Can humans and artificial intelligences share concepts and communicate? 'Making AI Intelligible' shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (for example, creditworthiness, recidivism, cancer, and combatants). If AIs can share our concepts, that will go some way towards justifying this reliance on AI. This ground-breaking study offers insight into how to take some first steps towards achieving Interpretable AI.

Network data is ubiquitous in many disciplines and application domains, including computer science, statistics, biology, and physics. These data, encoding relationships between units represented as nodes, are often accompanied by additional information about the nodes, usually referred to as node covariates, attributes, or metadata (Newman and Clauset, 2016; Liu, 2019; Chunaev, 2020). In these situations, a common goal is to understand the associations between the network connectivity and the node covariates. In our example, we consider international food commodity trade data represented as a network, where the nodes correspond to different countries and edge weights encode food commodity trade volumes between corresponding countries. The covariates at each node consist of economic and geographic information for each country, such as gross domestic product (GDP) per capita, birth rate and region. We wish to exploit that both datasets contain information about the nodes in order to better understand the structure of the network, node covariates and their relationship. Specifically, we seek to understand how economic and geographic factors explain the observed trade between countries, and identify additional information in the network that cannot be explained solely by these variables. There has been substantial work that incorporates network and node covariate information. Some examples include methods that use node covariates to improve community detection (Binkiewicz et al., 2017; Huang et al., 2023), dimensionality reduction (Zhao et al., 2022), regression with network information (Li et al., 2019) and mixed effect models for network edges (Hoff, 2005).

Data constitute the foundational component of the data economy and its marketplaces. Efficient and fair data valuation has emerged as a topic of significant interest.\ Many approaches based on marginal contribution have shown promising results in various downstream tasks. However, they are well known to be computationally expensive as they require training a large number of utility functions, which are used to evaluate the usefulness or value of a given dataset for a specific purpose. As a result, it has been recognized as infeasible to apply these methods to a data marketplace involving large-scale datasets. Consequently, a critical issue arises: how can the re-training of the utility function be avoided? To address this issue, we propose a novel data valuation method from the perspective of optimal control, named the neural dynamic data valuation (NDDV). Our method has solid theoretical interpretations to accurately identify the data valuation via the sensitivity of the data optimal control state. In addition, we implement a data re-weighting strategy to capture the unique features of data points, ensuring fairness through the interaction between data points and the mean-field states. Notably, our method requires only training once to estimate the value of all data points, significantly improving the computational efficiency. We conduct comprehensive experiments using different datasets and tasks. The results demonstrate that the proposed NDDV method outperforms the existing state-of-the-art data valuation methods in accurately identifying data points with either high or low values and is more computationally efficient.

Yesterday's Apple's Worldwide Developers Conference keynote teased a lot of what users can expect this fall when big iOS, iPadOS, macOS and watchOS updates hit their devices. Changes coming include RCS support in Messages, a new Passwords app, a revamped Calculator app for iPhone and iPad and a bunch of artificial intelligence (AI) infusions across the board with the new "Apple Intelligence" system. The latter will bring some of the biggest updates to Apple devices in years, including generative AI image creation, "Genmoji" custom emojis, text summarization and even some ChatGPT integration as well. If you weren't able to catch the news live, here's a rundown of everything announced at WWDC 2024. Apple revealed its plans to incorporate AI into its operating systems at WWDC this year.

Question generation for education assessments is a growing field within artificial intelligence applied to education. These question-generation tools have significant importance in the educational technology domain, such as intelligent tutoring systems and dialogue-based platforms. The automatic generation of assessment questions, which entail clear-cut answers, usually relies on syntactical and semantic indications within declarative sentences, which are then transformed into questions. Recent research has explored the generation of assessment educational questions in Arabic. The reported performance has been adversely affected by inherent errors, including sentence parsing inaccuracies, name entity recognition issues, and errors stemming from rule-based question transformation. Furthermore, the complexity of lengthy Arabic sentences has contributed to these challenges. This research presents an innovative Arabic question-generation system built upon a three-stage process: keywords and key phrases extraction, question generation, and subsequent ranking. The aim is to tackle the difficulties associated with automatically generating assessment questions in the Arabic language. The proposed approach and results show a precision of 83.50%, a recall of 78.68%, and an Fl score of 80.95%, indicating the framework high efficiency. Human evaluation further confirmed the model efficiency, receiving an average rating of 84%.

This paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for the Arabic language. The model was particularly finetuned on science textbooks in Palestinian curriculum. Our approach uses BERT's brilliant capabilities to automatically produce correct answers to questions in the field of science education. The model's ability to understand and extract pertinent information is improved by finetuning it using 11th and 12th grade biology book in Palestinian curriculum. This increases the model's efficacy in producing enlightening responses. Exact match (EM) and F1 score metrics are used to assess the model's performance; the results show an EM score of 20% and an F1 score of 51%. These findings show that the model can comprehend and react to questions in the context of Palestinian science book. The results demonstrate the potential of BERT-based QA models to support learning and understanding Arabic students questions.

Autonomous vehicles are expected to transform transportation systems with rapid technological advancement. Human mobility would become more accessible and safer with the emergence of driverless vehicles. To this end, autonomous shuttle services are currently introduced in different urban conditions throughout the world. As a result, studies are needed to assess the safety and mobility performance of such autonomous shuttle services. However, calibrating the movement of autonomous shuttles in a simulation environment has been a difficult task due to the absence of any real-world data. This study aims to calibrate autonomous shuttles in a microscopic traffic simulation model and consequently assess the impact of the shuttle service on urban road capacity through simulation experiments. For this analysis, a prototype of an operational shuttle system at Lake Nona, Orlando, Florida is emulated in a microscopic traffic simulator during different times of the day. The movements of autonomous vehicles are calibrated using real-world trajectory data which help replicate the driving behavior of the shuttle in the simulation. The analysis reveals that with increasing frequency of the shuttle service the delay time percentage of the shared road sections increases and traveling speed decreases. It is also found that increasing the speed of shuttles up to 5 mph during off-peak hours and 10 mph during peak hours will improve traffic conditions. The findings from this study will assist policymakers and transportation agencies to revise policies for deploying autonomous shuttles and for planning road infrastructures for shared road-use of autonomous shuttles and human driven vehicles.

Significant research has focused on improving the performance of large language model on code-related tasks due to their practical importance. Although performance is typically evaluated using public benchmark datasets, the existing datasets do not account for the concept of \emph{version}, which is crucial in professional software development. In this paper, we introduce VersiCode, the first comprehensive dataset designed to assess the ability of large language models to generate verifiable code for specific library versions. VersiCode encompasses 300 libraries across more than 2,000 versions spanning 9 years. We design two dedicated evaluation tasks: version-specific code completion (VSCC) and version-aware code editing (VACE). Comprehensive experiments are conducted to benchmark the performance of LLMs, revealing the challenging nature of these tasks and VersiCode, that even state-of-the-art LLMs struggle to generate version-correct code. This dataset, together with the proposed tasks, sheds light on LLMs' capabilities and limitations in handling version-specific code generation, and opens up an important new area of research for further investigation. The resources can be found at https://github.com/wutong8023/VersiCode.

Identifying and reconstructing what we see from brain activity gives us a special insight into investigating how the biological visual system represents the world. While recent efforts have achieved high-performance image classification and high-quality image reconstruction from brain signals collected by Functional Magnetic Resonance Imaging (fMRI) or magnetoencephalogram (MEG), the expensiveness and bulkiness of these devices make relevant applications difficult to generalize to practical applications. On the other hand, Electroencephalography (EEG), despite its advantages of ease of use, cost-efficiency, high temporal resolution, and non-invasive nature, has not been fully explored in relevant studies due to the lack of comprehensive datasets. To address this gap, we introduce EEG-ImageNet, a novel EEG dataset comprising recordings from 16 subjects exposed to 4000 images selected from the ImageNet dataset. EEG-ImageNet consists of 5 times EEG-image pairs larger than existing similar EEG benchmarks. EEG-ImageNet is collected with image stimuli of multi-granularity labels, i.e., 40 images with coarse-grained labels and 40 with fine-grained labels. Based on it, we establish benchmarks for object classification and image reconstruction. Experiments with several commonly used models show that the best models can achieve object classification with accuracy around 60% and image reconstruction with two-way identification around 64%. These results demonstrate the dataset's potential to advance EEG-based visual brain-computer interfaces, understand the visual perception of biological systems, and provide potential applications in improving machine visual models.

In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min framework focusing on fairness among the multiple goals and develop a relevant theory and a practical model-free algorithm under the max-min framework. The developed theory provides a theoretical advance in multi-objective reinforcement learning, and the proposed algorithm demonstrates a notable performance improvement over existing baseline methods.