Private wealth, trusts and estates
Canada | Publication | June 2023
Welcome to our private wealth, trusts and estates publication series, where you will find a range of articles that address some of the most common issues and questions that arise for clients in the context of estate planning, estate and trust administration, charities law and related areas. Our articles cover numerous issues pertaining to will planning, the use of trusts, succession and governance of privately held companies, probate fee planning, powers of attorney for property, health care representation agreements, private foundations, and marriage and cohabitation agreements.
Our team prides itself on being the trusted advisors that clients and other professionals turn to when the goals are important, the issues are complex, and the relationships matter. We offer the highest level of experience and service, delivered in the most efficient and effective way possible.
Recent publications
Publication
Two US decisions find that reproducing works to train large language models is fair use – Part 2: Kadrey v Meta
In Part 1 of this series on fair use in training large language models (LLMs), we discussed Judge Alsup’s decision of Bartz v Anthropic, which found that copying books to train an LLM was fair use, but using pirated books to create a central library was not.
Publication
L’AMF propose un encadrement pour l’usage de l’IA dans les services financiers : quelles obligations pour les institutions financières?
Selon un rapport conjoint du Bureau du surintendant des institutions financières (BSIF) et de l’Agence de la consommation en matière financière du Canada (ACFC), environ 70 % des institutions financières fédérales prévoient utiliser l’IA d’ici 2026 .
Publication
Two US decisions find that reproducing works to train large language models is fair use – Part 1: Bartz v Anthropic
Two recent judgments – decided just days apart – from different judges of the Northern District of California District Court determined that using copyrighted books to train large language models (LLMs) was fair use.
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