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As generative artificial intelligence (AI) systems become increasingly ubiquitous, their potential impact on society amplifies. These advanced language models possess remarkable capabilities, yet their inherent complexities raise concerns about unintended consequences and potential misuse. Consequently, the evolution of generative AI necessitates robust governance mechanisms to ensure responsible development and deployment. One crucial component of this governance framework is red teaming – a proactive approach to identifying and mitigating vulnerabilities and risks associated with these powerful technologies. Demystifying Red Teaming Red teaming is a cybersecurity practice that simulates real-world adversarial tactics, techniques, and procedures (TTPs) to evaluate an organization's defenses and…

We are excited to introduce Databricks Assistant Autocomplete now in Public Preview. This feature brings the AI-powered assistant to you in real-time, providing personalized code suggestions as you type. Directly integrated into the notebook and SQL editor, Assistant Autocomplete suggestions blend seamlessly into your development flow and allow you to stay focused in the editor. Boost Productivity with AI-generated Code SuggestionsDatabricks Assistant Autocomplete automatically provides fast code suggestions as you type in SQL and Python. AI code completion uses context from current and sounding code cells, Unity Catalog metadata, DataFrame data, and more to generate highly relevant suggestions as you type.SQL PythonGetting…

arXiv:2405.10346v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) can effectively model the ever-evolving nature of real-world knowledge, and their completeness and enhancement can be achieved by reasoning new events from existing ones. However, reasoning accuracy is adversely impacted due to an imbalance between new and recurring events in the datasets. To achieve more accurate TKG reasoning, we propose an attention masking-based contrastive event network (AMCEN) with local-global temporal patterns for the two-stage prediction of future events. In the network, historical and non-historical attention mask vectors are designed to control the attention bias towards historical and non-historical entities, acting as…

arXiv:2405.10347v1 Announce Type: new Abstract: The increasing prevalence of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in…

arXiv:2405.10385v1 Announce Type: new Abstract: The SemEval 2024 BRAINTEASER task represents a pioneering venture in Natural Language Processing (NLP) by focusing on lateral thinking, a dimension of cognitive reasoning that is often overlooked in traditional linguistic analyses. This challenge comprises of Sentence Puzzle and Word Puzzle subtasks and aims to test language models' capacity for divergent thinking. In this paper, we present our approach to the BRAINTEASER task. We employ a holistic strategy by leveraging cutting-edge pre-trained models in multiple choice architecture, and diversify the training data with Sentence and Word Puzzle datasets. To gain further improvement, we fine-tuned the…