Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing

• URL: http://arxiv.org/abs/2308.03176v1
• Date: Sun, 6 Aug 2023 18:05:59 GMT
• Title: Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing
• Authors: Shuang Ao
• Abstract summary: Current AI algorithms are unable to identify common causes for failure detection. Additional techniques are required to quantify the quality of predictions. This thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering.
• Score: 1.2183405753834557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for safety-critical tasks. One shared characteristic of these critical tasks is their risk sensitivity, where small mistakes can cause big consequences and even endanger life. There are several factors that could be guidelines for the successful deployment of AI systems in sensitive tasks: (i) failure detection and out-of-distribution (OOD) detection; (ii) overfitting identification; (iii) uncertainty quantification for predictions; (iv) robustness to data perturbations. These factors are also challenges of current AI systems, which are major blocks for building safe and reliable AI. Specifically, the current AI algorithms are unable to identify common causes for failure detection. Furthermore, additional techniques are required to quantify the quality of predictions. All these contribute to inaccurate uncertainty quantification, which lowers trust in predictions. Hence obtaining accurate model uncertainty quantification and its further improvement are challenging. To address these issues, many techniques have been proposed, such as regularization methods and learning strategies. As vision and language are the most typical data type and have many open source benchmark datasets, this thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering. In this thesis, we aim to build a safeguard by further developing current techniques to ensure the accurate model uncertainty for safety-critical tasks.

Related papers

• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset [23.155946032377052]
  We introduce a novel instance-wise calibration method based on an energy model. Our method incorporates energy scores instead of softmax confidence scores, allowing for adaptive consideration of uncertainty. In experiments, we show that the proposed method consistently maintains robust performance across the spectrum.
  arXiv  Detail & Related papers  (2024-07-17T06:14:55Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection [78.24964622317634]
  In this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty. To evaluate the quality of uncertainty, we consider the task of classifying anomalies with the additional option of rejecting predictions of high uncertainty. Our experiments demonstrate the effectiveness of the BAE and total anomaly uncertainty on a set of benchmark datasets and two real datasets for manufacturing.
  arXiv  Detail & Related papers  (2022-02-25T12:20:04Z)
• Statistical Perspectives on Reliability of Artificial Intelligence Systems [6.284088451820049]
  We provide statistical perspectives on the reliability of AI systems. We introduce a so-called SMART statistical framework for AI reliability research. We discuss recent developments in modeling and analysis of AI reliability.
  arXiv  Detail & Related papers  (2021-11-09T20:00:14Z)
• Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System [78.60415450507706]
  Recent advancements in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty.
  arXiv  Detail & Related papers  (2021-07-28T10:28:05Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [0.0]
  Key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. Different methods for uncertainty quantification have recently been proposed to measure the inevitable source of errors in data and models. These methods require a higher computational load, a higher memory footprint, and introduce extra latency, which can be prohibitive in safety-critical applications.
  arXiv  Detail & Related papers  (2020-06-26T18:55:10Z)
• Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty [30.97582874240214]
  Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. We identify and categorize the uncertainty patterns of benign examples (BEs) and AEs, and find that while BEs and AEs generated by existing methods do follow common uncertainty patterns, some other uncertainty patterns are largely missed.
  arXiv  Detail & Related papers  (2020-04-24T07:29:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.