issues where the capability binning isn’t accurate enough.As related to the previous question, for graphically rich content, we see a trend in mobile toward settings menus where users can choose to turn effects on or off, thereby allowing them to make performance choices that suit their preferences.Unity: Device memory and screen resolution are also important factors for choosing quality settings. Regarding textures, developers should be aware that Render Textures used by effects or post-processing can become a problem on devices with high resolution screens, but without a lot of memory to match.Given the breadth of configurations available (CPU, GPU, SOC, memory, mobile, desktop, console, etc.), can you suggest a way to categorize devices to reduce the number of tiers you need to optimize for?Arm: The number of tiers your team optimizes for is really a game design and business decision, and should be based on how important pushing visual quality is to the value proposition of the game. For some genres it might not matter at all, but for others, users will have high expectations for the visual fidelity.Does the texture memory limit differ among models and brands of Android devices that have the same amount of total system memory?Arm: To a first-order approximation, we would expect the total amount of texture memory to be similar across vendors and hardware generations. There will be minor differences caused by memory layout and alignment restrictions, so it won’t be exactly the same.Is it CPU or GPU usage that contributes the most to overheating on mobile devices?Arm: It’s entirely content dependent. The CPU, GPU, or the DRAM can individually overheat a high-end device if pushed hard enough, even if you ignore the other two completely. The exact balance will vary based on the workload you are running.What tips can you give for profiling on devices that have thermal throttling? What margin would you target to avoid thermal throttling (i.e., targeting 20 ms instead of 33 ms)?Arm: Optimizing for frame time can be misleading on Android because devices will constantly adjust frequency to optimize energy usage, making frame time an incomplete measure by itself. Preferably, monitor CPU and GPU cycles per frame, as well as GPU memory bandwidth per frame, to get some value that is independent of frequency. The cycle target you need will depend on each device’s chip design, so you’ll need to experiment.Any optimization helps when it comes to managing power consumption, even if it doesn’t directly improve frame rate. For example, reducing CPU cycles will reduce thermal load even if the CPU isn’t the critical path for your game.Beyond that, optimizing memory bandwidth is one of the biggest savings you can make. Accessing DRAM is orders of magnitude more expensive than accessing local data on-chip, so watch your triangle budget and keep data types in memory as small as possible.Unity: To limit the impact of CPU clock frequency on the performance metrics, we recommend trying to run at a consistent temperature. There are a couple of approaches for doing this:Run warm: Run the device for a while so that it reaches a stable warm state before profiling.Run cool: Leave the device to cool for a while before profiling. This strategy can eliminate confusion and inconsistency in profiling sessions by taking captures that are unlikely to be thermally throttled. However, such captures will always represent the best case performance a user will see rather than what they might actually see after long play sessions. This strategy can also delay the time between profiling runs due to the need to wait for the cooling period first.With some hardware, you can fix the clock frequency for more stable performance metrics. However, this is not representative of most devices your users will be using, and will not report accurate real-world performance. Basically, it’s a handy technique if you are using a continuous integration setup to check for performance changes in your codebase over time.Any thoughts on Vulkan vs OpenGL ES 3 on Android? Vulkan is generally slower performance-wise. At the same time, many devices lack support for various features on ES3.Arm: Recent drivers and engine builds have vastly improved the quality of the Vulkan implementations available; so for an equivalent workload, there shouldn’t be a performance gap between OpenGL ES and Vulkan (if there is, please let us know). The switch to Vulkan is picking up speed and we expect to see more people choosing Vulkan by default over the next year or two. If you have counterexamples of areas where Vulkan isn’t performing well, please get in touch with us. We’d love to hear from you.What tools can we use to monitor memory bandwidth (RAM <-> VRAM)?Arm: The Streamline Profiler in Arm Mobile Studio can measure bandwidth between Mali GPUs and the external DRAM (or system cache).Should you split graphical assets by device tiers or device resolution?Arm: You can get the best result by retuning assets, but it’s expensive to do. Start by reducing resolution and frame rate, or disabling some optional post-processing effects.What is the best way to record performance metric statistics from our development build?Arm: You can use the Performance Advisor tool in Arm Mobile Studio to automatically capture and export performance metrics from the Mali GPUs, although this comes with a caveat: The generation of JSON reports requires a Professional Edition license.Unity: The Unity Profiler can be used to view common rendering metrics, such as vertex and triangle counts in the Rendering module. Plus you can include custom packages, such as System Metrics Mali, in your project to add low-level Mali GPU metrics to the Unity Profiler.What are your recommendations for profiling shader code?You need a GPU Profiler to do this. The one you choose depends on your target platform. For example, on iOS devices, Xcode’s GPU Profiler includes the Shader Profiler, which breaks down shader performance on a line-by-line basis.Arm Mobile Studio supports Mali Offline Compiler, a static analysis tool for shader code and compute kernels. This tool provides some overall performance estimates and recommendations for the Arm Mali GPU family.When profiling, the general rule is to test your game or app on the target device(s). With the industry moving toward more types of chipsets (Apple M1, Arm, x86 by Intel, AMD, etc.), how can developers profile and pinpoint issues on the many different hardware configurations in a reasonable amount of time?The proliferation of chipsets is primarily a concern on desktop platforms. There are a limited number of hardware architectures to test for console games. On mobile, there’s Apple’s A Series for iOS devices and a range of Arm and Qualcomm architectures for Android – but selecting a manageable list of representative mobile devices is pretty straightforward.On desktop it’s trickier because there’s a wide range of available chipsets and architectures, and buying Macs and PCs for testing can be expensive. Our best advice is to do what you can. No studio has infinite time and money for testing. We generally wouldn’t expect any huge surprises when comparing performance between an Intel x86 CPU and a similarly specced AMD processor, for instance. As long as the game performs comfortably on your minimum spec machine, you should be reasonably confident about other machines. It’s also worth considering using analytics, such as Unity Analytics, to record frame rates, system specs, and player options’ settings to identify hotspots or problematic configurations.We’re seeing more studios move to using at least some level of automated testing for regular on-device profiling, with summary stats published where the whole team can keep an eye on performance across the range of target devices. With well-designed test scenes, this can usually be made into a mechanical process that’s suited for automation, so you don’t need an experienced technical artist or QA tester running builds through the process manually.Do you ever see performance issues on high-end devices that don’t occur on the low-end ones?It’s uncommon, but we have seen it. Often the issue lies in how the project is configured, such as with the use of fancy shaders and high-res textures on high-end devices, which can put extra pressure on the GPU or memory. Sometimes a high-end mobile device or console will use a high-res phone screen or 4K TV output as a selling point but not necessarily have enough GPU power or memory to live up to that promise without further optimization.If you make use of the current versions of the C# Job System, verify whether there’s a job scheduling overhead that scales with the number of worker threads, which in turn, scales with the number of CPU cores. This can result in code that runs more slowly on a 64+ core Threadripper™ than on a modest 4-core or 8-core CPU. This issue will be addressed in future versions of Unity, but in the meantime, try limiting the number of job worker threads by setting JobsUtility.JobWorkerCount.What are some pointers for setting a good frame budget?Most of the time when we talk about frame budgets, we’re talking about the overall time budget for the frame. You calculate 1000/target frames per second (fps) to get your frame budget: 33.33 ms for 30 fps, 16.66 ms for 60 fps, 8.33 ms for 120 Hz, etc. Reduce that number by around 35% if you’re on mobile to give the chips a chance to cool down between each frame. Dividing the budget up to get specific sub-budgets for different features and/or systems is probably overkill except for projects with very specific, predictable systems, or those that make heavy use of Time Slicing.Generally, profiling is the process of finding the biggest bottlenecks – and therefore, the biggest potential performance gains. So rather than saying, “Physics is taking 1.2 ms when the budget only allows for 1 ms,” you might look at a frame and say, “Rendering is taking 6 ms, making it the biggest main thread CPU cost in the frame. How can we reduce that?”It seems like profiling early and often is still not common knowledge. What are your thoughts on why this might be the case?Building, releasing, promoting, and managing a game is difficult work on multiple fronts. So there will always be numerous priorities vying for a developer’s attention, and profiling can fall by the wayside. They know it’s something they should do, but perhaps they’re unfamiliar with the tools and don’t feel like they have time to learn. Or, they don’t know how to fit profiling into their workflows because they’re pushed toward completing features rather than performance optimization.Just as with bugs and technical debt, performance issues are cheaper and less risky to address early on, rather than later in a project’s development cycle. Our focus is on helping to demystify profiling tools and techniques for those developers who are unfamiliar with them. That’s what the profiling e-book and its related blog post and webinar aim to support.Is there a way to exclude certain methods from instrumentation or include only specific methods when using Deep Profiling in the Unity Profiler? When using a lot of async/await tasks, we create large stack traces, but how can we avoid slowing down both the client and the Profiler when Deep Profiling?You can enable Allocation call stacks to see the full call stacks that lead to managed allocations (shown as magenta in the Unity CPU Profiler Timeline view). Additionally, you can – and should! – manually instrument long-running methods and processes by sprinkling ProfilerMarkers throughout your code. There’s currently no way to automatically enable Deep Profiling or disable profiling entirely in specific parts of your application. But manually adding ProfilerMarkers and enabling Allocation call stacks when required can help you dig down into problem areas without having to resort to Deep Profiling.As of Unity 2022.2, you can also use our IgnoredByDeepProfilerAttribute to prevent the Unity Profiler from capturing method calls. Just add the IgnoredByDeepProfiler attribute to classes, structures, and methods.Where can I find more information on Deep Profiling in Unity?Deep Profiling is covered in our Profiler documentation. Then there’s the most in-depth, single resource for profiling information, the Ultimate Guide to profiling Unity games e-book, which links to relevant documentation and other resources throughout.Is it correct that Deep Profiling is only useful for the Allocations Profiler and that it skews results so much that it’s not useful for finding hitches in the game?Deep Profiling can be used to find the specific causes of managed allocations, although Allocation call stacks can do the same thing with less overhead, overall. At the same time, Deep Profiling can be helpful for quickly investigating why one specific ProfilerMarker seems to be taking so long, as it’s more convenient to enable than to add numerous ProfilerMarkers to your scripts and rebuild your game. But yes, it does skew performance quite heavily and so shouldn’t be enabled for general profiling.Is VSync worth setting to every VBlank? My mobile game runs at a very low fps when it’s disabled.Mobile devices force VSync to be enabled at a driver/hardware level, so disabling it in Unity’s Quality settings shouldn’t make any difference on those platforms. We haven’t heard of a case where disabling VSync negatively affects performance. Try taking a profile capture with VSync enabled, along with another capture of the same scene but with VSync disabled. Then compare the captures using Profile Analyzer to try to understand why the performance is so different.How can you determine if the main thread is waiting for the GPU and not the other way around?This is covered in the Ultimate Guide to profiling Unity games. You can also get more information in the blog post, Detecting performance bottlenecks with Unity Frame Timing Manager.Generally speaking, the telltale sign is that the main thread waits for the Render thread while the Render thread waits for the GPU. The specific marker names will differ depending on your target platform and graphics API, but you should look out for markers with names such as “PresentFrame” or “WaitForPresent.”Is there a solid process for finding memory leaks in profiling?Use the Memory Profiler to compare memory snapshots and check for leaks. For example, you can take a snapshot in your main menu, enter your game and then quit, go back to the main menu, and take a second snapshot. Comparing these two will tell you whether any objects/allocations from the game are still hanging around in memory.Does it make sense to optimize and rewrite part of the code for the DOTS system, for mobile devices including VR/AR? Do you use this system in your projects?A number of game projects now make use of parts of the Data-Oriented Technology Stack (DOTS). Native Containers, the C# Job System, Mathematics, and the Burst compilerare all fully supported packages that you can use right away to write optimal, parallelized, high-performance C# (HPC#) code to improve your project’s CPU performance.A smaller number of projects are also using Entities and associated packages, such as the Hybrid Renderer, Unity Physics, and NetCode. However, at this time, the packages listed are experimental, and using them involves accepting a degree of technical risk. This risk derives from an API that is still evolving, missing or incomplete features, as well as the engineering learning curve required to understand Data-Oriented Design (DOD) to get the most out of Unity’s Entity Component System (ECS). Unity engineer Steve McGreal wrote a guide on DOTS best practices, which includes some DOD fundamentals and tips for improving ECS performance.How do you go about setting limits on SetPass calls or shader complexity? Can you even set limits beforehand?Rendering is a complex process and there is no practical way to set a hard limit on the maximum number of SetPass calls or a metric for shader complexity. Even on a fixed hardware platform, such as a single console, the limits will depend on what kind of scene you want to render, and what other work is happening on the CPU and GPU during a frame.That’s why the rule on when to profile is “early and often.” Teams tend to create a “vertical slice” demo early on during production – usually a short burst of gameplay developed to the level of visual fidelity intended for the final game. This is your first opportunity to profile rendering and figure out what optimizations and limits might be needed. The profiling process should be repeated every time a new area or other major piece of visual content is added.Here are additional resources for learning about performance optimization:BlogsOptimize your mobile game performance: Expert tips on graphics and assetsOptimize your mobile game performance: Expert tips on physics, UI, and audio settingsOptimize your mobile game performance: Expert tips on profiling, memory, and code architecture from Unity’s top engineersExpert tips on optimizing your game graphics for consolesProfiling in Unity 2021 LTS: What, when, and howHow-to pagesProfiling and debugging toolsHow to profile memory in UnityBest practices for profiling game performanceE-booksOptimize your console and PC game performanceOptimize your mobile game performanceUltimate guide to profiling Unity gamesLearn tutorialsProfiling CPU performance in Android builds with Android StudioProfiling applications – Made with UnityEven more advanced technical content is coming soon – but in the meantime, please feel free to suggest topics for us to cover on the forum and check out the full roundtable webinar recording.

Chances are, you've heard of the term "large language models," or LLMs, when people are talking about generative AI. But they aren't quite synonymous with the brand-name chatbots like ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and Anthropic's Claude.These AI chatbots can produce impressive results, but they don't actually understand the meaning of words the way we do. Instead, they're the interface we use to interact with large language models. These underlying technologies are trained to recognize how words are used and which words frequently appear together, so they can predict future words, sentences or paragraphs. Understanding how LLMs work is key to understanding how AI works. And as AI becomes increasingly common in our daily online experiences, that's something you ought to know.This is everything you need to know about LLMs and what they have to do with AI.What is a language model?You can think of a language model as a soothsayer for words."A language model is something that tries to predict what language looks like that humans produce," said Mark Riedl, professor in the Georgia Tech School of Interactive Computing and associate director of the Georgia Tech Machine Learning Center. "What makes something a language model is whether it can predict future words given previous words."This is the basis of autocomplete functionality when you're texting, as well as of AI chatbots.What is a large language model?A large language model contains vast amounts of words from a wide array of sources. These models are measured in what is known as "parameters."So, what's a parameter?Well, LLMs use neural networks, which are machine learning models that take an input and perform mathematical calculations to produce an output. The number of variables in these computations are parameters. A large language model can have 1 billion parameters or more."We know that they're large when they produce a full paragraph of coherent fluid text," Riedl said.How do large language models learn?LLMs learn via a core AI process called deep learning."It's a lot like when you teach a child -- you show a lot of examples," said Jason Alan Snyder, global CTO of ad agency Momentum Worldwide.In other words, you feed the LLM a library of content (what's known as training data) such as books, articles, code and social media posts to help it understand how words are used in different contexts, and even the more subtle nuances of language. The data collection and training practices of AI companies are the subject of some controversy and some lawsuits. Publishers like The New York Times, artists and other content catalog owners are alleging tech companies have used their copyrighted material without the necessary permissions.(Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed on Ziff Davis copyrights in training and operating its AI systems.)AI models digest far more than a person could ever read in their lifetime -- something on the order of trillions of tokens. Tokens help AI models break down and process text. You can think of an AI model as a reader who needs help. The model breaks down a sentence into smaller pieces, or tokens -- which are equivalent to four characters in English, or about three-quarters of a word -- so it can understand each piece and then the overall meaning.From there, the LLM can analyze how words connect and determine which words often appear together."It's like building this giant map of word relationships," Snyder said. "And then it starts to be able to do this really fun, cool thing, and it predicts what the next word is … and it compares the prediction to the actual word in the data and adjusts the internal map based on its accuracy."This prediction and adjustment happens billions of times, so the LLM is constantly refining its understanding of language and getting better at identifying patterns and predicting future words. It can even learn concepts and facts from the data to answer questions, generate creative text formats and translate languages. But they don't understand the meaning of words like we do -- all they know are the statistical relationships.LLMs also learn to improve their responses through reinforcement learning from human feedback."You get a judgment or a preference from humans on which response was better given the input that it was given," said Maarten Sap, assistant professor at the Language Technologies Institute at Carnegie Mellon University. "And then you can teach the model to improve its responses." LLMs are good at handling some tasks but not others.