Deep learning is another technological advance that has important implications for securities regulation.  In simple terms, deep learning is a form of machine learning that involves learning data representations and patterns using simulated neural networks.  Deep learning requires access to a very large amount of data and immense computing power.  I expect deep learning to be used by certain sophisticated traders, such as hedge funds and proprietary trading firms.

Two developments in technology, blockchain and deep learning, have implications for securities trading regulation.  The two technologies are different in scope and purpose and will raise different issues for securities regulators.  Both demonstrate how technological advances in the trading area can outpace current rules and regulations and cause regulators to rethink how to handle so-called “disruptive” technologies without impeding new structures and ideas.

Howard Kramer Comments - Early this year FINRA released a thoughtful white paper (or concept release) on distributed ledger technology (“DLT”), also referred to as blockchain, and its implications for securities regulation.  The white paper provided an overview of DLT and its securities industry applications and potential impact.  It also described the factors to consider when implementing DLT and the attendant regulatory considerations.