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Arxiv
1w
411
Image Credit: Arxiv
Marconi: Prefix Caching for the Era of Hybrid LLMs
- Hybrid models that combine the language modeling capabilities of Attention layers with the efficiency of Recurrent layers have gained traction for supporting long contexts in Large Language Model serving.
- Marconi is a system that supports efficient prefix caching with Hybrid LLMs.
- Marconi uses novel admission and eviction policies that assess potential cache entries based on recency, reuse likelihood, and compute savings.
- Marconi achieves significantly higher token hit rates compared to state-of-the-art prefix caching systems.
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Arxiv
1w
58
Image Credit: Arxiv
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
- Micro-Action Recognition (MAR) has gained attention for its role in non-verbal communication and emotion analysis.
- A novel approach called Prototypical Calibrating Ambiguous Network (PCAN) is proposed to address the ambiguity in MAR.
- PCAN employs a hierarchical action-tree to identify and categorize ambiguous samples into distinct sets.
- Extensive experiments demonstrate the superior performance of PCAN compared to existing approaches.
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Arxiv
1w
386
Image Credit: Arxiv
Tensor Product Attention Is All You Need
- The paper introduces Tensor Product Attention (TPA), a novel attention mechanism that uses tensor decompositions to represent queries, keys, and values compactly.
- TPA significantly reduces the memory overhead during inference by shrinking the size of the key-value (KV) cache.
- Based on TPA, the Tensor ProducT ATTenTion Transformer (T6) is introduced as a new model architecture for sequence modeling.
- T6 outperforms standard Transformer baselines in language modeling tasks, achieving improved model quality and memory efficiency.
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23 Likes
Arxiv
1w
243
Image Credit: Arxiv
Real-time Verification and Refinement of Language Model Text Generation
- Large language models (LLMs) sometimes generate factually incorrect answers, posing a critical challenge.
- The proposed Streaming-VR approach allows real-time verification and refinement of LLM outputs.
- Streaming-VR checks and corrects tokens as they are being generated, ensuring factual accuracy.
- Comprehensive evaluations show that Streaming-VR is an efficient solution compared to prior methods.
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14 Likes
Arxiv
1w
344
Image Credit: Arxiv
Dreamweaver: Learning Compositional World Models from Pixels
- Dreamweaver is a neural architecture designed to discover hierarchical and compositional representations from raw videos.
- The model leverages a Recurrent Block-Slot Unit (RBSU) to decompose videos into their constituent objects and attributes.
- Dreamweaver outperforms current state-of-the-art baselines for world modeling and allows the generation of novel videos by recombining attributes from previously seen objects.
- The research is evaluated under the DCI framework across multiple datasets.
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20 Likes
Medium
1w
403
Image Credit: Medium
Fueling the AI Revolution: Understanding Cloud Infrastructure for AI
- Cloud infrastructure plays a crucial role in fueling the AI revolution by providing flexibility, scalability, and raw power.
- Training modern AI models, especially deep learning systems, requires vast amounts of data and massive computational power, which cloud platforms support efficiently.
- Scalability in the cloud allows AI workloads to match varying needs, ensuring cost-efficiency by scaling resources up or down instantly.
- Access to powerful hardware like GPUs, TPUs, and custom chips from cloud providers eliminates the need for expensive in-house investments.
- The cloud enables fast experimentation, easy deployment, and access to managed services that streamline AI and machine learning processes.
- Cloud-based AI solutions not only offer flexibility and speed but also lead to cost-effectiveness by paying for what you use.
- Amazon Web Services, Google Cloud Platform, and Microsoft Azure offer deep AI capabilities alongside unique strengths in their AI tools and services.
- Choosing the right cloud platform for AI depends on factors like existing infrastructure, project focus, team familiarity, budget, and compliance requirements.
- Cloud infrastructure is essential for organizations looking to leverage AI effectively, enabling scalability, advanced hardware access, and managed tools for innovation.
- Understanding the strengths of different cloud platforms like AWS, GCP, and Azure is crucial for making informed decisions and driving long-term innovation.
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Medium
1w
382
Image Credit: Medium
Why Most AIs Are So Dumb
- Most Large Language Models (LLMs) are considered dumb, with IQs lower than expected.
- According to tracking data from TrackingAI.org, Gemini Pro 2.5, ChatGPT o1, and Claude Sonnet 3.7 are the most advanced models.
- Google's Bard model was considered extremely poor, and it has been replaced by Gemini Pro 2.5.
- The author prefers working with high IQ models like Gemini Pro 2.5, Claude Sonnet 3.7, and DeepSeek-R1.
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23 Likes
Medium
1w
210
Image Credit: Medium
One-Hot Encoding in NLP: A Gentle Introduction
- One-hot encoding is a simple yet foundational method in NLP that converts categorical data, like words, into numerical format.
- It represents each word with a binary vector, where only one position is 'hot' (1) and the rest remain 'cold' (0).
- One-hot encoding is used to transform text data for NLP tasks such as sentiment analysis before feeding it into machine learning algorithms or deep neural networks.
- While one-hot encoding has limitations, it serves as an entry point to more advanced NLP techniques and provides valuable insight into language interpretation by machines.
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12 Likes
Medium
1w
214
Image Credit: Medium
Understanding Model Context Protocol (MCP): What Every Tech Professional Should Know
- Model Context Protocol (MCP) is a foundational framework for large language models to process information and generate responses.
- MCP governs the context window, token management, and response parameters of AI models.
- MCP enables AI assistants to maintain conversation history, deliver consistent solutions, and analyze legal documents.
- Understanding MCP is essential for effective implementation of AI solutions.
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12 Likes
Medium
1w
406
Image Credit: Medium
From Boss to Bot Whisperer: The Weird Future of Work
- Companies are starting to hire humans to manage AI agents, as these agents are becoming co-workers.
- New AI agents can handle various tasks such as customer service, marketing content writing, and code generation.
- AI agents may require training, feedback, and alignment, similar to human employees.
- Roles such as project managers, team leads, QA specialists, and culture coaches may emerge to manage AI agents.
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19 Likes
Towards Data Science
1w
196
The Invisible Revolution: How Vectors Are (Re)defining Business Success
- Vectors play a crucial role in modern business success, offering a dynamic, multidimensional view of underlying connections and patterns.
- Understanding vector thinking is essential for decision-making, customer engagement, and data protection in today's data-focused world.
- Vectors represent a different concept of relationships, enabling accurate measurement in a multidimensional space for tasks like fraud detection and customer analysis.
- Using vector-based computing, businesses can analyze patterns and anomalies, leading to more precise decision-making and personalized customer experiences.
- Vector-based systems transform complex data into actionable insights, changing how businesses operate and make strategic decisions.
- Cosine similarity and Euclidean distance are key metrics that influence the performance and outcomes of recommendation engines, fraud detection systems, and more.
- Real-world applications, such as healthcare early detection systems using vector representations, showcase the practical impact of leveraging vectors for complex data analysis.
- Leaders who grasp vector concepts can make informed decisions, drive innovation in personalization and fraud detection, and position their businesses for success in an AI-driven landscape.
- Adopting vector-based thinking is crucial for businesses to stay competitive and thrive in an age where data and AI innovations define success.
- Embracing vectors as a fundamental tool for data literacy and decision-making can propel businesses forward in the evolving landscape of AI technology.
- Vectors are at the core of the invisible revolution reshaping business strategies, and mastering their principles is vital for leaders to navigate and lead in the data-driven future.
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10 Likes
Medium
1w
290
Image Credit: Medium
From Sensor Data to Smart Decisions: How We Built a Contactless Security System with AI
- During Deep Blue Season 6, a team of students tackled the urgent need to automate residential society gate security.
- The team designed an AI-powered contactless gate system using facial recognition, sensor data, and automation logic.
- Initially designed for building entry, the system was moved to the main gate to accommodate the varied ways people enter societies.
- The system had two parallel flows for walk-ins and vehicles, each with its own logic and triggers.
- The core components used in the system included Python for logic, OpenCV for face detection, and IR temperature sensors for monitoring.
- Challenges like face recognition with masks and gate triggering for vehicles were addressed through training and design adjustments.
- Real-world behaviors, such as drivers lowering windows for interaction, were considered to ensure smooth vehicle entry.
- Feedback mechanisms and defined fallback flows were implemented for every possible failure scenario.
- Lessons learned included the importance of user experience design and interaction with real-world users.
- The team presented a fully working prototype to mentors, showcasing real-time face recognition and gate control.
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17 Likes
Amazon
1w
105
Image Credit: Amazon
Reduce ML training costs with Amazon SageMaker HyperPod
- Training large-scale frontier models is computationally intensive and can take weeks to months to complete a single job, with potential hardware failures causing significant disruptions.
- High instance failure rates during distributed training highlight the challenges faced during large-scale model training.
- As cluster sizes grow, the likelihood of hardware failures increases, leading to decreased mean time between failures (MTBF).
- Amazon SageMaker HyperPod is a resilient solution that automates hardware issue detection and replacement, minimizing downtime and reducing training costs.
- By utilizing SageMaker HyperPod, manual interventions for hardware failures, root cause analysis, and system recovery are minimized, enhancing system reliability.
- HyperPod's automated mechanisms result in faster failure detection, shorter replacement times, and rapid job resumption, contributing to reduced total training time.
- SageMaker HyperPod's benefits are significant for large clusters, offering health monitoring agents, ML tool integrations, and insights into cluster performance for efficient model development.
- Empirical data shows that HyperPod reduces total training time by up to 32% in a 256-instance cluster with a 0.05% failure rate, translating to substantial cost savings.
- Automating hardware issue detection and resolution with SageMaker HyperPod enables faster time-to-market, leading to more effective innovation delivery.
- By addressing the reliability challenges of large-scale model training, HyperPod allows ML teams to focus on model innovation, streamlining infrastructure management.
- SageMaker HyperPod's contribution to reducing downtime and optimizing resource utilization makes it a valuable solution for organizations engaged in frontier model training.
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6 Likes
Towards Data Science
1w
187
How to Measure Real Model Accuracy When Labels Are Noisy
- Ground truth is never perfect, with errors in measurements and human annotations, raising concerns on evaluating models using imperfect labels.
- Exploring methods to estimate a model's 'true' accuracy when labels are noisy is essential.
- Errors in both model predictions and ground truth labels can mislead accuracy measurements.
- The true accuracy of a model can vary based on error correlations between the model and ground truth labels.
- Indications show that the model's true accuracy depends on the overlap of errors with ground truth errors.
- If errors are uncorrelated, a probabilistic estimate formula can help derive a more precise true accuracy.
- In practices where errors may be correlated, the true accuracy tends to lean towards the lower bound.
- Understanding the difference between measured and true accuracy is crucial for accurate model evaluation.
- Targeted error analysis and multiple independent annotations are recommended for handling noisy labels in model evaluation.
- In summary, the range of true accuracy depends on ground truth error rates, with considerations for error correlations in real-world scenarios.
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9 Likes
Siliconangle
1w
37
Image Credit: Siliconangle
Insurance technology startup Ominimo raises €10M in funding
- Insurance technology startup Ominimo has raised €10 million in funding from Zurich Insurance Group Ltd.
- Zurich Insurance took a 5% stake in Ominimo, valuing the startup at €200 million.
- Ominimo uses AI to generate quotes and streamline the sign-up process for customers.
- The funding will be used to expand into new markets and offer property insurance plans.
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2 Likes
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