American teenagers believe addressing the potential risks of artificial intelligence should be a top priority for lawmakers, according to a new poll that provides the first in-depth look into young people's concerns about the technology. The poll, carried out by youth-led advocacy group the Center for Youth and AI and polling organization YouGov, and shared exclusively with TIME, reveals a level of concern that rivals long standing issues like social inequality and climate change. The poll of 1,017 U.S. teens aged 13 to 18 was carried out in late July and early August, and found that 80% of respondents believed it was "extremely" or "somewhat" important for lawmakers to address the risks posed by AI, falling just below healthcare access and affordability in terms of issues they said were a top priority. That surpassed social inequality (78%) and climate change (77%). Although the sample size is fairly small, it gives an insight into how young people are thinking about technology, which has often been embedded in their lives from an early age.

The outlawed Kurdistan Workers' Party (PKK) has claimed responsibility for an attack on a Turkish state-run defence company near the capital, Ankara, that killed five people and wounded 22. The "act of sacrifice" in Ankara "was carried out by a team of the immortals battalion" of the PKK, the group said on the Telegram messaging app on Friday. Turkish Aerospace Industries (TUSAS) was attacked on Wednesday with fighters setting off explosives and opening fire using automatic rifles at the campus of the company that designs and manufactures civilian and military aircraft, unmanned aerial vehicles (UAVs) and other defence industry and space systems. On Friday, Turkey's Ministry of National Defence said it carried out air strikes for the second night in a row in northern Iraq, hitting 34 PKK targets in Hakurk, Gara, Qandil and Sinjar, destroying shelters, warehouses and other facilities. The overnight strikes followed a security meeting that Turkish President Recep Tayyip Erdogan chaired with key ministers and chiefs of the armed forces and intelligence agency in Istanbul. The Turkish government said earlier it had proof that the PKK, listed as a terror group by Turkey, the United States and the European Union, was behind the attack.

When Melissa Kono, the town clerk in Burnside, Wisconsin, began training election workers in 2015, their questions were relatively mundane. They asked about election rules, voter eligibility, and other basic procedures. The job was gratifying and enjoyable; they helped their neighbors while sipping coffee. But over the past few years, everything has changed. Kono now finds herself fielding questions about what to do when approached by suspicious voters who ask provocative questions or gripe about fraud.

If you're fluent with concepts like release schedules and calendars, you may notice Apple hasn't updated its computers in a while. It's nearly a full year since the iMac and MacBook Pro got speed bumps, and just over a year for the Mac Pro and Mac Studio. Now, Apple's head of marketing, Greg Joswiak, has announced an "exciting week of announcements," from Monday. It's easy to assume we'll see those models getting pushed from variants of the M3 to the M4. Given the M4's focus on AI, expect plenty of attention on Apple Intelligence, which comes to users as part of iOS 18.1's update at the same time.

Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skills, and occupations. Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. Similarly, they tend to attribute more skills and positive personality traits to women than to men, and we see a consistent tendency to associate negative personality traits with men. To eliminate the gender bias in these models, we find that finetuning-based debiasing methods achieve the best tradeoff between debiasing and retaining performance on downstream task. We argue for pre-deploying gender bias assessment in VLAs and motivate further development of debiasing strategies to ensure equitable societal outcomes. Rapid progress in large language models (LLMs) has sparked a wave of innovation fusing visual encoding modules with LLMs, which ...

Accurate prediction and synthesis of seismic waveforms are crucial for seismic hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such as Ground Motion Models and physics-based simulations, often fail to capture the full complexity of seismic wavefields, particularly at higher frequencies. This study introduces a novel, efficient, and scalable generative model for high-frequency seismic waveform generation. Our approach leverages a spectrogram representation of seismic waveform data, which is reduced to a lower-dimensional submanifold via an autoencoder. A state-of-the-art diffusion model is trained to generate this latent representation, conditioned on key input parameters: earthquake magnitude, recording distance, site conditions, and faulting type. The model generates waveforms with frequency content up to 50 Hz. Any scalar ground motion statistic, such as peak ground motion amplitudes and spectral accelerations, can be readily derived from the synthesized waveforms. We validate our model using commonly used seismological metrics, and performance metrics from image generation studies. Our results demonstrate that our openly available model can generate distributions of realistic high-frequency seismic waveforms across a wide range of input parameters, even in data-sparse regions. For the scalar ground motion statistics commonly used in seismic hazard and earthquake engineering studies, we show that the model accurately reproduces both the median trends of the real data and its variability. To evaluate and compare the growing number of this and similar 'Generative Waveform Models' (GWM), we argue that they should generally be openly available and that they should be included in community efforts for ground motion model evaluations.

The Multi-Capacity Fixed-Charge Network Flow (MC-FCNF) problem, a generalization of the Fixed-Charge Network Flow problem, aims to assign capacities to edges in a flow network such that a target amount of flow can be hosted at minimum cost. The cost model for both problems dictates that the fixed cost of an edge is incurred for any non-zero amount of flow hosted by that edge. This problem naturally arises in many areas including infrastructure design, transportation, telecommunications, and supply chain management. The MC-FCNF problem is NP-Hard, so solving large instances using exact techniques is impractical. This paper presents a genetic algorithm designed to quickly find high-quality flow solutions to the MC-FCNF problem. The genetic algorithm uses a novel solution representation scheme that eliminates the need to repair invalid flow solutions, which is an issue common to many other genetic algorithms for the MC-FCNF problem. The genetic algorithm's efficiency is displayed with an evaluation using real-world CO2 capture and storage infrastructure design data. The evaluation results highlight the genetic algorithm's potential for solving large-scale network design problems.

We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. Networks are considered untrainable when they overfit, underfit, or converge to poor results even when tuning their hyperparameters. For example, plain fully connected networks overfit on object recognition while deep convolutional networks without residual connections underfit. The traditional answer is to change the architecture to impose some inductive bias, although what that bias is remains unknown. We introduce guidance, where a guide network guides a target network using a neural distance function. The target is optimized to perform well and to match its internal representations, layer-by-layer, to those of the guide; the guide is unchanged. If the guide is trained, this transfers over part of the architectural prior and knowledge of the guide to the target. If the guide is untrained, this transfers over only part of the architectural prior of the guide. In this manner, we can investigate what kinds of priors different architectures place on untrainable networks such as fully connected networks. We demonstrate that this method overcomes the immediate overfitting of fully connected networks on vision tasks, makes plain CNNs competitive to ResNets, closes much of the gap between plain vanilla RNNs and Transformers, and can even help Transformers learn tasks which RNNs can perform more easily. We also discover evidence that better initializations of fully connected networks likely exist to avoid overfitting. Our method provides a mathematical tool to investigate priors and architectures, and in the long term, may demystify the dark art of architecture creation, even perhaps turning architectures into a continuous optimizable parameter of the network.

Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK Research Excellence Framework (REF) assesses Impact Case Studies (ICS), which are five-page evidence-based claims of societal impacts. This study investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support expert human assessors. For this, various parts of 6,220 public ICS from REF2021 were fed to ChatGPT 4o-mini along with the REF2021 evaluation guidelines, comparing the results with published departmental average ICS scores. The results suggest that the optimal strategy for high correlations with expert scores is to input the title and summary of an ICS but not the remaining text, and to modify the original REF guidelines to encourage a stricter evaluation. The scores generated by this approach correlated positively with departmental average scores in all 34 Units of Assessment (UoAs), with values between 0.18 (Economics and Econometrics) and 0.56 (Psychology, Psychiatry and Neuroscience). At the departmental level, the corresponding correlations were higher, reaching 0.71 for Sport and Exercise Sciences, Leisure and Tourism. Thus, ChatGPT-based ICS evaluations are simple and viable to support or cross-check expert judgments, although their value varies substantially between fields.

This review began with the modest goal of drafting a brief commentary on how the social work profession engages with and is impacted by artificial intelligence (AI). However, it quickly became apparent that a deeper exploration was required to adequately capture the profound influence of AI, one of the most transformative and debated innovations in modern history. As a result, this review evolved into an interdisciplinary endeavour, gathering seminal texts, critical articles, and influential voices from across industries and academia. This review aims to provide a comprehensive yet accessible overview, situating AI within broader societal and academic conversations as 2025 dawns. We explore perspectives from leading tech entrepreneurs, cultural icons, CEOs, and politicians alongside the pioneering contributions of AI engineers, innovators, and academics from fields as diverse as mathematics, sociology, philosophy, economics, and more. This review also briefly analyses AI's real-world impacts, ethical challenges, and implications for social work. It presents a vision for AI-facilitated simulations that could transform social work education through Advanced Personalised Simulation Training (APST). This tool uses AI to tailor high-fidelity simulations to individual student needs, providing real-time feedback and preparing them for the complexities of their future practice environments. We maintain a critical tone throughout, balancing our awe of AI's remarkable advancements with necessary caution. As AI continues to permeate every professional realm, understanding its subtleties, challenges, and opportunities becomes essential. Those who fully grasp the intricacies of this technology will be best positioned to navigate the impending AI Era.