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

AI News

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? However, Wilson warns of new questions on boundaries between personal and workplace data, spurred by such integrations.

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Best Financial Datasets for AI & Data Science in 2025

ODSC - Open Data Science

Whether its algorithmic trading , risk assessment, fraud detection , credit scoring, or market analysis, the accuracy and depth of financial data can make or break an AI-driven solution. Whether youre developing trading algorithms, forecasting economic trends, or detecting fraud, selecting high-quality data sources iscrucial.

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What CIOs and CTOs should consider before adopting generative AI for application modernization

IBM Journey to AI blog

But simultaneously, generative AI has the power to transform the process of application modernization through code reverse engineering, code generation, code conversion from one language to another, defining modernization workflow and other automated processes. Much more can be said about IT operations as a foundation of modernization.

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Promote resilience and responsible emissions management with the IBM Maximo Application Suite

IBM Journey to AI blog

For example, Sund & Baelt automated their inspection work to monitor and manage its critical infrastructures to help them reduce time and costs. Strategic planning and operational efficiency Strategic maintenance planning drives significant cost savings.

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Data Integrity: The Foundation for Trustworthy AI/ML Outcomes and Confident Business Decisions

ODSC - Open Data Science

As much as data quality is critical for AI, AI is critical for ensuring data quality, and for reducing the time to prepare data with automation. Furthermore, data enrichment can help ensure that AI algorithms are trained on diverse data, reducing the risk of bias. Data quality also works hand in hand with data governance.

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Harnessing Machine Learning for Climate Change Mitigation: A Roadmap to Sustainable Future

Heartbeat

Can algorithms, neural networks, and data analytics offer tangible solutions to mitigate the climate crisis? ML can sift through this data deluge by leveraging advanced algorithms and computational methodologies, uncovering hidden patterns, correlations, and insights that may elude human analysis.

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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

W&B Sweeps is a powerful tool to automate hyperparameter optimization. W&B Sweeps will automate this kind of exploration. Ilan Gleiser is a Principal Global Impact Computing Specialist at AWS leading the Circular Economy, Responsible AI and ESG businesses. Prior to AWS, he led AI Enterprise Solutions at Wells Fargo.

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