Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine learning (ML). For instance, traditional ML methods assume all data can be moved to a central repository. This is an unrealistic assumption when dealing with data sovereignty and security considerations or sensitive data like personally…
]]>Big data, new algorithms, and fast computation are three main factors that make the modern AI revolution possible. However, data poses many challenges for enterprises: difficulty in data labeling, ineffective data governance, limited data availability, data privacy, and so on. Synthetically generated data is a potential solution to address these challenges because it generates data points by…
]]>On April 21, 2021, the EU Commission of the European Union issued a proposal for a regulation to harmonize the rules governing the design and marketing of AI systems called the Artificial Intelligence Act (AIA). AI systems are considered to be risky by regulatory bodies. High-risk AI systems are subject to specific design and implementation obligations to improve transparency.
]]>Data Scientists and Machine Learning Engineers often face the dilemma of “machine learning compared to deep learning” classifier usage for their business problems. Depending upon the nature of the dataset, some data scientists prefer classical machine-learning approaches. Others apply the latest deep learning models, while still others pursue an “ensemble” model hoping to get the best of both…
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