'The advent of AI is drawing the world's attention to the extreme imbalance of power between employers and their employees.' 'The advent of AI is drawing the world's attention to the extreme imbalance of power between employers and their employees.' New technology has workers spooked, but experts say it's creating an opening for a resurgence in worker power In 2026, it's a scary time to work for a living. Gone are the days of quiet quitting, the Great Resignation, and the highly visible union-organizing battles that began the decade and signaled that perhaps worker power was on the rise again in the US. Instead, much of that momentum is being crowded out of our minds by anxieties: a worsening affordability crisis, geopolitical instability, and the specter of artificial intelligence looming over the workplace.

Various adverse weather conditions such as fog and rain pose a significant challenge to autonomous driving (AD) perception tasks like semantic segmentation, object detection, etc. The common domain adaption strategy is to minimize the disparity between images captured in clear and adverse weather conditions. However, domain adaption faces two challenges: (I) it typically relies on utilizing clear image as a reference, which is challenging to obtain in practice; (II) it generally targets single adverse weather condition and performs poorly when confronting the mixture of multiple adverse weather conditions. To address these issues, we introduce a reference-free and Adverse weather condition-independent (Advent) framework (rather than a specific model architecture) that can be implemented by various backbones and heads. This is achieved by leveraging the homogeneity over short durations, getting rid of clear reference and being generalizable to arbitrary weather condition. Specifically, Advent includes three integral components: (I) Locally Sequential Mechanism (LSM) leverages temporal correlations between adjacent frames to achieve the weather-condition-agnostic effect thanks to the homogeneity behind arbitrary weather condition; (II) Globally Shuffled Mechanism (GSM) is proposed to shuffle segments processed by LSM from different positions of input sequence to prevent the overfitting to LSM-induced temporal patterns; (III) Unfolded Regularizers (URs) are the deep unfolding implementation of two proposed regularizers to penalize the model complexity to enhance across-weather generalization. We take the semantic segmentation task as an example to assess the proposed Advent framework. Extensive experiments demonstrate that the proposed Advent outperforms existing state-of-the-art baselines with large margins.

I've been eagerly awaiting the advent of spatial computing. My home office desk setup, with multiple screens and browser windows, helps me be very productive. But on the go, I'm relegated to a laptop's 13-inch screen (or packing a portable monitor), and I'm not as efficient. Spatial computing--usually driven by a mixed reality headset or smart glasses--lets you craft a multi-monitor virtual workspace, where you can place apps and browser windows around your periphery, to replicate the experience you have set up at home or the office. Or you can take it a step further because you're only limited by your imagination.

If asked to come up with a quintessentially "human" work of art, one could do worse than to name a film by Studio Ghibli. The Japanese animation studio, founded by the legendary eighty-four-year-old director Hayao Miyazaki, is known for its hand-drawn imagery, lushly organic color palettes, epic narratives, and evocation of both the emotional ambiguities of childhood and the twisting path to becoming an adult. We American millennials were blessed to have the films translated and distributed in English just as we were growing up, and so movies including "My Neighbor Totoro," "Princess Mononoke," and "Spirited Away" are nigh-universally recognizable touchstones of our youth. Any Ghibli imagery is primed to make us feel a combination of pleasurable nostalgia and mournful shivers, evoking the doomed forest creatures, greedy bathhouse ghosts, and missed connections featured in Miyazaki's cinematic story lines. Unfortunately, that sense of poignancy quickly erodes when you are bombarded with thousands of Ghibli-esque copycat images, as we all were online last week, thanks to OpenAI's latest version of its ChatGPT tool.

It's a little weird to talk about Framework "launching" a new laptop given it just makes the same machine over and over again. That, of course, is the point, since it's building a fleet of modular, upgradeable and repairable machines that eliminate unnecessary e-waste. Let's agree that while launching isn't the right word, it is how we'll describe the advent of the updated AMD edition of the Framework 13, which now comes with the Ryzen AI 300 on board. Naturally, the big news is the fancier AMD unit welded to the mainboard, which boasts dramatically improved AI performance for Microsoft Copilot . But Framework has made its usual series of nips and tucks, adding Wi-Fi 7, a new thermal system, improved keyboard and new color options.

Nothing cheers up a tech columnist more than the sight of 600bn being wiped off the market cap of an overvalued tech giant in a single day. And yet last Monday that's what happened to Nvidia, the leading maker of electronic picks and shovels for the AI gold rush. It was the biggest one-day slump for any company in history, and it was not alone – shares of companies in semiconductor, power and infrastructure industries exposed to AI collectively shed more than 1tn in value on the same day. The proximate cause of this chaos was the news that a Chinese tech startup of whom few had hitherto heard had released DeepSeek R1, a powerful AI assistant that was much cheaper to train and operate than the dominant models of the US tech giants – and yet was comparable in competence to OpenAI's o1 "reasoning" model. Just to illustrate the difference: R1 was said to have cost only 5.58m to build, which is small change compared with the billions that OpenAI and co have spent on their models; and R1 is about 15 times more efficient (in terms of resource use) than anything comparable made by Meta. The DeepSeek app immediately zoomed to the top of the Apple app store, where it attracted huge numbers of users who were clearly unfazed by the fact that the terms and conditions and the privacy policy they needed to accept were in Chinese.

Google's central London office cost as much as a tech unicorn and the company's UK boss, Debbie Weinstein, says it pulses with a similar spirit. "It feels like a startup energy," she says. However, we are meeting on a morning when Google has been threatened with a reckoning reserved for members of the corporate establishment, not tech ingenues: a breakup. Hours earlier, the US Department of Justice had asked a federal judge to order the sale of Google's Chrome browser, along with a host of other actions including making its search index – a database of all the webpages it has crawled – available to competitors. It follows a ruling by the same judge in August that the 2tn company has built an illegal monopoly in the search market.

Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions is still unexplored. Addressing this gap, we introduce PECC, a novel benchmark derived from Advent Of Code (AoC) challenges and Project Euler, including 2396 problems. Unlike conventional benchmarks, PECC requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. A key feature of our dataset is the complexity added by natural language prompting in chat-based evaluations, mirroring real-world instruction ambiguities. Results show varying model performance between narrative and neutral problems, with specific challenges in the Euler math-based subset with GPT-3.5-Turbo passing 50% of the AoC challenges and only 8% on the Euler problems. By probing the limits of LLMs' capabilities, our benchmark provides a framework to monitor and assess the subsequent progress of LLMs as a universal problem solver.

Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advent of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and over-hyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.

How will superhuman artificial intelligence (AI) affect human decision making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 years (1950-2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.