Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences. We benchmark five objectives for pretraining with human feedback across three tasks and study how they affect the trade-off between alignment and capabilities of pretrained LMs. We find a Pareto-optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores given by a reward model. Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially-chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after task-specific finetuning. Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, i.e., learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.

EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the subject, yet XAI still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the combination of evidence stemming from the model and its input-output mapping, and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's inner workings and decision-making process) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are operationalized and it provides new insight into common explanation methods that we analyze as case studies.

Evaluations show that GPT-3 has gained The democratization of artificial intelligence (AI) through pretraining a surprisingly wide range of (Garvey, 2018; Rubeis et al., 2022) aims to provide knowledge, which can be transferred to downstream access to AI technologies to all members of tasks through knowledge distillation (Kim society, including individuals, small-and mediumsized et al., 2022). We present some examples in Appendix enterprises (SMEs), academic research labs, A.12. Due to the model architecture and and nonprofit organizations. Achieving this goal is pretraining tasks designed for auto-regressive generation, crucial for the promotion of innovation, economic GPT-3 is capable of generating human-like growth, and fairness and equality. As typical AI text and performing a broad array of NLP tasks, models are usually data-hungry, one significant obstacle such as machine translation, summarization, and of AI democratization is the preparation of question-answering.

Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Even a few hours of delay in the treatment of sepsis results in increased mortality. Early detection of sepsis during emergency department triage would allow early initiation of lab analysis, antibiotic administration, and other sepsis treatment protocols. The purpose of this study was to compare sepsis detection performance at ED triage (prior to the use of laboratory diagnostics) of the standard sepsis screening algorithm (SIRS with source of infection) and a machine learning algorithm trained on EHR triage data. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16participating hospitals. KATE Sepsis and standard screening were retrospectively evaluated on the adult population of 512,949 medical records. KATE Sepsis demonstrates an AUC of 0.9423 (0.9401 - 0.9441) with sensitivity of 71.09% (70.12% - 71.98%) and specificity of 94.81% (94.75% - 94.87%). Standard screening demonstrates an AUC of 0.6826 (0.6774 - 0.6878) with sensitivity of 40.8% (39.71% - 41.86%) and specificity of 95.72% (95.68% - 95.78%). The KATE Sepsis model trained to detect sepsis demonstrates 77.67% (75.78% -79.42%) sensitivity in detecting severe sepsis and 86.95% (84.2% - 88.81%) sensitivity in detecting septic shock. The standard screening protocol demonstrates 43.06% (41% - 45.87%) sensitivity in detecting severe sepsis and40% (36.55% - 43.26%) sensitivity in detecting septic shock. Future research should focus on the prospective impact of KATE Sepsis on administration of antibiotics, readmission rate, morbidity and mortality.

Visual scenes are composed of visual concepts and have the property of combinatorial explosion. An important reason for humans to efficiently learn from diverse visual scenes is the ability of compositional perception, and it is desirable for artificial intelligence to have similar abilities. Compositional scene representation learning is a task that enables such abilities. In recent years, various methods have been proposed to apply deep neural networks, which have been proven to be advantageous in representation learning, to learn compositional scene representations via reconstruction, advancing this research direction into the deep learning era. Learning via reconstruction is advantageous because it may utilize massive unlabeled data and avoid costly and laborious data annotation. In this survey, we first outline the current progress on reconstruction-based compositional scene representation learning with deep neural networks, including development history and categorizations of existing methods from the perspectives of the modeling of visual scenes and the inference of scene representations; then provide benchmarks, including an open source toolbox to reproduce the benchmark experiments, of representative methods that consider the most extensively studied problem setting and form the foundation for other methods; and finally discuss the limitations of existing methods and future directions of this research topic.

The United Nations secretary-general has warned that artificial intelligence (AI) is being used to spread disinformation and hate, as he backed a proposal for the creation of an international watchdog to monitor the technology. Speaking at the launch of a new policy on disinformation on Monday, Antonio Guterres said that while technological advancement has been used for some good, the risks posed by AI threatens democracy and human rights. Guterres said he backs a proposal by some artificial intelligence executives for the creation of a watchdog body similar to the International Atomic Energy Agency (IAEA). Generative AI technology – which can perform natural language processing tasks such as answering questions, summarising text and even generating lines of code – has captivated the public since ChatGPT launched six months ago. AI has also become a focus of concern over its ability to create misinformation and deep fakes, which are AI-generated images and videos that mimic people.

Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. Better evaluation of the semantic distance between the overlapped sentences benefits the language system's understanding and guides the generation. Since conventional semantic metrics are based on word representations, they are vulnerable to the disturbance of overlapped components with similar representations. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence (LCS) as neighboring words and use masked language modeling (MLM) from pre-trained language models (PLMs) to predict the distributions on their positions. Our metric, Neighboring Distribution Divergence (NDD), represent the semantic distance by calculating the divergence between distributions in the overlapped parts. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts. Based on the discovery, we further implement an unsupervised and training-free method for text compression, leading to a significant improvement on the previous perplexity-based method. The high scalability of our method even enables NDD to outperform the supervised state-of-the-art in domain adaption by a huge margin. Further experiments on syntax and semantics analyses verify the awareness of internal sentence structures, indicating the high potential of NDD for further studies.

Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. The popularity is partly due to their unique specifications originating from blockchain-related characteristics such as privacy, decentralisation, and untraceability. Despite their growing popularity, cryptocurrencies remain a high-risk investment due to their price volatility and uncertainty. The inherent volatility in cryptocurrency prices, coupled with internal cryptocurrency-related factors and external influential global economic factors makes predicting their prices and price movement directions challenging. Nevertheless, the knowledge obtained from predicting the direction of cryptocurrency prices can provide valuable guidance for investors in making informed investment decisions. To address this issue, this paper proposes a dynamic Bayesian network (DBN) approach, which can model complex systems in multivariate settings, to predict the price movement direction of five popular altcoins (cryptocurrencies other than Bitcoin) in the next trading day. The efficacy of the proposed model in predicting cryptocurrency price directions is evaluated from two perspectives. Firstly, our proposed approach is compared to two baseline models, namely an auto-regressive integrated moving average and support vector regression. Secondly, from a feature engineering point of view, the impact of twenty-three different features, grouped into four categories, on the DBN's prediction performance is investigated. The experimental results demonstrate that the DBN significantly outperforms the baseline models. In addition, among the groups of features, technical indicators are found to be the most effective predictors of cryptocurrency price directions.

Pre-training and fine-tuning is a paradigm for alleviating the data scarcity problem in end-to-end speech translation (E2E ST). The commonplace "modality gap" between speech and text data often leads to inconsistent inputs between pre-training and fine-tuning. However, we observe that this gap occurs in the early stages of fine-tuning, but does not have a major impact on the final performance. On the other hand, we find that there has another gap, which we call the "capacity gap": high resource tasks (such as ASR and MT) always require a large model to fit, when the model is reused for a low resource task (E2E ST), it will get a sub-optimal performance due to the over-fitting. In a case study, we find that the regularization plays a more important role than the well-designed modality adaption method, which achieves 29.0 for en-de and 40.3 for en-fr on the MuST-C dataset. Code and models are available at https://github.com/hannlp/TAB.

Hotel bathrooms are one of the most important places in terms of customer satisfaction, and where the most complaints are reported. To share their experiences, guests rate hotels, comment, and share images of their positive or negative ratings. An important part of the room images shared by guests is related to bathrooms. Guests tend to prove their satisfaction or dissatisfaction with the bathrooms with images in their comments. These Positive or negative comments and visuals potentially affect the prospective guests. In this study, two different versions of a deep learning algorithm were designed to classify hotel bathrooms as satisfactory (good) or unsatisfactory (bad, when any defects such as dirtiness, deficiencies, malfunctions were present) by analyzing images. The best-performer between the two models was determined as a result of a series of extensive experimental studies. The models were trained for each of 144 combinations of 5 hyper-parameter sets with a data set containing more than 11 thousand bathroom images, specially created for this study. The "HotelBath" data set was shared also with the community with this study. Four different image sizes were taken into consideration: 128, 256, 512 and 1024 pixels in both directions. The classification performances of the models were measured with several metrics. Both algorithms showed very attractive performances even with many combinations of hyper-parameters. They can classify bathroom images with very high accuracy. Suh that the top algorithm achieved an accuracy of 92.4% and an AUC (area under the curve) score of 0.967. In addition, other metrics also proved the success...