Legal Guidance and Research / Experts / Nathalie Richards
Nathalie Richards#9920

Nathalie Richards

Kilburn & Strode
Nathalie is a trainee patent attorney at Kilburn & Strode, where her practice focuses on AI and other emerging technology such as quantum computing. She prosecutes and drafts patent applications and advises clients on their freedom to operate. Nathalie graduated with a Bachelor in Chemistry from the University of Nottingham in 2018 where she particularly enjoyed quantum mechanics and particle simulation using computers. After graduating, she worked as a QA and then as a test engineer for a data company in London, where she implemented an automated testing framework. Prior to joining Kilburn & Strode Nathalie returned to academia to study Financial Computing at Queen Mary University, where she gained a Masters degree with Distinction. Nathalie read modules such as: Machine Learning, Trading and Risk System Development and Advanced Computing in Finance. Her Masters project concerned the Lorenz Lattice Gas Model which can be used to computationally model the behaviour of long chain polymers and finds applications in fields such a chemical engineering and in the understanding of proteins and DNA. With her cross-disciplinary background, Nathalie is ideally suited to advise clients on the intricacies of protecting inventions at the cutting edge of innovation combining computer science and life sciences and chemistry.

Practice Area

Panel

  • Contributing Author

Membership

  • Chartered Institute of Patent Attorneys
  • European Patent Institute

Education

  • Queen Mary University of London, IPCert Certificate in IP Law (2021)
  • Queen Mary University of London, MSc Financial Computing, Distinction (2020)
  • University of Nottingham, BSC, Chemistry, 2.1 (2018)

1 Contributions by Nathalie Richards

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 intelligence-explainability Artificial intelligence-intellectual property Artificial intelligence in the EU-the key legal issues AI clauses-overview AI procurement IP clauses-pro-supplier and Contractual considerations for the procurement of artificial intelligence-checklist. To follow the progress of key legal developments in relation to AI, see Practice Notes: UK artificial intelligence-tracker and EU Artificial intelligence-tracker. The history of AI and ML Although frequently perceived (and applied) as...
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