466 research outputs found
Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT
In this paper, we aimed to provide a review and tutorial for researchers in
the field of medical imaging using language models to improve their tasks at
hand. We began by providing an overview of the history and concepts of language
models, with a special focus on large language models. We then reviewed the
current literature on how language models are being used to improve medical
imaging, emphasizing different applications such as image captioning, report
generation, report classification, finding extraction, visual question
answering, interpretable diagnosis, and more for various modalities and organs.
The ChatGPT was specially highlighted for researchers to explore more potential
applications. We covered the potential benefits of accurate and efficient
language models for medical imaging analysis, including improving clinical
workflow efficiency, reducing diagnostic errors, and assisting healthcare
professionals in providing timely and accurate diagnoses. Overall, our goal was
to bridge the gap between language models and medical imaging and inspire new
ideas and innovations in this exciting area of research. We hope that this
review paper will serve as a useful resource for researchers in this field and
encourage further exploration of the possibilities of language models in
medical imaging
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development
path of the human-machine symbiotic art creation. We propose a classification
of the creative system with a hierarchy of 5 classes, showing the pathway of
creativity evolving from a mimic-human artist (Turing Artists) to a Machine
artist in its own right. We begin with an overview of the limitations of the
Turing Artists then focus on the top two-level systems, Machine Artists,
emphasizing machine-human communication in art creation. In art creation, it is
necessary for machines to understand humans' mental states, including desires,
appreciation, and emotions, humans also need to understand machines' creative
capabilities and limitations. The rapid development of immersive environment
and further evolution into the new concept of metaverse enable symbiotic art
creation through unprecedented flexibility of bi-directional communication
between artists and art manifestation environments. By examining the latest
sensor and XR technologies, we illustrate the novel way for art data collection
to constitute the base of a new form of human-machine bidirectional
communication and understanding in art creation. Based on such communication
and understanding mechanisms, we propose a novel framework for building future
Machine artists, which comes with the philosophy that a human-compatible AI
system should be based on the "human-in-the-loop" principle rather than the
traditional "end-to-end" dogma. By proposing a new form of inverse
reinforcement learning model, we outline the platform design of machine
artists, demonstrate its functions and showcase some examples of technologies
we have developed. We also provide a systematic exposition of the ecosystem for
AI-based symbiotic art form and community with an economic model built on NFT
technology. Ethical issues for the development of machine artists are also
discussed
Next Steps for Human-Centered Generative AI: A Technical Perspective
Through iterative, cross-disciplinary discussions, we define and propose
next-steps for Human-centered Generative AI (HGAI) from a technical
perspective. We contribute a roadmap that lays out future directions of
Generative AI spanning three levels: Aligning with human values; Accommodating
humans' expression of intents; and Augmenting humans' abilities in a
collaborative workflow. This roadmap intends to draw interdisciplinary research
teams to a comprehensive list of emergent ideas in HGAI, identifying their
interested topics while maintaining a coherent big picture of the future work
landscape
The Design of an Interactive Topic Modeling Application for Media Content
Topic Modeling has been widely used by data scientists to analyze the increasing amount of text documents. Documents can be assigned to a distribution of topics with techniques like LDA or NMF, that are related to unsupervised soft clustering but consider text semantics. More recently, Interactive Topic Modeling (ITM) has been introduced to incorporate human expertise in the modeling process. This enables real-time hyperparameter optimization and topic manipulation on document and keyword level. However, current ITM applications are mostly accessible to experienced data scientists, who lack domain knowledge. Domain experts, on the other hand, usually lack the data science expertise to build and use ITM applications.
This thesis presents an Interactive Topic Modeling application accessible to non-technical data analysts in the broadcasting domain. The application allows domain experts, like journalists, to explore themes in various produced media content in a dynamic, intuitive and efficient manner. An interactive interface, with an embedded NMF topic model, enables users to filter on various data sources, configure and refine the topic model, interpret and evaluate the output by visualizations, and analyze the data in wider context. This application was designed in collaboration with domain experts in focus group sessions, according to human-centered design principles.
An evaluation study with ten participants shows that journalists and data analysts without any natural language processing knowledge agree that the application is not only usable, but also very user-friendly, effective and efficient. A SUS score of 81 was received, and user experience and user perceptions of control questionnaires both received an average of 4.1 on a five-point Likert scale. The ITM application thus enables this specific user group to extract meaningful topics from their produced media content, and use these results in broader perspective to perform exploratory data analysis.
The success of the final application design presented in this thesis shows that the knowledge gap between data scientists and domain experts in the broadcasting field has been filled. In bigger perspective; machine learning applications can be made more accessible by translating hidden low-level details of complex models into high-level model interactions, presented in a user interface
Measuring Semantic Textual Similarity and Automatic Answer Assessment in Dialogue Based Tutoring Systems
This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for the last three years. The correlations between our models\u27predictions and the human judgments were above 0.80 for several datasets while ourmodels being very robust than many other top performing systems. Moreover, wehave proposed Bayesian. We have also proposed a novel Neural Network based word representationmapping approach which allows us to map the vector based representation of a wordfound in one model to the another model where the word representation is missing,effectively pooling together the vocabularies and corresponding representationsacross models. Our experiments show that the model coverage increased by few toseveral times depending on which model\u27s vocabulary is taken as a reference. Also,the transformed representations were well correlated to the native target modelvectors showing that the mapped representations can be used with condence tosubstitute the missing word representations in the target model. models to adapt similarity models across domains. Furthermore, we have proposed methods to improve open-ended answersassessment in dialogue based tutoring systems which is very challenging because ofthe variations in student answers which often are not self contained and need thecontextual information (e.g., dialogue history) in order to better assess theircorrectness. In that, we have proposed Probabilistic Soft Logic (PSL) modelsaugmenting semantic similarity information with other knowledge. To detect intra- and inter-sentential negation scope and focus in tutorialdialogs, we have developed Conditional Random Fields (CRF) models. The resultsindicate that our approach is very effective in detecting negation scope and focus intutorial dialogue context and can be further developed to augment the naturallanguage understanding systems. Additionally, we created resources (datasets, models, and tools) for fosteringresearch in semantic similarity and student response understanding inconversational tutoring systems
Blockchain Applications in Lifelong Learning and the Role of the Semantic Blockchain
The emergence of the blockchain promises to revolutionise not only the financial world but also lifelong learning in various ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and tutoring services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This chapter explores the different aspects of lifelong learning that are affected by this new paradigm and describes an ecosystem that places the learner at the centre of the learning process and its associated data. This chapter also discusses the possibilities that will be afforded by the combination of trustworthy educational data enhanced with meaningful web-accessible linked data, and what these developments will mean for learners, educators, and the employment market
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