Unite.AI

MARCH 14, 2025

A McKinsey survey found that while one-quarter of respondents were concerned about accuracy, many struggled just as much with security, explainability, intellectual property (IP) management, and regulatory compliance. Keep your stakeholders and leadership informed on progress. Document everything.

Automation 113

Automation Data Quality LLM Generative AI 113

Unite.AI

JULY 19, 2024

That's an AI hallucination, where the AI fabricates incorrect information. The consequences of relying on inaccurate information can be severe for these industries. These tools help identify when AI makes up information or gives incorrect answers, even if they sound believable. What Are AI Hallucination Detection Tools?

LLM 279

LLM AI AI Data Quality 279

AWS Machine Learning Blog

NOVEMBER 15, 2024

For now, we consider eight key dimensions of responsible AI: Fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. Such words can include offensive terms or undesirable outputs, like product or competitor information.

Responsible AI 107

Responsible AI Prompt Engineering Prompt Engineer AI 107

Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). These AI systems must perform accurately and provide explainable results to comply with regulatory requirements.

Explainable AI 107

Explainable AI Explainability Neural Network Algorithm 107

AssemblyAI

OCTOBER 31, 2024

A 21% increase in accuracy in alphanumerics across critical data like phone numbers, zip codes, and other numerical identifiers for smoother customer experiences, better critical data management, and clearer escalation and reporting. The IMEI is 35-824919-198374-1, and I'm getting error code AX-2103."

Automation 147

Automation Data Quality Explainability AI 147

Flipboard

DECEMBER 27, 2023

Presented by SQream The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how …

Generative AI 181

Generative AI Data Quality Explainability AI 181

IBM Journey to AI blog

APRIL 10, 2023

Everyone would be using the same data set to make informed decisions which may range from goal setting to prioritizing investments in sustainability. Data fabric can help model, integrate and query data sources, build data pipelines, integrate data in near real-time, and run AI-driven applications.

ESG 259

ESG Metadata AI AI 259

Unite.AI

JANUARY 9, 2024

Building a strong data foundation. 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.

Metadata 290

Metadata AI AI AI Tools 290

IBM Journey to AI blog

OCTOBER 12, 2023

Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors. A single point of entry eliminates the need to duplicate sensitive data for various purposes or move critical data to a less secure (and possibly non-compliant) environment.

Data Scientist 234

Data Scientist Metadata Explainability Responsible AI 234

Unite.AI

FEBRUARY 17, 2025

But applications combining predictive, generative, and soon agentic AI with specialized vertical knowledge sources and workflows can pull information from disparate sources enterprise-wide, speed and automate repetitive tasks, and make recommendations for high-impact actions.

Automation 147

Automation Responsible AI Generative AI AI Modeling 147

Flipboard

FEBRUARY 19, 2025

As weve seen from Andurils experience with Alfred, building a robust data infrastructure using AWS services such as Amazon Bedrock , Amazon SageMaker AI , Amazon Kendra , and Amazon DynamoDB in AWS GovCloud (US) creates the essential backbone for effective information retrieval and generation.

Responsible AI 81

Responsible AI Natural Language Processing Explainability Large Language Models 81

Pickl AI

JANUARY 12, 2025

Introduction Are you struggling to decide between data-driven practices and AI-driven strategies for your business? Besides, there is a balance between the precision of traditional data analysis and the innovative potential of explainable artificial intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

IBM Journey to AI blog

MAY 8, 2023

Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality. Crucially, the insurance sector is a financially regulated industry where the transparency, explainability and auditability of algorithms is of key importance to the regulator.

Algorithm 218

Algorithm AI AI Generative AI 218

AWS Machine Learning Blog

MARCH 18, 2025

In the rapidly evolving healthcare landscape, patients often find themselves navigating a maze of complex medical information, seeking answers to their questions and concerns. However, accessing accurate and comprehensible information can be a daunting task, leading to confusion and frustration.

LLM 79

LLM NLP Data Integration AI 79

Towards AI

NOVEMBER 6, 2024

How CatBoost Works CatBoost incorporates a few novel approaches to overcome the limitations of traditional gradient boosting: Ordered Target Encoding: Traditional methods of encoding categorical variables often lead to data leakage, where information from the validation or test set can “leak” into the model during training.

Categorization 114

Categorization Algorithm Machine Learning Python 114

IBM Journey to AI blog

DECEMBER 16, 2022

So, instead of wandering the aisles in hopes you’ll stumble across the book, you can walk straight to it and get the information you want much faster. An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more.

Metadata 130

Metadata Data Quality Data Discovery Data Scientist 130

Unite.AI

JANUARY 20, 2025

Headquartered in Oregon, the company is at the forefront of transforming how healthcare data is shared, monetized, and applied, enabling secure collaboration between data custodians and data consumers. Can you explain how datma.FED utilizes AI to revolutionize healthcare data sharing and analysis?

Automation 130

Automation Data Platform Data Quality Explainability 130

Pickl AI

FEBRUARY 17, 2025

Introduction: The Reality of Machine Learning Consider a healthcare organisation that implemented a Machine Learning model to predict patient outcomes based on historical data. However, once deployed in a real-world setting, its performance plummeted due to data quality issues and unforeseen biases.

Machine Learning 52

Machine Learning Algorithm Explainability Data Quality 52

Unite.AI

SEPTEMBER 30, 2024

As AI takes center stage, AI quality assurance can empower teams to deliver higher-quality software faster. This article explains how AI in quality assurance streamlines software testing while improving product performance. What is AI-powered Quality Assurance?

Automation 317

Automation AI AI DevOps 317

Towards AI

FEBRUARY 11, 2025

Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models. Author(s): Richie Bachala Originally published on Towards AI.

Data Quality 54

Data Quality Neural Network ETL Computer Vision 54

PyImageSearch

JANUARY 6, 2025

In quality control, an outlier could indicate a defect in a manufacturing process. By understanding and identifying outliers, we can improve data quality, make better decisions, and gain deeper insights into the underlying patterns of the data. finance, healthcare, and quality control). Huot, and P. Thakur, eds.,

Python 112

Python Explainability Computer Vision Deep Learning 112

Unite.AI

MARCH 4, 2024

Knowledge workers use their specialized skills, expertise, and creativity to generate, process, and communicate information. Knowledge workers are confused regarding AI due to exposure to conflicting and contradictory information and uncertainty about its impact on their professional lives. Why Are Knowledge Workers Confused About AI?

AI 162

AI AI Algorithm Explainability 162

Unite.AI

JUNE 10, 2024

The company is developing its flagship product, ThinkLabs Copilot, a digital assistant that comprehends the real world through proprietary physics-informed AI digital twins, providing a foundational model for engineering systems. Can you explain what a physics-informed AI digital twin is and how it benefits grid reliability?

Continuous Learning 130

Continuous Learning Automation Data Quality AI 130

Snorkel AI

MARCH 4, 2025

Instead, DPO trains the LLM directly on human preference data. Researchers at Stanford demonstrated that LLMs inherently encode information that can approximate human preferences, which can be leveraged for alignment without explicitly training a separate reward model. Learn how to get more value from your PDF documents! Sign up here!

LLM 52

LLM Large Language Models Data Quality Prompt Engineering 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Unite.AI

APRIL 23, 2024

Aiimi's tech allows companies to find and make sense of their data – bringing together what can be a sprawling mass of data, documents, and digital information – helping teams access information instantly. The more data businesses accumulated and stored in different systems, the worse issues became.

Insight Engine 130

Insight Engine Auto-complete AI Tools Data Quality 130

AWS Machine Learning Blog

FEBRUARY 18, 2025

Anthropics Claude 3 models (the latest model at the time of development) were used to orchestrate and generate high-quality responses, maintaining accurate and relevant information from the chat assistant. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.

Generative AI 69

Generative AI ETL LLM AI 69

Towards AI

OCTOBER 3, 2024

Yet, despite these advancements, AI still faces significant limitations — particularly in adaptability, energy consumption, and the ability to learn from new situations without forgetting old information. Mimicking the brain’s neuron firing mechanism, SNNs process information only when spikes occur, leading to energy-efficient computations.

Neural Network 124

Neural Network Algorithm Continuous Learning AI 124

IBM Journey to AI blog

JANUARY 8, 2024

 It includes a built-in schema registry to validate event data from applications as expected, improving data quality and reducing errors. Events may be more actionable when augmented with external data, or when they occur along with other events in a particular time period.

Automation 231

Automation Data Quality Explainability 231

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making.

ETL 52

ETL Explainability Data Integration Data Extraction 52

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Can you debug system information? Integration with ML tools and libraries: Provide you with flexibility and extensibility. Can you compare images? Can you customize the UI to your needs?

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Pickl AI

OCTOBER 14, 2024

Each technique is explained with real-world use cases to illustrate its application. Understanding these methods enables organisations to gather accurate data, driving informed decision-making and enhancing strategies in various fields, from business to research.

Explainability 52

Explainability Data Quality 52

Aiiot Talk

JULY 7, 2023

That extra verification step can prevent a cybercriminal from stealing your identity with breached information. Consequently, you should keep a close eye on your bank accounts and any online profiles that hold your financial data. Always make sure a service is trustworthy before giving it any permissions or information.

Robotics 130

Robotics Data Quality Artificial Intelligence Artificial Intelligence 130

Towards AI

DECEMBER 19, 2024

capabilities for information retrieval and summarization. A Streamlit application showcases the agents functionality: users input a query, and the agent scrapes data, processes it using Llama 3.3, The GenAI DLP Black Book: Everything You Need to Know About Data Leakage from LLM By Mohit Sewak, Ph.D. The agent leverages Llama 3.3s

Data Ingestion 105

Data Ingestion Explainability AI Researcher AI Research 105

Unite.AI

OCTOBER 2, 2023

To explain this limitation, it is important to understand that the chemistry of sensory-based products is largely focused on quality control, i.e., how much of this analyte is in that mixture? When it comes to data quality, we realized a valid training set could not be generated from existing commercial or crowd-sourced data.

Machine Learning 278

Machine Learning Data Quality Data Science Explainability 278