Beneath the Sizzle of Artificial Intelligence

Beneath the Sizzle of Artificial Intelligence

Beneath the Sizzle of Artificial Intelligence

Over the past two years, we have seen many mentions in the press and at conferences about the financial applications of artificial intelligence, machine learning, deep learning and big data. Is this the next frontier, or is it just snake oil? To adapt a famous advertising phrase, “We’ve seen the sizzle; now where is the steak?”

Artificial intelligence research has a long history, but most of the investment community’s views of AI are shaped by the media. The robot in Lost in Space, HAL 9000 in 2001: A Space Odyssey, Skynet in The Terminator — these media tropes subconsciously shape our perceptions of what AI is and what it could be capable of. We dream of (or maybe dread) a HAL 9000 computer sitting next to us as we trade. As we move our mouse to buy some shares, it will sonorously intone, “I don’t think you should do that, Dave,” or maybe it will just lock us out of the trading room when we are about to make a mistake. Robert Harris’s 2011 novel The Fear Index develops this theme near the dramatic conclusion. Maybe some clever computer scientists will plug in SkynetFinance, and rather than trying to wipe out the human race, it will instantaneously understand every scrap of financial information and make more money than there is money before scientists can switch it off — assuming that they would want to, of course.

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Sadly, the truth is somewhat less exciting. Although AI techniques and deep learning have had very significant successes (most notably, mastering the board game Go earlier this year), these have always occurred when AI is given problems that have very specialized features. First, the AI is designed on the basis of a well-defined goal — for example, “win the game” or “present a Netflix user with films they will probably like.” This is absolutely not the case in finance, where the goal is often nuanced and complex. Between the two extremes of being 100 percent in cash and betting your entire asset base on a single coin toss, there are lots of conflicting and complicated constraints and goals.

Second, finance is dominated by randomness. In the game of Go, there is one opponent, who can make one (highly constrained) move at a time.


Comments 1

  1. The AI of now is more likely to be based on descision trees. Accumulated statistics of either the data as it was yesterday or a partial sample taken of that data. The best it can be is older than now.

    When we build an AI capability that can base decisions on unrelated data faster than the data currently available , then we will have AI of the future.

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