AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment. To provide a comprehensive and up-to-date overview of the alignment field, in this survey, we delve into the core concepts, methodology, and practice of alignment. First, we identify four principles as the key objectives of AI alignment: Robustness, Interpretability, Controllability, and Ethicality (RICE). Guided by these four principles, we outline the landscape of current alignment research and decompose them into two key components: forward alignment and backward alignment. The former aims to make AI systems aligned via alignment training, while the latter aims to gain evidence about the systems' alignment and govern them appropriately to avoid exacerbating misalignment risks. On forward alignment, we discuss techniques for learning from feedback and learning under distribution shift. On backward alignment, we discuss assurance techniques and governance practices. We also release and continually update the website (www.alignmentsurvey.com) which features tutorials, collections of papers, blog posts, and other resources.

In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features. Usually, images taken in close temporal proximity (e.g., belonging to the same age class) share some common content properties. Such content bias can be exploited by a neural network. In this work, a novel approach is proposed that evaluates the influence of image content. This approach is verified using synthetic images (where content bias can be ruled out) with an age signal embedded. Based on the proposed approach, it is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content. As a possible countermeasure, two different models from the field of image steganalysis, along with three different preprocessing techniques to increase the signal-to-noise ratio (age signal to image content), are evaluated using the proposed method.

Pre-trained Vision-Language Models (VLMs) utilizing extensive image-text paired data have demonstrated unprecedented image-text association capabilities, achieving remarkable results across various downstream tasks. A critical challenge is how to make use of existing large-scale pre-trained VLMs, which are trained on common objects, to perform the domain-specific transfer for accomplishing domain-related downstream tasks. A critical challenge is how to make use of existing large-scale pre-trained VLMs, which are trained on common objects, to perform the domain-specific transfer for accomplishing domain-related downstream tasks. In this paper, we propose a new framework that includes the Domain pre-trained Vision-Language Model (DVLM), bridging the gap between the General Vision-Language Model (GVLM) and domain-specific downstream tasks. Moreover, we present an image-text paired dataset in the field of remote sensing (RS), RS5M, which has 5 million RS images with English descriptions. The dataset is obtained from filtering publicly available image-text paired datasets and captioning label-only RS datasets with pre-trained VLM. These constitute the first large-scale RS image-text paired dataset. Additionally, we fine-tuned the CLIP model and tried several Parameter-Efficient Fine-Tuning methods on RS5M to implement the DVLM. Experimental results show that our proposed dataset is highly effective for various tasks, and our model GeoRSCLIP improves upon the baseline or previous state-of-the-art model by $3\%\sim20\%$ in Zero-shot Classification (ZSC), $3\%\sim6\%$ in Remote Sensing Cross-Modal Text-Image Retrieval (RSCTIR) and $4\%\sim5\%$ in Semantic Localization (SeLo) tasks. Dataset and models have been released in: \url{https://github.com/om-ai-lab/RS5M}.

Music, enchanting and poetic, permeates every corner of human civilization. Although music is not unfamiliar to people, our understanding of its essence remains limited, and there is still no universally accepted scientific description. This is primarily due to music being regarded as a product of both reason and emotion, making it difficult to define. In this article, we focus on the fundamental elements of music and construct an evolutionary network from the perspective of music as a natural language, aligning with the statistical characteristics of texts. Through this approach, we aim to comprehend the structural differences in music across different periods, enabling a more scientific exploration of music. Relying on the advantages of structuralism, we can concentrate on the relationships and order between the physical elements of music, rather than getting entangled in the blurred boundaries of science and philosophy. The scientific framework we present not only conforms to past conclusions in music, but also serves as a bridge that connects music to natural language processing and knowledge graphs.

Highway traffic crashes exert a considerable impact on both transportation systems and the economy. In this context, accurate and dependable emergency responses are crucial for effective traffic management. However, the influence of crashes on traffic status varies across diverse factors and may be biased due to selection bias. Therefore, there arises a necessity to accurately estimate the heterogeneous causal effects of crashes, thereby providing essential insights to facilitate individual-level emergency decision-making. This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed. The Neyman-Rubin Causal Model (RCM) is employed to formulate this problem from a causal perspective. The Conditional Shapley Value Index (CSVI) is proposed based on causal graph theory to filter adverse variables, and the Structural Causal Model (SCM) is then adopted to define the statistical estimand for causal effects. The treatment effects are estimated by Doubly Robust Learning (DRL) methods, which combine doubly robust causal inference with classification and regression machine learning models. Experimental results from 4815 crashes on Highway Interstate 5 in Washington State reveal the heterogeneous treatment effects of crashes at varying distances and durations. The rear-end crashes cause more severe congestion and longer durations than other types of crashes, and the sideswipe crashes have the longest delayed impact. Additionally, the findings show that rear-end crashes affect traffic greater at night, while crash to objects has the most significant influence during peak hours. Statistical hypothesis tests, error metrics based on matched "counterfactual outcomes", and sensitive analyses are employed for assessment, and the results validate the accuracy and effectiveness of our method.

Since language models produce fake text quickly and easily, there is an oversupply of such content in the public domain. The degree of sophistication and writing style has reached a point where differentiating between human authored and machine-generated content is nearly impossible. As a result, works generated by language models rather than human authors have gained significant media attention and stirred controversy.Concerns regarding the possible influence of advanced language models on society have also arisen, needing a fuller knowledge of these processes. Natural language generation (NLG) and generative pre-trained transformer (GPT) models have revolutionized a variety of sectors: the scope not only permeated throughout journalism and customer service but also reached academia. To mitigate the hazardous implications that may arise from the use of these models, preventative measures must be implemented, such as providing human agents with the capacity to distinguish between artificially made and human composed texts utilizing automated systems and possibly reverse-engineered language models. Furthermore, to ensure a balanced and responsible approach, it is critical to have a full grasp of the socio-technological ramifications of these breakthroughs. This literature survey aims to compile and synthesize accomplishments and developments in the aforementioned work, while also identifying future prospects. It also gives an overview of machine-generated text trends and explores the larger societal implications. Ultimately, this survey intends to contribute to the development of robust and effective approaches for resolving the issues connected with the usage and detection of machine-generated text by exploring the interplay between the capabilities of language models and their possible implications.

Objective: The growing use of large language models (LLMs) stimulates a need for a comprehensive review of their applications and outcomes in mental health care contexts. This scoping review aims to critically analyze the existing development and applications of LLMs in mental health care, highlighting their successes and identifying their challenges and limitations in these specialized fields. Materials and Methods: A broad literature search was conducted in November 2023 using six databases (PubMed, Web of Science, Google Scholar, arXiv, medRxiv, and PsyArXiv) following the 2020 version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 313 publications were initially identified, and after applying the study inclusion criteria, 34 publications were selected for the final review. Results: We identified diverse applications of LLMs in mental health care, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. Conclusion: LLMs show promising potential in advancing mental health care, with applications in diagnostics, and patient support. Continued advancements depend on collaborative, multidisciplinary efforts focused on framework enhancement, rigorous dataset development, technological refinement, and ethical integration to ensure the effective and safe application of LLMs in mental health care.

Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.

Cancer diagnosis is a well-studied problem in machine learning since early detection of cancer is often the determining factor in prognosis. Supervised deep learning achieves excellent results in cancer image classification, usually through transfer learning. However, these models require large amounts of labelled data and for several types of cancer, large labelled datasets do not exist. In this paper, we demonstrate that a model pre-trained using a self-supervised learning algorithm known as Barlow Twins can outperform the conventional supervised transfer learning pipeline. We juxtapose two base models: i) pretrained in a supervised fashion on ImageNet; ii) pretrained in a self-supervised fashion on ImageNet. Both are subsequently fine tuned on a small labelled skin lesion dataset and evaluated on a large test set. We achieve a mean test accuracy of 70\% for self-supervised transfer in comparison to 66\% for supervised transfer. Interestingly, boosting performance further is possible by self-supervised pretraining a second time (on unlabelled skin lesion images) before subsequent fine tuning. This hints at an alternative path to collecting more labelled data in settings where this is challenging - namely just collecting more unlabelled images. Our framework is applicable to cancer image classification models in the low-labelled data regime.

The revolution of natural language processing via large language models has motivated its use in multidisciplinary areas that include social sciences and humanities and more specifically, comparative religion. Sentiment analysis provides a mechanism to study the emotions expressed in text. Recently, sentiment analysis has been used to study and compare translations of the Bhagavad Gita, which is a fundamental and sacred Hindu text. In this study, we use sentiment analysis for studying selected chapters of the Bible. These chapters are known as the Sermon on the Mount. We utilize a pre-trained language model for sentiment analysis by reviewing five translations of the Sermon on the Mount, which include the King James version, the New International Version, the New Revised Standard Version, the Lamsa Version, and the Basic English Version. We provide a chapter-by-chapter and verse-by-verse comparison using sentiment and semantic analysis and review the major sentiments expressed. Our results highlight the varying sentiments across the chapters and verses. We found that the vocabulary of the respective translations is significantly different. We detected different levels of humour, optimism, and empathy in the respective chapters that were used by Jesus to deliver his message.