The journey of embedding ethics into AI workflows begins at the design phase. This stage is crucial for setting the ethical foundation of AI systems. Designers and developers must consider the implications of their creations on privacy, security, and fairness. Ethical AI design involves implementing privacy-by-design principles, ensuring data protection, and mitigating biases that could perpetuate inequality or discrimination.…
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The accuracy of Artificial Intelligence (AI) and Machine Learning (ML) models depends significantly on the quality of the data used for training. High-quality data, which is accurate, complete, and representative, leads to more reliable and robust models. Conversely, poor-quality data can result in models that make inaccurate predictions or exhibit biased behavior. The quality of…
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User-Friendly On-Premise HPC Access Universities can easily provide access to on-premise High-Performance Computers (HPC) to students, faculty, and staff using the Analytics Gateway™. Users’ access, storage quotas, and computing time can all be controlled by system administrators using the dashboard. Once authorized, users can immediately start using any of the provided applications such as Jupyter,…
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