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Amazon
3h
188
Image Credit: Amazon
Deploy Amazon SageMaker Projects with Terraform Cloud
- Amazon SageMaker Projects empower data scientists to self-serve AWS tooling for organizing ML lifecycle and standardizing resources.
- Enabling SageMaker Projects with Terraform Cloud removes CloudFormation dependency for AWS enterprises.
- SageMaker Projects map to AWS Service Catalog products, now designated for Terraform Cloud via AWS-native infrastructure.
- Prerequisites for deployment include AWS account access, SageMaker Studio domain, Terraform, and Terraform Cloud account.
- Deployment involves cloning the repository, creating a Service Catalog portfolio, and setting up necessary variables.
- Initiating and applying the Terraform Cloud workspace are crucial steps in deploying SageMaker Projects.
- Customize the example with additional Terraform in the project template and manage cleanup using Terraform commands.
- The process enables deploying and provisioning SageMaker Projects solely through Terraform in an enterprise setup.
- The author, Max Copeland, is a Machine Learning Engineer at AWS specializing in ML-Ops, data science, and AI.
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Amazon
3h
124
Image Credit: Amazon
Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI
- Generative AI is revolutionizing business operations with applications like Amazon's Rufus and a Seller Assistant, including non-conversational implementations leveraging large language models for autonomous operations.
- Amazon.com showcases examples of generative AI applications: listing creation, prescription processing in Amazon Pharmacy, review highlights, and Ads creative image/video generation.
- Generative AI simplifies high-quality listing creation on Amazon by proactively acquiring product information from multiple sources, benefiting selling partners and customers.
- Amazon Pharmacy utilizes an AI system for prescription processing by integrating human experts with LLMs in an agentic workflow design for more accurate medication instructions.
- Amazon implements AI-generated customer review highlights to distill customer feedback at scale, enabling quick decision-making for shoppers through concise summaries.
- Amazon Ads leverages generative AI for creative image and video generation, democratizing visual content creation for advertisers and improving click-through rates.
- Key technical learnings from these implementations highlight the importance of task decomposition, hybrid architectures, cost optimization, quality assurance, HITL, and responsible AI practices.
- The comprehensive approach to generative AI solutions involves combining various models, maintaining quality, and adhering to responsible AI practices for efficiency and scalability.
- Successful generative AI implementation requires a strong partnership between AWS services, engineering practices, and business understanding to solve real business problems effectively.
- Authors of the article include experts in AI/ML, product knowledge, technology, and engineering disciplines, emphasizing the diverse skillsets driving generative AI innovations.
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Siliconangle
6h
20
Image Credit: Siliconangle
IBM’s data-first AI strategy puts hybrid in the driver’s seat
- IBM is focusing on a data-first AI strategy with a hybrid data architecture that adapts to data location and usage.
- The company is experiencing promising early results with expectations of significant future gains.
- IBM emphasizes unifying structured and unstructured data sources through watsonx.data for better accuracy and more use cases.
- IBM's hybrid data architecture ensures consistent performance across on-prem, cloud, and hybrid environments, with governance playing a crucial role in enabling insight generation and compliance.
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Medium
8h
67
Image Credit: Medium
The Best of CVPR 2025 Series — Day 3
- The Best of CVPR 2025 Series — Day 3 summarizes four groundbreaking papers challenging conventional boundaries in vision research.
- FLAIR (Fine-grained Language-informed Image Representations) enhances the fine-grained alignment between image regions and textual descriptions, surpassing CLIP in localized understanding.
- FLAIR's token-level grounding superiority is highlighted in image-text associations, leading to state-of-the-art results in multimodal retrieval and zero-shot segmentation.
- OpenMIBOOD introduces a benchmark suite for Out-of-Distribution (OOD) detection in medical imaging, aiming to improve models' reliability in handling unexpected inputs.
- DyCON, a semi-supervised learning framework for medical image segmentation, utilizes uncertainty as a signal to enhance lesion segmentation accuracy with minimal annotation.
- RANGE generates multi-resolution geo-embeddings by retrieving visual features, surpassing traditional contrastive models in geospatial tasks and performance.
- FLAIR, OpenMIBOOD, DyCON, and RANGE exemplify the advancements in vision research, emphasizing domain-specific reliability, interpretability, and performance.
- These innovative approaches promise more capable, trustworthy, and transparent AI models applicable to real-world critical workflows.
- The article concludes the coverage of CVPR 2025, highlighting the importance of smarter learning and purpose-driven advancements in AI research.
- For deeper insights into AI and professional growth, connections on LinkedIn and participation in Voxel51 events are encouraged.
- Links are provided for further reading on the featured papers from CVPR 2025.
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Medium
8h
3
Image Credit: Medium
The Best of CVPR 2025 Series — Day 2
- The Best of CVPR 2025 Series — Day 2 focuses on highlighting research papers that address safety, trust, fairness, and usability across industries in computer vision.
- It introduces SmartHome-Bench, a benchmark for video anomaly detection in smart homes, emphasizing trust, transparency, and reasoning.
- SmartHome-Bench achieved a notable 11.62% improvement in anomaly detection accuracy and showcased the highest accuracy of 79.05% with Claude-3.5-sonnet.
- CSR (Concept-based Similarity Reasoning) was introduced for medical image analysis, offering interpretability, transparency, and real-time doctor interaction.
- OFER was presented as a method for reconstructing 3D faces with diverse expressions from single occluded images, improving quality and diversity of expression under occlusion.
- Multi-Flow, a multi-view industrial anomaly detection architecture, outperformed prior baselines, offering better reliability in spotting anomalies across different views of objects.
- The research presented in the article showcases advancements in AI that prioritize trust, transparency, and real-world practicality in fields like smart homes, medical imaging, and industrial anomaly detection.
- The work addresses the limitations of existing models and offers new frameworks and methodologies to enhance the performance and usability of AI systems in various domains.
- Researchers are striving to improve collaboration between humans and AI, enabling more transparent and interactive processes in decision-making and problem-solving.
- The findings from CVPR 2025 Day 2 have significant implications for the development of more trustworthy, explainable, and robust AI systems for a wide range of applications.
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Medium
8h
208
Image Credit: Medium
The Best of CVPR 2025 Series — Day 1
- The "Best of CVPR" virtual meetup and blog series highlights research in computer vision, aiming to connect it to real-world problems and showcase its potential impact on communities.
- OpticalNet, an AI benchmark introduced in CVPR 2025, breaks traditional optical resolution limits to enable affordable, non-invasive subwavelength imaging for various applications.
- SkeletonDiffusion, a latent diffusion model for human motion prediction, introduces structural awareness for more realistic and accurate forecasts, benefiting applications like autonomous driving and healthcare.
- A lightweight, few-shot adaptation of the Grounding-DINO object detection model is tailored for agricultural tasks, allowing accurate detection with minimal annotated data.
- Drive4C, a benchmark for language-guided autonomous driving, exposes weaknesses in current large language models, emphasizing the need for improvements in spatial, temporal, and physical understanding.
- The showcased papers emphasize the necessity for AI systems that can reason, adapt, and explain, especially in high-stakes domains like healthcare, agriculture, and autonomous driving.
- CVPR 2025 encourages deeper thinking with vision beyond just improved imaging, focusing on developing systems that reason effectively. Stay tuned for more insights in the upcoming days of the series.
- Connect with the author on LinkedIn for further AI discussions and growth opportunities. Explore Voxel51 events and job openings for potential involvement in the field.
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Medium
11h
331
Image Credit: Medium
What is Artificial Intelligence (AI) and How Does it Work
- Artificial Intelligence (AI) has a long history, with roots dating back to ancient times when humans fantasized about objects with human-like reasoning powers.
- In the 1940s, significant developments in AI emerged, with figures like John Von Neumann and Warren McCulloch contributing to the field.
- The 1950s marked pivotal moments with Alan Turing's 'Turing Test' and the creation of the first AI program, Logic Theorist.
- The 1950s and 1960s saw advancements like General Problem Solver (GPS) and the Lisp programming language in AI.
- The 1970s and 1980s experienced a slowdown in AI research, but the 1980s brought hope with deep learning techniques and expert systems.
- Since the 1990s, AI has rapidly developed, showcasing achievements like IBM's Deep Blue defeating Garry Kasparov and Watson dominating Jeopardy.
- AI today utilizes machine learning (ML) and natural language processing (NLP) to excel in various fields, from education and healthcare to technology and manufacturing.
- AI systems analyze data, identify correlations, and predict future patterns, all based on cognitive processes that enable decision-making.
- AI is distinguished from cognitive computing, with the former simulating human intelligence independently while the latter aids humans in decision-making.
- AI benefits include quick data processing, enhanced accuracy, and efficiency surpassing human capabilities in certain tasks.
- The article explores different types of AI like Narrow AI, General AI, and Super AI, emphasizing the increasing role AI plays in daily life applications.
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Medium
1h
261
Using MinIO to Store Machine Learning Data for Training and Inference
- MinIO is a high-performance, Kubernetes-native object storage system that is fully S3-compatible and optimized for speed and simplicity.
- Reasons to use MinIO in ML Pipelines include S3 API compatibility, on-prem & hybrid friendliness, high-speed storage, easy scalability, and Kubernetes-native deployment.
- An important use case of MinIO is storing ML training data for future inference, such as uploading images for training a computer vision model and later using them for predictions.
- MinIO serves as a central storage layer for ML workflows, allowing the upload of raw data, preprocessing outputs, versioning datasets, sharing data across training jobs, and integrating with popular ML frameworks for inferencing.
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Medium
2h
182
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What AI Still Sucks At (And Why That’s a Good Thing for You)
- AI excels in some areas but still struggles with understanding emotions and creating genuine connections that humans can provide.
- AI is great at recognizing patterns but often fails to generate creative or unconventional ideas like humans can.
- AI lacks the ability to sense feelings or instinctively pick up on cues, making human intuition and interpersonal skills invaluable.
- AI may offer suggestions based on data, but it cannot fully comprehend an individual's personal preferences, values, or experiences like humans can.
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Amazon
3h
67
Image Credit: Amazon
How ZURU improved the accuracy of floor plan generation by 109% using Amazon Bedrock and Amazon SageMaker
- ZURU Tech developed Dreamcatcher, a user-friendly platform for building design and construction collaboration.
- They collaborated with AWS to create a more accurate text-to-floor plan generator using generative AI.
- ZURU's evaluation framework ensured accuracy in generating 2D floor plans based on user prompts.
- They found success using a GPT2 LLM approach for accurate floor plan generation.
- ZURU employed prompt engineering and fine-tuning with Llama 3B variants to improve model accuracy.
- Dataset preparation involved gathering floor plans and streamlining the review process using a custom application.
- Dynamic few-shot prompting and prompt decomposition methods enhanced the relevancy and quality of generated content.
- The workflow involved using Amazon Bedrock and Amazon SageMaker for AI model optimization.
- Fine-tuning approaches included full parameter fine-tuning and Low-Rank Adaptation for optimized performance.
- The evaluation framework compared different approaches, with prompt engineering and full fine-tuning showing improved accuracy over baseline models.
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Amazon
4h
196
Image Credit: Amazon
Architect a mature generative AI foundation on AWS
- Generative AI applications are complex systems involving workflows, FMs, and domain-specific data, utilizing patterns like Retrieval Augmented Generation (RAG).
- Organizations suffer from siloed AI initiatives resulting in inefficiencies, redundancies, and inconsistent governance frameworks.
- Unified generative AI platforms are adopted to centralize governance and operations, offering core services and reusable components.
- A generative AI foundation streamlines development, enables scaling, reduces risk, optimizes costs, and accelerates innovation.
- Key components of the foundation include hubs for models and tools, gateways for secure access, and orchestration for workflows.
- Model customization techniques like continued pre-training and fine-tuning are crucial for customization depth and task-specific learning.
- Data management involves integrating data sources, processing pipelines, and tools for cataloging data to support RAG and model customization.
- GenAIOps encompasses managing AI systems, from operationalizing applications to training models, with a focus on governance and lifecycle management.
- Observability in generative AI systems requires collecting logs, metrics, and traces to optimize performance and troubleshoot issues effectively.
- To address challenges responsibly, tools and techniques aligning with responsible AI dimensions like privacy, transparency, fairness, and governance are crucial.
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Amazon
5h
27
Image Credit: Amazon
Using Amazon OpenSearch ML connector APIs
- Amazon OpenSearch offers machine learning (ML) connectors for data augmentation before ingestion.
- Two highlighted connectors are Amazon Comprehend for language detection and Amazon Bedrock for semantic search.
- To use Amazon Comprehend with OpenSearch, roles, permissions, and connectors need to be set up.
- For Amazon Bedrock, an ML connector is created to utilize the Titan Text Embeddings model v2.
- Steps involve setting up connectors, registering APIs, and creating pipelines for ML integration.
- Testing Amazon Comprehend API involves detecting language in text, while Amazon Bedrock enables multilingual semantic search.
- Post successful setup, language documents are indexed with embeddings for semantic searches.
- The ML connector approach offers simplified architecture, operational benefits, and cost efficiency.
- The full demo available on GitHub showcases the process of using ML connectors with OpenSearch.
- Authors of the post are John Trollinger, Principal Solutions Architect, and Shwetha Radhakrishnan, Solutions Architect at AWS.
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Amazon
5h
61
Image Credit: Amazon
Bridging the gap between development and production: Seamless model lifecycle management with Amazon Bedrock
- Amazon Bedrock is a managed service offering foundation models and capabilities for generative AI applications, facilitating seamless model lifecycle management.
- It introduces Model Share and Model Copy features to streamline AI development lifecycle, enabling collaboration, resource utilization, and security.
- Model Share allows sharing fine-tuned custom models between AWS accounts within the same organization, aiding in development-to-production workflows.
- Model Copy replicates custom models across Regions for global deployment, improving performance, availability, disaster recovery, and compliance.
- These features ensure smooth transitions, team collaboration, and resource optimization for AI applications, aligning with AWS best practices.
- Model Copy reduces latency, enhances availability, improves disaster recovery, and supports compliance by distributing models across Regions.
- A practical use case highlights how Model Share and Copy can streamline moving a model from development to production, emphasizing security and efficiency.
- By implementing these features and best practices, organizations can enhance agility, efficiency, and competitiveness in AI model management and deployment.
- Continuous monitoring, evaluation, and refinement are essential for maintaining model effectiveness and alignment with business needs during the AI development process.
- Amazon Bedrock equips organizations to effectively manage AI models, optimize workflows, and maximize the value of investments in model customization and fine-tuning.
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Medium
5h
117
Image Credit: Medium
Adversarial Robustness Is Not Just Related to AI— It’s a Physics Problem
- Adversarial vulnerability in AI models stems from a lack of physical understanding of the world.
- Neural networks rely on statistical correlations rather than causal, physical reasons for object recognition.
- Humans use physical priors like gravity and light reflection for consistent object perception.
- AI systems lack physical grounding, making them vulnerable to adversarial perturbations.
- Physics provides invariances and symmetries essential for robust perception in humans.
- AI's learned representations exist apart from the physical manifold of the environment, leading to vulnerabilities.
- The uncertainty of reality's dimensionality and structure presents a challenge to achieving adversarial robustness.
- Neural networks have fundamental limitations due to their disconnect from actual physical perception.
- To enhance AI robustness, a physics-informed approach with differential geometry and causal relationships is needed.
- Adopting a physics-grounded framework can lead to AI systems that understand and reason about the world reliably.
- Embracing uncertainty, interdisciplinary research, and collaborations are key to advancing AI with a physics-informed perspective.
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Global Fintech Series
11h
323
Image Credit: Global Fintech Series
How FinTechs Are Using AI for Smarter AML Surveillance
- AI is revolutionizing AML surveillance by providing more efficient and intelligent detection mechanisms to combat financial crime.
- AI-driven analytics allow for the identification of hidden patterns, real-time detection of suspicious behaviors, and streamlined compliance efforts.
- FinTechs are leading the transformation by integrating AI into their AML strategies to enhance risk assessment and fraud detection.
- AI shifts AML surveillance from rule-based to dynamic approaches, enabling real-time analysis, risk assessment, and prioritization of high-risk cases.
- AI minimizes false positives, anticipates risks with predictive analytics, automates compliance processes, and evolves to counter evolving financial crimes.
- Challenges for small financial institutions in adopting AI for AML include high costs, lack of expertise, data availability issues, regulatory complexities, and legacy system integration challenges.
- Strategies to overcome AI adoption barriers include industry partnerships, cloud-based solutions, upskilling the workforce, phased implementation, and exploring pre-configured AI solutions.
- AI is enhancing financial institutions' ability to prevent financial crimes by reducing false positives, automating compliance tasks, and leveraging predictive analytics.
- The integration of AI and human expertise is crucial for a resilient and trustworthy AML ecosystem, ensuring ethical oversight and accurate interpretation of financial behaviors.
- The future of AML surveillance lies in information sharing, regulatory advancements, and further developments in AI-driven technologies.
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