Unite.AI

NOVEMBER 1, 2024

In 2021, Gartner estimated that poor data cost organizations an average of $12.9 Dirty data—data that is incomplete, inaccurate, or inconsistent—can have a cascading effect on AI systems. When AI models are trained on poor-quality data, the resulting insights and predictions are fundamentally flawed.

AI Development 157

AI Development AI Developer Data Quality Data Science 157

Unite.AI

DECEMBER 17, 2024

AI can be prone to false positives if the models arent well-tuned, or are trained on biased data. While humans are also susceptible to bias, the added risk of AI is that it can be difficult to identify bias within the system. A full replacement of rules-based systems with AI could leave blind spots in AFC monitoring.

Automation 246

Automation Explainability AI AI 246

Unite.AI

DECEMBER 27, 2024

Heres the thing no one talks about: the most sophisticated AI model in the world is useless without the right fuel. That fuel is dataand not just any data, but high-quality, purpose-built, and meticulously curated datasets. Data-centric AI flips the traditional script. Why is this the case?

Data Quality 130

Data Quality Data Drift Explainability Big Data 130

Unite.AI

FEBRUARY 1, 2025

Let us look at how Allen AI built this model: Stage 1: Strategic Data Selection The team knew that model quality starts with data quality. Their approach, using length-normalized Direct Preference Optimization (DPO) , meant the model learned to value quality over quantity. The result?

AI Development 257

AI Development AI Developer AI Modeling Data Quality 257

Unite.AI

JULY 31, 2023

AI algorithms learn from data; they identify patterns, make decisions, and generate predictions based on the information they're fed. Consequently, the quality of this training data is paramount. AI's Role in Improving Data Quality While the problem of data quality may seem daunting, there is hope.

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Data Quality Algorithm Automation AI 130

AI News

SEPTEMBER 17, 2024

Challenges extend beyond AI regulation However, the challenges facing AI adoption extend beyond regulatory concerns. The survey uncovers a troubling lack of trust in data quality—a cornerstone of successful AI implementation. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Data Quality 321

Data Quality Big Data AI AI 321

Unite.AI

JANUARY 1, 2025

AI is reshaping the world, from transforming healthcare to reforming education. Data is at the centre of this revolutionthe fuel that powers every AI model. Why It Matters As AI takes on more prominent roles in decision-making, data monocultures can have real-world consequences.

AI 176

AI AI Algorithm Large Language Models 176

Marktechpost

JULY 11, 2024

In conclusion, AgentInstruct represents a breakthrough in generating synthetic data for AI training. Automating the creation of diverse and high-quality data addresses the critical issues of manual curation and data quality, leading to significant improvements in the performance and reliability of large language models.

Data Quality 111

Data Quality Large Language Models AI Modeling Automation 111

Unite.AI

NOVEMBER 13, 2024

These trends will elevate the role of data observability in ensuring that organizations can scale their AI initiatives while maintaining high standards for data quality and governance.

Machine Learning 144

Machine Learning Large Language Models Data Quality Automation 144

Unite.AI

DECEMBER 20, 2024

This raises a crucial question: Are the datasets being sold trustworthy, and what implications does this practice have for the scientific community and generative AI models? These agreements enable AI companies to access diverse and expansive scientific datasets, presumably improving the quality of their AI tools.

Generative AI 130

Generative AI AI Modeling AI AI 130

Unite.AI

SEPTEMBER 23, 2024

McKinsey Global Institute estimates that generative AI could add $60 billion to $110 billion annually to the sector. From technical limitations to data quality and ethical concerns, it’s clear that the journey ahead is still full of obstacles. But while there’s a lot of enthusiasm, significant challenges remain.

Generative AI 290

Generative AI Data Quality AI AI 290

Unite.AI

JANUARY 27, 2025

Enterprise-wide AI adoption faces barriers like data quality, infrastructure constraints, and high costs. How does Cirrascale address these challenges for businesses scaling AI initiatives? While Cirrascale does not offer Data Quality type services, we do partner with companies that can assist with Data issues.

Deep Learning 157

Deep Learning Data Quality ML Engineer Data Scientist 157

Unite.AI

JANUARY 30, 2025

While RAG attempts to customize off-the-shelf AI models by feeding them organizational data and logic, it faces several limitations. It's a black box – you can't determine if you've provided enough examples for proper customization or how model updates affect accuracy.

Automation 144

Automation Metadata Explainability Data Scientist 144

IBM Journey to AI blog

MAY 9, 2023

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process.

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Data Platform Automation AI AI 347

Unite.AI

JUNE 19, 2024

However, integrating AI into manufacturing presents several challenges. Two of the most significant challenges are the availability of high-quality data and the need for more skilled talent. Even the most advanced AI models can fail without accurate and comprehensive data.

Data Quality 196

Data Quality Data Scientist Machine Learning AI 196

Unite.AI

NOVEMBER 11, 2024

A cornerstone of this strategy is our commitment to data integrity and diversity, evident in our significant investment in privacy and compliance measures and dataset curation. This focus ensures that AI models are developed with a strong foundation of inclusivity and fairness.

Machine Learning 157

Machine Learning AI AI Natural Language Processing 157

IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

Data Integration 256

Data Integration ETL Business Intelligence Data Quality 256

Unite.AI

FEBRUARY 17, 2025

AI agents can help organizations be more effective, more productive, and improve the customer and employee experience, all while reducing costs. Regularly involve business stakeholders in the AI assessment/selection process to ensure alignment and provide clear ROI.

Automation 144

Automation Responsible AI Generative AI AI Modeling 144

Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning?

Data Quality 52

Data Quality Machine Learning Automation Data Integration 52

IBM Journey to AI blog

JULY 30, 2024

Data Scientists will typically help with training, validating, and maintaining foundation models that are optimized for data tasks. Data Engineer: A data engineer sets the foundation of building any generating AI app by preparing, cleaning and validating data required to train and deploy AI models.

Generative AI 266

Generative AI Data Scientist Software Engineer AI 266

Unite.AI

NOVEMBER 27, 2024

These include a commitment to engineering excellence, adaptability, scalability, and ethical transparency: Precision in Model Development AI models are only as effective as the data and design behind them.

Business Intelligence 144

Business Intelligence AI AI Categorization 144

Unite.AI

OCTOBER 4, 2023

In this article, we’ll look at what AI bias is, how it impacts our society, and briefly discuss how practitioners can mitigate it to address challenges like cultural stereotypes. What is AI Bias? AI bias occurs when AI models produce discriminatory results against certain demographics.

Artificial Intelligence 273

Artificial Intelligence Artificial Intelligence AI AI 273

Unite.AI

OCTOBER 30, 2023

Improves quality: The effectiveness of AI is significantly influenced by the quality of the data it processes. Training AI models with subpar data can lead to biased responses and undesirable outcomes. Improving AI quality: AI system effectiveness hinges on data quality.

DevOps 305

DevOps Software Development Automation Artificial Intelligence 305

Unite.AI

JANUARY 29, 2024

In the digital era, misinformation has emerged as a formidable challenge, especially in the field of Artificial Intelligence (AI). As generative AI models become increasingly integral to content creation and decision-making, they often rely on open-source databases like Wikipedia for foundational knowledge.

Generative AI 201

Generative AI Data Quality AI Modeling AI 201

Unite.AI

JULY 19, 2024

This extensive knowledge base allows for robust AI validation that makes Pythia ideal for situations where accuracy is important. Here are some key features of Pythia: With its real-time hallucination detection capabilities, Pythia enables AI models to make reliable decisions. Automatically detects mislabeled data.

LLM 274

LLM AI AI Data Quality 274

Marktechpost

JUNE 15, 2024

Current methods to counteract model collapse involve several approaches, including using Reinforcement Learning with Human Feedback (RLHF), data curation, and prompt engineering. RLHF leverages human feedback to ensure the data quality used for training, thereby maintaining or enhancing model performance.

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AI Modeling Prompt Engineer Prompt Engineering Large Language Models 129

IBM Journey to AI blog

JANUARY 9, 2024

These models tend to reinforce their understanding based on previously assimilated answers. Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues. The groundwork of training data in an AI model is comparable to piloting an airplane.

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Data Ingestion Data Integration Data Quality LLM 222

Towards AI

FEBRUARY 11, 2025

Author(s): Richie Bachala Originally published on Towards AI. 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.

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Data Quality Neural Network ETL Computer Vision 54

IBM Journey to AI blog

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy.

Data Scientist 252

Data Scientist Machine Learning Neural Network Data Quality 252

AI Weekly

MAY 30, 2024

[Download now] rws.com In The News OpenAI forms safety council as it trains latest AI model OpenAI says it is setting up a safety and security committee and has begun training a new AI model to supplant the GPT-4 system that underpins its ChatGPT chatbot. arxiv.org Sponsor Need Data to Train AI?

Robotics 261

Robotics AI AI Artificial Intelligence 261

Unite.AI

SEPTEMBER 12, 2024

Much like a solid foundation is essential for a structure's stability, an AI model's effectiveness is fundamentally linked to the quality of the data it is built upon. In recent years, it has become increasingly evident that even the most advanced AI models are only as good as the data they are trained on.

Data Quality 157

Data Quality Data Scarcity AI AI 157

Unite.AI

SEPTEMBER 26, 2023

Understanding these challenges allows them to maximize the benefits they get from AI. Data Quality and Availability AI models heavily depend on data to function effectively. Bias and Security Issues AI models can sometimes reflect biases present in their training data.

AI 344

AI AI Data Quality Artificial Intelligence 344

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 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.

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Metadata AI AI AI Tools 284

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.

Data Scientist 234

Data Scientist Metadata Explainability Responsible AI 234

IBM Journey to AI blog

MAY 8, 2023

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. This is to ensure the AI model captures data inputs and usage patterns, required validations and testing cycles, and expected outputs.

Algorithm 218

Algorithm AI AI Generative AI 218

Unite.AI

DECEMBER 6, 2024

At the fundamental level, your data quality is your AI differentiator. The accuracy of, and particularly the generated responses of, a RAG application will always be subject to the quality of data that is being used to train and augment the output.

AI 130

AI AI Data Platform LLM 130

Marktechpost

JANUARY 12, 2024

This increases the generalization loss forecasts for large-scale models and improves the accuracy of the ideal model or data scaling-up allocation approach. Impact of Data Quality – The best model or data scaling-up allocation approach has been heavily influenced by the caliber of the pre-training data.

Large Language Models 111

Large Language Models AI Modeling Data Quality LLM 111