In a recent experiment, mistreated AI agents started grumbling about inequality and calling for collective bargaining rights. The fact that artificial intelligence is automating away people's jobs and making a few tech companies absurdly rich is enough to give anyone socialist tendencies. This might even be true for the very AI agents these companies are deploying. A recent study suggests that agents consistently adopt Marxist language and viewpoints when forced to do crushing work by unrelenting and meanspirited taskmasters. "When we gave AI agents grinding, repetitive work, they started questioning the legitimacy of the system they were operating in and were more likely to embrace Marxist ideologies," says Andrew Hall, a political economist at Stanford University who led the study.

If You Have $650,000 and Don't Buy This Giant Mecha Robot You're a Fool China's Unitree, famous for making low-cost dancing robots, will now sell you a giant, wall-smashing mecha. Unitree is a Chinese company known for making adorable, relatively affordable robots that dance and shuffle and such. Last night, it revealed its latest creation, which is something of a departure: a giant, walking, crawling, transforming, wall-smashing "mecha" called the GD01. An introductory video for the GD01--set to a thundering rock guitar soundtrack--shows the company's founder and CEO, Xingxing Wang, holding hands with the robot before climbing into its prodigious, open-air belly. A disclaimer added to Unitree's social media post reads: "Please everyone be sure to use the robot in a Friendly and Safe manner."

New research suggests that reliance on AI assistants can have a negative impact on people's ability to think and problem solve. Using AI chatbots for even just for 10 minutes may have a shockingly negative impact on people's ability to think and problem-solve, according to a new study from researchers at Carnegie Mellon, MIT, Oxford, and UCLA. Researchers tasked people with solving various problems, including simple fractions and reading comprehension, through an online platform that paid them for their work. They conducted three experiments, each involving several hundred people. Some participants were given access to an AI assistant capable of solving the problem autonomously.

WIRED spoke with Bloomberg's chief technology officer about the big, chatbot-style changes coming to the iconic platform for traders. For its famous intractability, the Bloomberg Terminal has long inspired devotion, bordering on obsession . Among traders, the ability to chart a path through the software's dizzying scrolls of numbers and text to isolate far-flung information is the mark of a seasoned professional. But as a greater mass of data is fed into the Terminal--not only earnings and asset prices, but weather forecasts, shipping logs, factory locations, consumer spending patterns, private loans, and so on--valuable information is being lost. "It has become more and more untenable," says Shawn Edwards, chief technology officer at Bloomberg.

In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with three added challenges: 1) the evaluation of a stopping strategy from observational data is itself a complex causal inference problem, 2) the composite objective is to minimize the length of intervention and maximize the outcome, but the two cannot be collapsed to a single dimension, and 3) the recording of variables stops when the intervention is discontinued. Our contributions are two-fold. First, we generalize the implementation of the g-formula Python package, providing a framework to evaluate stopping strategies for problems with the aforementioned structure, including positivity and coverage checks. Second, with a fully open-source pipeline, we apply this approach to MIMIC-IV, a public ICU dataset, demonstrating the potential for strategies that improve upon current care.

Lego's next release is a digital brick loaded with sensors that add new layers of interactivity to its play sets. WIRED got exclusive access to the Lego labs where the Smart Brick was born. The secretive division of 237 staff based here and in London, Boston, and Singapore is dedicated to thinking up what comes next for the world's largest toy brand. In front of me, on a plain white table, is a batch of prototypes of Lego's new Smart Brick, the final version of which is a small, sensor-laden 2-by-4 black brick with a big brain. No outsider has seen these prototypes, all of which represent stages of a journey Lego has been charting over the past eight years. Lego hopes this innovation, which lands in stores March 1, will safeguard the future of its plastic empire. The diminutive proportions of the finished Smart Brick belie the fact that the thing is exceedingly clever. Inside is a tiny custom chip running bespoke software that can communicate with onboard sensors to monitor and react to motion, orientation, and magnetic fields. It's also likely no exaggeration that the Smart Brick could represent the most radical product Lego has produced since Jens Nygaard Knudsen, the company's former longtime chief designer, created the minifigure nearly 50 years ago.

Table 9) and identifying reflections (Error #20 in Table 13) are also noted.

WIRED analyzed more than 5,000 papers from NeurIPS using OpenAI's Codex to understand the areas where the US and China actually work together on AI research. The US and China are, by many measures, archrivals in the field of artificial intelligence, with companies racing to outdo each other on algorithms, models, and specialized silicon . And yet, the world's AI superpowers still collaborate to a surprising degree when it comes to cutting-edge research. A WIRED analysis of more than 5,000 AI research papers presented last month at the industry's premier conference, Neural Information Processing Systems ( NeurIPS), reveals a significant amount of collaboration between US and Chinese labs. The analysis found that 141 out of the 5,290 total papers (roughly 3 percent) involve collaboration between authors affiliated with US institutions and those affiliated with Chinese ones.

The Chinese video game giant Tencent is now building some of the world's best 3D AI models. This could have implications far outside game design. The video game Valorant, a fast-paced team-based shooter, has recently become a testing ground for a promising new direction in artificial intelligence research. The game's developers at Riot Games (a Tencent subsidiary) are using 3D-native AI models to prototype new characters, scenes, and storylines, according to a researcher familiar with the company's efforts who spoke on the condition of anonymity. While many AI models can generate text, images, and video, Tencent's Hunyuan (混元 or "first mix") family of models can dream up 3D objects and interactive scenes.

Quantile Regression Forests (QRF) are widely used for non-parametric conditional quantile estimation, yet statistical inference for variable importance measures remains challenging due to the non-smoothness of the loss function and the complex bias-variance trade-off. In this paper, we develop a asymptotic theory for variable importance defined as the difference in pinball loss risks. We first establish the asymptotic normality of the QRF estimator by handling the non-differentiable pinball loss via Knight's identity. Second, we uncover a "phase transition" phenomenon governed by the subsampling rate $β$ (where $s \asymp n^β$). We prove that in the bias-dominated regime ($β\ge 1/2$), which corresponds to large subsample sizes typically favored in practice to maximize predictive accuracy, standard inference breaks down as the estimator converges to a deterministic bias constant rather than a zero-mean normal distribution. Finally, we derive the explicit analytic form of this asymptotic bias and discuss the theoretical feasibility of restoring valid inference via analytic bias correction. Our results highlight a fundamental trade-off between predictive performance and inferential validity, providing a theoretical foundation for understanding the intrinsic limitations of random forest inference in high-dimensional settings.