16,811 research outputs found

    On becoming a physicist of mind

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    In 1976, the German Max Planck Society established a new research enterprise in psycholinguistics, which became the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands. I was fortunate enough to be invited to direct this institute. It enabled me, with my background in visual and auditory psychophysics and the theory of formal grammars and automata, to develop a long-term chronometric endeavor to dissect the process of speaking. It led, among other work, to my book Speaking (1989) and to my research team's article in Brain and Behavioral Sciences “A Theory of Lexical Access in Speech Production” (1999). When I later became president of the Royal Netherlands Academy of Arts and Sciences, I helped initiate the Women for Science research project of the Inter Academy Council, a project chaired by my physicist sister at the National Institute of Standards and Technology. As an emeritus I published a comprehensive History of Psycholinguistics (2013). As will become clear, many people inspired and joined me in these undertakings

    Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

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    The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

    The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

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    Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling

    Artificial Intelligence in the Context of Human Consciousness

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    Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

    From Exploration to Sensemaking: an Interactive Exploratory Search System

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    exist to satisfy people's information needs. Many search systems have been designed for well-formed and explicit information needs but few for exploratory purposes. By exploration, we mean the information need is ambiguous initially and evolves during the search process. There are mainly two parts within the thesis. In the first part, we will develop an interactive exploratory search system for the arXiv database, an open e-print archive for scientific articles. The part is mostly based on an initial study, in which a search engine, Scinet, based on an intent estimation model is proposed. With this search engine, users can direct their exploration by giving feedbacks to the estimated search intents, which are represented by relevant keywords. Intents are visualized and arranged into a radial layout where the radius measures relevance and the angle measures similarity. Users can drag a keyword closer to the center to indicate higher relevance or click on a keyword to assign full relevance and then the retrieved documents will be updated accordingly. Compared to the initial search system, a mind-map functionality is also added as a new feature. With this mind-map, users can temporarily store the keywords or titles that they find interesting. To verify the interactive exploratory ability, we have designed and conducted a small-scale experiment based on the arXiv dataset. Particularly, the keywords for arXiv articles are extracted by an automatic keyword extraction algorithm since most of the arXiv articles do not provide keywords by the authors. For the second part, we investigate a potential novel functionality of the Scinet search system on a large database of scientific articles. The ability of the system to support information seeking was shown in previous publication. Here we propose that this system will also support sensemaking, namely, help users to make sense of the results. We suggest that this advantage arises because in Scinet, not only are intents estimated but also the relationships between them are indicated on the interface. In order to better support sensemaking, the new functionality is also added to the prototype system in the initial study. We believe that this search system will help people to better understand and interpret the search results

    Reading in the Disciplines: The Challenges of Adolescent Literacy

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    A companion report to Carnegie's Time to Act, focuses on the specific skills and literacy support needed for reading in academic subject areas in higher grades. Outlines strategies for teaching content knowledge and reading strategies together

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    Natural Language Processing (NLP) – A Solution for Knowledge Extraction from Patent Unstructured Data

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    AbstractPatents are valuable source of knowledge and are extremely important for assisting engineers and decisions makers through the inventive process. This paper describes a new approach of automatic extraction of IDM (Inventive Design Method) related knowledge from patent documents. IDM derives from TRIZ, the theory of Inventive problem solving, which is largely based on patent's observation to theorize the act of inventing. Our method mainly consists in using natural language techniques (NLP) to match and extract knowledge relevant to IDM Ontology. The purpose of this paper is to investigate on the contribution of NLP techniques to effective knowledge extraction from patent documents. We propose in this paper to firstly report on progress made so far in data mining before describing our approach

    Machine Reading at Scale: A Search Engine for Scientific and Academic Research

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    The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance.This work has received funding from the following projects: UIDB/00760/2020 and UIDP/00760/2020.info:eu-repo/semantics/publishedVersio
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