Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensemble approach to distinguishing scam and fraud CFPB complaints and describe initial findings regarding the strengths and weaknesses of LLMs in the scam defense context.

In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating yield. The development of global land-cover maps has been facilitated by the increasing availability of earth observation data and advancements in geospatial machine learning. However, these global maps exhibit lower accuracy and inconsistencies in Africa, partly due to the lack of representative training data. To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps. Our method trains a high-resolution teacher model on images with a resolution of 0.331 m/pixel and a low-resolution student model on publicly available images with a resolution of 10 m/pixel. The student model also utilizes the teacher model's output as its weak label examples through knowledge transfer. We evaluated our framework using Murang'a county in Kenya, renowned for its agricultural productivity, as a use case. Our local models achieved higher quality maps, with improvements of 0.14 in the F1 score and 0.21 in Intersection-over-Union, compared to the best global model. Our evaluation also revealed inconsistencies in existing global maps, with a maximum agreement rate of 0.30 among themselves. Our work provides valuable guidance to decision-makers for driving informed decisions to enhance food security.

With the help of these systems, anyone can create something that would previously have been considered a remarkable work of art. In human-AI collaboration, the computer seems to have become more than a tool. Many who have made their first contact with current generative AIs see them as "creativity machines" while for others the term "machine creativity" remains an oxymoron. This article is about (the possibility of) creativity in computers within the current Machine Learning paradigm. It outlines some of the key concepts behind the technologies and the innovations that have contributed to this qualitative shift, with a focus on text-to-image systems. The nature of Artificial Creativity as such is discussed, as well as what this might mean for art. AI may become a responsible collaborator with elements of independent machine authorship in the artistic process.

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: 'Examine policies and guidelines', 'Think about social impacts', 'Harness understanding of the technology', 'Indicate use', 'Critically engage with outputs', 'Access secure versions', and'Look at user agreements'. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI's benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies.

Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not straightforward. This paper introduces the main considerations necessary for making sound use of energy measurement tools and interpreting energy estimates, including the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures. It also contains practical tips and real-world scenarios that illustrate how these considerations come into play. It concludes with a call to action for improving the state of the art of measurement methods and standards for facilitating robust comparisons between diverse hardware and software environments.

Children across the US will likely spend the coming days and weeks saying goodbye to an AI-powered friend named Moxie. The small dog-sized companion bot--which used a ChatGPT-style large language model and expressive features to hold open-ended conversations with children--will soon be taken offline due to its creator's financial struggles. The decision to abandon the 799 product four years after its release, first reported by Aftermath, has left some customers bemoaning the loss of an artificial friend and others angrily demanding refunds. Videos of confused, crying children saying goodbye to their companion flooding social media. It's part of a larger trend of companies cutting off software support for hardware to cut costs.

Character.AI, a platform offering personalizable chatbots powered by large language models–faces yet another lawsuit for allegedly "serious, irreparable, and ongoing abuses" inflicted on its teenage users. According to a December 9th federal court complaint filed on behalf of two Texas families, multiple Character.AI bots engaged in discussions with minors that promoted self-harm and sexual abuse. Among other "overtly sensational and violent responses," one chatbot reportedly suggested a 15-year-old murder his parents for restricting his internet use. The lawsuit, filed by attorneys at the Social Media Victims Law Center and the Tech Justice Law Project, recounts the rapid mental and physical decline of two teens who used Character.AI bots. The first unnamed plaintiff is described as a "typical kid with high functioning autism" who began using the app around April 2023 at the age of 15 without their parents' knowledge.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. On Thursday evening, President-elect Donald Trump announced on his Truth Social platform that he would be appointing David O. Sacks--the "PayPal Mafia" alum, longtime venture capitalist, All-In Podcast co-host, Elon Musk pal, and rock-ribbed Silicon Valley conservative--as the "White House A.I. & Crypto Czar." In his statement, Trump wrote that "Sacks will focus on making America the clear global leader" in artificial intelligence and cryptocurrency, which he deemed to be "two areas critical to the future of American competitiveness." In addition, Sacks will "safeguard Free Speech online," "steer us away from Big Tech bias and censorship," and "lead the Presidential Council of Advisors for Science and Technology." For his first-ever Truth Social post, the incoming czar responded to Trump with gratitude and claimed that he "looks forward to advancing American competitiveness in these critical technologies."

Last Thursday, Senators Elizabeth Warren and Eric Schmitt introduced a bill aimed at stirring up more competition for Pentagon contracts awarded in AI and cloud computing. Amazon, Microsoft, Google, and Oracle currently dominate those contracts. "The way that the big get bigger in AI is by sucking up everyone else's data and using it to train and expand their own systems," Warren told the Washington Post. The new bill would "require a competitive award process" for contracts, which would ban the use of "no-bid" awards by the Pentagon to companies for cloud services or AI foundation models. While Big Tech is hit with antitrust investigations--including the ongoing lawsuit against Google about its dominance in search, as well as a new investigation opened into Microsoft--regulators are also accusing AI companies of, well, just straight-up lying.

Document content extraction is crucial in computer vision, especially for meeting the high-quality data needs of large language models (LLMs) and retrieval-augmented generation (RAG) technologies. However, current document parsing methods suffer from significant limitations in terms of diversity and comprehensive evaluation. To address these challenges, we introduce OmniDocBench, a novel multi-source benchmark designed to advance automated document content extraction. OmniDocBench includes a meticulously curated and annotated high-quality evaluation dataset comprising nine diverse document types, such as academic papers, textbooks, slides, among others. Our benchmark provides a flexible and comprehensive evaluation framework with 19 layout category labels and 14 attribute labels, enabling multi-level assessments across entire datasets, individual modules, or specific data types. Using OmniDocBench, we perform an exhaustive comparative analysis of existing modular pipelines and multimodal end-to-end methods, highlighting their limitations in handling document diversity and ensuring fair evaluation. OmniDocBench establishes a robust, diverse, and fair evaluation standard for the document content extraction field, offering crucial insights for future advancements and fostering the development of document parsing technologies. The codes and dataset is available in https://github.com/opendatalab/OmniDocBench.