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Dmytry comments on AI Risk and Opportunity: Humanity's Efforts So Far - Less Wrong Discussion
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Not sure how non-wireheaded though. One doesn't literally need a wire to the head to have a shortcut. Watching movies or reading fiction is pretty wireheaded effort.
I'm not quite sure its how it works. The pain and the pleasure seem to be the reinforcement values for neural network training, rather than actual utilities of any kind. Suppose you are training a dog not to chew stuff, by reinforcements. The reinforcement value is not proportional to utility of behaviour, but is set as to optimize the training process.
See, if there's two actions, one result in utility 1000 and other result in utility 100, this method can choose the one that results in utility 100 because it is reachable by imperfect backwards tracing while the 1000 one isn't (and is lost in giant space that one can't search). At that point, you could of course declare that being backwards traceable to is a very desirable property of an action, and goalpost shift so that this action has utility 2000, but its clear that this is a screwy approach.
And how do you improve the models you use for expectations?
Of course you can describe literally anything as 'utility maximization', the issue is that agent which is maximizing something, doesn't really even need to know what it is maximizing, doesn't necessarily do calculation of utilities, et cetera. You don't really have model here, you just have a description, and if you are to model it as utility maximizer, you'll be committing fallacy as with that blue minimizing robot
Maybe - if the person rewarding the dog is doing it wrong. Normally, you would want those things to keep reasonably in step.
So, in the "forecasting/evaluation/tree pruning" framework, that sounds as though it is a consequence of tree pruning.
Pruning is inevitablle in resource-limited agents. I wouldn't say it stopped them from being expected utility maximisers, though.
a) get more data; b) figure out how to compress it better;
Alas, I don't like that post very much. It is an attack on the concept of utility-maximization, which hardly seems productive to me. Anyway, I think I see your point here - though I am less clear about how it relates to the previous conversation (about expectations of utility vs utility - or more generally about utility maximisation being somehow "sub-optimal").
If the dog chews something really expensive up, there is no point punishing the dog proportionally more for that. That would be wrong; some level of punishment is optimal for training; beyond this is just letting anger out.
It's not mere pruning. You need a person to be able to feed your pets, you need them to get through the door, you need a key, you can get a key at key duplicating place, you go to key duplicating place you know of to make a duplicate.
That stops them from being usefully modelled as 'choose action that gives maximum utility'. You can't assume that it makes action that results in maximum utility. You can say that it makes action which results in as much utility as this agent with its limitations could get out of that situation, but that's almost tautological at this point. Also, see
http://en.wikipedia.org/wiki/Intelligent_agent
for terminology.
Well, the utility agent as per wiki article, is clearly stupid because it won't reason backwards. And the utility maximizers discussed by purely theoretical AI researchers, likewise.
Needs something better than trying all models and seeing what fits, though. One should ideally be able to use the normal reasoning to improve models. It feels that a better model has bigger utility.
You would probably want to let the dog know that some of your chewable things are really expensive. You might also want to tell it about the variance in the value of your chewable items. I'm sure there are some cases where the owner might want to manipulate the dog by giving it misleading reward signals - but honest signals are often best.
These are the researchers who presume no computational resource limitation? They have no need to use optimisation heuristics - such as the ones you are proposing - they assume unlimited computing resources.
Needs something better than trying all models and seeing what fits, though. One should ideally be able to use the normal reasoning to improve models. [...]
Sure. Humans use "normal reasoning" to improve their world models.
Well, i don't think that quite works, dogs aren't terribly clever. Back to humans, e.g. significant injuries hurt a lot less than you'd think they would, my guess is that small self inflicted ones hurt so much for effective conditioning.
The ugly is when some go on and talk about the AGIs certainly killing everyone unless designed in some way that isn't going to work. And otherwise paint wrong pictures of AGI.
Ya. Sometimes even resulting in breakage, when they modify world models to fit with some pre-existing guess.
The idea and its explanation both seem pretty speculative to me.
Pavlovian conditioning is settled science; the pain being negative utility value for intelligence etc, not so much.
The "idea" was:
...and its explanation was:
I'm inclined towards scepticism - significant injuries often hurt a considerable amount - and small ones do not hurt by disproportionally large amounts - at least as far as I know.
There do seem to be some ceiling-llike effects - to try and prevent people passing out and generally going wrong. I don't think that is to do with your hypothesis.
The very fact that you can pass out from pain and otherwise the pain interfering with thought and actions, implies that the pain doesn't work remotely like utility should. Of course one does factor in pain into the utility, but that is potentially dangerous for survival (as you may e.g. have to cut your hand off when its stuck under boulder and you already determined that cutting the hand off is the best means of survival). You can expect interference along the lines of passing out from the network training process. You can't expect interference from utility values being calculated.
edit:
Okay for the middle ground: would you agree that pain has Pavlovian conditioning role? The brain also assigns it negative utility, but the pain itself isn't utility, it evolved long before brains could think very well. And in principle you'd be better off assigning utility to lasting damage rather than to pain (and most people do at least try).
edit: that is to say, removing your own appendix got to be easy (for surgeons) if pain was just utility, properly summed with other utilities, making you overall happy that you got the knife for the procedure and can save yourself, through the entire process. It'd be like giving up an item worth $10 for $10 000 000 . There the values are properly summed first, not making you feel the loss and feel the gain separately.
You don't think consciousness should be sacrificed - no matter what the degree of damage - in an intelligently designed machine? Nature sacrifices consciousness under a variety of circumstances. Can you defend your intuition about this issue? Why is nature wrong to permit fainting and passing out from excessive pain?
Of course pain should really hurt. It is supposed to distract you and encourage you to deal with it. Creatures in which pain didn't really, really hurt are likely to have have left fewer descendants.