I think you underrate the money that a good AGI could make on the stock market. An AGI that integrates information from a variety of different sources could potentially make 2% per day in day-trading by anticipating moves of the various market participants.
All those layers of human middle management would quickly game any incentives I could design (even if their intentions were pure; selection pressure suffices).
Today, I saw a story about people complaining that Amazon replaces some middle management with AI.
I definitely think an AI could make that kind of money with a relatively small bank account, although those kinds of returns get a lot more difficult at scale. Regardless, it’s still at least plausible that it’s harder than that, or data requirements are more important than processing/reasoning, or that scalability problems kick in earlier than I’d expect, or.… The story is conditioning on the world where such things are hard.
This is correct. The reason is the stock market has exhaustible gradients. Suppose you have an algorithm that can find market beating investment opportunities. Due to EMH there will be a limited number of these and there will only be finite shares for sale at a market beating price. Once you buy out all the underpriced shares, or sell all the overpriced shares you are holding (by “shares” I also include derivatives) the market price will trend to the efficient price as a result of your own action.
And you have a larger effect the more money you have. This is why successful hedge funds are victims of their own success.
The EMH is about the opportunities that can be exploited by current tools getting exploited. If you have an AGI that uses NSA, Google or Facebook data to find out who is in a clinical trial for a drug and what the outcomes on the person are that allows it to make trades outside of the kind of trades that are currently made.
A hedge fund is usually limited in brain power and can’t simply double it’s cognitive capacity the same way an AGI can to look at more opportunities to exploit.
Error in paragraph one. Suppose the drug company stock is $10 and from your sleuthing you predict it will be $20 once the trial results release. There are a finite number of shares you can buy in the interval between (10 and 20). In the short term you will exhaust the order book for the market and longer term you will drive the price to $20. Hedge funds who can leverage trillions routinely cause things like this.
Error in paragraph 2: the return on increasing intelligence is diminishing. You will not get double the results for double the intelligence. (Note I still think the singularity is possible but because the intelligence increase would be on the order of a million to a billion times the combined intelligence of humanity once you build enough computers and network them with enough bandwidth)
If the AGI can simply double it’s cognitive throughput, it can just repeat the action “sleuth to find an under-priced stock” as needed. This does not exhaust the order book until the entire market is operating at AGI-comparable efficiency, at which point the AGI probably controls a large (or majority) share of the trading volume.
Also, the other players would have limited ability to imitate the AGI’s tactics, so its edge would last until they left the market.
This is true. Keep in mind that the AGI is trying to make money, it’s having to find securities where it predicts humans are going to change the price in a predictable direction in a short time horizon.
Most securities will change their price purely by random chance (or in a pattern no algorithm can find) and you cannot beat the market.
Now there is another strategy. This has been used by highly successful hedges. If you are the news you can make the market move in the direction you predict. Certain hedges do their research and from a mixture of publicly available and probably insider data find companies in weak financial positions. They then sell them short with near term strike prices on the options and announce publicly their findings.
This is a strategy AGI could probably do extremely well.
Increasing intelligence and increasing cognitive capacity are two different things. If you take two hedge funds with 10 employees each, taken together they have double the cognitive capacity as one alone but not double the intelligence. We do see two headfunds with 10 employees each having double the results as one headfund with 10 employees.
Increasing intelligence is something that an AGI might do but it’s hard to do. On the other hand increasing cognitive capacity is just a matter of needing more hardware to have more instances running.
While I agree these are 2 different quantities when we say “intelligence test” we mean cognitive capacity. Every problem on an IQ test can be eventually solved by someone without gross brain deficits. They might need some weeks of training first to understand the “trick” a test maker looks for but after this they can solve every question. So an IQ test score measures problems solved by a time limit (that cannot provide enough time for any living human being to solve all questions or the test has an upper range it can measure) plotter on a gaussian.
So IQ testing an AI system will be tough since obviously it would need about a second to run all questions in parallel though however many stages of neural networks and other algorithms it uses. And then it will either miss a question because it doesn’t have the algorithm to answer one of a particular type or because it doesn’t have information that the test maker assumed all human beings would have.
While I agree these are 2 different quantities when we say “intelligence test” we mean cognitive capacity. Every problem on an IQ test can be eventually solved by someone without gross brain deficits.
What do you mean with “eventually solved”? It seems to me a strange way to think about test questions.
As in if there were no time limit and the test taker were allowed to read any reference that doesn’t directly have the answer and had unlimited lifespan and focus. Note also that harder iq test questions as they are written today in absolute terms the questions are wrong in that multiple valid solutions that satisfy all constraints exist. (With the usual cop out of “best” answer without defining the algorithm used to sort answers for best)
The MCAT and the dental one is another example of such a test. Every well prepared student has the ability to answer every question but there is a time limit.
There are intelligence tests where time alone gets you to be able to answer all correctly. There are others where you won’t reduce your errors to zero by spending more time. To the extend that it’s valuable for certain application of IQ testing to have a test that could be passed at maximum score that tells us nothing about the underlying nature of intelligence.
There are mental tasks that are complex and require you to hold a lot of information at the same time in your head. The mental task involved in making good GPJ-Open predictions is not one that’s just about spending more time.
Depends on how deterministic you think various things are, but if you can predict the market’s movements sufficiently well then trading on shorter time scales is where it is at and you should be able to print money until such time as you extract enough that the market loses liquidity as people become afraid to trade for anything except the long term (first options, then almost anything at all). Question is when that happens, after which you basically get to collect the spread on every economic trade forever, and quite a big one.
I think you underrate the money that a good AGI could make on the stock market. An AGI that integrates information from a variety of different sources could potentially make 2% per day in day-trading by anticipating moves of the various market participants.
Today, I saw a story about people complaining that Amazon replaces some middle management with AI.
I definitely think an AI could make that kind of money with a relatively small bank account, although those kinds of returns get a lot more difficult at scale. Regardless, it’s still at least plausible that it’s harder than that, or data requirements are more important than processing/reasoning, or that scalability problems kick in earlier than I’d expect, or.… The story is conditioning on the world where such things are hard.
This is correct. The reason is the stock market has exhaustible gradients. Suppose you have an algorithm that can find market beating investment opportunities. Due to EMH there will be a limited number of these and there will only be finite shares for sale at a market beating price. Once you buy out all the underpriced shares, or sell all the overpriced shares you are holding (by “shares” I also include derivatives) the market price will trend to the efficient price as a result of your own action.
And you have a larger effect the more money you have. This is why successful hedge funds are victims of their own success.
The EMH is about the opportunities that can be exploited by current tools getting exploited. If you have an AGI that uses NSA, Google or Facebook data to find out who is in a clinical trial for a drug and what the outcomes on the person are that allows it to make trades outside of the kind of trades that are currently made.
A hedge fund is usually limited in brain power and can’t simply double it’s cognitive capacity the same way an AGI can to look at more opportunities to exploit.
Error in paragraph one. Suppose the drug company stock is $10 and from your sleuthing you predict it will be $20 once the trial results release. There are a finite number of shares you can buy in the interval between (10 and 20). In the short term you will exhaust the order book for the market and longer term you will drive the price to $20. Hedge funds who can leverage trillions routinely cause things like this. Error in paragraph 2: the return on increasing intelligence is diminishing. You will not get double the results for double the intelligence. (Note I still think the singularity is possible but because the intelligence increase would be on the order of a million to a billion times the combined intelligence of humanity once you build enough computers and network them with enough bandwidth)
If the AGI can simply double it’s cognitive throughput, it can just repeat the action “sleuth to find an under-priced stock” as needed. This does not exhaust the order book until the entire market is operating at AGI-comparable efficiency, at which point the AGI probably controls a large (or majority) share of the trading volume.
Also, the other players would have limited ability to imitate the AGI’s tactics, so its edge would last until they left the market.
This is true. Keep in mind that the AGI is trying to make money, it’s having to find securities where it predicts humans are going to change the price in a predictable direction in a short time horizon.
Most securities will change their price purely by random chance (or in a pattern no algorithm can find) and you cannot beat the market.
Now there is another strategy. This has been used by highly successful hedges. If you are the news you can make the market move in the direction you predict. Certain hedges do their research and from a mixture of publicly available and probably insider data find companies in weak financial positions. They then sell them short with near term strike prices on the options and announce publicly their findings.
This is a strategy AGI could probably do extremely well.
Increasing intelligence and increasing cognitive capacity are two different things. If you take two hedge funds with 10 employees each, taken together they have double the cognitive capacity as one alone but not double the intelligence. We do see two headfunds with 10 employees each having double the results as one headfund with 10 employees.
Increasing intelligence is something that an AGI might do but it’s hard to do. On the other hand increasing cognitive capacity is just a matter of needing more hardware to have more instances running.
While I agree these are 2 different quantities when we say “intelligence test” we mean cognitive capacity. Every problem on an IQ test can be eventually solved by someone without gross brain deficits. They might need some weeks of training first to understand the “trick” a test maker looks for but after this they can solve every question. So an IQ test score measures problems solved by a time limit (that cannot provide enough time for any living human being to solve all questions or the test has an upper range it can measure) plotter on a gaussian.
So IQ testing an AI system will be tough since obviously it would need about a second to run all questions in parallel though however many stages of neural networks and other algorithms it uses. And then it will either miss a question because it doesn’t have the algorithm to answer one of a particular type or because it doesn’t have information that the test maker assumed all human beings would have.
What do you mean with “eventually solved”? It seems to me a strange way to think about test questions.
As in if there were no time limit and the test taker were allowed to read any reference that doesn’t directly have the answer and had unlimited lifespan and focus. Note also that harder iq test questions as they are written today in absolute terms the questions are wrong in that multiple valid solutions that satisfy all constraints exist. (With the usual cop out of “best” answer without defining the algorithm used to sort answers for best)
The MCAT and the dental one is another example of such a test. Every well prepared student has the ability to answer every question but there is a time limit.
There are intelligence tests where time alone gets you to be able to answer all correctly. There are others where you won’t reduce your errors to zero by spending more time. To the extend that it’s valuable for certain application of IQ testing to have a test that could be passed at maximum score that tells us nothing about the underlying nature of intelligence.
There are mental tasks that are complex and require you to hold a lot of information at the same time in your head. The mental task involved in making good GPJ-Open predictions is not one that’s just about spending more time.
A person can write things down, I suspect that an incorrect answer on a test with unlimited time is :
The person got bored and didn’t check enough to catch every error or didn’t possess a fact that the test writer expected every taker to know.
The question itself is wrong. (a correct question is one where after all constraints are applied one and only one answer exists)
Depends on how deterministic you think various things are, but if you can predict the market’s movements sufficiently well then trading on shorter time scales is where it is at and you should be able to print money until such time as you extract enough that the market loses liquidity as people become afraid to trade for anything except the long term (first options, then almost anything at all). Question is when that happens, after which you basically get to collect the spread on every economic trade forever, and quite a big one.