120,145 research outputs found
Large AI Model-Based Semantic Communications
Semantic communication (SC) is an emerging intelligent paradigm, offering
solutions for various future applications like metaverse, mixed-reality, and
the Internet of everything. However, in current SC systems, the construction of
the knowledge base (KB) faces several issues, including limited knowledge
representation, frequent knowledge updates, and insecure knowledge sharing.
Fortunately, the development of the large AI model provides new solutions to
overcome above issues. Here, we propose a large AI model-based SC framework
(LAM-SC) specifically designed for image data, where we first design the
segment anything model (SAM)-based KB (SKB) that can split the original image
into different semantic segments by universal semantic knowledge. Then, we
present an attention-based semantic integration (ASI) to weigh the semantic
segments generated by SKB without human participation and integrate them as the
semantic-aware image. Additionally, we propose an adaptive semantic compression
(ASC) encoding to remove redundant information in semantic features, thereby
reducing communication overhead. Finally, through simulations, we demonstrate
the effectiveness of the LAM-SC framework and the significance of the large AI
model-based KB development in future SC paradigms.Comment: Plan to submit it to journal for possible publicatio
The Semantic Web Revisited
The original Scientific American article on the Semantic Web appeared in 2001. It described the evolution of a Web that consisted largely of documents for humans to read to one that included data and information for computers to manipulate. The Semantic Web is a Web of actionable information--information derived from data through a semantic theory for interpreting the symbols.This simple idea, however, remains largely unrealized. Shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks; they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the Semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years. Furthermore, we see the use of ontologies in the e-science community presaging ultimate success for the Semantic Web--just as the use of HTTP within the CERN particle physics community led to the revolutionary success of the original Web. This article is part of a special issue on the Future of AI
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
In recent years, ubiquitous semantic Metaverse has been studied to
revolutionize immersive cyber-virtual experiences for augmented reality (AR)
and virtual reality (VR) users, which leverages advanced semantic understanding
and representation to enable seamless, context-aware interactions within
mixed-reality environments. This survey focuses on the intelligence and
spatio-temporal characteristics of four fundamental system components in
ubiquitous semantic Metaverse, i.e., artificial intelligence (AI),
spatio-temporal data representation (STDR), semantic Internet of Things (SIoT),
and semantic-enhanced digital twin (SDT). We thoroughly survey the
representative techniques of the four fundamental system components that enable
intelligent, personalized, and context-aware interactions with typical use
cases of the ubiquitous semantic Metaverse, such as remote education, work and
collaboration, entertainment and socialization, healthcare, and e-commerce
marketing. Furthermore, we outline the opportunities for constructing the
future ubiquitous semantic Metaverse, including scalability and
interoperability, privacy and security, performance measurement and
standardization, as well as ethical considerations and responsible AI.
Addressing those challenges is important for creating a robust, secure, and
ethically sound system environment that offers engaging immersive experiences
for the users and AR/VR applications.Comment: 18 pages, 7 figures, 3 table
Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview
Semantic communication is poised to play a pivotal role in shaping the
landscape of future AI-driven communication systems. Its challenge of
extracting semantic information from the original complex content and
regenerating semantically consistent data at the receiver, possibly being
robust to channel corruptions, can be addressed with deep generative models.
This ICASSP special session overview paper discloses the semantic communication
challenges from the machine learning perspective and unveils how deep
generative models will significantly enhance semantic communication frameworks
in dealing with real-world complex data, extracting and exploiting semantic
information, and being robust to channel corruptions. Alongside establishing
this emerging field, this paper charts novel research pathways for the next
generative semantic communication frameworks.Comment: Submitted to IEEE ICASS
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
A tool that could suggest new personalized research directions and ideas by
taking insights from the scientific literature could significantly accelerate
the progress of science. A field that might benefit from such an approach is
artificial intelligence (AI) research, where the number of scientific
publications has been growing exponentially over the last years, making it
challenging for human researchers to keep track of the progress. Here, we use
AI techniques to predict the future research directions of AI itself. We
develop a new graph-based benchmark based on real-world data -- the
Science4Cast benchmark, which aims to predict the future state of an evolving
semantic network of AI. For that, we use more than 100,000 research papers and
build up a knowledge network with more than 64,000 concept nodes. We then
present ten diverse methods to tackle this task, ranging from pure statistical
to pure learning methods. Surprisingly, the most powerful methods use a
carefully curated set of network features, rather than an end-to-end AI
approach. It indicates a great potential that can be unleashed for purely ML
approaches without human knowledge. Ultimately, better predictions of new
future research directions will be a crucial component of more advanced
research suggestion tools.Comment: 13 pages, 7 figures. Comments welcome
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A field that might benefit from such an approach is artificial intelligence (AI) research, where the number of scientific publications has been growing exponentially over the last years, making it challenging for human researchers to keep track of the progress. Here, we use AI techniques to predict the future research directions of AI itself. We develop a new graph-based benchmark based on real-world data -- the Science4Cast benchmark, which aims to predict the future state of an evolving semantic network of AI. For that, we use more than 100,000 research papers and build up a knowledge network with more than 64,000 concept nodes. We then present ten diverse methods to tackle this task, ranging from pure statistical to pure learning methods. Surprisingly, the most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach. It indicates a great potential that can be unleashed for purely ML approaches without human knowledge. Ultimately, better predictions of new future research directions will be a crucial component of more advanced research suggestion tools
The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP
I propose a paradigm for scientific progress in NLP centered around
developing scalable, data-driven theories of linguistic structure. The idea is
to collect data in tightly scoped, carefully defined ways which allow for
exhaustive annotation of behavioral phenomena of interest, and then use machine
learning to construct explanatory theories of these phenomena which can form
building blocks for intelligible AI systems. After laying some conceptual
groundwork, I describe several investigations into data-driven theories of
shallow semantic structure using Question-Answer driven Semantic Role Labeling
(QA-SRL), a schema for annotating verbal predicate-argument relations using
highly constrained question-answer pairs. While this only scratches the surface
of the complex language behaviors of interest in AI, I outline principles for
data collection and theoretical modeling which can inform future scientific
progress. This note summarizes and draws heavily on my PhD thesis.Comment: 13 pages, 3 figures, 2 tables. Presented at The Big Picture Workshop
at EMNLP 202
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