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AI and Machine Learning: A Technical Primer for Lawyers—Data, Training, Algorithms, Neural Networks, Deep Learning, and Key Issues (Explainability, Bias, Data Protection and IP)
PRACTICE NOTES
AI and Machine Learning: A Technical Primer for Lawyers—Data, Training, Algorithms, Neural Networks, Deep Learning, and Key Issues (Explainability, Bias, Data Protection and IP)
This Practice Note outlines the fundamentals of artificial intelligence (AI) and machine learning (ML) technology. It includes: A brief history of AI and ML Why data matters How ML models are trained Categories of ML Factors when choosing or evaluating an ML algorithm Neural networks What deep learning means Typical neural network architectures Examples of other widely used ML algorithms Core challenges for AI and ML-transparency, explainability and bias Privacy and data protection Safeguarding AI technology This Practice Note does not address legal or regulatory matters arising from the use or development of AI or ML technologies. For more on these topics, see Practice Notes: Artificial intelligence-data protection Artificial intelligence-UK regulation and the National AI Strategy Artificial
TMT
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