IBM Journey to AI blog

MAY 9, 2023

It sounds like a joke, but it’s not, as anyone who has tried to solve business problems with AI may know. 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.

Data Platform 347

Data Platform Automation AI AI 347

Unite.AI

DECEMBER 20, 2024

These agreements enable AI companies to access diverse and expansive scientific datasets, presumably improving the quality of their AI tools. The pitch from publishers is straightforward: licensing ensures better AI models, benefitting society while rewarding authors with royalties.

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 294

Generative AI Data Quality AI AI 294

Unite.AI

JANUARY 9, 2024

While the stakes may not be as high for RCM as they are on the clinical side, the repercussions of poorly designed AI solutions are nonetheless significant. Poorly trained AI tools being used to conduct prospective claims audits might miss instances of undercoding, which means missed revenue opportunities.

Metadata 290

Metadata AI AI AI Tools 290

IBM Journey to AI blog

JANUARY 9, 2024

The emergence of generative AI prompted several prominent companies to restrict its use because of the mishandling of sensitive internal data. According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT.

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Data Ingestion Data Integration Data Quality LLM 222

Marktechpost

DECEMBER 3, 2024

Supporting a wide range of document types and retaining all information during parsing reduces manual effort while enhancing the quality of input data for LLMs. Check out the GitHub Page. All credit for this research goes to the researchers of this project. Don’t Forget to join our 60k+ ML SubReddit.

LLM 102

LLM AI Tools Large Language Models Data Ingestion 102

Unite.AI

FEBRUARY 17, 2025

At the next level, AI agents go beyond predictive AI algorithms and software with their ability to operate autonomously, adapt to changing environments, and make decisions based on both pre-programmed rules and learned behaviors.

Automation 147

Automation Responsible AI Generative AI AI Modeling 147

Unite.AI

FEBRUARY 21, 2025

Akeneos Product Cloud solution has PIM, syndication, and supplier data manager capabilities, which allows retailers to have all their product data in one spot. While personalization is nothing new to brands, AI and ML technology allows brands to enter new levels of customer personalization to meet the high consumer expectations.

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IBM Journey to AI blog

MAY 8, 2023

Human-centric governance To mitigate the usage risk a three-pronged approach is proposed: Start with a training program to create mandatory awareness for staff involved in developing, selecting, or using AI tools to ensure alignment with expectations.

Algorithm 218

Algorithm AI AI Generative AI 218

Marktechpost

MARCH 6, 2025

The researchers focused on optimizing data quality by implementing a rigorous pipeline that curates high-quality training datasets from multiple sources. Unlike previous models, Babel includes widely spoken but often overlooked languages such as Bengali, Urdu, Swahili, and Javanese.

Large Language Models 67

Large Language Models LLM NLP Data Quality 67

Unite.AI

SEPTEMBER 18, 2024

Best Practices for Leveraging AI in Sales In order to fully leverage the power of AI in sales and create success in business, sales leaders should use these best practices… Integrate AI Seamlessly: Ensure that AI tools are seamlessly integrated into existing sales processes to minimise disruption and maximise widespread adoption.

Automation 200

Automation AI AI Algorithm 200

Pickl AI

JANUARY 12, 2025

However, there are also challenges that businesses must address to maximise the various benefits of data-driven and AI-driven approaches. Data quality : Both approaches’ success depends on the data’s accuracy and completeness. Step 2: Identify AI Implementation Areas.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

Unite.AI

SEPTEMBER 30, 2024

Emerging Trends in AI Software Quality Control AI is reshaping how QA teams operate, from speeding up test creation to enhancing test data management. Here are a few emerging trends in AI software quality control: AI-powered Test Automation Creating test cases is now faster and more accurate with AI.

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Automation AI AI DevOps 317

Flipboard

JUNE 22, 2023

Ask computer vision, machine learning, and data science questions : VoxelGPT is a comprehensive educational resource providing insights into fundamental concepts and solutions to common data quality issues. ’s Power to Generate Python Code for Computer Vision Dataset Analysis appeared first on MarkTechPost.

Computer Vision 138

Computer Vision Python Machine Learning AI Tools 138

Flipboard

JANUARY 21, 2025

The deployment of generative AI tools across enterprise software has driven a rise in the need for skills related to AI modeling and data annotation amid the full lifecycle of AI solutions, according to Upwork. Professionals are seeking freelancers to help themselves upskill and develop amid technological changes.

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Data Mining Data Extraction Prompt Engineer Prompt Engineering 127

Unite.AI

OCTOBER 11, 2024

That doesn’t mean that businesses should steer clear of AI, but they should recognize the importance of setting a sustainable pace, defining clear goals, and meticulously planning their journey. Pitfall 2: Data Quality and Integrity Using poor quality data with AI is like putting diesel into a gasoline car.

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Data Quality AI AI Continuous Learning 147

Marktechpost

JUNE 27, 2023

In this paper, they investigate how the data quality might be improved along a different axis. Higher quality data produces better results; for instance, data cleaning is a crucial step in creating current datasets and can result in relatively smaller datasets or the ability to run the data through more iterations.

Large Language Models 122

Large Language Models Python Neural Network Data Quality 122

Unite.AI

SEPTEMBER 16, 2024

It offers both open-source and enterprise/paid versions and facilitates big data management. Key Features: Seamless integration with cloud and on-premise environments, extensive data quality, and governance tools. Pros: Scalable, strong data governance features, support for big data.

Data Integration 173

Data Integration ETL Big Data Automation 173

Unite.AI

MARCH 6, 2024

It’s also where AI can perform the yeoman’s share of the work. There are generative AI tools on the market that can scrub a prospect’s social media pages, uncover where they went to college and what concert they attended last weekend, and then forcefully wedge that information into an email.

Automation 147

Automation AI Tools AI Strategy AI 147

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Unite.AI

APRIL 23, 2024

At Aiimi, we believe that AI should give users more, not less, control over their data. AI should be a driver of data quality and brand-new insights that genuinely help businesses make their most important decisions with confidence. The risks of ‘shadow’ AI can be substantial for businesses.

Insight Engine 130

Insight Engine Auto-complete AI Tools Data Quality 130

Marktechpost

AUGUST 9, 2024

and SAM2, have demonstrated the importance of robust synthetic data pipelines for achieving cutting-edge performance. IRIS is an AI tool that can simplify the tagging of visual data. Data annotation is much easier and faster thanks to this tool, which can interpret and react to picture-related commands.

Computer Vision 107

Computer Vision Categorization Data Quality Artificial Intelligence 107

Flipboard

JUNE 17, 2023

In particular, Tart achieves the necessary goals: • Task-neutral: Tart’s inference module must be trained once with fictitious data. Quality: Performs better than basic LLM across the board and closes the gap using task-specific fine-tuning techniques. Data-scalable: Handling 10 times as many instances as in-context learning.

LLM 143

LLM Large Language Models NLP Prompt Engineering 143

Flipboard

JULY 4, 2023

Advantages of vector databases Spatial Indexing – Vector databases use spatial indexing techniques like R-trees and Quad-trees to enable data retrieval based on geographical relationships, such as proximity and confinement, which makes vector databases better than other databases.

Large Language Models 138

Large Language Models Machine Learning Natural Language Processing BERT 138

ODSC - Open Data Science

NOVEMBER 29, 2024

Artificial Intelligence (AI) stands at the forefront of transforming data governance strategies, offering innovative solutions that enhance data integrity and security. In this post, let’s understand the growing role of AI in data governance, making it more dynamic, efficient, and secure.

Data Integration 40

Data Integration Automation Machine Learning AI 40

Unite.AI

DECEMBER 27, 2023

The sheer size and complexity of LLMs require extensive training data to operate effectively across various domains and tasks. The quality and quantity of this data will greatly impact the performance of LLMs, and by extension, a company’s suite of AI tools. is absolutely massive.”

LLM 130

LLM AI AI Large Language Models 130

Unite.AI

JANUARY 23, 2025

AI tools have seen widespread business adoption since ChatGPT's 2022 launch, with 98% of small businesses surveyed by the US Chamber of Commerce using them. Moreover, as the industry evolves and agentic AI systems that can make autonomous decisions become more widespread, the stakes for responsible implementation grow higher.

Responsible AI 113

Responsible AI AI Tools Data Quality Data Analysis 113

Marktechpost

APRIL 15, 2024

These include the high energy requirements of training large AI models and the need for substantial improvements in data quality and accessibility.

Machine Learning 106

Machine Learning Data Analysis ML Artificial Intelligence 106

AssemblyAI

AUGUST 3, 2023

The exploding popularity of conversational AI tools has also raised serious concerns about AI safety. While such studies are still missing a full view of the landscape, they suggest that focusing on the data quality might be way more beneficial than prioritizing scalability when fine-tuning LLMs.

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LLM ChatGPT Chatbots OpenAI 238

Marktechpost

AUGUST 10, 2024

High-Risk AI: These include critical applications like medical AI tools or recruitment software. They must meet strict standards for accuracy, security, and data quality, with ongoing human oversight. Content like deep fakes should be labeled to show it’s artificially made.

Artificial Intelligence 132

Artificial Intelligence Artificial Intelligence AI AI 132

ODSC - Open Data Science

NOVEMBER 20, 2024

Despite these advantages, Yves cautioned that AI-generated content can still appear formulaic if not carefully edited, noting that the human element is still essential for engaging, impactful messaging. Yves Mulkers stressed the need for clean, reliable data as a foundation for AI success.

Generative AI 71

Generative AI Prompt Engineering Prompt Engineer Data Quality 71

Unite.AI

JUNE 17, 2024

By cultivating these three competencies, individuals can navigate the AI era with confidence and create their own irreplaceable value proposition. How can organizations ensure that AI tools are augmenting rather than replacing human workers? Another critical factor is to involve employees in the AI implementation process.

Automation 147

Automation Artificial Intelligence Artificial Intelligence Generative AI 147

Marktechpost

JUNE 23, 2023

While important for complying with privacy regulations, anonymization often reduces data quality, which hampers computer vision development. Several challenges exist, such as data degradation, balancing privacy and utility, creating efficient algorithms, and negotiating moral and legal issues.

Computer Vision 56

Computer Vision AI Tools Data Quality AI 56

TransOrg Analytics

MAY 16, 2024

To effectively integrate AI into customer segmentation, CPG companies should consider the following steps: Data Consolidation: Collect and unify data from various sources, including sales, customer service interactions, online engagement, and third party demographic information.

Data Quality 52

Data Quality Machine Learning AI AI 52

Unite.AI

JUNE 24, 2024

Astro provides robust data-centric alerting with customizable notifications that can be sent through various channels like Slack and PagerDuty. Data validation tests, unit tests, and data quality checks play vital roles in ensuring the reliability, accuracy, and efficiency of data pipelines and ultimately the data that powers your business.

Data Scientist 130

Data Scientist Large Language Models Machine Learning Software Engineer 130

Pickl AI

DECEMBER 16, 2024

Cost Savings One of the most compelling benefits of AI in procurement is its ability to identify cost-saving opportunities. By analysing spending patterns and supplier performance, AI tools can recommend optimal purchasing strategies that lead to lower costs. Data Quality The effectiveness of AI depends on high-quality data.

Automation 52

Automation Artificial Intelligence Artificial Intelligence AI 52

ODSC - Open Data Science

OCTOBER 25, 2024

As AI adoption accelerates, teams face pressure to create AI that not only boosts efficiency but is also intuitive and trustworthy. While data quality and model performance are crucial, building human-centric AI requires collaboration across data, business, design, and legal teams to ensure AI tools genuinely improve productivity.

AI 52

AI AI AI Tools Automation 52