- Oslo, Oslo, Norway
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Interactive continuous learning is an important characteristic of a cognitive agent that is supposed to operate and evolve in an everchanging environment. In this paper we present representations and mechanisms that are necessary for... more
Interactive continuous learning is an important characteristic of a cognitive agent that is supposed to operate and evolve in an everchanging environment. In this paper we present representations and mechanisms that are necessary for continuous learning of visual concepts in dialogue with a tutor. We present an approach for modelling beliefs stemming from multiple modalities and we show how these beliefs are created by processing visual and linguistic information and how they are used for learning. We also present a system that exploits these representations and mechanisms, and demonstrate these principles in the case of learning about object colours and basic shapes in dialogue with the tutor.
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There are many different approaches to building a system that can engage in autonomous mental development. In this paper, we present an approach based on what we term self-understanding, by which we mean the explicit representation of and... more
There are many different approaches to building a system that can engage in autonomous mental development. In this paper, we present an approach based on what we term self-understanding, by which we mean the explicit representation of and reasoning about what a system does and does not know, and how that knowledge changes under action. We present an architecture and a set of representations used in two robot systems that exhibit a limited degree of autonomous mental development, which we term self-extension. The contributions include: representations of gaps and uncertainty for specific kinds of knowledge, and a goal management and planning system for setting and achieving learning goals.
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In CoSy, our robots were to be able to interact with human. These interactions served to help the robot learn more about its environment, or to plan and carry out actions. For a robot to make sense of such dialogues, it needs to... more
In CoSy, our robots were to be able to interact with human. These interactions served to help the robot learn more about its environment, or to plan and carry out actions. For a robot to make sense of such dialogues, it needs to understand how a dialogue can relate to, and refer to, “the world” – local visuo-spatial scenes, as in the Playmate scenario (9), or the spatial organization of an indoor environment in the Explorer scenario (10).
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Abstract Speech disfluencies such as filled pauses, repetitions and self-corrections are unavoidable and pervasive in natural spoken dialogue. A robust dialogue system should therefore be able to accommodate such linguistic constructions.... more
Abstract Speech disfluencies such as filled pauses, repetitions and self-corrections are unavoidable and pervasive in natural spoken dialogue. A robust dialogue system should therefore be able to accommodate such linguistic constructions. In this paper, we present ...
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Spoken dialogue is notoriously hard to process with standard language processing technologies. Dialogue systems must indeed meet two major challenges. First, natural spoken dialogue is replete with disfluent, partial, elided or... more
Spoken dialogue is notoriously hard to process with standard language processing technologies. Dialogue systems must indeed meet two major challenges. First, natural spoken dialogue is replete with disfluent, partial, elided or ungrammatical utterances. Second, speech recognition remains a highly error-prone task, especially for complex, open-ended domains. We present an integrated approach for addressing these two issues, based on a robust incremental parser. The parser takes word lattices as input and is able to handle ill-formed and misrecognised utterances by selectively relaxing its set of grammatical rules. The choice of the most relevant interpretation is then realised via a discriminative model augmented with contextual information. The approach is fully implemented in a dialogue system for autonomous robots. Evaluation results on a Wizard of Oz test suite demonstrate very significant improvements in accuracy and robustness compared to the baseline.
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Much of the legal and technical literature on data anonymization has focused on structured data such as tables. However, unstructured data such as text documents or images are far more common, and the legal requirements that must be... more
Much of the legal and technical literature on data anonymization has focused on structured data such as tables. However, unstructured data such as text documents or images are far more common, and the legal requirements that must be fulfilled to properly anonymize such data formats remain unclear and underaddressed by the literature. In the absence of a definition of the term ‘anonymous data’ in the General Data Protection Regulation (GDPR), we examine its antithesis—personal data—and the identifiability test in Recital 26 GDPR to understand what conditions must be in place for the anonymization of unstructured data. This article examines the two contrasting approaches for determining identifiability that are prevalent today: (i) the risk-based approach and (ii) the strict approach in the Article 29 Working Party’s Opinion on Anonymization Techniques (WP 216). Through two case studies, we illustrate the challenges encountered when trying to anonymize unstructured datasets. We show that, while the risk-based approach offers a more nuanced test consistent with the purposes of the GDPR, the strict approach of WP 216 makes anonymization of unstructured data virtually impossible as long as the original data continues to exist. The concluding section considers the policy implications of the strict approach and technological developments that assist identification, and proposes a way forward