mstyslavity/prisma_v3 huggingface dataset:
Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledge-driven nature of human driving, recent approaches explore the potential of large language models (LLMs) to improve understanding and decision-making in traffic scenarios. They find that the pretrain-finetune paradigm of LLMs on downstream data with the Chain-of-Thought (CoT) reasoning process can enhance explainability and scene understanding. However, such a popular strategy proves to suffer from the notorious problems of misalignment between the crafted CoTs against the consequent decision-making, which remains untouched by previous LLM-based AD methods. To address this problem, we motivate an end-to-end decision-making model based on multimodality-augmented LLM, which simultaneously executes CoT reasoning and carries out planning results. Furthermore, we propose a reasoning-decision alignment constraint between the paired CoTs and planning results, imposing the correspondence between reasoning and decision-making. Moreover, we redesign the CoTs to enable the model to comprehend complex scenarios and enhance decision-making performance. We dub our proposed large language planners with reasoning-decision alignment as RDA-Driver. Experimental evaluations on the nuScenes and DriveLM-nuScenes benchmarks demonstrate the effectiveness of our RDA-Driver in enhancing the performance of end-to-end AD systems. Specifically, our RDA-Driver achieves state-of-the-art planning performance on the nuScenes dataset with 0.80 L2 error and 0.32 collision rate, and also achieves leading results on challenging DriveLM-nuScenes benchmarks with 0.82 L2 error and 0.38 collision rate.
We investigate the nonequilibrium dynamics of the magnetization in an Ising chain subjected to a slowly rotating transverse field. The magnetization oscillations are found to be explained by the contributions from different particle excitations in the quantum $E_8$ model. We study the magnetization in the frequency domain in detail, uncovering a series of singular peaks for the $z$ (Ising) component. These singular peaks are split into two sets for the magnetization along $x$ and $y$ directions with frequency shifts set by the rotational-field frequency. The peaks include both $\delta$-function type and edge-singularity type peaks. The $\delta$-function peaks can be attributed to particle excitations involving an $E_8$ particle with either the vacuum or a different particle. The edge-singularity peaks are contributed by particle excitations of two $E_8$ particles with either the vacuum or another particle, or by particle excitations that contain two sets of two particles with each set including at least a particle of the same type. We propose a Rydberg qubit array for possible experimental investigation.
In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI, such as Large Multimodal Models (LMMs) like GPT4 (Omni), have shown promise in understanding and interpreting process diagrams for Visual Question Answering (VQA). However, proprietary models pose data privacy risks, and their computational complexity prevents knowledge editing for domain-specific customization on consumer hardware. To overcome these challenges, we propose a secure, on-premises enterprise solution using a hierarchical, multi-agent Retrieval Augmented Generation (RAG) framework for open-domain question answering (ODQA) tasks, offering enhanced data privacy, explainability, and cost-effectiveness. Our novel multi-agent framework employs introspective and specialized sub-agents using open-source, small-scale multimodal models with the ReAct (Reason+Act) prompting technique for PFD and P&ID analysis, integrating multiple information sources to provide accurate and contextually relevant answers. Our approach, supported by iterative self-correction, aims to deliver superior performance in ODQA tasks. We conducted rigorous experimental studies, and the empirical results validated the proposed approach effectiveness.
The Lunar landing has drawn great interest in lunar exploration in recent years, and autonomous lunar landing navigation is fundamental to this task. AI is expected to play a critical role in autonomous and intelligent space missions, yet human experts question the reliability of AI solutions. Thus, the \gls{xai} for vision-based lunar landing is studied in this paper, aiming at providing transparent and understandable predictions for intelligent lunar landing. Attention-based Darknet53 is proposed as the feature extraction structure. For crater detection and navigation tasks, attention-based YOLOv3 and attention-Darknet53-LSTM are presented respectively. The experimental results show that the offered networks provide competitive performance on relative crater detection and pose estimation during the lunar landing. The explainability of the provided networks is achieved by introducing an attention mechanism into the network during model building. Moreover, the PCC is utilised to quantitively evaluate the explainability of the proposed networks, with the findings showing the functions of various convolutional layers in the network.
Diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) from fMRI functional connectivity (FC) has gained popularity, but most FC-based diagnostic models are black boxes lacking casual reasoning so they contribute little to the knowledge about FC-based neural biomarkers of cognitive decline.To enhance the explainability of diagnostic models, we propose a generative counterfactual attention-guided network (GCAN), which introduces counterfactual reasoning to recognize cognitive decline-related brain regions and then uses these regions as attention maps to boost the prediction performance of diagnostic models. Furthermore, to tackle the difficulty in the generation of highly-structured and brain-atlas-constrained FC, which is essential in counterfactual reasoning, an Atlas-Aware Bidirectional Transformer (AABT) method is developed. AABT employs a bidirectional strategy to encode and decode the tokens from each network of brain atlas, thereby enhancing the generation of high-quality target label FC. In the experiments of hospital-collected and ADNI datasets, the generated attention maps closely resemble FC abnormalities in the literature on SCD and MCI. The diagnostic performance is also superior to baseline models. The code is available at https://github.com/SXR3015/GCAN
Assessing an AI system's behavior-particularly in Explainable AI Systems-is sometimes done empirically, by measuring people's abilities to predict the agent's next move-but how to perform such measurements? In empirical studies with humans, an obvious approach is to frame the task as binary (i.e., prediction is either right or wrong), but this does not scale. As output spaces increase, so do floor effects, because the ratio of right answers to wrong answers quickly becomes very small. The crux of the problem is that the binary framing is failing to capture the nuances of the different degrees of wrongness." To address this, we begin by proposing three mathematical bases upon which to measure "partial wrongness." We then uses these bases to perform two analyses on sequential decision-making domains: the first is an in-lab study with 86 participants on a size-36 action space; the second is a re-analysis of a prior study on a size-4 action space. Other researchers adopting our operationalization of the prediction task and analysis methodology will improve the rigor of user studies conducted with that task, which is particularly important when the domain features a large output space.""
Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level information that may be scarce or unavailable in some scenarios (e.g., cold-start). Large Language Models (LLMs) have shown good generalization and reasoning capabilities and are rich in geographical knowledge, allowing us to believe that these models can act as zero-shot next-location predictors. We tested more than 15 LLMs on three real-world mobility datasets and we found that LLMs can obtain accuracies up to 36.2%, a significant relative improvement of almost 640% when compared to other models specifically designed for human mobility. We also test for data contamination and explored the possibility of using LLMs as text-based explainers for next-location prediction, showing that, regardless of the model size, LLMs can explain their decision.
Deep Neural Networks (DNNs) have revolutionized various fields by enabling task automation and reducing human error. However, their internal workings and decision-making processes remain obscure due to their black box nature. Consequently, the lack of interpretability limits the application of these models in high-risk scenarios. To address this issue, the emerging field of eXplainable Artificial Intelligence (XAI) aims to explain and interpret the inner workings of DNNs. Despite advancements, XAI faces challenges such as the semantic gap between machine and human understanding, the trade-off between interpretability and performance, and the need for context-specific explanations. To overcome these limitations, we propose a novel multimodal framework named VALE Visual and Language Explanation. VALE integrates explainable AI techniques with advanced language models to provide comprehensive explanations. This framework utilizes visual explanations from XAI tools, an advanced zero-shot image segmentation model, and a visual language model to generate corresponding textual explanations. By combining visual and textual explanations, VALE bridges the semantic gap between machine outputs and human interpretation, delivering results that are more comprehensible to users. In this paper, we conduct a pilot study of the VALE framework for image classification tasks. Specifically, Shapley Additive Explanations (SHAP) are used to identify the most influential regions in classified images. The object of interest is then extracted using the Segment Anything Model (SAM), and explanations are generated using state-of-the-art pre-trained Vision-Language Models (VLMs). Extensive experimental studies are performed on two datasets: the ImageNet dataset and a custom underwater SONAR image dataset, demonstrating VALEs real-world applicability in underwater image classification.
Deep learning models are complex due to their size, structure, and inherent randomness in training procedures. Additional complexity arises from the selection of datasets and inductive biases. Addressing these challenges for explainability, Kim et al. (2018) introduced Concept Activation Vectors (CAVs), which aim to understand deep models' internal states in terms of human-aligned concepts. These concepts correspond to directions in latent space, identified using linear discriminants. Although this method was first applied to image classification, it was later adapted to other domains, including natural language processing. In this work, we attempt to apply the method to electroencephalogram (EEG) data for explainability in Kostas et al.'s BENDR (2021), a large-scale transformer model. A crucial part of this endeavor involves defining the explanatory concepts and selecting relevant datasets to ground concepts in the latent space. Our focus is on two mechanisms for EEG concept formation: the use of externally labeled EEG datasets, and the application of anatomically defined concepts. The former approach is a straightforward generalization of methods used in image classification, while the latter is novel and specific to EEG. We present evidence that both approaches to concept formation yield valuable insights into the representations learned by deep EEG models.
The human brain has an inherent ability to fill in gaps to perceive figures as complete wholes, even when parts are missing or fragmented. This phenomenon is known as Closure in psychology, one of the Gestalt laws of perceptual organization, explaining how the human brain interprets visual stimuli. Given the importance of Closure for human object recognition, we investigate whether neural networks rely on a similar mechanism. Exploring this crucial human visual skill in neural networks has the potential to highlight their comparability to humans. Recent studies have examined the Closure effect in neural networks. However, they typically focus on a limited selection of Convolutional Neural Networks (CNNs) and have not reached a consensus on their capability to perform Closure. To address these gaps, we present a systematic framework for investigating the Closure principle in neural networks. We introduce well-curated datasets designed to test for Closure effects, including both modal and amodal completion. We then conduct experiments on various CNNs employing different measurements. Our comprehensive analysis reveals that VGG16 and DenseNet-121 exhibit the Closure effect, while other CNNs show variable results. We interpret these findings by blending insights from psychology and neural network research, offering a unique perspective that enhances transparency in understanding neural networks. Our code and dataset will be made available on GitHub.
We often use "explainable" Artificial Intelligence (XAI)" and "interpretable AI (IAI)" interchangeably when we apply various XAI tools for a given dataset to explain the reasons that underpin machine learning (ML) outputs. However, these notions can sometimes be confusing because interpretation often has a subjective connotation, while explanations lean towards objective facts. We argue that XAI is a subset of IAI. The concept of IAI is beyond the sphere of a dataset. It includes the domain of a mindset. At the core of this ambiguity is the duality of reasons, in which we can reason either outwards or inwards. When directed outwards, we want the reasons to make sense through the laws of nature. When turned inwards, we want the reasons to be happy, guided by the laws of the heart. While XAI and IAI share reason as the common notion for the goal of transparency, clarity, fairness, reliability, and accountability in the context of ethical AI and trustworthy AI (TAI), their differences lie in that XAI emphasizes the post-hoc analysis of a dataset, and IAI requires a priori mindset of abstraction. This hypothesis can be proved by empirical experiments based on an open dataset and harnessed by High-Performance Computing (HPC). The demarcation of XAI and IAI is indispensable because it would be impossible to determine regulatory policies for many AI applications, especially in healthcare, human resources, banking, and finance. We aim to clarify these notions and lay the foundation of XAI, IAI, EAI, and TAI for many practitioners and policymakers in future AI applications and research.
The responsible AI (RAI) community has introduced numerous processes and artifacts (e.g., Model Cards, Transparency Notes, Data Cards) to facilitate transparency and support the governance of AI systems. While originally designed to scaffold and document AI development processes in technology companies, these artifacts are becoming central components of regulatory compliance under recent regulations such as the EU AI Act. Much prior work has explored the design of new RAI artifacts or their use by practitioners within technology companies. However, as RAI artifacts begin to play key roles in enabling external oversight, it becomes critical to understand how stakeholders--particularly those situated outside of technology companies who govern and audit industry AI deployments--perceive the efficacy of RAI artifacts. In this study, we conduct semi-structured interviews and design activities with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. While participants believe that RAI artifacts are a valuable contribution to the broader AI governance ecosystem, many are concerned about their potential unintended, longer-term impacts on actors outside of technology companies (e.g., downstream end-users, policymakers, civil society stakeholders). We organize these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry, impeding civil society and legal stakeholders' ability to protect downstream end-users from potential AI harms. Participants envision how structural changes, along with changes in how RAI artifacts are designed, used, and governed, could help redirect the role of artifacts to support more collaborative and proactive external oversight of AI systems. We discuss research and policy implications for RAI artifacts.
We present our results regarding the automatic construction of a knowledge graph from historical documents related to the Chilean dictatorship period (1973-1990). Our approach consists on using LLMs to automatically recognize entities and relations between these entities, and also to perform resolution between these sets of values. In order to prevent hallucination, the interaction with the LLM is grounded in a simple ontology with 4 types of entities and 7 types of relations. To evaluate our architecture, we use a gold standard graph constructed using a small subset of the documents, and compare this to the graph obtained from our approach when processing the same set of documents. Results show that the automatic construction manages to recognize a good portion of all the entities in the gold standard, and that those not recognized are mostly explained by the level of granularity in which the information is structured in the graph, and not because the automatic approach misses an important entity in the graph. Looking forward, we expect this report will encourage work on other similar projects focused on enhancing research in humanities and social science, but we remark that better evaluation metrics are needed in order to accurately fine-tune these types of architectures.
Recently, Meta has shifted towards AI-mediated ad targeting mechanisms that do not require advertisers to provide detailed targeting criteria, likely driven by excitement over AI capabilities as well as new data privacy policies and targeting changes agreed upon in civil rights settlements. At the same time, Meta has touted their ad preference controls as an effective mechanism for users to control the ads they see. Furthermore, Meta markets their targeting explanations as a transparency tool that allows users to understand why they saw certain ads and inform actions to control future ads. Our study evaluates the effectiveness of Meta's "See less" ad control and the actionability of ad targeting explanations following the shift to AI-mediated targeting. We conduct a large-scale study, randomly assigning 1304 participants to mark "See less" to Body Weight Control or Parenting topics, and collecting the ads and targeting explanations Meta shows to participants before and after the intervention. We find that utilizing the "See less" ad control for the topics we study does not significantly reduce the number of ads shown by Meta on these topics, and that the control is less effective for some users whose demographics are correlated with the topic. Furthermore, we find that the majority of ad targeting explanations for local ads made no reference to location-specific targeting criteria, and did not inform users why ads related to the topics they marked to "See less" of continued to be delivered. We hypothesize that the poor effectiveness of controls and lack of actionability in explanations are the result of the shift to AI-mediated targeting, for which explainability and transparency tools have not yet been developed. Our work thus provides evidence for the need of new methods for transparency and user control, suitable and reflective of increasingly complex AI-mediated ad delivery systems.
In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional explanatory methods like Class Activation Mapping (CAM) and attention mechanisms, although effective in highlighting regions vital for decisions in various contexts, prove inadequate for HAR. This inadequacy stems from the inherently abstract nature of HAR data, rendering these explanations obscure. In contrast, state-of-th-art post-hoc interpretation techniques for time series can explain the model from other perspectives. However, this requires extra effort. It usually takes 10 to 20 seconds to generate an explanation. To overcome these challenges, we proposes a novel, model-agnostic framework that enhances both the interpretability and efficacy of HAR models through the strategic use of competitive data augmentation. This innovative approach does not rely on any particular model architecture, thereby broadening its applicability across various HAR models. By implementing competitive data augmentation, our framework provides intuitive and accessible explanations of model decisions, thereby significantly advancing the interpretability of HAR systems without compromising on performance.
Mercari is the largest C2C e-commerce marketplace in Japan, having more than 20 million active monthly users. Search being the fundamental way to discover desired items, we have always had a substantial amount of data with implicit feedback. Although we actively take advantage of that to provide the best service for our users, the correlation of implicit feedback for such tasks as image quality assessment is not trivial. Many traditional lines of research in Machine Learning (ML) are similarly motivated by the insatiable appetite of Deep Learning (DL) models for well-labelled training data. Weak supervision is about leveraging higher-level and/or noisier supervision over unlabeled data. Large Language Models (LLMs) are being actively studied and used for data labelling tasks. We present how we leverage a Chain-of-Thought (CoT) to enable LLM to produce image aesthetics labels that correlate well with human behavior in e-commerce settings. Leveraging LLMs is more cost-effective compared to explicit human judgment, while significantly improving the explainability of deep image quality evaluation which is highly important for customer journey optimization at Mercari. We propose a cost-efficient LLM-driven approach for assessing and predicting image quality in e-commerce settings, which is very convenient for proof-of-concept testing. We show that our LLM-produced labels correlate with user behavior on Mercari. Finally, we show our results from an online experimentation, where we achieved a significant growth in sales on the web platform.