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

AI News

In 2025, open-source AI solutions will emerge as a dominant force in closing this gap, he explains. With so many examples of algorithmic bias leading to unwanted outputs and humans being, well, humans behavioural psychology will catch up to the AI train, explained Mortensen. The solutions?

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The ethics of AI and how they affect you

AI News

Data privacy, data protection and data governance Adequate data protection frameworks and data governance mechanisms should be established or enhanced to ensure that the privacy and rights of individuals are maintained in line with legal guidelines around data integrity and personal data protection.

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How Quality Data Fuels Superior Model Performance

Unite.AI

Lastly, balancing data volume and quality is an ongoing struggle. While massive, overly influential datasets can enhance model performance , they often include redundant or noisy information that dilutes effectiveness. This collaborative approach allows organizations to share knowledge without compromising sensitive information.

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Re-evaluating data management in the generative AI age

IBM Journey to AI blog

Data lineage becomes even more important as the need to provide “Explainability” in models is required by regulatory bodies. Enterprise data is often complex, diverse and scattered across various repositories, making it difficult to integrate into gen AI solutions.

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Top 5 AI Hallucination Detection Solutions

Unite.AI

That's an AI hallucination, where the AI fabricates incorrect information. The consequences of relying on inaccurate information can be severe for these industries. These tools help identify when AI makes up information or gives incorrect answers, even if they sound believable. What Are AI Hallucination Detection Tools?

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Amazon AI Introduces DataLore: A Machine Learning Framework that Explains Data Changes between an Initial Dataset and Its Augmented Version to Improve Traceability

Marktechpost

Data scientists and engineers frequently collaborate on machine learning ML tasks, making incremental improvements, iteratively refining ML pipelines, and checking the model’s generalizability and robustness. To build a well-documented ML pipeline, data traceability is crucial. Examples of DATALORE utilization.

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors. Security and privacy —When all data scientists and AI models are given access to data through a single point of entry, data integrity and security are improved.