Check Out Highlights From WIRED's Big Interview Event On December 4, WIRED sat down with some of the biggest names in tech, culture, business, and science for a day full of in-depth interviews. In 2024, we brought those talks to a stage in San Francisco for the very first time. This year, we did it again, bringing together AMD CEO Lisa Su, director Jon M. Chu, Anthropic cofounder Daniela Amodei, Cloudflare CEO Matthew Prince, and many more. The Big Interview, a one-day, in-person event held at The Midway in San Francisco on December 4, featured a series of in-depth, illuminating Q&As with some of the biggest names in innovation today, each led by a WIRED journalist. We also hosted our take on a modern-day science fair, complete with hands-on demos and other fun experiences.

Meta wants to make its AI hardware slicker and more fashion-forward. It also needs to make its software more usable. The way to do all that appears to be hiring design maestros away from Apple. Meta has made a big move to hire two prominent designers away from rival tech giant Apple, likely putting them to work on designing Meta's next generation of AI hardware and the software that runs on it. Alan Dye, formerly Apple's vice president of Human Interface Design, will join Meta to head up a new design studio within Meta's Reality Labs.

Sahil Lavingia, previously a DOGE operative at the Department of Veterans Affairs, is now a career employee at the IRS. He said at WIRED's Big Interview event that he expects to work there 10 years. Sahil Lavingia, the former member of Elon Musk's so-called Department of Government Efficiency (DOGE) first identified by WIRED, has a new job in government at the Internal Revenue Service (IRS). Lavingia joined the IRS in November. In a conversation at WIRED's Big Interview event with former acting commissioner of the Social Security Administration (SSA) Leland Dudek and David Foote, outside counsel for the US Institute of Peace, Lavingia said, "I'm working at IRS for online accounts."

Waterstones would stock books created using artificial intelligence, the company's boss has said, as long as they were clearly labelled, and if customers wanted them. However, James Daunt, a veteran of the bookselling industry, said he personally did not expect that to happen. There's a huge proliferation of AI generated content and most of it are not books that we should be selling, he said. But it would be up to the reader. An explosion in the use of artificial intelligence, or AI, has prompted heated debate in the publishing industry, with writers concerned about the impact on their livelihoods.

The United States Inspector General report reviewing Secretary of Defense Pete Hegseth's text messaging mess recommends a single change to keep classified material secure. A United States Inspector General report publicly released today found that Secretary of Defense Pete Hegseth could have put US troops and military operations at risk by using the consumer messaging service Signal to share sensitive, real-time details in March about a planned attack on Houthi rebels in Yemen. The IG first shared the classified report with Congress on Tuesday. The report contains only one direct recommendation: that the chief of US Central Command's Special Security Office "review the command's classification procedures for compliance" with Department of Defense regulations "and issue additional procedures, as necessary, to ensure proper portion marking of classified information." The report also references another IG publication about use of "non-DOD-controlled electronic messaging systems" and points to its recommendations that DOD "improve training for senior DOD officials on the proper use of electronic devices."

Boolean matrix factorization (BMF) approximates a given binary input matrix as the product of two smaller binary factors. Unlike binary matrix factorization based on standard arithmetic, BMF employs the Boolean OR and AND operations for the matrix product, which improves interpretability and reduces the approximation error. It is also used in role mining and computer vision. In this paper, we first propose algorithms for BMF that perform alternating optimization (AO) of the factor matrices, where each subproblem is solved via integer programming (IP). We then design different approaches to further enhance AO-based algorithms by selecting an optimal subset of rank-one factors from multiple runs. To address the scalability limits of IP-based methods, we introduce new greedy and local-search heuristics. We also construct a new C++ data structure for Boolean vectors and matrices that is significantly faster than existing ones and is of independent interest, allowing our heuristics to scale to large datasets. We illustrate the performance of all our proposed methods and compare them with the state of the art on various real datasets, both with and without missing data, including applications in topic modeling and imaging.

A standard assumption in deep learning is that the inductive bias introduced by a neural network architecture must persist from training through inference. The architecture you train with is the architecture you deploy. This assumption constrains the community from selecting architectures that may have desirable efficiency or design properties due to difficulties with optimization. We challenge this assumption with Network of Theseus (NoT), a method for progressively converting a trained, or even untrained, guide network architecture part-by-part into an entirely different target network architecture while preserving the performance of the guide network. At each stage, components in the guide network architecture are incrementally replaced with target architecture modules and aligned via representational similarity metrics. This procedure largely preserves the functionality of the guide network even under substantial architectural changes-for example, converting a convolutional network into a multilayer perceptron, or GPT-2 into a recurrent neural network. By decoupling optimization from deployment, NoT expands the space of viable inference-time architectures, opening opportunities for better accuracy-efficiency tradeoffs and enabling more directed exploration of the architectural design space.

As healthcare increasingly turns to AI for scalable and trustworthy clinical decision support, ensuring reliability in model reasoning remains a critical challenge. Individual large language models (LLMs) are susceptible to hallucinations and inconsistency, whereas naive ensembles of models often fail to deliver stable and credible recommendations. Building on our previous work on LLM Chemistry, which quantifies the collaborative compatibility among LLMs, we apply this framework to improve the reliability in medication recommendation from brief clinical vignettes. Our approach leverages multi-LLM collaboration guided by Chemistry-inspired interaction modeling, enabling ensembles that are effective (exploiting complementary strengths), stable (producing consistent quality), and calibrated (minimizing interference and error amplification). We evaluate our Chemistry-based Multi-LLM collaboration strategy on real-world clinical scenarios to investigate whether such interaction-aware ensembles can generate credible, patient-specific medication recommendations. Preliminary results are encouraging, suggesting that LLM Chemistry-guided collaboration may offer a promising path toward reliable and trustworthy AI assistants in clinical practice.

Long short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunication forecasting, where temporal correlations and nonlinear dependencies dominate. However, conventional LSTMs suffer from high parameter redundancy and limited nonlinear expressivity. In this work, we propose the Quantum-inspired Kolmogorov-Arnold Long Short-Term Memory (QKAN-LSTM), which integrates Data Re-Uploading Activation (DARUAN) modules into the gating structure of LSTMs. Each DARUAN acts as a quantum variational activation function (QVAF), enhancing frequency adaptability and enabling an exponentially enriched spectral representation without multi-qubit entanglement. The resulting architecture preserves quantum-level expressivity while remaining fully executable on classical hardware. Empirical evaluations on three datasets, Damped Simple Harmonic Motion, Bessel Function, and Urban Telecommunication, demonstrate that QKAN-LSTM achieves superior predictive accuracy and generalization with a 79% reduction in trainable parameters compared to classical LSTMs. We extend the framework to the Jiang-Huang-Chen-Goan Network (JHCG Net), which generalizes KAN to encoder-decoder structures, and then further use QKAN to realize the latent KAN, thereby creating a Hybrid QKAN (HQKAN) for hierarchical representation learning. The proposed HQKAN-LSTM thus provides a scalable and interpretable pathway toward quantum-inspired sequential modeling in real-world data environments.

Generative models augmented with external tools and update mechanisms (or \textit{agents}) have demonstrated capabilities beyond intelligent prompting of base models. As agent use proliferates, dynamic multi-agent systems have naturally emerged. Recent work has investigated the theoretical and empirical properties of low-dimensional representations of agents based on query responses at a single time point. This paper introduces the Temporal Data Kernel Perspective Space (TDKPS), which jointly embeds agents across time, and proposes several novel hypothesis tests for detecting behavioral change at the agent- and group-level in black-box multi-agent systems. We characterize the empirical properties of our proposed tests, including their sensitivity to key hyperparameters, in simulations motivated by a multi-agent system of evolving digital personas. Finally, we demonstrate via natural experiment that our proposed tests detect changes that correlate sensitively, specifically, and significantly with a real exogenous event. As far as we are aware, TDKPS is the first principled framework for monitoring behavioral dynamics in black-box multi-agent systems -- a critical capability as generative agent deployment continues to scale.