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A New Google DeepMind Research Reveals a New Kind of Vulnerability that Could Leak User Prompts in MoE Model The routing mechanism of MoE models evokes a great privacy challenge. Optimize LLM large language model performance by selectively activating only a fraction of its total parameters while making it highly susceptible to adversarial data extraction through routing-dependent interactions. This risk, most obviously present with the ECR mechanism, would let an attacker siphon out user inputs by putting their crafted queries in the same processing batch as the targeted input. Read the full article: https://lnkd.in/e9FUDjfP Paper: https://lnkd.in/eMQ3P5v9

Top 15+ GPU Server Hosting Providers in 2025 High-performance computing has become crucial for various businesses, including scientific research and Artificial Intelligence (AI), in today’s data-driven society. By providing strong, scalable, and reasonably priced cloud computing resources, GPU hosting companies are essential in enabling these taxing workloads. Selecting the best GPU hosting company is essential for guaranteeing performance, dependability, and cost-effectiveness in light of the growing need for AI, machine learning, deep learning, and other data-intensive application Read the full article: https://lnkd.in/eVyK6Mhj

Microsoft Researchers Introduce Magentic-One: A Modular Multi-Agent System Focused on Enhancing AI Adaptability and Task Completion Across Benchmark Tests Agentic systems are a progressive branch of artificial intelligence that aims to create solutions capable of autonomously handling complex, multi-step tasks across various environments. These systems go beyond the typical scope of machine learning models by incorporating capabilities that allow them to perceive and act within real-world digital settings, integrating knowledge, reasoning, and adaptable decision-making processes. Read the full article: https://lnkd.in/e2gApynp Paper: https://lnkd.in/eSxnQtG3

DELTA: A Novel AI Method that Efficiently (10x Faster) Tracks Every Pixel in 3D Space from Monocular Videos Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. Existing methods face challenges in achieving detailed 3D tracking because they often track only a few points, which need more detail for full-scene understanding. They also demand computational power, making it difficult to handle long videos efficiently. Additionally, many of them must be fixed to maintain accuracy over extended sequences, as problems like camera movement and object occlusion cause the model to lose track or introduce errors. Read the full article: https://lnkd.in/dQJRgESz Paper: https://lnkd.in/dUyP4Nxw

Anthropic Introduces Claude 3.5 Sonnet: The AI That Understands Text, Images, and More in PDFs Information overload presents significant challenges in extracting insights from documents containing both text and visuals, such as charts, graphs, and images. Despite advancements in language models, analyzing these multimodal documents remains difficult. Conventional AI models are limited to interpreting plain text, often struggling to process complex visual elements embedded in documents, which hinders effective document analysis and knowledge extraction. Read the full article: https://lnkd.in/e3yNjHqi

UniMTS: A Unified Pre-Training Procedure for Motion Time Series that Generalizes Across Diverse Device Latent Factors and Activities Recognition of human motion using time series from mobile and wearable devices is commonly used as key context information for various applications, from health condition monitoring to sports activity analysis to user habit studies. However, collecting large-scale motion time series data remains challenging due to security or privacy concerns. In the motion time series domain, the lack of datasets and an effective pre-training task makes it difficult to develop similar models that can operate with limited data. Read the full article: https://lnkd.in/eMUvNqCy Paper: https://lnkd.in/eZyT_9Ad

Top 20 AI Graphic Design Tools in 2025 The advent of AI has revolutionized the landscape of graphic design. AI graphic design tools are reshaping the way designers work, offering unprecedented efficiency, creativity, and innovation. These tools can automate repetitive tasks, generate fresh ideas, and accelerate the design process, empowering designers to focus on higher-level creative endeavors. Read the full article: https://lnkd.in/e5p6rFD2

How to Become a Data Analyst? Step by Step Guide In today’s data-driven world, data analysts play a crucial role in various domains. Businesses use data extensively to inform strategy, enhance operations, and obtain a competitive edge. Professionals known as data analysts enable this by turning complicated raw data into understandable, useful insights that help in decision-making. They navigate the whole data analysis cycle, from discovering and collecting pertinent data to getting it ready for analysis, interpreting the findings, and formulating suggestions. Read the full article: https://lnkd.in/e8qq6c8K

OpenAI Introduces ‘Predicted Outputs’ Feature: Speeding Up GPT-4o by ~5x for Tasks like Editing Docs or Refactoring Code The use of large language models like GPT-4o and GPT-4o-mini has brought significant advancements in natural language processing, enabling high-quality response generation, document rewriting, and productivity enhancements across numerous applications. However, one of the biggest challenges these models face is latency. Whether it’s updating a blog post or refining lines of code, the lag associated with response generation can hinder seamless user experiences. Read the full article: https://lnkd.in/eJ7vEWFb

ShadowKV: A High-Throughput Inference System for Long-Context LLM Inference Large language models (LLMs) are getting better at scaling and handling long contexts. As they are being used on a large scale, there has been a growing demand for efficient support of high-throughput inference. However, efficiently serving these long-context LLMs presents challenges related to the key-value (KV) cache, which stores previous key-value activations to avoid re-computation. But as the text they handle gets longer, the increasing memory footprint and the need to access it for each token generation both result in low throughput when serving long-context LLMs. Read the full article: https://lnkd.in/eq5G2c9i Paper: https://lnkd.in/eVuJtJcD