The brand's Milan Design Week exhibit reveals the connection between Range Rover's past and present.

Here's what you need to know about surveillance technology and protests—and how you can best protect yourself.

The day before the visit by Jensen Huang, lawmakers in Washington said they were investigating whether the chipmaker’s sales in China violated U.S. rules.

Macworld Tax day has come and gone which means that tax returns will be coming soon. If you’re getting a big return, you might be tempted to blow it on an expensive purchase: A new phone, computer, or tablet. Instead of hitting the mall with that money burning a hole in your pocket, consider investing in a cheaper (but no less robust) option: This Microsoft Surface SE is 49% off, bringing the total to just $189.99. This is the perfect computer for all-day productivity for students or workers. The 11.6-inch display provides sharp visuals while being compact and lightweight enough to easily fit into any bag or briefcase. Plus, a full battery will give you 16 hours of power with typical usage. It has 8GB of RAM and 128 GB of storage for photos, files, or other projects. The Intel Celeron N4120 is an efficient processor that can handle tasks like web browsing, document editing, and streaming seamlessly. This open-box model was released in 2022 and runs on Windows 11 SE, so you’ll get the same processing speed, operating system, and power that you know and love, just at a much lower cost. Open-box items are practically new, usually having been overstocked, display models, or unopened returns. Get the seamless Windows 11 SE interface and a powerful processor at a fraction of the price with the MS Surface SE for just $189.99 (MSRP $378.99) Microsoft Surface SE 11.6″ Laptop (2022) Celeron N4120 8GB RAM 128GB SSD Win11 SE (Open Box)See Deal StackSocial prices subject to change.

OpenAI has released o3 and 04-mini, two reasoning AI models designed to be extra good at programming, math, and science and that can use images to “think,” according to Engadget, This means that users can upload sketches or diagrams, for example, and even if they are of low quality, o3 and 04-mini will understand what is meant. The new models are also capable of generating images and browsing on their own. A Chat GPT Plus, Pro or Team subscription is required to use the o3- and 04-mini models. And OpenAI said a more powerful o3-pro variant will be released in a few weeks. The company first released o3-mini in January.

Google DeepMind’s latest update to a top Gemini AI model includes a dial to control how much the system “thinks” through a response. The new feature is ostensibly designed to save money for developers, but it also concedes a problem: Reasoning models, the tech world’s new obsession, are prone to overthinking, burning money and energy in the process. Since 2019, there have been a couple of tried and true ways to make an AI model more powerful. One was to make it bigger by using more training data, and the other was to give it better feedback on what constitutes a good answer. But toward the end of last year, Google DeepMind and other AI companies turned to a third method: reasoning. “We’ve been really pushing on ‘thinking,’” says Jack Rae, a principal research scientist at DeepMind. Such models, which are built to work through problems logically and spend more time arriving at an answer, rose to prominence earlier this year with the launch of the DeepSeek R1 model. They’re attractive to AI companies because they can make an existing model better by training it to approach a problem pragmatically. That way, the companies can avoid having to build a new model from scratch.  When the AI model dedicates more time (and energy) to a query, it costs more to run. Leaderboards of reasoning models show that one task can cost upwards of $200 to complete. The promise is that this extra time and money help reasoning models do better at handling challenging tasks, like analyzing code or gathering information from lots of documents.  “The more you can iterate over certain hypotheses and thoughts,” says Google DeepMind chief technical officer Koray Kavukcuoglu, the more “it’s going to find the right thing.” This isn’t true in all cases, though. “The model overthinks,” says Tulsee Doshi, who leads the product team at Gemini, referring specifically to Gemini Flash 2.5, the model released today that includes a slider for developers to dial back how much it thinks. “For simple prompts, the model does think more than it needs to.”  When a model spends longer than necessary on a problem only to arrive at a mediocre answer, it makes the model expensive to run for developers and worsens AI’s environmental footprint. Nathan Habib, an engineer at Hugging Face who has studied the proliferation of such reasoning models, says overthinking is abundant. In the rush to show off smarter AI, companies are reaching for reasoning models like hammers even where there’s no nail in sight, Habib says. Indeed, when OpenAI announced a new model in February, it said it would be the company’s last nonreasoning model.  The performance gain is “undeniable” for certain tasks, Habib says, but not for many others where people normally use AI. Even when reasoning is used for the right problem, things can go awry. Habib showed me an example of a leading reasoning model that was asked to work through an organic chemistry problem. It started out okay, but halfway through its reasoning process the model’s responses started resembling a meltdown: It sputtered “Wait, but …” hundreds of times. It ended up taking far longer than a nonreasoning model would spend on one task. Kate Olszewska, who works on evaluating Gemini models at DeepMind, says Google’s models can also get stuck in loops. Google’s new “reasoning” dial is one attempt to solve that problem. For now, it’s built not for the consumer version of Gemini but for developers who are making apps. Developers can set a budget for how much computing power the model should spend on a certain problem, the idea being to turn down the dial if the task shouldn’t involve much reasoning at all. Outputs from the model are about six times more expensive to generate when reasoning is turned on. Another reason for this flexibility is that it’s not yet clear when more reasoning will be required to get a better answer. “It’s really hard to draw a boundary on, like, what’s the perfect task right now for thinking?” Rae says.  Obvious tasks include coding (developers might paste hundreds of lines of code into the model and then ask for help), or generating expert-level research reports. The dial would be turned way up for these, and developers might find the expense worth it. But more testing and feedback from developers will be needed to find out when medium or low settings are good enough. Habib says the amount of investment in reasoning models is a sign that the old paradigm for how to make models better is changing. “Scaling laws are being replaced,” he says.  Instead, companies are betting that the best responses will come from longer thinking times rather than bigger models. It’s been clear for several years that AI companies are spending more money on inferencing—when models are actually “pinged” to generate an answer for something—than on training, and this spending will accelerate as reasoning models take off. Inferencing is also responsible for a growing share of emissions. (While on the subject of models that “reason” or “think”: an AI model cannot perform these acts in the way we normally use such words when talking about humans. I asked Rae why the company uses anthropomorphic language like this. “It’s allowed us to have a simple name,” he says, “and people have an intuitive sense of what it should mean.” Kavukcuoglu says that Google is not trying to mimic any particular human cognitive process in its models.) Even if reasoning models continue to dominate, Google DeepMind isn’t the only game in town. When the results from DeepSeek began circulating in December and January, it triggered a nearly $1 trillion dip in the stock market because it promised that powerful reasoning models could be had for cheap. The model is referred to as “open weight”—in other words, its internal settings, called weights, are made publicly available, allowing developers to run it on their own rather than paying to access proprietary models from Google or OpenAI. (The term “open source” is reserved for models that disclose the data they were trained on.)  So why use proprietary models from Google when open ones like DeepSeek are performing so well? Kavukcuoglu says that coding, math, and finance are cases where “there’s high expectation from the model to be very accurate, to be very precise, and to be able to understand really complex situations,” and he expects models that deliver on that, open or not, to win out. In DeepMind’s view, this reasoning will be the foundation of future AI models that act on your behalf and solve problems for you. “Reasoning is the key capability that builds up intelligence,” he says. “The moment the model starts thinking, the agency of the model has started.”

CEO Tim Cook's working relationship with President Donald Trump has once again helped Apple escape issues in the U.S.-China tariff battle. Here's how.Tim Cook [left], President Donald Trump [right]On April 11, following after a week of increases to the import tariff for Chinese goods entering the United States, President Trump made an announcement. While many products would be affected by a high import tariff of 145% at the time, Trump decided he was giving a reprieve on a variety of tech products and components.While the reprieve itself is not permanent, with a semiconductor tariff expected to arrive in the future, the exemptions were immediately helpful to Apple. Indeed, a few days later, Trump confirmed that he was in talks with Tim Cook, and that he "helped" him with the tariff exemption. Continue Reading on AppleInsider | Discuss on our Forums

The LA Times has provided some frontline insights on the ground in Los Angeles, where building contractors are feeling the impacts of tariffs acutely as they plan an unprecedented post-fire rebuilding effort that must account for 16,000 lost homes and structures.  "We’re padding a 5% to 10% contingency for what we’re calling 'market volatility' into the budget," one contractor told the outlet. The anticipated but hard-to-predict rise in costs comes at the tail end of an economic downturn in architecture that had shown signs of improvement by the end of last year. Now, with a 145% levy on Chinese goods in place and others looming, ABC chief economist Anirban Basu says bluntly, "the construction outlook today is not nearly as sanguine as it was seven or eight weeks ago."

Sandfall Interactive debuted a new trailer for Clair Obscur: Expedition 33 during the first-ever Galaxies showcase. It focuses on the role-playing game’s lore with Maelle (voiced by Jennifer English)explaining the events leading up to the present day. Several decades prior, the city of Lumiere was separated from the main continent under mysterious circumstances. Around the same time, the Paintress emerged and started painting numbers. Anyone aged the same would subsequently die.  Lumiere sends out Expeditions to try and stop her but to no avail. As part of Expedition 33, Maelle – alongside Gustave, Lune, Sciel, and other members – ventures out, potentially to succeed where previous expeditions failed. Clair Obscur: Expedition 33 launches on April 24th for Xbox Series X/S, PS5, and PC. Check out the latest character trailer here, showcasing Monoco, a Gestral who joins the party and can copy Nevron’s abilities. You can also learn more about the game in our feature.

1120 Ossington. Photo credit: Doublespace Photography “As Canada approaches a federal election, the future of deeply affordable, supportive housing will depend on sustained investment and coordinated leadership,” says Naama Blonder of Toronto-based Smart Density. The firm, together with mcCallumSather serving as Architects of Record, recently unveiled 1120 Ossington, a new 25-unit supportive housing development which aims to deliver affordable, rent-geared-to-income homes for individuals experiencing chronic homelessness. The rapidly deployed modular project was delivered by Assembly Corp. Blonder says that 1120 Ossington is “proof that when funding, policy, and thoughtful design come together, exceptional housing outcomes are not only possible—they’re scalable.” The project, developed by St. Clare’s Multifaith Housing Society, aims to demonstrate how permanent supportive housing can be delivered without compromising on design quality or integration within the existing urban fabric. 1120 Ossington. Photo credit: Doublespace Photography The project is located on a surplus portion of land adjacent to a previously converted church, and is owned and operated by St. Clare’s. The goal for the project was to “make the land work harder,” says Blonder, by increasing the site’s total unit count as well as improving operational efficiency, while addressing a need for supportive housing in the city. It uses mass timber and a panelized, tilt-up construction method, which allowed the structure to be assembled in 17 days. This offsite approach reduced build time, minimized disruption, and improved energy and cost performance. The building is modeled to perform 40 per cent better than the NECB baseline, with an R40 nominal envelope and a Passive House-rated slab from Legalett. A building automation system (BAS) will monitor energy use in real-time to measure performance against TEDI and TEUI targets. 1120 Ossington. Photo credit: Doublespace Photography Each unit is a private micro-unit with a kitchenette and bathroom and supports autonomy and dignity for residents who have experienced chronic homelessness. Shared spaces also provide access to on-site services, and an exterior corridor improves natural ventilation and energy efficiency. The design constraints of modular, mass timber construction were addressed through integrated architectural art. Wind Garden, by artist Leo Krukowski, features perforated metal panels mounted outside the windows. These screens provide shading and privacy while also casting shifting patterns of light and shadow. The installation, which was inspired by the forms of native plants, enhances the building façade and reinforces the idea that supportive housing contributes meaningfully to the public realm. 1120 Ossington. Photo credit: Doublespace Photography The project was made possible through funding and support from all levels of government, including the City of Toronto’s Open Doors and Concept 2 Keys programs, CMHC, the Province of Ontario, and private donors. All 25 units are rent-geared-to-income, with most tenants paying $582; the shelter component of ODSP. The post This is what deeply affordable housing success looks like. Can it endure after the election? appeared first on Canadian Architect.