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20 Nov

Google Kubernetes Engine Now Supports Trillion-Parameter AI Models

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(Image source: Pepperdata) The exponential growth in large language model (LLM) size and the resulting need for high-performance computing (HPC) infrastructure is reshaping the AI landscape. Some of the newer GenAI models have grown to well over a billion parameters, with some approaching 2 trillion. Google Cloud announced that in anticipation of even larger models, it has upgraded its Kubernetes Engine’s capacity to support 65,000-node clusters, up from 15,000-node clusters. This enhancement enables Google Kubernetes Engine (GKE) to operate at a 10x scale compared to two other major cloud providers, according to Google Cloud. While Google Cloud did not specify…

20 Nov

Game Development and Cloud Computing: Benefits of Cloud-Native Game Servers

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Cloud computing is transforming game development, allowing studios to create, launch, and manage games more efficiently than ever. One of the most significant advancements is the use of cloud-native game servers, which are specially designed to operate within cloud environments. Such servers provide game developers with great benefits, including the ability to cope with high player traffic, lower latency easily, and efficient use of resources for better costs. As a result of using cloud-native servers, game developers are able to develop games that are more robust, ubiquitous, and appealing to gamers irrespective of geographical boundaries. What are Cloud-Native Game Servers?…

20 Nov

Celebrating Innovation: Announcing the Finalists of the Databricks Generative AI Startup Challenge

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We are thrilled to unveil the finalists for the Databricks Generative AI Startup Challenge, a competition designed to spotlight innovative early-stage startups harnessing the power of Generative AI on the Databricks Data Intelligence Platform. In collaboration with AWS, this challenge has attracted an impressive array of participants, all striving to push the boundaries of technology and solve real-world problems. With over $1 million in prizes and potential Databricks Ventures funding available for the three winners, the stakes are high.Meet Our FinalistsAfter a rigorous selection process, we are announcing the following four startups as finalists:ChipStackChipStack is tackling one of the most…

20 Nov

Learning the Simplicity of Scattering Amplitudes

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arXiv:2408.04720v2 Announce Type: replace-cross Abstract: The simplification and reorganization of complex expressions lies at the core of scientific progress, particularly in theoretical high-energy physics. This work explores the application of machine learning to a particular facet of this challenge: the task of simplifying scattering amplitudes expressed in terms of spinor-helicity variables. We demonstrate that an encoder-decoder transformer architecture achieves impressive simplification capabilities for expressions composed of handfuls of terms. Lengthier expressions are implemented in an additional embedding network, trained using contrastive learning, which isolates subexpressions that are more likely to simplify. The resulting framework is capable of reducing expressions with…

20 Nov

AI Guided Early Screening of Cervical Cancer

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arXiv:2411.12681v1 Announce Type: cross Abstract: In order to support the creation of reliable machine learning models for anomaly detection, this project focuses on preprocessing, enhancing, and organizing a medical imaging dataset. There are two classifications in the dataset: normal and abnormal, along with extra noise fluctuations. In order to improve the photographs' quality, undesirable artifacts, including visible medical equipment at the edges, were eliminated using central cropping. Adjusting the brightness and contrast was one of the additional preprocessing processes. Normalization was then performed to normalize the data. To make classification jobs easier, the dataset was methodically handled by combining several…

20 Nov

Large Language Models for Combinatorial Optimization of Design Structure Matrix

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arXiv:2411.12571v1 Announce Type: cross Abstract: Combinatorial optimization (CO) is essential for improving efficiency and performance in engineering applications. As complexity increases with larger problem sizes and more intricate dependencies, identifying the optimal solution become challenging. When it comes to real-world engineering problems, algorithms based on pure mathematical reasoning are limited and incapable to capture the contextual nuances necessary for optimization. This study explores the potential of Large Language Models (LLMs) in solving engineering CO problems by leveraging their reasoning power and contextual knowledge. We propose a novel LLM-based framework that integrates network topology and domain knowledge to optimize the sequencing…

20 Nov

Monte Carlo Brings GenAI to Data Observability

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(Treecha/Shutterstock) Monte Carlo has made a name for itself in the field of data observability, where it uses machine learning and other statistical methods to identify quality and reliability issues hiding in big data. With this week’s update, which it made during its IMPACT 2024 event, the company is adopting generative AI to help it take its data observability capabilities to a new level. When it comes to data observability, or any type of IT observability discipline for that matter, there is no magic bullet (or ML model) that can detect all of the potential ways data can go bad.…

20 Nov

Multi-LoRA Composition for Image Generation

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[Submitted on 26 Feb 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled Multi-LoRA Composition for Image Generation, by Ming Zhong and 8 other authors View PDF HTML (experimental) Abstract:Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images. Nonetheless, existing methods face challenges in effectively composing multiple LoRAs, especially as the number of LoRAs to be integrated grows, thus hindering the creation of complex imagery. In this paper, we study multi-LoRA composition through a decoding-centric perspective.…

20 Nov

3D Reconstruction by Looking: Instantaneous Blind Spot Detector for Indoor SLAM through Mixed Reality

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arXiv:2411.12514v1 Announce Type: cross Abstract: Indoor SLAM often suffers from issues such as scene drifting, double walls, and blind spots, particularly in confined spaces with objects close to the sensors (e.g. LiDAR and cameras) in reconstruction tasks. Real-time visualization of point cloud registration during data collection may help mitigate these issues, but a significant limitation remains in the inability to in-depth compare the scanned data with actual physical environments. These challenges obstruct the quality of reconstruction products, frequently necessitating revisit and rescan efforts. For this regard, we developed the LiMRSF (LiDAR-MR-RGB Sensor Fusion) system, allowing users to perceive the in-situ…

20 Nov

Child Speech Recognition in Human-Robot Interaction: Problem Solved?

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[Submitted on 26 Apr 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled Child Speech Recognition in Human-Robot Interaction: Problem Solved?, by Ruben Janssens and 5 other authors View PDF HTML (experimental) Abstract:Automated Speech Recognition shows superhuman performance for adult English speech on a range of benchmarks, but disappoints when fed children's speech. This has long sat in the way of child-robot interaction. Recent evolutions in data-driven speech recognition, including the availability of Transformer architectures and unprecedented volumes of training data, might mean a breakthrough for child speech recognition and social robot…