one where AI systems are trusted with enormous sums of money
Kinda. They are carefully watched and have separate risk management systems which impose constraints and limits on what they can do.
E.g. one company apparently lost $440 million in less than an hour due to a glitch in their software.
Yes, but that has nothing to do with AI: “To err is human, but to really screw up you need a computer”. Besides, there are equivalent human errors (fat fingers, add a few zeros to a trade inadvertently) with equivalent magnitude of losses.
have separate risk management systems which impose constraints and limits on what they can do.
If those risk management systems are themselves software, that doesn’t really change the overall picture.
Yes, but that has nothing to do with AI:
If we’re talking about “would companies place AI systems in a role where those systems could cost the company lots of money if they malfunctioned”, then examples of AI systems having been placed in roles where they cost the company a lot of money have everything to do with the discussion.
In the usual way. Contemporary trading systems are not black boxes full of elven magic. They are models, that is, a bunch of code and some data. If the model doesn’t do what you want it to do, you stick your hands in there and twiddle the doohickeys until it stops outputting twaddle.
Besides, in most trading systems the sophisticated part (“AI”) is an oracle. Typically it outputs predictions (e.g. of prices of financial assets) and its utility function is some loss function on the difference between the prediction and the actual. It has no concept of trades, or dollars, or position limits.
Translating these predictions into trades is usually quite straightforward.
Kinda. They are carefully watched and have separate risk management systems which impose constraints and limits on what they can do.
Yes, but that has nothing to do with AI: “To err is human, but to really screw up you need a computer”. Besides, there are equivalent human errors (fat fingers, add a few zeros to a trade inadvertently) with equivalent magnitude of losses.
If those risk management systems are themselves software, that doesn’t really change the overall picture.
If we’re talking about “would companies place AI systems in a role where those systems could cost the company lots of money if they malfunctioned”, then examples of AI systems having been placed in roles where they cost the company a lot of money have everything to do with the discussion.
It does because the issue is complexity and opaqueness. A simple gatekeeper filter along the lines of
is not an “AI system”.
In which case the AI splits the transaction into 2 transactions, each just below a gazillion.
I’m talking about contemporary-level-of-technology trading systems, not about future malicious AIs.
So? An opaque neural net would quickly learn how to get around trade size restrictions if given the proper motivations.
At which point the humans running this NN will notice that it likes to go around risk control measures and will… persuade it that it’s a bad idea.
It’s not like no one is looking at the trades it’s doing.
How? By instituting more complex control measures? Then you’re back to the problem Kaj mentioned above.
In the usual way. Contemporary trading systems are not black boxes full of elven magic. They are models, that is, a bunch of code and some data. If the model doesn’t do what you want it to do, you stick your hands in there and twiddle the doohickeys until it stops outputting twaddle.
Besides, in most trading systems the sophisticated part (“AI”) is an oracle. Typically it outputs predictions (e.g. of prices of financial assets) and its utility function is some loss function on the difference between the prediction and the actual. It has no concept of trades, or dollars, or position limits.
Translating these predictions into trades is usually quite straightforward.