Viral News - CybAI news

23 May

The Role of Synthetic Data in Cybersecurity

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Data's value is something of a double-edged sword. On one hand, digital data lays the groundwork for powerful AI applications, many of which could change the world for the better. Conversely, storing so many details on people creates huge privacy risks. Synthetic data provides a possible solution. What Is Synthetic Data? Synthetic data is a subset of anonymized data – data that doesn't reveal any real-world details. More specifically, it refers to information that looks and acts like real-world data but has no ties to actual people, places or events. In short, it's fake data that can produce real results. In…

22 May

Announcing General Availability of Liquid Clustering

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We’re excited to announce the General Availability of Delta Lake Liquid Clustering in the Databricks Data Intelligence Platform. Liquid Clustering is an innovative data management technique that replaces table partitioning and ZORDER so you no longer have to fine-tune your data layout to achieve optimal query performance. Liquid clustering significantly simplifies data layout-related decisions and provides the flexibility to redefine clustering keys without data rewrites. It allows data layout to evolve alongside analytic needs over time – something you could never do with partitioning on Delta. Since the Public Preview of Liquid Clustering at the Data and AI Summit last year, we’ve…

22 May

Top Data Validation Tools for Machine Learning in 2024

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Image generated with MidjourneyIt was challenging to stop myself from starting this article with some variation of the popular phrase "garbage in, garbage out." Well, I did it anyway. But jokes aside, we can easily imagine a situation in which we have built and deployed a machine learning model (possibly a black box) that accepts some input and returns some predictions. So far, so good.However, with tons of complexity happening before the model (data preprocessing and manipulation), the model itself, and any post-processing of the outputs, many things can go wrong. And in some mission-critical fields (finance, healthcare, or security),…

22 May

Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning

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arXiv:2405.12228v1 Announce Type: new Abstract: Stochastic optimization algorithms, particularly stochastic policy gradient (SPG), report significant success in reinforcement learning (RL). Nevertheless, up to now, that how to speedily acquire an optimal solution for RL is still a challenge. To tackle this issue, this work develops a fast SPG algorithm from the perspective of utilizing a momentum, coined SPG-NM. Specifically, in SPG-NM, a novel type of the negative momentum (NM) technique is applied into the classical SPG algorithm. Different from the existing NM techniques, we have adopted a few hyper-parameters in our SPG-NM algorithm. Moreover, the computational complexity is nearly same…

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…