5 Key AI problems related to Data Privacy. How addressing issues with your machine learning models will improve data privacy compliance
Privacy is a concern not only related to Artificial Intelligence (AI) but any data-related field in general. It is about people having control over their personal data and decisions taken based on those.
In Europe, the General Data Protection Regulation (GDPR) that came into force in 2018 regulates the collection and use of personal data. Data protection law does not refer explicitly to AI or Machine Learning but there is a significant focus on large-scale automated processing of personal data and automated decision-making. This means that where AI uses personal data it falls within the scope of the regulation and GDPR principles apply. This can be through the use of personal data to train, test or deploy an AI system. Failure to comply with the GDPR may result in enormous penalties for the companies involved.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
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There are many intersections and overlaps between AI and data science. AI has numerous subsets, like Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). With many career opportunities in both fields, there are lots of conflicting perspectives on educational paths for starting a career in one of these fields.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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