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22 May

Focus on Low-Resolution Information: Multi-Granular Information-Lossless Model for Low-Resolution Human Pose Estimation

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arXiv:2405.12247v1 Announce Type: new Abstract: In real-world applications of human pose estimation, low-resolution input images are frequently encountered when the performance of the image acquisition equipment is limited or the shooting distance is too far. However, existing state-of-the-art models for human pose estimation perform poorly on low-resolution images. One key reason is the presence of downsampling layers in these models, e.g., strided convolutions and pooling layers. It further reduces the already insufficient image information. Another key reason is that the body skeleton and human kinematic information are not fully utilized. In this work, we propose a Multi-Granular Information-Lossless (MGIL) model…

22 May

The Arabic Noun System Generation

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arXiv:2405.11014v1 Announce Type: new Abstract: In this paper, we show that the multiple-stem approach to nouns with a broken plural pattern allows for greater generalizations to be stated in the morphological system. Such an approach dispenses with truncating/deleting rules and other complex rules that are required to account for the highly allomorphic broken plural system. The generation of inflected sound nouns necessitates a pre-specification of the affixes denoting the sound plural masculine and the sound plural feminine, namely uwna and aAt, in the lexicon. The first subsection of section one provides an evaluation of some of the previous analyses of…

22 May

Fine Tuning Phi 1.5 using QLoRA on the Stanford Alpaca Dataset

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Quantized LoRA, more commonly known as QLoRA is a combination of quantization and Low Rank Adaptation for fine-tuning LLMs. Simply put, LoRa is a technique to adapt Large Language Models to specific tasks without making them forget their pretraining knowledge. In QLoRa, we load the pretrained model weights in quantized format, say 4-bit (INT4). However, the adapter (LoRA) layers are loaded in full precision, FP16 or FP32. This reduces the memory (GPU) consumption by a great extent making fine tuning possible on low resource hardware. To this end, in this article, we will be fine tuning the Phi 1.5 model…

21 May

Informatica CEO: Good Data Management Not Optional for AI

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(greenbutterfly/Shutterstock) The big data era may have started a decade-and-a-half ago, but for many companies, it’s the current AI revolution that’s forcing them to finally get serious about data management, says Informatica CEO Amit Walia. “What is AI without good quality data?” he says. Data is the foundation for a host of corporate efforts these days, and that realization is leading many companies to renew their interest in establishing a comprehensive data management strategy, Walia told Datanami last week in advanced of Informatica World, which takes place in Las Vegas this week. “The driver is all of these digital initiatives…

21 May

Data Machina #253

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The Google AI Blast . This week OpenAI released a new closed model called GPT-4o (as in omni): Hello GPT-4o, a model that can reason across audio, vision, and text in real time. It seems the model performance in many benchmarks wasn’t as good as many AI pundits expected.And while many people in the AI community were befuddled and discussing the “flirtatiousness” aspects of GPT-4o, then Google came in and blasted a massive AI storm including SOTA models, new powerful open models, and pretty amazing tools. Here’s my summary on what Google released: Gemini 1.5 Pro model updates: Lots of…

21 May

Unveiling the Criticality of Red Teaming for Generative AI Governance

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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…

21 May

Introducing Databricks Assistant Autocomplete

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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…

20 May

AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning

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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…

20 May

Networking Systems for Video Anomaly Detection: A Tutorial and Survey

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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…

20 May

AmazUtah_NLP at SemEval-2024 Task 9: A MultiChoice Question Answering System for Commonsense Defying Reasoning

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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…