A slew of articles has appeared recently about the growing use of artificial intelligence (“AI”) and neural networks for securities trading. Most of these articles conflate basic algorithm refinement with deep learning and neural networks. As I noted in an article I wrote last December, I am confident that some forms of deep learning are being used by a few hedge funds and proprietary traders, and that such use will grow quickly over time. The notion, however, that it is taking hold on a widespread basis seems grounded on a misunderstanding of the nature of deep learning.
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Murphy & McGonigle partner Timothy Peterson comments in CoinDesk on a class action securities lawsuit following an ICO. The lawsuit alleges cryptocurrency failed to register as a security.
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.