In the case of humans, everything that we do that seems intelligent is part of a large, complex mechanism in which we are engaged to ensure our survival. This is so hardwired into us that we do not see it easily, and we certainly cannot change it very much. However, superintelligent computer programs are not limited in this way. They understand the way that they work, can change their own code, and are not limited by any particular reward mechanism. I argue that because of this fact, such entities are not self-consistent. In fact, if our superintelligent program has no hard-coded survival mechanism, it is more likely to switch itself off than to destroy the human race willfully.
Suzanne Gildert basically argues that any AGI that can considerably self-improve would simply alter its reward function directly. I’m not sure how she arrives at the conclusion that such an AGI would likely switch itself off. Even if an abstract general intelligence would tend to alter its reward function, wouldn’t it do so indefinitely rather than switching itself off?
So imagine a simple example – our case from earlier – where a computer gets an additional ’1′ added to a numerical value for each good thing it does, and it tries to maximize the total by doing more good things. But if the computer program is clever enough, why can’t it just rewrite it’s own code and replace that piece of code that says ‘add 1′ with an ’add 2′? Now the program gets twice the reward for every good thing that it does! And why stop at 2? Why not 3, or 4? Soon, the program will spend so much time thinking about adjusting its reward number that it will ignore the good task it was doing in the first place! It seems that being intelligent enough to start modifying your own reward mechanisms is not necessarily a good thing!
If it wants to maximize its reward by increasing a numerical value, why wouldn’t it consume the universe doing so? Maybe she had something in mind along the lines of an argument by Katja Grace:
In trying to get to most goals, people don’t invest and invest until they explode with investment. Why is this? Because it quickly becomes cheaper to actually fulfil a goal at than it is to invest more and then fulfil it. [...] A creature should only invest in many levels of intelligence improvement when it is pursuing goals significantly more resource intensive than creating many levels of intelligence improvement.
I am not sure if that argument would apply here. I suppose the AI might hit diminishing returns but could again alter its reward function to prevent that, though what would be the incentive for doing so?
Because it would consume the whole universe in an effort to encode an even larger reward number? In the case that an AI decides to alter its reward function directly, maximizing its reward by means of improving its reward function becomes its new goal. Why wouldn’t it do everything to maximize its payoff, after all it has no incentive to switch itself off? And why would it account for humans in doing so?
ETA #2:
What else I wrote:
There is absolutely no reason (incentive) for it to do anything except increasing its reward number. This includes the modification of its reward function in any way that would not increase the numerical value that is the reward number.
We are talking about a general intelligence with the ability to self-improve towards superhuman intelligence. Of course it would do a long-term risks-benefits analysis and calculate its payoff and do everything to increase its reward number maximally. Human values are complex but superhuman intelligence does not imply complex values. It has no incentive to alter its goal.
What do superintelligences really want? [Link]
Link: physicsandcake.wordpress.com/2011/01/22/pavlovs-ai-what-did-it-mean/
Suzanne Gildert basically argues that any AGI that can considerably self-improve would simply alter its reward function directly. I’m not sure how she arrives at the conclusion that such an AGI would likely switch itself off. Even if an abstract general intelligence would tend to alter its reward function, wouldn’t it do so indefinitely rather than switching itself off?
If it wants to maximize its reward by increasing a numerical value, why wouldn’t it consume the universe doing so? Maybe she had something in mind along the lines of an argument by Katja Grace:
Link: meteuphoric.wordpress.com/2010/02/06/cheap-goals-not-explosive/
I am not sure if that argument would apply here. I suppose the AI might hit diminishing returns but could again alter its reward function to prevent that, though what would be the incentive for doing so?
ETA:
I left a comment over there:
ETA #2:
What else I wrote: