Marktechpost

FEBRUARY 22, 2025

OctoTools is a modular, training-free, and extensible framework that standardizes how AI models interact with external tools. Unlike previous frameworks that require predefined tool configurations, OctoTools introduces tool cards, which encapsulate tool functionalities and metadata.

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Metadata Large Language Models Algorithm AI 110

Marktechpost

MAY 9, 2024

Rightsify’ s Global Copyright Exchange (GCX) offers vast collections of copyright-cleared music datasets tailored for machine learning and generative AI music initiatives. Text, Stem, MIDI, and sheet music pairings for audio are bundled with their AI music datasets, furnishing comprehensive resources for ML projects.

Metadata 122

Metadata Categorization AI AI 122

Unite.AI

JANUARY 29, 2025

Instead of solely focusing on whos building the most advanced models, businesses need to start investing in robust, flexible, and secure infrastructure that enables them to work effectively with any AI model, adapt to technological advancements, and safeguard their data. AI models are just one part of the equation.

LLM 162

LLM Explainability AI AI 162

Unite.AI

MARCH 26, 2025

So, how can AI help with curating trial site selection? By training AI models with the historical and real-time data of potential sites, trial sponsors can predict patient enrollment rates and a sites performance optimizing site allocation, reducing over- or under-enrollment, and improving overall efficiency and cost.

Artificial Intelligence 157

Artificial Intelligence Artificial Intelligence Metadata Large Language Models 157

Unite.AI

JANUARY 30, 2025

The platform automatically analyzes metadata to locate and label structured data without moving or altering it, adding semantic meaning and aligning definitions to ensure clarity and transparency. When onboarding customers, we automatically retrain these ontologies on their metadata.

Automation 144

Automation Metadata Explainability Data Scientist 144

AI News

MARCH 4, 2024

Furthermore, the document outlines plans for implementing a “consent popup” mechanism to inform users about potential defects or errors produced by AI. It also mandates the labelling of deepfakes with permanent unique metadata or other identifiers to prevent misuse.

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Large Language Models Big Data Metadata LLM 336

AWS Machine Learning Blog

JANUARY 7, 2025

It is critical for AI models to capture not only the context, but also the cultural specificities to produce a more natural sounding translation. When using the FAISS adapter (vector search), translation unit groupings are parsed and turned into vectors using the selected embedding model from Amazon Bedrock.

Large Language Models 98

Large Language Models Prompt Engineer Prompt Engineering Metadata 98

IBM Journey to AI blog

OCTOBER 12, 2023

The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly. Here’s what’s involved in making that happen.

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Data Scientist Metadata Explainability Responsible AI 234

AWS Machine Learning Blog

APRIL 3, 2025

Meanwhile, structured metadata and processed results are housed in Amazon RDS, enabling fast queries and integration with enterprise applications. For example, if an AI model detects a critical defect, Step Functions can initiate a maintenance request in SAP, notify engineers, and schedule repairs without human intervention.

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Computer Vision Automation AI AI 96

Flipboard

MARCH 7, 2025

An AWS Batch job reads these documents, chunks them into smaller slices, then creates embeddings of the text chunks using the Amazon Titan Text Embeddings model through Amazon Bedrock and stores them in an Amazon OpenSearch Service vector database. Vaibhav Singh is a Product Innovation Analyst at Verisk, based out of New Jersey.

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Generative AI Prompt Engineer Prompt Engineering Software Development 151

Unite.AI

JANUARY 9, 2024

Best Practices to Mitigate Generative AI Plagiarism Here are some best practices both AI developers and users can adopt to minimize plagiarism risks: For AI developers: Carefully vet training data sources to exclude copyrighted or licensed material without proper permissions. Record metadata like licenses, tags, creators, etc.

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AWS Machine Learning Blog

MARCH 20, 2025

With robust security measures, data privacy safeguards, and a cost-effective pay-as-you-go model, Amazon Bedrock offers a secure, flexible, and cost-efficient service to harness generative AIs potential in enhancing customer service analytics, ultimately leading to improved customer experiences and operational efficiencies.

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Generative AI Metadata AI AI 73

IBM Journey to AI blog

MAY 9, 2023

A lack of confidence to operationalize AI Many organizations struggle when adopting AI. According to Gartner , 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.

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Metadata Explainability Automation Responsible AI 227

NVIDIA

MAY 1, 2024

Editor’s note: This post is part of the AI Decoded series , which demystifies AI by making the technology more accessible, and which showcases new hardware, software, tools and accelerations for RTX PC users. The new ChatRTX release also lets people chat with their data using their voice.

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AI Modeling Neural Network Chatbots Metadata 134

AI News

SEPTEMBER 29, 2024

AI models often need access to real-time data for training and inference, so the database must offer low latency to enable real-time decision-making and responsiveness. This is one of the biggest challenges organisations face when building AI-powered applications, and it’s precisely what MongoDB is designed to handle.

Big Data 311

Big Data Generative AI ETL Data Ingestion 311

IBM Journey to AI blog

MAY 9, 2023

1] Users can access data through a single point of entry, with a shared metadata layer across clouds and on-premises environments. With watsonx.data, businesses will be able to build trustworthy AI models and automate AI life cycles on multicloud architectures, taking full advantage of interoperability with IBM and third-party services.

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Data Scientist Machine Learning Automation Metadata 276

Unite.AI

JANUARY 9, 2024

There are three areas of AI in particular that will always require human involvement to achieve optimal outcomes. Building a robust data foundation is critical, as the underlying data model with proper metadata, data quality, and governance is key to enabling AI to achieve peak efficiencies. Continuous training.

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Metadata AI AI AI Tools 284

IBM Journey to AI blog

OCTOBER 16, 2023

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Track models and drive transparent processes. Increase trust in AI outcomes.

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Metadata Explainability Automation Explainable AI 220

Marktechpost

JUNE 18, 2024

The approach incorporates over 20 modalities, including SAM segments, 3D human poses, Canny edges, color palettes, and various metadata and embeddings. The method incorporates a wide range of modalities, including RGB, geometric, semantic, edges, feature maps, metadata, and text.

AI Modeling 122

AI Modeling Metadata Large Language Models Neural Network 122

IBM Journey to AI blog

APRIL 4, 2023

For years IBM has been using cutting-edge AI to improve the digital experiences found in the Masters app. We taught an AI model to analyze Masters video and produce highlight reels for every player, minutes after their round is complete. We built models that generate scoring predictions for every player on every hole.

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Large Language Models Neural Network Metadata AI Modeling 242

Unite.AI

OCTOBER 15, 2024

By embedding metadata into images and other digital files, Adobe enables artists to assert ownership and trace the origin of their work. Additionally, Adobe has implemented licensing mechanisms within Firefly that empower artists to be part of the AI training process on their own terms.

Metadata 157

Metadata Generative AI AI AI 157

Unite.AI

OCTOBER 10, 2024

The funding will allow ApertureData to scale its operations and launch its new cloud-based service, ApertureDB Cloud, a tool designed to simplify and accelerate the management of multimodal data, which includes images, videos, text, and related metadata. ApertureData’s flagship product, ApertureDB , addresses this challenge head-on.

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Metadata Machine Learning Robotics Computer Vision 144

AWS Machine Learning Blog

MARCH 20, 2025

This is achieved through responsible AI, with Amazon Bedrock Data Automation passing every process through a responsible AI model to help ensure fairness, accuracy, and compliance in document automation. These analytics are implemented with either Amazon Comprehend , or separate prompt engineering with FMs.

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Automation IDP Generative AI Prompt Engineer 104

AWS Machine Learning Blog

NOVEMBER 26, 2024

The image was generated using the Stability AI (SDXL 1.0) model on Amazon Bedrock. The following screenshot shows the prompt and the model’s response. For example, we provide the following image of a cake to the model to extract the recipe. The image was generated using the Stability AI model (SDXL 1.0)

Convolutional Neural Networks 96

Convolutional Neural Networks LLM Metadata Explainability 96

Unite.AI

SEPTEMBER 14, 2023

Even today, a vast chunk of machine learning and deep learning techniques for AI models rely on a centralized model that trains a group of servers that run or train a specific model against training data, and then verifies the learning using validation or training dataset.

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Algorithm AI AI Machine Learning 316

Marktechpost

SEPTEMBER 15, 2024

FineVideo addresses this gap by enabling researchers to explore various video features, from mood transitions to plot twists, providing a fertile ground for training AI models capable of context-aware video analysis. With an average video length of 4.7 or “What is the mood of the operator during the training?”

Metadata 102

Metadata AI Modeling AI AI 102

Unite.AI

JULY 31, 2023

This approach is known as self-supervised learning , and it’s one of the most efficient methods to build ML and AI models that have the “ common sense ” or background knowledge to solve problems that are beyond the capabilities of AI models today.

Computer Vision 246

Computer Vision Metadata Natural Language Processing ML 246

IBM Journey to AI blog

AUGUST 17, 2023

Next, the teams trained a foundation model using watsonx.ai , a powerful studio for training, validating, tuning and deploying generative AI models for business.

Generative AI 246

Generative AI Metadata AI AI 246

AssemblyAI

APRIL 3, 2024

AI notetakers that can now generate highly accurate and hallucination-free meeting notes to serve as the basis for LLM-powered summaries, action items, and other metadata generation with accurate proper noun, speaker, and timing information included.   At AssemblyAI, we use a combination of models to produce your results.

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Metadata OpenAI Automation AI Modeling 347

AssemblyAI

SEPTEMBER 13, 2024

Extract and generate data : Find out how to extract tags and descriptions from your audio to enhance metadata and searchability with LeMUR. Turbo AI model for intelligent processing, and ElevenLabs for speech synthesis.  Summarize audio data : Discover how to quickly summarize your audio data with key takeaways using LeMUR.

Large Language Models 147

Large Language Models Python Metadata OpenAI 147

IBM Journey to AI blog

APRIL 11, 2024

It “…provides a structured approach to the safe development, deployment and use of generative AI. In doing so, the framework highlights gaps and opportunities in addressing safety concerns, viewed from the perspective of four primary actors: AI model creators, AI model adapters, AI model users, and AI application users.”

Responsible AI 202

Responsible AI AI AI AI Modeling 202

IBM Journey to AI blog

FEBRUARY 8, 2024

For industries providing essential services to clients such as insurance, banking and retail, the law requires the use of a fundamental rights impact assessment that details how the use of AI will affect the rights of customers. Higher risk tiers have more transparency requirements including model evaluation, documentation and reporting.

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Artificial Intelligence Artificial Intelligence AI AI 223

IBM Journey to AI blog

JANUARY 9, 2024

These models tend to reinforce their understanding based on previously assimilated answers. The groundwork of training data in an AI model is comparable to piloting an airplane. The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions.

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Data Ingestion Data Integration Data Quality LLM 222

IBM Journey to AI blog

JUNE 26, 2023

Concerns to consider with off the shelf generative AI models include: Internet data is not always fair and accurate At the heart of much of generative AI today is vast amounts of data from sources such as Wikipedia, websites, articles, image or audio files, etc. What is watsonx.governance?

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Generative AI Explainability Explainable AI Natural Language Processing 203

AWS Machine Learning Blog

NOVEMBER 21, 2024

As generative AI models advance in creating multimedia content, the difference between good and great output often lies in the details that only human feedback can capture. The path to creating effective AI models for audio and video generation presents several distinct challenges.

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Generative AI Metadata AI Modeling Natural Language Processing 84

Unite.AI

SEPTEMBER 18, 2023

Utilizing blockchain technology to record and store the training data, input and output of the models, and parameters, ensuring accountability, and transparency in model audits. Using blockchain frameworks to deploy AI models to achieve decentralization services among models, and enhancing the scalability and stability of the system.

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Deep Learning Artificial Intelligence Artificial Intelligence AI 318

IBM Journey to AI blog

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. An enterprise data catalog automates the process of contextualizing data assets by using: Business metadata to describe an asset’s content and purpose. Technical metadata to describe schemas, indexes and other database objects.

Metadata 130

Metadata Data Quality Data Discovery Data Scientist 130