Unite.AI

DECEMBER 27, 2024

The future of AI demands both, but it starts with the data. Why Data Quality Matters More Than Ever According to one survey, 48% of businesses use big data , but a much lower number manage to use it successfully. No matter how advanced an algorithm is, noisy, biased, or insufficient data can bottleneck its potential.

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Data Quality Data Drift Explainability Big Data 130

Unite.AI

AUGUST 28, 2024

Challenges of Using AI in Healthcare Physicians, doctors, nurses, and other healthcare providers face many challenges integrating AI into their workflows, from displacement of human labor to data quality issues. Additionally, biases in training data could result in unequal treatment suggestions or misdiagnosis.

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Unite.AI

NOVEMBER 1, 2024

This is creating a major headache for corporate data science teams who have had to increasingly focus their limited resources on cleaning and organizing data. In a recent state of engineering report conducted by DBT , 57% of data science professionals cited poor data quality as a predominant issue in their work.

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AI Development AI Developer Data Quality Data Science 162

Flipboard

NOVEMBER 13, 2024

In the quest to uncover the fundamental particles and forces of nature, one of the critical challenges facing high-energy experiments at the Large Hadron Collider (LHC) is ensuring the quality of the vast amounts of data collected. The new system was deployed in the barrel of the ECAL in 2022 and in the endcaps in 2023.

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Algorithm Data Quality Machine Learning Neural Network 162

Analytics Vidhya

MAY 30, 2023

Introduction In the realm of machine learning, the veracity of data holds utmost significance in the triumph of models. Inadequate data quality can give rise to erroneous predictions, unreliable insights, and overall performance.

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Machine Learning Data Quality Data Analysis Algorithm 325

AI News

NOVEMBER 21, 2023

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. Companies have struggled with data quality and data hygiene.

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Big Data Data Quality Generative AI Explainability 334

Unite.AI

SEPTEMBER 23, 2024

From technical limitations to data quality and ethical concerns, it’s clear that the journey ahead is still full of obstacles. Another challenge is the data itself. AI algorithms depend on massive datasets for training, and while the pharmaceutical industry has plenty of data, it’s often noisy, incomplete, or biased.

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Generative AI Data Quality AI AI 294

Unite.AI

NOVEMBER 11, 2024

Jumio’s industry-leading AI-powered platform has evolved to integrate continually advanced AI and machine learning algorithms to analyze biometric data more effectively. We anticipate the increasing use of synthetic data generation, which offers greater controllability, data privacy and a focus on data quality rather than quantity.

Machine Learning 162

Machine Learning AI AI Natural Language Processing 162

AI News

MAY 3, 2024

“Managing dynamic data quality, testing and detecting for bias and inaccuracies, ensuring high standards of data privacy, and ethical use of AI systems all require human oversight,” he said.

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Big Data Generative AI Explainability AI 318

Marktechpost

SEPTEMBER 29, 2024

This dependency on large datasets makes traditional methods unsuitable for real-world applications, where data collection is time-consuming, expensive, and potentially dangerous. The researchers’ modifications include a straightforward regularizer for OOD state-action values, which can be integrated into any zero-shot RL algorithm.

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Algorithm Artificial Intelligence Artificial Intelligence Data Quality 106

Towards AI

MARCH 10, 2025

We began by preprocessing the images to enhance data quality. Once the binary mask is created, the connected components algorithm is applied. Applications: 4-connectivity is often used in algorithms where diagonal connections are not considered, thus providing a more restrictive form of connectivity.

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Python Algorithm Data Quality Data Analysis 98

Towards AI

NOVEMBER 6, 2024

This story explores CatBoost, a powerful machine-learning algorithm that handles both categorical and numerical data easily. CatBoost is a powerful, gradient-boosting algorithm designed to handle categorical data effectively. But what if we could predict a student’s engagement level before they begin?

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Categorization Algorithm Machine Learning Python 114

Unite.AI

JULY 19, 2024

Some rely on machine learning algorithms, while others use rule-based systems or statistical methods. The tool employs advanced algorithms to deliver precision hallucination detection. Key features of Cleanlab include: Cleanlab's AI algorithms can automatically identify label errors, outliers, and near-duplicates.

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LLM AI AI Data Quality 279

Marktechpost

JULY 7, 2024

Researchers from the University of Toronto present an insightful examination of the advanced algorithms used in modern ad and content recommendation systems. This survey examines these systems’ most effective retrieval algorithms, highlighting their underlying mechanisms and challenges.

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Algorithm Neural Network Metadata Large Language Models 105

Unite.AI

FEBRUARY 21, 2025

One of the most practical use cases of AI today is its ability to automate data standardization, enrichment, and validation processes to ensure accuracy and consistency across multiple channels. Leveraging customer data in this way allows AI algorithms to make broader connections across customer order history, preferences, etc.,

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Natural Language Processing NLP Categorization Algorithm 147

Unite.AI

OCTOBER 4, 2023

For example, in August 2020, Robert McDaniel became the target of a criminal act due to the Chicago Police Department’s predictive policing algorithm labeling him as a “person of interest.” Similarly, the AI-generated image of a South Sudan Barbie was shown holding a gun at her side, reflecting the deeply rooted bias in AI algorithms.

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Artificial Intelligence Artificial Intelligence AI AI 278

IBM Journey to AI blog

MAY 8, 2023

Technological risk—security AI algorithms are the parameters that optimizes the training data that gives the AI its ability to give insights. Should the parameters of an algorithm be leaked, a third party may be able to copy the model, causing economic and intellectual property loss to the owner of the model.

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Algorithm AI AI Generative AI 218

IBM Journey to AI blog

MAY 9, 2023

Data: the foundation of your foundation model Data quality matters. An AI model trained on biased or toxic data will naturally tend to produce biased or toxic outputs. When objectionable data is identified, we remove it, retrain the model, and repeat. Data curation is a task that’s never truly finished.

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Data Platform Automation AI AI 347

Unite.AI

AUGUST 13, 2024

BM42 is a state-of-the-art retrieval algorithm designed by Qdrant to enhance RAG's capabilities. This algorithm addresses the limitations of previous methods, making it a key development for improving the accuracy and efficiency of AI systems. At its core, RAG first retrieves relevant data points from a large corpus of information.

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Algorithm Chatbots AI AI 255

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

Towards AI

OCTOBER 31, 2024

It covers the concept of embedding, its importance for machine learning algorithms, and how it is used in LangChain for various applications. It covers key considerations like balancing data quality versus quantity, ensuring data diversity, and selecting the right tuning method.

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LLM NLP BERT Large Language Models 116

AWS Machine Learning Blog

JANUARY 28, 2025

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. Evaluation algorithm Computes evaluation metrics to model outputs.

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LLM Large Language Models ML Algorithm 119

IBM Journey to AI blog

JANUARY 9, 2024

Challenges in rectifying biased data: If the data is biased from the beginning, “ the only way to retroactively remove a portion of that data is by retraining the algorithm from scratch.” This may also entail working with new data through methods like web scraping or uploading.

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Data Ingestion Data Integration Data Quality LLM 222

Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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Unite.AI

FEBRUARY 17, 2025

The wide availability of affordable, highly effective predictive and generative AI has addressed the next level of more complex business problems requiring specialized domain expertise, enterprise-class security, and the ability to integrate diverse data sources.

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Automation Responsible AI Generative AI AI Modeling 147

Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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Data Quality ML Machine Learning Natural Language Processing 52

IBM Journey to AI blog

SEPTEMBER 3, 2024

” For example, synthetic data represents a promising way to address the data crisis. This data is created algorithmically to mimic the characteristics of real-world data and can serve as an alternative or supplement to it. In this context, data quality often outweighs quantity.

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IBM Journey to AI blog

JANUARY 25, 2024

Consider these questions: Do you have a platform that combines statistical analyses, prescriptive analytics and optimization algorithms? Do you have purpose-built algorithms to improve intermittent and variable demand forecasting? Master data enrichment to enhance categorization and materials attributes.

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Algorithm Categorization Data Quality Artificial Intelligence 233

Unite.AI

OCTOBER 12, 2023

Could you discuss the types of machine learning algorithms that you work on at LXT? Artificial intelligence solutions are transforming businesses across all industries, and we at LXT are honored to provide the high-quality data to train the machine learning algorithms that power them.

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Machine Learning Deep Learning Conversational AI Data Quality 278

Unite.AI

SEPTEMBER 26, 2023

Data Quality and Availability AI models heavily depend on data to function effectively. If businesses don't provide clean, structured and comprehensive data, these models can produce inaccurate results, leading the system to make erroneous predictions.

AI 345

AI AI Data Quality Artificial Intelligence 345

Unite.AI

OCTOBER 17, 2023

By collecting extensive data (including purchase history, farm size, types of crops grown, irrigation methods used, technology adoption, automation rate, and more), and letting AI algorithms analyze it, the firm detected that farm size is one of the most critical factors that influence a farmer’s purchasing decision.

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AI AI Machine Learning Algorithm 277

Unite.AI

SEPTEMBER 12, 2024

Traditionally, AI research and development have focused on refining models, enhancing algorithms, optimizing architectures, and increasing computational power to advance the frontiers of machine learning. However, a noticeable shift is occurring in how experts approach AI development, centered around Data-Centric AI.

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Data Quality Data Scarcity AI AI 162

Marktechpost

AUGUST 24, 2023

As an alternative, offline RL algorithms are more computationally efficient and less vulnerable to reward hacking because they learn from a predefined dataset of samples. However, the characteristics of the offline dataset are inextricably linked to the quality of the policy learned offline. .

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Algorithm Large Language Models Data Quality LLM 98

Unite.AI

SEPTEMBER 22, 2023

Jay Mishra is the Chief Operating Officer (COO) at Astera Software , a rapidly-growing provider of enterprise-ready data solutions. And then I found certain areas in computer science very attractive such as the way algorithms work, advanced algorithms. What initially attracted you to computer science?

Large Language Models 278

Large Language Models Automation Artificial Intelligence Artificial Intelligence 278

Unite.AI

MAY 23, 2024

Taking stock of which data the company has available and identifying any blind spots can help build out data-gathering initiatives. From there, a brand will need to set data governance rules and implement frameworks for data quality assurance, privacy compliance, and security.

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Machine Learning AI AI AI Engineer 269

Unite.AI

JANUARY 20, 2025

Can you explain how datma.FED utilizes AI to revolutionize healthcare data sharing and analysis? What trends in AI and healthcare data do you foresee having the biggest impact in the next five years? AI in healthcare, is tempered by concerns for privacy, security and limited only by data quality.

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Automation Data Platform Data Quality Explainability 130

IBM Journey to AI blog

DECEMBER 20, 2023

Establish a data governance framework to manage data effectively. Algorithms: Algorithms are the rules or instructions that enable machines to learn, analyze data and make decisions. A model represents what was learned by a machine learning algorithm.

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