Transcript
My name is Angelo Calvello, along with my partner Julie Bonafide, I co-founded Rosetta Analytics in 2016. We are an asset manager and an asset manager that's been using advanced AI.  We have been using deep learning since 2017 and deep reinforcement learning since 2020 to run money and life strategies for U.S. institutional investors. And by any measure, the models work and the strategies provide investors with the investment outcomes they seek.

Yet we face a strong headwind in our business development because of what I can only described as a cultural belief. And I'll get to that in a minute. Also, for a fuller delineation of the following ideas, I encourage you to read my essay in Institutional Investor entitled "The Most Powerful Artificial Intelligence Knows Nothing About Investing. And that's perfectly OK."

It was published in February of 2021. Let me start with my assessment of the investment industry's use of AI, or what I prefer to call machine learning or its use of AML. Today's become quite fashionable for managers to claim to be using machine learning as part of their investment process. If they're truthful and let's be clear, there's a lot of hand-waving in this space, but if they're truthful, then they have likely integrated what I would call traditional machine learning techniques, such as support vector machines, random forests, nearest neighbor into their existing investment processes.

These traditional machine learning techniques are generally used to augment an existing human based investment process, reducing AI at the end of the day to a handmaiden to human intelligence. And while such machine learning techniques have proven helpful, they remain bound by the constraints of human intelligence, and they fail to achieve the superhuman performing that comes with what many call a new wave of A.I. systems.

With this new wave, these are programs that operate unlike traditional AI programs. They are not hard coded, and therefore they're not restricted to working within the confines of what is already known. This new wave of A.I. is inspired by neuroscience and is capable of learning on their own first principles. This new wave includes such systems as deep learning and deep reinforcement learning, which, unlike traditional machine learning systems, are capable of finding patterns in data directly and making predictions and decisions entirely on their own, independent of human intelligence or human judgment.

And this is where Rosetta differs from other managers using ML. We embrace this new wave of AI to the point that our A.I. is our investment process. Our models develop their own predictions, their own decisions directly from the data. Most of that raw, unstructured time series data It is precisely this nonhuman dimension that makes this new wave of AI so powerful.

As David Silver of Google's DeepMind points out, deep learning is more powerful than previous approaches because by not using human data or human expertize in any fashion, we've removed the constraints of human knowledge, and it is able to create knowledge itself. Investment managers, especially quants and quants that have adopted traditional machine learning. Readily concede that these powerful algorithms can be used to solve incredibly complex problems in medicine, autonomous driving, engineering, robotics and other verticals.

But they staunchly deny that deep learning and deep reinforcement learning can be used to solve investment problems and build successful autonomous investment strategies. In my view, that denial will be their downfall.

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