Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinions model-generated responses are more similar to. We first build a dataset, GlobalOpinionQA, comprised of questions and answers from cross-national surveys designed to capture diverse opinions on global issues across different countries. Next, we define a metric that quantifies the similarity between LLM-generated survey responses and human responses, conditioned on country. With our framework, we run three experiments on an LLM trained to be helpful, honest, and harmless with Constitutional AI. By default, LLM responses tend to be more similar to the opinions of certain populations, such as those from the USA, and some European and South American countries, highlighting the potential for biases. When we prompt the model to consider a particular country's perspective, responses shift to be more similar to the opinions of the prompted populations, but can reflect harmful cultural stereotypes. When we translate GlobalOpinionQA questions to a target language, the model's responses do not necessarily become the most similar to the opinions of speakers of those languages. We release our dataset for others to use and build on. Our data is at https://huggingface.co/datasets/Anthropic/llm_global_opinions. We also provide an interactive visualization at https://llmglobalvalues.anthropic.com.

We investigate how hallucination in large language models (LLM) is characterized in peer-reviewed literature using a critical examination of 103 publications across NLP research. Through a comprehensive review of sociological and technological literature, we identify a lack of agreement with the term `hallucination.' Additionally, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis underscores the necessity for explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society.

Deep Learning (DL) has made significant strides in various medical applications in recent years, achieving remarkable results. In the field of medical imaging, DL models can assist doctors in disease diagnosis by classifying pathologies in Chest X-ray images. However, training on new data to expand model capabilities and adapt to distribution shifts is a notable challenge these models face. Continual Learning (CL) has emerged as a solution to this challenge, enabling models to adapt to new data while retaining knowledge gained from previous experiences. Previous studies have analyzed the behavior of CL strategies in medical imaging regarding classification performance. However, when considering models that interact with sensitive information, such as in the medical domain, it is imperative to disaggregate the performance of socially salient groups. Indeed, DL algorithms can exhibit biases against certain sub-populations, leading to discrepancies in predictive performance across different groups identified by sensitive attributes such as age, race/ethnicity, sex/gender, and socioeconomic status. In this study, we go beyond the typical assessment of classification performance in CL and study bias evolution over successive tasks with domain-specific fairness metrics. Specifically, we evaluate the CL strategies using the well-known CheXpert (CXP) and ChestX-ray14 (NIH) datasets. We consider a class incremental scenario of five tasks with 12 pathologies. We evaluate the Replay, Learning without Forgetting (LwF), LwF Replay, and Pseudo-Label strategies. LwF and Pseudo-Label exhibit optimal classification performance, but when including fairness metrics in the evaluation, it is clear that Pseudo-Label is less biased. For this reason, this strategy should be preferred when considering real-world scenarios in which it is crucial to consider the fairness of the model.

Autonomous systems are becoming increasingly prevalent in new vehicles. Due to their environmental friendliness and their remarkable capability to significantly enhance road safety, these vehicles have gained widespread recognition and acceptance in recent years. Automated Driving Systems (ADS) are intricate systems that incorporate a multitude of sensors and actuators to interact with the environment autonomously, pervasively, and interactively. Consequently, numerous studies are currently underway to keep abreast of these rapid developments. This paper aims to provide a comprehensive overview of recent advancements in ADS technologies. It provides in-depth insights into the detailed information about how data and information flow in the distributed system, including autonomous vehicles and other various supporting services and entities. Data validation and system requirements are emphasised, such as security, privacy, scalability, and data ownership, in accordance with regulatory standards. Finally, several current research directions in the AVs field will be discussed.

AI-empowered technologies' impact on the world is undeniable, reshaping industries, revolutionizing how humans interact with technology, transforming educational paradigms, and redefining social codes. However, this rapid growth is accompanied by two notable challenges: a lack of diversity within the AI field and a widening AI divide. In this context, This paper examines the intersection of AI and identity as a pathway to understand biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves understanding the associations between the individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper proposes the need for a comprehensive approach to fostering a more inclusive and responsible AI ecosystem through the lens of identity.

In this paper, we introduce GoodDrag, a novel approach to improve the stability and image quality of drag editing. Unlike existing methods that struggle with accumulated perturbations and often result in distortions, GoodDrag introduces an AlDD framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. We also propose an information-preserving motion supervision operation that maintains the original features of the starting point for precise manipulation and artifact reduction. In addition, we contribute to the benchmarking of drag editing by introducing a new dataset, Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy Index and Gemini Score, utilizing Large Multimodal Models. Extensive experiments demonstrate that the proposed GoodDrag compares favorably against the state-of-the-art approaches both qualitatively and quantitatively. The project page is https://gooddrag.github.io.

In the near future, mobile networks are expected to broaden their services and coverage to accommodate a larger user base and diverse user needs. Thus, they will increasingly rely on artificial intelligence (AI) to manage network operation and control costs, undertaking complex decision-making roles. This shift will necessitate the application of techniques that incorporate critical thinking abilities, including reasoning and planning. Symbolic AI techniques already facilitate critical thinking based on existing knowledge. Yet, their use in telecommunications is hindered by the high cost of mostly manual curation of this knowledge and high computational complexity of reasoning tasks. At the same time, there is a spurt of innovations in industries such as telecommunications due to Generative AI (GenAI) technologies, operating independently of human-curated knowledge. However, their capacity for critical thinking remains uncertain. This paper aims to address this gap by examining the current status of GenAI algorithms with critical thinking capabilities and investigating their potential applications in telecom networks. Specifically, the aim of this study is to offer an introduction to the potential utilization of GenAI for critical thinking techniques in mobile networks, while also establishing a foundation for future research.

Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in computer vision and pattern recognition. For the achievement of state-of-the-art performances in diverse domains, the deep learning used different architectures and these architectures used activation functions to perform various computations between hidden and output layers of any architecture. This paper presents a survey on the existing studies of deep learning in handwriting recognition field. Even though the recent progress indicates that the deep learning methods has provided valuable means for speeding up or proving accurate results in handwriting recognition, but following from the extensive literature survey, the present study finds that the deep learning has yet to revolutionize more and has to resolve many of the most pressing challenges in this field, but promising advances have been made on the prior state of the art. Additionally, an inadequate availability of labelled data to train presents problems in this domain. Nevertheless, the present handwriting recognition survey foresees deep learning enabling changes at both bench and bedside with the potential to transform several domains as image processing, speech recognition, computer vision, machine translation, robotics and control, medical imaging, medical information processing, bio-informatics, natural language processing, cyber security, and many others.

Dimensionality reduction represents the process of extracting low dimensional structure from high dimensional data. High dimensional data include multimedia databases, gene expression microarrays, and financial time series, for example. In order to deal with such real-world data properly, it is better to reduce its dimensionality to avoid undesired properties of high dimensions such as the curse of dimensionality [14, 11]. As a result, classification, visualization, and compression of data can be expedited, for example [14]. In many problems, it is presumed that the dimensionality of the measured data is only artificially high; the measured data are high-dimensional but data nearly have a lower-dimensional structure, since they are multiple, indirect measurements of an underlying factors, which typically cannot be directly calibrated [4].

Language models trained on internet-scale data sets have shown an impressive ability to solve problems in Natural Language Processing and Computer Vision. However, experience is showing that these models are frequently brittle in unexpected ways, and require significant scaffolding to ensure that they operate correctly in the larger systems that comprise "language-model agents." In this paper, we argue that behavior trees provide a unifying framework for combining language models with classical AI and traditional programming. We introduce Dendron, a Python library for programming language model agents using behavior trees. We demonstrate the approach embodied by Dendron in three case studies: building a chat agent, a camera-based infrastructure inspection agent for use on a mobile robot or vehicle, and an agent that has been built to satisfy safety constraints that it did not receive through instruction tuning or RLHF.