776 research outputs found

    Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

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    Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy

    Meta-learning algorithms and applications

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    Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples. Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number. Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation. More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents

    Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models

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    We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. Code is available at https://github.com/gyhandy/One-Class-AnythingComment: 16 pages (including appendix and references), 3 figure

    A Review of the Role of Causality in Developing Trustworthy AI Systems

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    State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.Comment: 55 pages, 8 figures. Under revie

    How as a Signal of an Invariant Meaning

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    This dissertation aims to explain why speakers and writers use how in the communicative contexts in which they do, and its central claim is that how is a signal of one invariant meaning. The form’s diverse communicative contributions can be explained by hypothesizing a single meaning that contributes to different message effects, or contextual interpretations, on different occasions of its use. The present analysis rests on the crucial distinction in Columbia School (CS) linguistics, the theoretical framework guiding this project, between meaning and message. A meaning is a signal’s invariant semantic contribution, while messages are the context-unique interpretations that stem from, but are underdetermined by, linguistic utterances (Diver, 1975/2012; Huffman, 2001; Stern, 2019, among many others). How contributes to overlapping messages including — though not limited to — degree, characterization/assessment, personal perspective, and manner, but its invariant semantic contribution is a great deal more abstract than any of these things. How’s hypothesized meaning draws on the CS constructs of both substance and value (Diver, 1995/2012; Davis, 2004). Its substance pertains to Elaboration – it signals that additional, elaborating information is pertinent to some aspect of the ongoing discourse. Elaborating information may in principle be relevant in any communicative context, but how explicitly signals that this is so. How’s value (its contrast with other forms) is seen in its membership in the grammatical system of Elaboration, constituted by what are traditionally termed the wh-words (who, what, which, where, when, why and how). Thus, while the other wh-words signal the Relevance of Elaboration with respect to something comparatively specific — a PERSON, ENTITY, LOCATION, TIME, REASON — how signals the Relevance of unspecified Elaboration, or Elaboration (OTHER). In Diverian terms, how is the residual member of its semantic domain (Diver 1978/2012, 1995/2012). It opens the deictic field to its widest setting, signaling that Elaboration in the broadest possible sense is Relevant. It is how’s role as a residual member of a grammatical system that accounts for the widely diverse and heterogeneous message effects that follow from its use. These may involve persons, entities, locations, etc., but the Relevant Elaboration signaled by how never centers on, and is thus never reducible to, any one of these things. Evidence in favor of this analysis includes both qualitative and quantitative data. The qualitative data spans several sources, including two full length books; quantitative data from a large corpus, the Corpus of Contemporary American English (Davies, 2008- ), shows that how is more likely to co-occur with other forms that suggest the relevance of Elaboration as part of the communication. The analysis offered in this dissertation more successfully accounts for how’s distribution than the many categories identified in traditional grammars, and more successfully than the three categories posited in generative syntax — manner adverb, degree adverb and complementizer/conjunction (Willis, 2007; van Gelderen, 2013, 2015). These constructs prove to be analytically unreliable, in that they overlap significantly and exhibit a considerable degree of indeterminacy. They are also descriptively inadequate, in that some attested occurrences of how cannot be accounted for by any of them. In contrast, the present analysis takes a fresh perspective. Freed from the limitations of sentence-based, traditional categories and based on careful review of attested data, we have discovered that how is a signal with a meaning. The form’s heterogeneous message-effects follow from the invariant meaning proposed here, Elaboration (OTHER) is Relevant — a meaning which is utilized by speakers and writers in pursuit of their communicative goals

    On marked declaratives, exclamatives, and discourse particles in Castilian Spanish

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    This book provides a new perspective on prosodically marked declaratives, wh-exclamatives, and discourse particles in the Madrid variety of Spanish. It argues that some marked forms differ from unmarked forms in that they encode modal evaluations of the at-issue meaning. Two epistemic evaluations that can be shown to be encoded by intonation in Spanish are linguistically encoded surprise, or mirativity, and obviousness. An empirical investigation via an audio-enhanced production experiment finds that mirativity and obviousness are associated with distinct intonational features under constant focus scope, with stances of (dis)agreement showing an impact on obvious declaratives. Wh-exclamatives are found not to differ significantly in intonational marking from neutral declaratives, showing that they need not be miratives. Moreover, we find that intonational marking on different discourse particles in natural dialogue correlates with their meaning contribution without being fully determined by it. In part, these findings quantitatively confirm previous qualitative findings on the meaning of intonational configurations in Madrid Spanish. But they also add new insights on the role intonation plays in the negotiation of commitments and expectations between interlocutors

    Provable Offline Reinforcement Learning with Human Feedback

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    In this paper, we investigate the problem of offline reinforcement learning with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed algorithm consists of two main steps: (1) estimate the implicit reward using Maximum Likelihood Estimation (MLE) with general function approximation from offline data and (2) solve a distributionally robust planning problem over a confidence set around the MLE. We consider the general reward setting where the reward can be defined over the whole trajectory and provide a novel guarantee that allows us to learn any target policy with a polynomial number of samples, as long as the target policy is covered by the offline data. This guarantee is the first of its kind with general function approximation. To measure the coverage of the target policy, we introduce a new single-policy concentrability coefficient, which can be upper bounded by the per-trajectory concentrability coefficient. We also establish lower bounds that highlight the necessity of such concentrability and the difference from standard RL, where state-action-wise rewards are directly observed. We further extend and analyze our algorithm when the feedback is given over action pairs

    Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities

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    There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through two public sector procurement checklists, identifying what we can do now, what we should be able to do with technical innovation in AI, and what requirements necessitate a more interdisciplinary approach

    Transparency Helps Reveal When Language Models Learn Meaning

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    AbstractMany current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations (i.e., languages with strong transparency), both autoregressive and masked language models successfully learn to emulate semantic relations between expressions. However, when denotations are changed to be context-dependent with the language otherwise unmodified, this ability degrades. Turning to natural language, our experiments with a specific phenomenon—referential opacity—add to the growing body of evidence that current language models do not represent natural language semantics well. We show this failure relates to the context-dependent nature of natural language form-meaning mappings
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