AI News

OCTOBER 17, 2024

Data integrity and security emerged as the biggest deterrents to implementing new AI solutions. Executives also reported encountering various AI performance issues, including: Data quality issues (e.g., Additionally, 65% expressed concern about IP infringement and data security.

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Unite.AI

AUGUST 28, 2024

Artificial intelligence (AI) integration in healthcare has begun, unlocking many use cases for healthcare providers and patients. The AI healthcare software and hardware market is expected to surpass $34 billion by 2025 globally. These tools remove siloed data and improve interoperability.

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IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. Unified, governed data can also be put to use for various analytical, operational and decision-making purposes. There are several styles of data integration.

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Unite.AI

MAY 23, 2024

When integrated successfully, AI technology can have massive ROI, leading to better sales, more satisfied customers, and streamlined operations that save thousands of dollars each year. With all of this in mind, it’s no surprise that investment in AI is projected to top $200 billion by 2025.

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Pickl AI

NOVEMBER 4, 2024

Data modelling is crucial for structuring data effectively. It reduces redundancy, improves data integrity, and facilitates easier access to data. Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity.

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IBM Journey to AI blog

OCTOBER 12, 2023

AI-optimized data stores enable cost-effective AI workload scalability AI models rely on secure access to trustworthy data, but organizations seeking to deploy and scale these models face an increasingly large and complicated data landscape.

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Marktechpost

AUGUST 10, 2024

They must meet strict standards for accuracy, security, and data quality, with ongoing human oversight. With this new legislation, organizations that design and employ AI, particularly those with high-risk systems, must adhere to stringent requirements of openness, data integrity, and human monitoring.

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