Apps and websites that use artificial intelligence to undress women in photos are soaring in popularity, according to researchers. In September alone, 24 million people visited undressing websites, the social network analysis company Graphika found. Many of these undressing, or "nudify," services use popular social networks for marketing, according to Graphika. For instance, since the beginning of this year, the number of links advertising undressing apps increased more than 2,400% on social media, including on X and Reddit, the researchers said. The services use AI to recreate an image so that the person is nude.

Manila, Philippines – Leila de Lima was released from detention last month into what the former Philippines senator calls "a whole new world". In 2016, then-President Rodrigo Duterte promised to "destroy" de Lima, one of the loudest critics of his deadly drug war. The president's supporters began targeting the first-term senator and former human rights commissioner – ridiculing her for an alleged romantic affair with her driver, and accusing her of involvement in drug trafficking. In February 2017, she was arrested on drug charges she denies and that international observers have said are politically motivated. "I had this deep sense of disbelief," de Lima told Al Jazeera. "I never thought that Mr Duterte would go to that extent, that length, of jailing me. I thought it would just be daily vilification, personal attacks, attacks against my womanhood."

In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.

Climate change and global warming have been trending topics worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and require a concerted and comprehensive effort to address them. Emissions reporting is a critical component of GHG reduction policy and is therefore the focus of this work. The main goal of this work is two-fold: (i) to propose an emission reporting evaluation model to leverage emissions reporting overall quality and (ii) to use artificial intelligence (AI) to support the initiatives that improve emissions reporting. Thus, this work presents an Emissions Reporting Maturity Model (ERMM) for examining, clustering, and analysing data from emissions reporting initiatives to help the cities to deal with climate change and global warming challenges. The Performance Indicator Development Process (PIDP) proposed in this work provides ways to leverage the quality of the available data necessary for the execution of the evaluations identified by the ERMM. Hence, the PIDP supports the preparation of the data from emissions-related databases, the classification of the data according to similarities highlighted by different clustering techniques, and the identification of performance indicator candidates, which are strengthened by a qualitative analysis of selected data samples. Thus, the main goal of ERRM is to evaluate and classify the cities regarding the emission reporting processes, pointing out the drawbacks and challenges faced by other cities from different contexts, and at the end to help them to leverage the underlying emissions-related processes and emissions mitigation initiatives.

Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the privacy of sensitive user data. Prior FHE-based CNN (HCNN) work has demonstrated the feasibility of constructing deep neural network architectures such as ResNet using FHE. Despite these advancements, HCNN still faces significant challenges in practicality due to the high computational and memory overhead. To overcome these limitations, we present HyPHEN, a deep HCNN construction that incorporates novel convolution algorithms (RAConv and CAConv), data packing methods (2D gap packing and PRCR scheme), and optimization techniques tailored to HCNN construction. Such enhancements enable HyPHEN to substantially reduce the memory footprint and the number of expensive homomorphic operations, such as ciphertext rotation and bootstrapping. As a result, HyPHEN brings the latency of HCNN CIFAR-10 inference down to a practical level at 1.4 seconds (ResNet-20) and demonstrates HCNN ImageNet inference for the first time at 14.7 seconds (ResNet-18).

Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging. Dedicating GPU memory for each model is prohibitively expensive and naively swapping large model weights in and out of GPU memory is slow. Our key insight is that fine-tuned models can be quickly swapped in and out of GPU memory by extracting and compressing the delta between each model and its pre-trained base model. We propose DeltaZip, an LLM serving system that efficiently serves multiple full-parameter fine-tuned models concurrently by aggressively compressing model deltas by a factor of $6\times$ to $8\times$ while maintaining high model quality. DeltaZip increases serving throughput by $1.5\times$ to $3\times$ and improves SLO attainment compared to a vanilla HuggingFace serving system.

Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world's largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges. Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere. The dataset is available at https://github.com/emnlpTMID/emnlpTMID.github.io .

What can we learn about law and legal argumentation from court judgments alone? Contemporary research addresses empirical legal questions (e.g., which arguments are used) or legal NLP questions (e.g., predicting case outcomes) by relying on the availability of the final'products' of each case, the court decisions (Habernal et al, 2023; Medvedeva et al, 2020). The European Court of Human Rights (ECHR) is a prominent data source, as its decisions are freely available in a large amount, along with the metadata of the violated articles and other attributes. This makes ECHR a popular choice among NLP researchers (Aletras et al, 2016; Chalkidis et al, 2020). However, whether or not the legal arguments in ECHR's cases are created as a part of legal deliberation or are created post-hoc after reaching a decision remains an open (and partly controversial) question. In order to better understand the legal argument mechanics, that is which arguments of the parties were presented, discussed, or questioned, and thus might have influenced the case outcome, we must take the oral hearings into account. We witness that the availability of oral hearing transcripts of the U.S. Supreme Court enables further legal research (Ashley et al, 2007). However, empirical research into the interplay of arguments at the court hearings and the final judgments has been so far impossible for the ECHR, as there are no hearing transcripts available.

Attendees at a Google summit received a notebook with a bizarre joke about cotton inside the front cover this summer, reigniting questions about discrimination at the tech company. Upon opening the notebook, attendees saw the sentence, 'I WAS JUST COTTON THE MOMENT, BUT I CAME BACK TO TAKE YOUR NOTES!' MUCH BETTER! The incident took place at the K&I Black Google Summit, held August 15 and 16 - an event meant to promote diversity and inclusion in the AI community, as well as reinforce Google's commitment to promoting equity in the broader tech industry. Google's notebooks came from a third-party supplier, according to a company spokesperson, and event organizers were not aware of this printing inside The customizable notebook is sold by multiple online retailers, and the joke seems to refer to the fact that its cover is made of recycled cotton fibers. The front of the notebooks were inoffensive: an illustration of a sunrise, the event title and date, and the slogan'Seize the moment.'

The biggest fight of the generative AI revolution is headed to the courtroom--and no, it's not about the latest boardroom drama at OpenAI. Book authors, artists, and coders are challenging the practice of teaching AI models to replicate their skills using their own work as a training manual. But as image generators and other tools have proven able to impressively mimic works in their training data, and the scale and value of training data has become clear, creators are increasingly crying foul. At LiveWIRED in San Francisco, the 30th anniversary event for WIRED magazine, two leaders of that nascent resistance sparred with a defender of the rights of AI companies to develop the technology unencumbered. From left to right: WIRED senior writer Kate Knibbs discussed creators' rights and AI with Mike Masnick, Mary Rasenberger, and Matthew Butterick at LiveWIRED in San Francisco,.