Why Cryptofinance Needs Decentralized AGI

This article was first published on SingularityNET - Medium
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While the pace has picked up in recent years, AI has been revolutionizing finance behind the scenes for decades now. Due to the complexity of the data, systems, and strategies involved in financial markets, there are numerous situations where the actionable, monetizable patterns elude human perception and understanding but succumb to AI algorithmics.

Typical AI technologies applied to financial markets have also shown some significant limitations — mostly notably the inability to deal with ‘regime changes’, i.e. situations where fundamental characteristics of a market change radically during a short period of time. A typical pattern is that a certain quantitative or AI technique works really well for a certain interval on a certain market, yielding dramatic positive returns with manageable risk, and then suddenly the market regime shifts and the performance rapidly becomes quite the opposite.

Given the nature of the beast, this sort of phenomenon will probably never be entirely conquered. However, the clear potential exists to do a much better job of robust AI market prediction and financial management by leveraging ensemble-based AI tools and progress toward Artificial General Intelligence.

AI in Finance: Transcending the Curve-Fitting Trap

The use of AI in finance is a natural extension of the use of advanced statistical methods, which arguably dates back at least to Bachelier’s 1900 thesis ‘The theory of speculation’. Machine learning methods, and broader sorts of AI techniques, exceed standard statistical methods in their ability to deal with complex interdependencies between multiple variables, which are ever-present in finance, due to the complexities of the economic systems underlying financial instruments and also the diversity of players in real-world markets. Today AI methods are everpresent on the back end of financial applications including process automation, security, underwriting and credit scoring, robo-advisory, algorithmic trading, and more.

Fundamentally, though, every AI-for-finance application still suffers from ...

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