Learning object-centric representations from complex natural environments enables both humans and machines with reasoning abilities from low-level perceptual features. To capture compositional entities of the scene, we proposed cyclic walks between perceptual features extracted from vision transformers and object entities. First, a slot-attention module interfaces with these perceptual features and produces a finite set of slot representations. These slots can bind to any object entities in the scene via inter-slot competitions for attention. Next, we establish entity-feature correspondence with cyclic walks along high transition probability based on the pairwise similarity between perceptual features (aka "parts") and slot-binded object representations (aka "whole").

Abstract--Large language models (LLMs) face the challenge of hallucinations - outputs that seem coherent but are actually incorrect. A particularly damaging type is fact-conflicting hallucination (FCH), where generated content contradicts established facts. Addressing FCH presents three main challenges: 1) Automatically constructing and maintaining large-scale benchmark datasets is difficult and resource-intensive; 2) Generating complex and efficient test cases that the LLM has not been trained on - especially those involving intricate temporal features - is challenging, yet crucial for eliciting hallucinations; and 3) Validating the reasoning behind LLM outputs is inherently difficult, particularly with complex logical relationships, as it requires transparency in the model's decision-making process. LLMs are tested using these cases through template-based prompts, which require them to generate both answers and reasoning steps. T o validate the reasoning, we propose two semantic-aware oracles that compare the semantic structure of LLM outputs to the ground truths. Key insights reveal that LLMs struggle with out-of-distribution knowledge and logical reasoning. These findings highlight the importance of continued efforts to detect and mitigate hallucinations in LLMs. Large Language Models (LLMs) have revolutionized language processing, demonstrating impressive text generation and comprehension capabilities with diverse applications. However, despite their growing use, LLMs face significant security and privacy challenges [1], [2], [3], [4], [5], which affect their overall effectiveness and reliability . A critical issue is the phenomenon of hallucination, where LLMs generate outputs that are coherent but factually incorrect or irrelevant. This tendency to produce misleading information compromises the safety and usability of LLM-based systems. This paper focuses on fact-conflicting hallucina tion (FCH), the most prominent form of hallucination in LLMs. FCH occurs when LLMs generate content that directly contradicts established facts. For instance, as illustrated in Figure 1, an LLM incorrectly asserts that " Haruki Murakami won the Nobel Prize in Literature in 2016 ", whereas the fact is that "Haruki Murakami has not won the Nobel Prize, though he has received numerous other literary awards ". Such inaccuracies can significantly lead to user confusion and undermine the trust and reliability that are crucial for LLM applications. N. Li, K. Wang, and H. Wang are with Huazhong University of Science and T echnology, China. Song is with the National University of Singapore, Singapore. Li is with the University of New South Wales, Australia.

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SINGAPORE - Singapore is developing a new national blueprint to throw its hat into 6G developments to stay in the global digital race. What is 6G and how is it superior to predecessor technologies such as 5G and 4G? The Straits Times' tech editor Irene Tham looks at the mobile evolution and what each upgrade can do for users here. Together, 5G and AI are expected to stage the next industrial revolution to welcome new realities such as self-driving cars, remote surgery and smart cities complete with smart power grids, e-surveillance and payments by facial scans. Nations with these technologies hold the keys to the future.

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.

Singapore, Singapore--(Newsfile Corp. - October 8, 2021) - PlatON, a next-generation Internet infrastructure protocol based on the fundamental properties of blockchain, recently combined blockchain, artificial intelligence and privacy computing technology to establish a decentralized collaborative privacy artificial intelligence network, which would take utilization of data to a new level. This network will serve as the infrastructure of autonomous artificial intelligence (AI) agents, who can facilitate advanced artificial intelligence and explore the road to Artificial General Intelligence. Based on blockchain network, PlatON developers designed a decentralized privacy computing network to connect data, algorithms and hashrate through privacy computing protocols. Autonomous AI agents can obtain the required resources at low cost, train artificial intelligence models and publish them on the network, and interact with other artificial intelligence services or agents to form a self-organizing and cooperative artificial intelligence network. On this network, anyone can obtain artificial intelligence technology or become a stakeholder in its development, and realize the autonomy of artificial intelligence.

Singapore – Singapore has tested patrol robots that blast warnings at people engaging in "undesirable social behavior," adding to an arsenal of surveillance technology in the tightly controlled city-state that is fueling privacy concerns. From vast numbers of CCTV cameras to trials of lampposts kitted out with facial recognition tech, Singapore is seeing an explosion of tools to track its inhabitants. Officials have long pushed a vision of a hyper-efficient, tech-driven "smart nation," but activists say privacy is being sacrificed and people have little control over what happens to their data. Singapore is frequently criticized for curbing civil liberties and people are accustomed to tight controls, but there is still growing unease over intrusive tech. The government's latest surveillance devices are robots on wheels, with seven cameras, that issue warnings to the public and detect "undesirable social behavior."