#AI-powered data annotation | Explore Tumblr posts and blogs

apcseo · 10 months ago

Text

Meta and Microsoft Unveil Llama 2: An Open-Source, Versatile AI Language Model

In a groundbreaking collaboration, Meta and Microsoft have unleashed Llama 2, a powerful large language AI model designed to revolutionise the AI landscape. This sophisticated language model is available for public use, free of charge, and boasts exceptional versatility. In a strategic move to enhance accessibility and foster innovation, Meta has shared the code for Llama 2, allowing researchers to explore novel approaches for refining large language models.

Llama 2 is no ordinary AI model. Its unparalleled versatility allows it to cater to diverse use cases, making it an ideal tool for established businesses, startups, lone operators, and researchers alike. Unlike fine-tuned models that are engineered for specific tasks, Llama 2’s adaptability enables developers to explore its vast potential in various applications.

Microsoft, as a key partner in this venture, will integrate Llama 2 into its cloud computing platform, Azure, and its renowned operating system, Windows. This strategic collaboration is a testament to Microsoft’s commitment to supporting open and frontier models, as well as their dedication to advancing AI technology. Notably, Llama 2 will also be available on other platforms, such as AWS and Hugging Face, providing developers with the freedom to choose the environment that suits their needs best.

During the Microsoft Inspire event, the company announced plans to embed Llama 2’s AI tools into its 360 platform, further streamlining the integration process for developers. This move is set to open new possibilities for innovative AI solutions and elevate user experiences across various industries.

Meta’s collaboration with Qualcomm promises an exciting future for Llama 2. The companies are working together to bring Llama 2 to laptops, phones, and headsets, with plans for implementation starting next year. This expansion into new devices demonstrates Meta’s dedication to making Llama 2’s capabilities more accessible to users on-the-go.

Llama 2’s prowess is partly attributed to its extensive pretraining on publicly available online data sources, including Llama-2-chat. Leveraging publicly available instruction datasets and over 1 million human annotations, Meta has honed Llama 2’s understanding and responsiveness to human language.

In a Facebook post, Mark Zuckerberg, the visionary behind Meta, highlighted the significance of open-source technology. He firmly believes that an open ecosystem fosters innovation by empowering a broader community of developers to build with new technology. With the release of Llama 2’s code, Meta is exemplifying this belief, creating opportunities for collective progress and inspiring the AI community.

The launch of Llama 2 marks a pivotal moment in the AI race, as Meta and Microsoft collaborate to offer a highly versatile and accessible AI language model. With its open-source approach and availability on multiple platforms, Llama 2 invites developers and researchers to explore its vast potential across various applications. As the ecosystem expands, driven by Meta’s vision for openness and collaboration, we can look forward to witnessing groundbreaking AI solutions that will shape the future of technology.

This post was originally published on: Apppl Combine

#Apppl Combine #Ad Agency #AI Model #AI Tools #Llama 2 #facebook #Llama 2 Chat #META AI Model #Meta and Microsoft #Microsoft #Technology

2 notes · View notes

outsourcebigdata · 1 year ago

Text

Best data extraction services in USA

In today's fiercely competitive business landscape, the strategic selection of a web data extraction services provider becomes crucial. Outsource Bigdata stands out by offering access to high-quality data through a meticulously crafted automated, AI-augmented process designed to extract valuable insights from websites. Our team ensures data precision and reliability, facilitating decision-making processes.

For more details, visit: https://outsourcebigdata.com/data-automation/web-scraping-services/web-data-extraction-services/.

About AIMLEAP

Outsource Bigdata is a division of Aimleap. AIMLEAP is an ISO 9001:2015 and ISO/IEC 27001:2013 certified global technology consulting and service provider offering AI-augmented Data Solutions, Data Engineering, Automation, IT Services, and Digital Marketing Services. AIMLEAP has been recognized as a ‘Great Place to Work®’.

With a special focus on AI and automation, we built quite a few AI & ML solutions, AI-driven web scraping solutions, AI-data Labeling, AI-Data-Hub, and Self-serving BI solutions. We started in 2012 and successfully delivered IT & digital transformation projects, automation-driven data solutions, on-demand data, and digital marketing for more than 750 fast-growing companies in the USA, Europe, New Zealand, Australia, Canada; and more. 

-An ISO 9001:2015 and ISO/IEC 27001:2013 certified  -Served 750+ customers  -11+ Years of industry experience  -98% client retention  -Great Place to Work® certified  -Global delivery centers in the USA, Canada, India & Australia 

Our Data Solutions

APISCRAPY: AI driven web scraping & workflow automation platform APISCRAPY is an AI driven web scraping and automation platform that converts any web data into ready-to-use data. The platform is capable to extract data from websites, process data, automate workflows, classify data and integrate ready to consume data into database or deliver data in any desired format. 

AI-Labeler: AI augmented annotation & labeling solution AI-Labeler is an AI augmented data annotation platform that combines the power of artificial intelligence with in-person involvement to label, annotate and classify data, and allowing faster development of robust and accurate models.

AI-Data-Hub: On-demand data for building AI products & services On-demand AI data hub for curated data, pre-annotated data, pre-classified data, and allowing enterprises to obtain easily and efficiently, and exploit high-quality data for training and developing AI models.

PRICESCRAPY: AI enabled real-time pricing solution An AI and automation driven price solution that provides real time price monitoring, pricing analytics, and dynamic pricing for companies across the world. 

APIKART: AI driven data API solution hub  APIKART is a data API hub that allows businesses and developers to access and integrate large volume of data from various sources through APIs. It is a data solution hub for accessing data through APIs, allowing companies to leverage data, and integrate APIs into their systems and applications. 

Locations: USA: 1-30235 14656  Canada: +1 4378 370 063  India: +91 810 527 1615  Australia: +61 402 576 615 Email: [email protected]

#data extraction services #web crawling services

2 notes · View notes

rishiaca · 2 years ago

Text

ChatGPT and Machine Learning: Advancements in Conversational AI

Introduction: In recent years, the field of natural language processing (NLP) has witnessed significant advancements with the development of powerful language models like ChatGPT. Powered by machine learning techniques, ChatGPT has revolutionized conversational AI by enabling human-like interactions with computers. This article explores the intersection of ChatGPT and machine learning, discussing their applications, benefits, challenges, and future prospects.

The Rise of ChatGPT: ChatGPT is an advanced language model developed by OpenAI that utilizes deep learning algorithms to generate human-like responses in conversational contexts. It is based on the underlying technology of GPT (Generative Pre-trained Transformer), a state-of-the-art model in NLP, which has been fine-tuned specifically for chat-based interactions.

How ChatGPT Works: ChatGPT employs a technique called unsupervised learning, where it learns from vast amounts of text data without explicit instructions or human annotations. It utilizes a transformer architecture, which allows it to process and generate text in a parallel and efficient manner.

The model is trained using a massive dataset and learns to predict the next word or phrase given the preceding context.

Applications of ChatGPT: Customer Support: ChatGPT can be deployed in customer service applications, providing instant and personalized assistance to users, answering frequently asked questions, and resolving common issues.

Virtual Assistants: ChatGPT can serve as intelligent virtual assistants, capable of understanding and responding to user queries, managing calendars, setting reminders, and performing various tasks.

Content Generation: ChatGPT can be used for generating content, such as blog posts, news articles, and creative writing, with minimal human intervention.

Language Translation: ChatGPT's language understanding capabilities make it useful for real-time language translation services, breaking down barriers and facilitating communication across different languages.

Benefits of ChatGPT: Enhanced User Experience: ChatGPT offers a more natural and interactive conversational experience, making interactions with machines feel more human-like.

Increased Efficiency: ChatGPT automates tasks that would otherwise require human intervention, resulting in improved efficiency and reduced response times.

Scalability: ChatGPT can handle multiple user interactions simultaneously, making it scalable for applications with high user volumes.

Challenges and Ethical Considerations: Bias and Fairness: ChatGPT's responses can sometimes reflect biases present in the training data, highlighting the importance of addressing bias and ensuring fairness in AI systems.

Misinformation and Manipulation: ChatGPT's ability to generate realistic text raises concerns about the potential spread of misinformation or malicious use. Ensuring the responsible deployment and monitoring of such models is crucial.

Future Directions: Fine-tuning and Customization: Continued research and development aim to improve the fine-tuning capabilities of ChatGPT, enabling users to customize the model for specific domains or applications.

Ethical Frameworks: Efforts are underway to establish ethical guidelines and frameworks for the responsible use of conversational AI models like ChatGPT, mitigating potential risks and ensuring accountability.

Conclusion: In conclusion, the emergence of ChatGPT and its integration into the field of machine learning has opened up new possibilities for human-computer interaction and natural language understanding. With its ability to generate coherent and contextually relevant responses, ChatGPT showcases the advancements made in language modeling and conversational AI.

We have explored the various aspects and applications of ChatGPT, including its training process, fine-tuning techniques, and its contextual understanding capabilities. Moreover, the concept of transfer learning has played a crucial role in leveraging the model's knowledge and adapting it to specific tasks and domains.

While ChatGPT has shown remarkable progress, it is important to acknowledge its limitations and potential biases. The continuous efforts by OpenAI to gather user feedback and refine the model reflect their commitment to improving its performance and addressing these concerns. User collaboration is key to shaping the future development of ChatGPT and ensuring it aligns with societal values and expectations.

The integration of ChatGPT into various applications and platforms demonstrates its potential to enhance collaboration, streamline information gathering, and assist users in a conversational manner. Developers can harness the power of ChatGPT by leveraging its capabilities through APIs, enabling seamless integration and expanding the reach of conversational AI.

Looking ahead, the field of machine learning and conversational AI holds immense promise. As ChatGPT and similar models continue to evolve, the focus should remain on user privacy, data security, and responsible AI practices. Collaboration between humans and machines will be crucial, as we strive to develop AI systems that augment human intelligence and provide valuable assistance while maintaining ethical standards.

With further advancements in training techniques, model architectures, and datasets, we can expect even more sophisticated and context-aware language models in the future. As the dialogue between humans and machines becomes more seamless and natural, the potential for innovation and improvement in various domains is vast.

In summary, ChatGPT represents a significant milestone in the field of machine learning, bringing us closer to human-like conversation and intelligent interactions. By harnessing its capabilities responsibly and striving for continuous improvement, we can leverage the power of ChatGPT to enhance user experiences, foster collaboration, and push the boundaries of what is possible in the realm of artificial intelligence.

#chatgpt #artificial intelligence #machine learning #data science

2 notes · View notes

jcmarchi · 12 hours ago

Text

Why Agentic Document Extraction Is Replacing OCR for Smarter Document Automation

New Post has been published on https://thedigitalinsider.com/why-agentic-document-extraction-is-replacing-ocr-for-smarter-document-automation/

Why Agentic Document Extraction Is Replacing OCR for Smarter Document Automation

For many years, businesses have used Optical Character Recognition (OCR) to convert physical documents into digital formats, transforming the process of data entry. However, as businesses face more complex workflows, OCR’s limitations are becoming clear. It struggles to handle unstructured layouts, handwritten text, and embedded images, and it often fails to interpret the context or relationships between different parts of a document. These limitations are increasingly problematic in today’s fast-paced business environment.

Agentic Document Extraction, however, represents a significant advancement. By employing AI technologies such as Machine Learning (ML), Natural Language Processing (NLP), and visual grounding, this technology not only extracts text but also understands the structure and context of documents. With accuracy rates above 95% and processing times reduced from hours to just minutes, Agentic Document Extraction is transforming how businesses handle documents, offering a powerful solution to the challenges OCR cannot overcome.

Why OCR is No Longer Enough

For years, OCR was the preferred technology for digitizing documents, revolutionizing how data was processed. It helped automate data entry by converting printed text into machine-readable formats, streamlining workflows across many industries. However, as business processes have evolved, OCR’s limitations have become more apparent.

One of the significant challenges with OCR is its inability to handle unstructured data. In industries like healthcare, OCR often struggles with interpreting handwritten text. Prescriptions or medical records, which often have varying handwriting and inconsistent formatting, can be misinterpreted, leading to errors that may harm patient safety. Agentic Document Extraction addresses this by accurately extracting handwritten data, ensuring the information can be integrated into healthcare systems, improving patient care.

In finance, OCR’s inability to recognize relationships between different data points within documents can lead to mistakes. For example, an OCR system might extract data from an invoice without linking it to a purchase order, resulting in potential financial discrepancies. Agentic Document Extraction solves this problem by understanding the context of the document, allowing it to recognize these relationships and flag discrepancies in real-time, helping to prevent costly errors and fraud.

OCR also faces challenges when dealing with documents that require manual validation. The technology often misinterprets numbers or text, leading to manual corrections that can slow down business operations. In the legal sector, OCR may misinterpret legal terms or miss annotations, which requires lawyers to intervene manually. Agentic Document Extraction removes this step, offering precise interpretations of legal language and preserving the original structure, making it a more reliable tool for legal professionals.

A distinguishing feature of Agentic Document Extraction is the use of advanced AI, which goes beyond simple text recognition. It understands the document’s layout and context, enabling it to identify and preserve tables, forms, and flowcharts while accurately extracting data. This is particularly useful in industries like e-commerce, where product catalogues have diverse layouts. Agentic Document Extraction automatically processes these complex formats, extracting product details like names, prices, and descriptions while ensuring proper alignment.

Another prominent feature of Agentic Document Extraction is its use of visual grounding, which helps identify the exact location of data within a document. For example, when processing an invoice, the system not only extracts the invoice number but also highlights its location on the page, ensuring the data is captured accurately in context. This feature is particularly valuable in industries like logistics, where large volumes of shipping invoices and customs documents are processed. Agentic Document Extraction improves accuracy by capturing critical information like tracking numbers and delivery addresses, reducing errors and improving efficiency.

Finally, Agentic Document Extraction’s ability to adapt to new document formats is another significant advantage over OCR. While OCR systems require manual reprogramming when new document types or layouts arise, Agentic Document Extraction learns from each new document it processes. This adaptability is especially valuable in industries like insurance, where claim forms and policy documents vary from one insurer to another. Agentic Document Extraction can process a wide range of document formats without needing to adjust the system, making it highly scalable and efficient for businesses that deal with diverse document types.

The Technology Behind Agentic Document Extraction

Agentic Document Extraction brings together several advanced technologies to address the limitations of traditional OCR, offering a more powerful way to process and understand documents. It uses deep learning, NLP, spatial computing, and system integration to extract meaningful data accurately and efficiently.

At the core of Agentic Document Extraction are deep learning models trained on large amounts of data from both structured and unstructured documents. These models use Convolutional Neural Networks (CNNs) to analyze document images, detecting essential elements like text, tables, and signatures at the pixel level. Architectures like ResNet-50 and EfficientNet help the system identify key features in the document.

Additionally, Agentic Document Extraction employs transformer-based models like LayoutLM and DocFormer, which combine visual, textual, and positional information to understand how different elements of a document relate to each other. For example, it can connect a table header to the data it represents. Another powerful feature of Agentic Document Extraction is few-shot learning. It allows the system to adapt to new document types with minimal data, speeding up its deployment in specialized cases.

The NLP capabilities of Agentic Document Extraction go beyond simple text extraction. It uses advanced models for Named Entity Recognition (NER), such as BERT, to identify essential data points like invoice numbers or medical codes. Agentic Document Extraction can also resolve ambiguous terms in a document, linking them to the proper references, even when the text is unclear. This makes it especially useful for industries like healthcare or finance, where precision is critical. In financial documents, Agentic Document Extraction can accurately link fields like “total_amount” to corresponding line items, ensuring consistency in calculations.

Another critical aspect of Agentic Document Extraction is its use of spatial computing. Unlike OCR, which treats documents as a linear sequence of text, Agentic Document Extraction understands documents as structured 2D layouts. It uses computer vision tools like OpenCV and Mask R-CNN to detect tables, forms, and multi-column text. Agentic Document Extraction improves the accuracy of traditional OCR by correcting issues such as skewed perspectives and overlapping text.

It also employs Graph Neural Networks (GNNs) to understand how different elements in a document are related in space, such as a “total” value positioned below a table. This spatial reasoning ensures that the structure of documents is preserved, which is essential for tasks like financial reconciliation. Agentic Document Extraction also stores the extracted data with coordinates, ensuring transparency and traceability back to the original document.

For businesses looking to integrate Agentic Document Extraction into their workflows, the system offers robust end-to-end automation. Documents are ingested through REST APIs or email parsers and stored in cloud-based systems like AWS S3. Once ingested, microservices, managed by platforms like Kubernetes, take care of processing the data using OCR, NLP, and validation modules in parallel. Validation is handled both by rule-based checks (like matching invoice totals) and machine learning algorithms that detect anomalies in the data. After extraction and validation, the data is synced with other business tools like ERP systems (SAP, NetSuite) or databases (PostgreSQL), ensuring that it is readily available for use.

By combining these technologies, Agentic Document Extraction turns static documents into dynamic, actionable data. It moves beyond the limitations of traditional OCR, offering businesses a smarter, faster, and more accurate solution for document processing. This makes it a valuable tool across industries, enabling greater efficiency and new opportunities for automation.

5 Ways Agentic Document Extraction Outperforms OCR

While OCR is effective for basic document scanning, Agentic Document Extraction offers several advantages that make it a more suitable option for businesses looking to automate document processing and improve accuracy. Here’s how it excels:

Accuracy in Complex Documents

Agentic Document Extraction handles complex documents like those containing tables, charts, and handwritten signatures far better than OCR. It reduces errors by up to 70%, making it ideal for industries like healthcare, where documents often include handwritten notes and complex layouts. For example, medical records that contain varying handwriting, tables, and images can be accurately processed, ensuring critical information such as patient diagnoses and histories are correctly extracted, something OCR might struggle with.

Context-Aware Insights

Unlike OCR, which extracts text, Agentic Document Extraction can analyze the context and relationships within a document. For instance, in banking, it can automatically flag unusual transactions when processing account statements, speeding up fraud detection. By understanding the relationships between different data points, Agentic Document Extraction allows businesses to make more informed decisions faster, providing a level of intelligence that traditional OCR cannot match.

Touchless Automation

OCR often requires manual validation to correct errors, slowing down workflows. Agentic Document Extraction, on the other hand, automates this process by applying validation rules such as “invoice totals must match line items.” This enables businesses to achieve efficient touchless processing. For example, in retail, invoices can be automatically validated without human intervention, ensuring that the amounts on invoices match purchase orders and deliveries, reducing errors and saving significant time.

Scalability

Traditional OCR systems face challenges when processing large volumes of documents, especially if the documents have varying formats. Agentic Document Extraction easily scales to handle thousands or even millions of documents daily, making it perfect for industries with dynamic data. In e-commerce, where product catalogs constantly change, or in healthcare, where decades of patient records need to be digitized, Agentic Document Extraction ensures that even high-volume, varied documents are processed efficiently.

Future-Proof Integration

Agentic Document Extraction integrates smoothly with other tools to share real-time data across platforms. This is especially valuable in fast-paced industries like logistics, where quick access to updated shipping details can make a significant difference. By connecting with other systems, Agentic Document Extraction ensures that critical data flows through the proper channels at the right time, improving operational efficiency.

Challenges and Considerations in Implementing Agentic Document Extraction

Agentic Document Extraction is changing the way businesses handle documents, but there are important factors to consider before adopting it. One challenge is working with low-quality documents, like blurry scans or damaged text. Even advanced AI can have trouble extracting data from faded or distorted content. This is primarily a concern in sectors like healthcare, where handwritten or old records are common. However, recent improvements in image preprocessing tools, like deskewing and binarization, are helping address these issues. Using tools like OpenCV and Tesseract OCR can improve the quality of scanned documents, boosting accuracy significantly.

Another consideration is the balance between cost and return on investment. The initial cost of Agentic Document Extraction can be high, especially for small businesses. However, the long-term benefits are significant. Companies using Agentic Document Extraction often see processing time reduced by 60-85%, and error rates drop by 30-50%. This leads to a typical payback period of 6 to 12 months. As technology advances, cloud-based Agentic Document Extraction solutions are becoming more affordable, with flexible pricing options that make it accessible to small and medium-sized businesses.

Looking ahead, Agentic Document Extraction is evolving quickly. New features, like predictive extraction, allow systems to anticipate data needs. For example, it can automatically extract client addresses from recurring invoices or highlight important contract dates. Generative AI is also being integrated, allowing Agentic Document Extraction to not only extract data but also generate summaries or populate CRM systems with insights.

For businesses considering Agentic Document Extraction, it is vital to look for solutions that offer custom validation rules and transparent audit trails. This ensures compliance and trust in the extraction process.

The Bottom Line

In conclusion, Agentic Document Extraction is transforming document processing by offering higher accuracy, faster processing, and better data handling compared to traditional OCR. While it comes with challenges, such as managing low-quality inputs and initial investment costs, the long-term benefits, such as improved efficiency and reduced errors, make it a valuable tool for businesses.

As technology continues to evolve, the future of document processing looks bright with advancements like predictive extraction and generative AI. Businesses adopting Agentic Document Extraction can expect significant improvements in how they manage critical documents, ultimately leading to greater productivity and success.

0 notes

activeslr · 3 days ago

Text

Discover the Best Tools for Your Literature Review

Conducting a comprehensive literature review is a foundational step in any academic research project. Whether you're a student, a research scholar, or a seasoned academic, having access to the right tools can significantly streamline your process and improve your outcomes. In this article, we explore the best software for literature review and provide insights into how these tools can elevate the quality and efficiency of your work.

Why Literature Review Software Matters

A literature review is more than just collecting references. It involves critical evaluation, synthesis, and the ability to draw meaningful conclusions from a vast body of existing knowledge. Traditional methods—manual sorting, note-taking, and referencing—are not only time-consuming but also prone to error. This is where technology comes in.

The best literature review software offers features like reference management, keyword tagging, note-taking, collaboration tools, and advanced search capabilities. These functionalities help researchers to organize, analyze, and review vast amounts of literature quickly and accurately.

Key Features to Look For in Review Software

When selecting a software for literature review, several features stand out as essential:

Reference Management

The software should support easy importing, exporting, and organizing of citations in various.

PDF Annotation

Many tools allow users to highlight, annotate, and comment on PDFs, making it easier to extract relevant information for your review.

Cloud Synchronization

Working from multiple devices is common today. Cloud-based systems ensure you can access your library from anywhere without losing data.

Search and Filter Options

Robust search capabilities can drastically reduce the time you spend finding relevant literature.

Collaboration Tools

If you're working in a research team, the ability to share notes and references is a huge advantage.

Top Software Options for Literature Reviews

There is no one-size-fits-all solution, but here are some popular tools among researchers:

Zotero

Zotero is a free and open-source reference management tool. It allows users to save, organize, and cite sources from the web with a single click. Its group library feature is great for collaborative work.

Mendeley

Owned by Elsevier, Mendeley provides both reference management and academic social networking. Its PDF viewer and annotation features are particularly useful for researchers who work extensively with academic journals.

EndNote

A more advanced tool with extensive features, EndNote is favored by seasoned researchers. It offers powerful citation tools and integration with Microsoft Word, ideal for long-form writing and dissertations.

Citavi

Citavi combines reference management and task planning. It's perfect for organizing not only your literature but also your research process.

These tools are widely regarded as the best software for literature review, especially when productivity and academic accuracy are priorities.

How Literature Review Software Enhances Research

The real benefit of using specialized software is not just in saving time but in improving the quality of research. With automatic citation formatting, consistent tagging, and easy navigation of large datasets, you can ensure your literature review is both thorough and professionally presented.

Additionally, many of these tools offer AI-powered recommendations and analytics to help identify gaps in the existing literature. This not only aids in writing but also assists in forming research hypotheses.

For a reliable, efficient, and modern approach to literature management, consider exploring platforms like activeslr.io, which offer advanced solutions tailored specifically for literature review automation and data extraction.

0 notes

oliviiaagraceeeeee · 10 days ago

Text

Leading ASX AI Stocks Powering Industry Growth

Highlights

Surge in machine learning solutions across listed Australian companies

Diverse platforms span data annotation, edge computing, and logistics automation

Emphasis on cloud integration and neural networking advancements

The Australian technology sector has witnessed a surge in firms leveraging artificial intelligence across varied industries. ASX AI Stocks represent a segment of publicly listed entities focusing on AI driven solutions in domains such as language processing, computer vision, and automation. These offerings support data processing services and feature scalable platforms that address demands in healthcare, logistics, and digital services. As data volumes increase, these listed firms adapt their architectures to enable cloud integration and robust machine learning models. The presence of these ASX AI Stocks highlights the maturation of AI capabilities within local capital markets.

Key Company Profiles

Within the ASX AI Stocks landscape, several firms attract attention for their specialized platforms. One company delivers crowd sourced data annotation that fuels reliable language learning applications. Another group focuses on edge computing chips designed to accelerate neural networks in embedded devices. A logistics software provider integrates artificial intelligence modules to streamline supply chain operations. Each entity maintains a record of service expansions and welcomes partnerships aimed at enhancing algorithm accuracy. Collectively, these ASX AI Stocks illustrate the diversity of AI applications among Australian market participants.

Technological Advancements

Continuous innovation drives the performance of ASX AI Stocks as research teams refine deep learning techniques and introduce new model architectures. Enhanced data sets enable more efficient training pipelines, while advances in neural networking reduce computational overhead. Investment in cloud infrastructure supports real time insights and allows enterprises to deploy scalable AI services. Cross sector collaboration fosters development of custom solutions in areas like remote sensing and predictive maintenance. These technological milestones reinforce the appeal of ASX AI Stocks to businesses seeking to harness emerging machine intelligence capabilities.

Market Dynamics

Market observers note that ASX AI Stocks benefit from increasing corporate demand for intelligent automation across finance, retail, and government services. Regulatory frameworks in Australia support research partnerships and provide avenues for data sovereignty compliance. Public listings of these companies create visibility among stakeholders tracking digital transformation metrics. Transparent reporting of software deployments and collaboration agreements informs interest in the evolving ecosystem. Ongoing developments in data privacy and ethical AI management shape strategies employed by ASX AI Stocks to align with emerging governance standards.

Regulatory Framework

Australian authorities have introduced legislation and guidelines that shape the development and deployment of artificial intelligence solutions. Data privacy obligations under national acts ensure that personal information is collected and processed in compliance with established principles. Ethical frameworks published by government agencies promote transparency, accountability, and fairness in algorithmic decision making. Collaboration between regulators and industry bodies supports the creation of standardized protocols for data security and model validation. These requirements apply across sectors spanning healthcare services, financial reporting, and public infrastructure management, reinforcing a consistent approach to technology governance.

Call to Action

Subscribers are invited to explore detailed disclosures and official corporate communications related to ASX AI Stocks to gain deeper insights into service offerings and technology roadmaps. Subscribing to sector updates and monitoring announcements from listed entities offers access to the latest platform enhancements and strategic partnerships. For those interested in progress within the Australian artificial intelligence landscape, visiting company websites and reviewing corporate relations portals dedicated to ASX AI Stocks can provide comprehensive updates. Exploring thought leadership materials about ASX AI Stocks helps maintain awareness of advancements driven by these innovative firms in the public market.

0 notes

generativeinai · 10 days ago

Text

Step-by-Step Breakdown of AI Video Analytics Software Development: Tools, Frameworks, and Best Practices for Scalable Deployment

AI Video Analytics is revolutionizing how businesses analyze visual data. From enhancing security systems to optimizing retail experiences and managing traffic, AI-powered video analytics software has become a game-changer. But how exactly is such a solution developed? Let’s break it down step by step—covering the tools, frameworks, and best practices that go into building scalable AI video analytics software.

Introduction: The Rise of AI in Video Analytics

The explosion of video data—from surveillance cameras to drones and smart cities—has outpaced human capabilities to monitor and interpret visual content in real-time. This is where AI Video Analytics Software Development steps in. Using computer vision, machine learning, and deep neural networks, these systems analyze live or recorded video streams to detect events, recognize patterns, and trigger automated responses.

Step 1: Define the Use Case and Scope

Every AI video analytics solution starts with a clear business goal. Common use cases include:

Real-time threat detection in surveillance

Customer behavior analysis in retail

Traffic management in smart cities

Industrial safety monitoring

License plate recognition

Key Deliverables:

Problem statement

Target environment (edge, cloud, or hybrid)

Required analytics (object detection, tracking, counting, etc.)

Step 2: Data Collection and Annotation

AI models require massive amounts of high-quality, annotated video data. Without clean data, the model's accuracy will suffer.

Tools for Data Collection:

Surveillance cameras

Drones

Mobile apps and edge devices

Tools for Annotation:

CVAT (Computer Vision Annotation Tool)

Labelbox

Supervisely

Tip: Use diverse datasets (different lighting, angles, environments) to improve model generalization.

Step 3: Model Selection and Training

This is where the real AI work begins. The model learns to recognize specific objects, actions, or anomalies.

Popular AI Models for Video Analytics:

YOLOv8 (You Only Look Once)

OpenPose (for human activity recognition)

DeepSORT (for multi-object tracking)

3D CNNs for spatiotemporal activity analysis

Frameworks:

TensorFlow

PyTorch

OpenCV (for pre/post-processing)

ONNX (for interoperability)

Best Practice: Start with pre-trained models and fine-tune them on your domain-specific dataset to save time and improve accuracy.

Step 4: Edge vs. Cloud Deployment Strategy

AI video analytics can run on the cloud, on-premises, or at the edge depending on latency, bandwidth, and privacy needs.

Cloud:

Scalable and easier to manage

Good for post-event analysis

Edge:

Low latency

Ideal for real-time alerts and privacy-sensitive applications

Hybrid:

Initial processing on edge devices, deeper analysis in the cloud

Popular Platforms:

NVIDIA Jetson for edge

AWS Panorama

Azure Video Indexer

Google Cloud Video AI

Step 5: Real-Time Inference Pipeline Design

The pipeline architecture must handle:

Video stream ingestion

Frame extraction

Model inference

Alert/visualization output

Tools & Libraries:

GStreamer for video streaming

FFmpeg for frame manipulation

Flask/FastAPI for inference APIs

Kafka/MQTT for real-time event streaming

Pro Tip: Use GPU acceleration with TensorRT or OpenVINO for faster inference speeds.

Step 6: Integration with Dashboards and APIs

To make insights actionable, integrate the AI system with:

Web-based dashboards (using React, Plotly, or Grafana)

REST or gRPC APIs for external system communication

Notification systems (SMS, email, Slack, etc.)

Best Practice: Create role-based dashboards to manage permissions and customize views for operations, IT, or security teams.

Step 7: Monitoring and Maintenance

Deploying AI models is not a one-time task. Performance should be monitored continuously.

Key Metrics:

Accuracy (Precision, Recall)

Latency

False Positive/Negative rate

Frame per second (FPS)

Tools:

Prometheus + Grafana (for monitoring)

MLflow or Weights & Biases (for model versioning and experiment tracking)

Step 8: Security, Privacy & Compliance

Video data is sensitive, so it’s vital to address:

GDPR/CCPA compliance

Video redaction (blurring faces/license plates)

Secure data transmission (TLS/SSL)

Pro Tip: Use anonymization techniques and role-based access control (RBAC) in your application.

Step 9: Scaling the Solution

As more video feeds and locations are added, the architecture should scale seamlessly.

Scaling Strategies:

Containerization (Docker)

Orchestration (Kubernetes)

Auto-scaling with cloud platforms

Microservices-based architecture

Best Practice: Use a modular pipeline so each part (video input, AI model, alert engine) can scale independently.

Step 10: Continuous Improvement with Feedback Loops

Real-world data is messy, and edge cases arise often. Use real-time feedback loops to retrain models.

Automatically collect misclassified instances

Use human-in-the-loop (HITL) systems for validation

Periodically retrain and redeploy models

Conclusion

Building scalable AI Video Analytics Software is a multi-disciplinary effort combining computer vision, data engineering, cloud computing, and UX design. With the right tools, frameworks, and development strategy, organizations can unlock immense value from their video data—turning passive footage into actionable intelligence.

#ai #artificial intelligence

0 notes

macgenceblog · 12 days ago

Text

Image Annotation Services: Powering AI with Precision

Image annotation services play a critical role in training computer vision models by adding metadata to images. This process involves labeling objects, boundaries, and other visual elements within images, enabling machines to recognize and interpret visual data accurately. From bounding boxes and semantic segmentation to landmark and polygon annotations, these services lay the groundwork for developing AI systems used in self-driving cars, facial recognition, retail automation, and more.

High-quality image annotation requires a blend of skilled human annotators and advanced tools to ensure accuracy, consistency, and scalability. Industries such as healthcare, agriculture, and e-commerce increasingly rely on annotated image datasets to power applications like disease detection, crop monitoring, and product categorization.

At Macgence, our image annotation services combine precision, scalability, and customization. We support a wide range of annotation types tailored to specific use cases, ensuring that your AI models are trained on high-quality, well-structured data. With a commitment to quality assurance and data security, we help businesses accelerate their AI initiatives with confidence.

Whether you're building object detection algorithms or fine-tuning machine learning models, image annotation is the foundation that drives performance and accuracy—making it a vital step in any AI development pipeline.

#image annotation #datasets #data annotation #macgence

0 notes

anujashinde · 13 days ago

Text

The Power of Data Annotation in Machine Learning

In machine learning, algorithms are only as good as the data that feeds them. But what if your data is messy, unlabeled, or inconsistent?

That’s where data annotation comes in — and why it’s one of the most important steps in training AI models.

🏷️ What Is Data Annotation?

Data annotation is the process of labeling data — like drawing boxes around objects in images, tagging parts of speech in sentences, or identifying key sounds in audio. These labels teach AI models how to “see,” “read,” or “listen” more accurately.

Without annotation, your AI can’t learn. It’s just guessing.

💡 Why It Matters

✅ Better predictions

✅ Reduced bias

✅ More reliable AI models

✅ Real-world readiness

Whether you're training a computer vision model or building a chatbot, annotated data is what makes your AI smart.

🚀 How Dserve AI Helps

At Dserve AI, we specialize in high-quality AI data annotation services for industries like:

Healthcare AI

Conversational AI

Biometric AI

Computer Vision

NLP and more

We combine domain expertise with scalable tools to deliver machine learning datasets that drive results.

👉 Learn more: https://dserveai.com

#machinelearning #artificial intelligence #machinelearningdatasets

1 note · View note

preludeinsights · 13 days ago

Text

Top AI Trends in Medical Record Review for 2025 and Beyond

When every minute counts and the volume of documentation keeps growing, professionals handling medical records often face a familiar bottleneck—navigating through massive, redundant files to pinpoint crucial medical data. Whether for independent medical exams (IMEs), peer reviews, or litigation support, delays, and inaccuracies in reviewing records can disrupt decision-making, increase overhead, and pose compliance risks.

That's where AI is stepping in—not as a future solution but as a game changer.

From Data Overload to Data Precision

Manual review processes often fall short when records include thousands of pages with duplicated reports, handwritten notes, and scattered information. AI-powered medical records review services now bring precision, speed, and structure to this chaos.

AI/ML model scans entire sets of medical documents, learns from structured and unstructured data, and identifies critical data points—physician notes, prescriptions, lab values, diagnosis codes, imaging results, and provider details. The system then indexes and sorts records according to the date of injury and treatment visits, ensuring clear chronological visibility.

Once organized, the engine produces concise summaries tailored for quick decision-making—ideal for deposition summaries, peer reviews, and IME/QME reports.

Key AI Trends Reshaping 2025 and Beyond

1. Contextual AI Summarization

Summaries are no longer just text extractions. AI models are becoming context-aware, producing focused narratives that eliminate repetition and highlight medically significant events—exactly what reviewers need when building a case or evaluating medical necessity.

2. Intelligent Indexing & Chronology Sorting

Chronological sorting is moving beyond simple date alignment. AI models now associate events with the treatment cycle, grouping diagnostics, prescriptions, and physician notes by the injury timeline—offering a cleaner, more logical flow of information.

3. Deduplication & Version Control

Duplicate documents create confusion and waste time. Advanced AI can now detect and remove near-identical reports, earlier versions, and misfiled documents while preserving audit trails. This alone reduces review fatigue and administrative overhead.

4. Custom Output Formats

Different reviewers prefer different formats. AI-driven platforms offer customizable outputs—hyperlinked reports, annotated PDFs, or clean deposition summaries—ready for court proceedings or clinical assessments.

Why This Matters Now

The pressure to process records faster, more accurately, and at scale is growing. Workers' comp cases and utilization reviews depend on fast and clear insights. AI-powered medical records review service providers bring the tools to meet that demand—not just for efficiency but also for risk mitigation and quality outcomes.

Why Partner with an AI-Driven Medical Records Review Provider?

A reliable partner can bring scalable infrastructure, domain-trained AI models, and compliance-ready outputs. That's not just an operational upgrade—it's a strategic advantage. As the demand for faster, more intelligent medical records review services grows, those who invest in AI-driven solutions will lead the next phase of review excellence.

#ai medical records review #medical records #healthcare #medical summary #medical indexing

0 notes

360edukraft · 16 days ago

Text

How do I empower non-technical teams to explore Power BI data independently in 2025?

Introduction: The Rise of Data Empowerment in 2025

2025 has officially become the era of data democratization. Where once data was the exclusive domain of analysts and developers, today, business users from marketing to HR demand access and autonomy. The real question isn’t whether non-technical teams should explore data, but how we enable them to do so—confidently, independently, and effectively.

Understanding the Non-Technical User Mindset

Non-technical users don’t want to code, debug, or decipher complex SQL queries. They want insights—clear, timely, and contextual. They’re not looking to become data scientists. They’re looking to solve problems, make decisions, and innovate. Empowering them begins with empathy

The Democratization of Data

Data democratization is more than a buzzword. It's a strategic shift. It’s about granting self-service access to data while ensuring security, governance, and reliability. Tools like Power BI are leading the charge in removing barriers to entry.

Why Power BI is the Tool of Choice

Power BI isn’t just powerful—it’s intuitive. Its interface mimics Excel, integrates with countless data sources, and allows drag-and-drop visualizations. Microsoft’s relentless focus on usability makes Power BI the ideal platform to empower those without a technical background.

Shifting from Data Gatekeeping to Data Enabling

In many organizations, data is locked behind departments and systems. By decentralizing access and creating governed environments, data teams can pivot from gatekeepers to enablers—helping business users explore Power BI data independently without risk.

Crafting a User-Friendly Power BI Experience

Dashboards overloaded with KPIs and filters can overwhelm non-technical users. Simplicity is key. Create focused, curated views for each department. Use consistent colors, clear labels, and intuitive layouts. Design for interpretation, not interrogation.

Building Role-Based Dashboards

One size fits none. A finance manager needs very different data than a content marketer. Role-based dashboards ensure relevance and clarity. Tailor filters, visuals, and datasets to match real-world use cases.

Using Natural Language Queries for Accessibility

Thanks to AI advancements, Power BI’s Q&A feature lets users type natural language questions like “Show me sales by region for Q1 2025.” No formulas. No coding. Just answers. It’s Google for your business intelligence.

Importance of Data Literacy Workshops

Empowerment without education is a recipe for chaos. Host data literacy workshops to teach basics like understanding data types, interpreting charts, and avoiding common pitfalls. These sessions bridge the gap between access and insight.

Leveraging the Power BI Institute in Washington for Upskilling

The power bI institute in washington offers targeted programs designed for non-tech professionals. From beginner bootcamps to scenario-based simulations, their curriculum is ideal for building comfort with data exploration.

Collaborating with the Power BI Institute in San Francisco for Hands-On Learning

The power bI institute in san francisco excels in hands-on, project-based learning. Their workshops immerse users in real-world data challenges, helping them gain not just technical skills, but analytical thinking prowess.

Enrolling in Programs from the Power BI Institute in New York for Certification

For teams looking to formalize their skills, the power bI institute in new york offers certification tracks tailored for business users. These programs validate skills while building confidence—essential for large-scale empowerment initiatives.

Embedding Storytelling in Power BI Dashboards

Numbers tell a story—but only if you help them speak. Use narrative arcs, visual cues, and annotations to guide users through the insights. Every dashboard should answer the question: “So what?”

Encouraging a Culture of Data Curiosity

Tools don’t spark curiosity—cultures do. Celebrate exploration. Share wins. Host internal “data jams” where departments showcase how they’ve used Power BI to solve challenges or discover opportunities.

Integrating AI Assistance for On-the-Fly Insights

AI features in Power BI—like smart narratives, anomaly detection, and forecasting—empower users to uncover insights without lifting a finger. AI becomes their co-pilot, making data discovery faster and less intimidating.

#power bi #microsoft

0 notes