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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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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IBM Journey to AI blog

OCTOBER 20, 2023

.” When observing its potential impact within industry, McKinsey Global Institute estimates that in just the manufacturing sector, emerging technologies that use AI will by 2025 add as much as USD 3.7 AutoML tools: Automated machine learning, or autoML, supports faster model creation with low-code and no-code functionality.

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NVIDIA

OCTOBER 23, 2024

Members of the Indian healthcare ecosystem are leading the charge by advancing neuroscience research, combating antibiotic resistance, accelerating drug discovery, automating diagnostic scan analysis and more — all with AI’s help.

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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. The global data warehouse as a service market was valued at USD 9.06 They are crucial in ensuring data is readily available for analysis and reporting.

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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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