Legal Guidance and Research / Experts / Alexander Korenberg

Alexander Korenberg , PhD

Described as “Tech maestro with a solid grip on the legal aspects of patent prosecution and extremely technically savvy” (IAM Patent 1000, 2020), Alexander is known to “build futureproofed patent strategies in cutting-edge areas such as AI” (IAM Patent 1000, 2021). He is listed as one of the world's leading IP Strategists in Intellectual Asset Management's IAM Strategy 300 and have co-edited a book on IP Strategy for the boardroom, based on years of experience advising clients on how to make strategic choices in the wider business context. Alexander leads the firm's AI practice and publishes a monthly newsletter on patenting AI.

Alexander has an MSci in Physics from Imperial College and a PhD in Computational Neuroscience from University College London. His particular technical expertise is in Artificial Intelligence (AI) and Machine Learning (ML), having worked at the Gatsby Computational Neuroscience Unit, a centre of excellence for computational and theoretical neuroscience and statistical machine learning. During his undergraduate degree, Alexander read molecular biophysics and medical physics and spent a year in Grenoble, France, working on protein crystallography and molecular simulations. With an academic background and a practice at the intersection of physics, computer science and life sciences, Alexander is perfectly placed to protect innovation in this expanding field of technology.

Practice Area

Panel

  • Contributing Author

Qualified Year

  • 2006

Membership

  • Intellectual Property Organisation, Artificial and Emerging Technology Committee
  • Chartered Institute of Patent Attorneys
  • European Patent Institute

Qualifications

  • EPA (2006)
  • CPA (2006)

Education

  • Imperial College, MSci Physics (1998)
  • University College London, PhD Computational Neuroscience (2002)

1 Contributions by Alexander Korenberg

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...
TMT
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