"As soon as individual securities start to fly away from the flock, that's one signal to Wallace that says, 'Zero in on this, why is this security behaving more independently versus its peers.' And often independence of behavior is indicative of knowledge that's imprinted on the security. Often, when people don't really know anything, they tend to act in lockstep with others."
The hedge fund's staff now spend their time trying to "break" Wallace's system, by throwing in "unknown unknowns" and offering the AI new data. In one case, the team fed Wallace satellite images of Walmart (WMT) parking lots, to see whether the information could help the machine predict consumer behavior.
A former investment banker with little previous quantitative-trading experience, Valois-Franklin claimed that Wallace's main selling point over rival AI-powered hedge funds is its ability to constantly refine its own models using evolutionary processes that have been likened to selective breeding.
Speaking to MarketWatch, Valois-Franklin described Wallace as a multi-manager hedge fund in which virtual portfolio managers are constantly "battling each other to see who is the most fit in this environment." But the hedge fund chief notes that unlike human portfolio managers, Wallace never needs to sleep and never needs a pep talk.
In simple terms, Wallace's evolutionary process sees the machine create thousands of differently-weighted virtual investment portfolios each day, which are tested and ranked according to their suitability to current market conditions, Valois-Franklin said. In a recurring eight-hour cycle, Wallace picks out its top-performing portfolios and gives them priority to "breed."
"On a daily basis, Wallace will make thousands of copies of itself, each a virtual portfolio manager with different characteristics. It will turn certain weightings and patterns up and down, or on or off, and then determine whether each portfolio manager is better or worse suited in this market environment we're in today... If it's better, it increases the probability that it breeds" Valois-Franklin said.
Speaking to MarketWatch, Castle Ridge's chief scientific officer, Alex Bogdan, argued Wallace's evolutionary approach allows for a deeper level of understanding compared to the neural networks used by systems like ChatGPT, which are modeled on the human brain.
In Bogdan's view, these evolutionary processes represent the future of AI, in allowing machines to go beyond simple mimicry. Bogdan explained that neural networks, which are most prominent in the form of large language models (LLMs) like OpenAI's ChatGPT, simply "mimic the responses that a human would make given the same input."
In contrast, Wallace's "genetic algorithms" work to combine the individual bits of knowledge it has, to build on its own understanding and become "incrementally smarter." "What GPTs are, are clever algorithms," Bogdan said. "We have enough mimicking. We need understanding, not cleverness."
發表於 2024-3-25 21:25:48
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