The spatial and temporal distribution of precipitation has a significant impact on human lives by determining freshwater resources and agricultural yield, but also rainfall-driven hazards like flooding or landslides. While the ERA5 reanalysis dataset provides consistent long-term global precipitation information that allows investigations of these impacts, it lacks the resolution to capture the high spatio-temporal variability of precipitation. ERA5 misses intense local rainfall events that are crucial drivers of devastating flooding - a critical limitation since extreme weather events become increasingly frequent. Here, we introduce spateGAN-ERA5, the first deep learning based spatio-temporal downscaling of precipitation data on a global scale. SpateGAN-ERA5 uses a conditional generative adversarial neural network (cGAN) that enhances the resolution of ERA5 precipitation data from 24 km and 1 hour to 2 km and 10 minutes, delivering high-resolution rainfall fields with realistic spatio-temporal patterns and accurate rain rate distribution including extremes. Its computational efficiency enables the generation of a large ensemble of solutions, addressing uncertainties inherent to the challenges of downscaling. Trained solely on data from Germany and validated in the US and Australia considering diverse climate zones, spateGAN-ERA5 demonstrates strong generalization indicating a robust global applicability. SpateGAN-ERA5 fulfils a critical need for high-resolution precipitation data in hydrological and meteorological research, offering new capabilities for flood risk assessment, AI-enhanced weather forecasting, and impact modelling to address climate-driven challenges worldwide.

The domain of computational design, driven by advancements in Generative AI, is transforming creative fields. We explore the transformative effects of Generative AI on the architectural design process and discuss the role of the architect. The case of architecture is interesting as designing houses is complex, involving extensive customer interaction. We employ a within-subject experiment using a popular general-purpose text-to-image tool for generating designs and providing feedback on existing designs, followed by expert interviews. The study reveals that AI can disrupt the ideation phase by enabling clients to engage in the design process through rapid visualization of their own ideas. In turn, the architect's role shifts more towards assessing the feasibility of designs generated conjointly by clients and AI. Our study also shows that while AI can provide valuable feedback on designs, it might fail to generate such designs, allowing for interesting connections to foundations in computer science, i.e., NP-completeness. AI's feedback also tends to hamper creativity and innovation by suggesting altering novel, innovative approaches toward more standardized designs. Our study also reveals that there is uncertainty among architects about the interpretative sovereignty of architecture and loss of meaning and identity when AI increasingly takes over authorship in the design process.

In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence. Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation helps disentangle the treatment features of interest, such as specific sentiments and certain topics, from other possibly unknown confounding features. Unlike the existing methods, our proposed approach eliminates the need to learn causal representation from the data and hence produces more accurate and efficient estimates. We formally establish the conditions required for the nonparametric identification of the average treatment effect, propose an estimation strategy that avoids the violation of the overlap assumption, and derive the asymptotic properties of the proposed estimator through the application of double machine learning. Finally, using an instrumental variables approach, we extend the proposed methodology to the settings, in which the treatment feature is based on human perception rather than is assumed to be fixed given the treatment object. The proposed methodology is also applicable to text reuse where an LLM is used to regenerate the existing texts. We conduct simulation and empirical studies, using the generated text data from an open-source LLM, Llama 3, to illustrate the advantages of our estimator over the state-of-the-art causal representation learning algorithms.

Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is requesting bank login information. However, existing ML/DM classifiers are limited in their ability to understand natural languages w.r.t the context and nuances. The aforementioned challenges are overcome with the arrival of Gen-AI techniques, along with their inherent ability w.r.t translation between languages, fine-tuning between various tasks and domains. In this manuscript, we provide a comprehensive overview of the various work done while using Gen-AI techniques w.r.t user safety. In particular, we first provide the various domains (e.g. phishing, malware, content moderation, counterfeit, physical safety) across which Gen-AI techniques have been applied. Next, we provide how Gen-AI techniques can be used in conjunction with various data modalities i.e. text, images, videos, audio, executable binaries to detect violations of user-safety. Further, also provide an overview of how Gen-AI techniques can be used in an adversarial setting. We believe that this work represents the first summarization of Gen-AI techniques for user-safety.

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models might memorize or guess. To address these limitations, we introduce Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines such as mathematics, cryptography, cybersecurity, and computer science. The accompanying dataset, DIA-Bench, contains a diverse collection of challenge templates with mutable parameters presented in various formats, including text, PDFs, compiled binaries, visual puzzles, and CTF-style cybersecurity challenges. Our framework introduces four new metrics to assess a model's reliability and confidence across multiple attempts. These metrics revealed that even simple questions are frequently answered incorrectly when posed in varying forms, highlighting significant gaps in models' reliability. Notably, API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. In self-assessment, OpenAI's o1-mini proved to have the best judgement on what tasks it should attempt to solve. We evaluated 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence, even with simpler questions. The DIA framework sets a new standard for assessing not only problem-solving but also a model's adaptive intelligence and ability to assess its limitations. The dataset is publicly available on the project's page: https://github.com/DIA-Bench.

Astrophysicist Stephen Hawking told Last Week Tonight's John Oliver a chilling but memorable hypothetical story a decade ago about the potential dangers of AI. The gist is a group of scientists build a superintelligent computer and ask it, "Is there a God?" The computer answers, "There is now" and a bolt of lightning zaps the plug preventing it from being shut down. Let's hope that's not what happened with OpenAI and some missing evidence from the New York Times' plagiarism lawsuit. Wired reported that a court declaration filed by the New York Times on Wednesday says that OpenAI's engineers accidentally erased evidence of the AI's training data that took a long time to research and compile.

What are the key differences between ChatGPT, Claude, Gemini, Copilot, and other top AI tools? How deeply should you be concerned about AI systems using your personal data? Are the reported inaccuracies and biases of the AI platforms really as bad as what I read? Earlier this month, we asked you, WIRED subscribers, to send in your questions about generative artificial intelligence, and these are just some of the excellent queries we received. On Wednesday November 20, we hosted a live Q&A session with WIRED's Reece Rogers, the author of the "AI Unlocked" newsletter, the monthly AI ethics and advice column "The Prompt," and countless news and how-to articles about the impact AI is having on our lives.

This week, the Times alleged that OpenAI's engineers inadvertently erased data the paper's team spent more than 150 hours extracting as potential evidence. OpenAI was able to recover much of the data, but the Times' legal team says it's still missing the original file names and folder structure. According to a declaration filed to the court Wednesday by Jennifer B. Maisel, a lawyer for the newspaper, this means the information "cannot be used to determine where the news plaintiffs' copied articles" may have been incorporated into OpenAI's artificial intelligence models. "We disagree with the characterizations made and will file our response soon," OpenAI spokesperson Jason Deutrom told WIRED in a statement. The New York Times declined to comment.

The first paper describes how OpenAI directs an extensive network of human testers outside the company to vet the behavior of its models before they are released. The second paper presents a new way to automate parts of the testing process, using a large language model like GPT-4 to come up with novel ways to bypass its own guardrails. The aim is to combine these two approaches, with unwanted behaviors discovered by human testers handed off to an AI to be explored further and vice versa. Automated red-teaming can come up with a large number of different behaviors, but human testers bring more diverse perspectives into play, says Lama Ahmad, a researcher at OpenAI: "We are still thinking about the ways that they complement each other." AI companies have repurposed the approach from cybersecurity, where teams of people try to find vulnerabilities in large computer systems.

Creators who sell assets on itch.io will now have to be a lot more upfront about using generative AI. The marketplace for independent digital creators has introduced a new rule that requires users to label their projects if they were produced using generative AI tools, such as ChatGPT and Midjourney. Users will see an AI generation disclosure box when they upload their projects. If they confirm that their project contains AI-generated output, they'll be required to indicate what kinds of content were made with generative AI, whether they're graphics, sounds, text and dialogue or code. If they have a public asset page, they'll see a dialog box when they access their dashboard, making it easy to bulk tag their projects.