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Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

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.

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AI in 2025: Purpose-driven models, human integration, and more

AI News

For this article, AI News caught up with some of the worlds leading minds to see what they envision for the year ahead. Smaller, purpose-driven models Grant Shipley, Senior Director of AI at Red Hat , predicts a shift away from valuing AI models by their sizeable parameter counts.

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Trust meets efficiency: AI and blockchain mutuality

AI News

Giza shows how AI can optimise decentralised finance and is a project that uses the two technologies to good effect. Blockchain as AI’s backbone Blockchain offers AI a decentralised infrastructure to foster trust and collaboration. AI algorithms and training datasets can be recorded on-chain so they’re auditable.

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Optimizing Company Workflows with AI Agents: Myth or Reality?

Unite.AI

If the input data is outdated, incomplete, or biased, the results will inevitably be subpar. Unfortunately, organizations sometimes overlook this fundamental aspect, expecting AI to perform miracles despite flaws in the data. Integration challenges also pose significant obstacles.

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Multimodal Data Integration: How Artificial Intelligence Is Revolutionizing Cancer Care

Towards AI

Last Updated on November 5, 2023 by Editorial Team Author(s): Max Charney Originally published on Towards AI. Introspection of histology image model features. the authors of the multimodal data integration in oncology paper. Some of the required information and potential applications of multimodal data integration.

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The High Cost of Dirty Data in AI Development

Unite.AI

In 2021, Gartner estimated that poor data cost organizations an average of $12.9 Dirty datadata 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.

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Ashish Nagar, CEO & Founder of Level AI – Interview Series

Unite.AI

This makes us the central hub, collecting data from all these sources and serving as the intelligence layer on top. However, the challenge is that some of these systems are based on non-cloud, on-premise technology, or even cloud technology that lacks APIs or clean data integrations. With the recent $39.4