Mark,
The main motivation behind my setup was to avoid the wirehead
scenario. That is why I make the explicit goodness/pleasure
distinction. Whatever good is, it cannot be something directly
observable, or the AI will just wirehead itself (assuming it gets
intelligent enough to do so, of course). But, goodness cannot be
completely unobservable, or the AI will have no idea what it should
do.
So, the AI needs to have a concept of external goodness, with a weak
probabilistic correlation to its directly observable pleasure. That
way, the system will go after pleasant things, but won't be able to
fool itself with things that are maximally pleasant. For example, if
it were to consider rewiring its visual circuits to see only
skin-color, it would not like the idea, because it would know that
such a move would make it less able to maximize goodness in general.
(It would know that seeing only tan does not mean that the entire
world is made of pure goodness.) An AI that was trying to maximize
pleasure would see nothing wrong with self-stimulation of this sort.
So, I think that pushing the problem of goal-setting back to
pleasure-setting is very useful for avoiding certain types of
undesirable behavior.
By the way, where does this term "wireheading" come from? I assume
from context that it simply means self-stimulation.
-Abram Demski
On Wed, Aug 27, 2008 at 2:58 PM, Mark Waser <[EMAIL PROTECTED]> wrote:
> Hi,
>
> A number of problems unfortunately . . . .
>
>> -Learning is pleasurable.
>
> . . . . for humans. We can choose whether to make it so for machines or
> not. Doing so would be equivalent to setting a goal of learning.
>
>> -Other things may be pleasurable depending on what we initially want
>> the AI to enjoy doing.
>
> See . . . all you've done here is pushed goal-setting to pleasure-setting
> . . . .
>
> = = = = =
>
> Further, if you judge goodness by pleasure, you'll probably create an AGI
> whose shortest path-to-goal is to wirehead the universe (which I consider to
> be a seriously suboptimal situation - YMMV).
>
>
>
>
> ----- Original Message ----- From: "Abram Demski" <[EMAIL PROTECTED]>
> To: <agi@v2.listbox.com>
> Sent: Wednesday, August 27, 2008 2:25 PM
> Subject: **SPAM** Re: AGI goals (was Re: Information theoretic approaches to
> AGI (was Re: [agi] The Necessity of Embodiment))
>
>
>> Mark,
>>
>> OK, I take up the challenge. Here is a different set of goal-axioms:
>>
>> -"Good" is a property of some entities.
>> -Maximize good in the world.
>> -A more-good entity is usually more likely to cause goodness than a
>> less-good entity.
>> -A more-good entity is often more likely to cause pleasure than a
>> less-good entity.
>> -"Self" is the entity that causes my actions.
>> -An entity with properties similar to "self" is more likely to be good.
>>
>> Pleasure, unlike goodness, is directly observable. It comes from many
>> sources. For example:
>> -Learning is pleasurable.
>> -A full battery is pleasurable (if relevant).
>> -Perhaps the color of human skin is pleasurable in and of itself.
>> (More specifically, all skin colors of any existing race.)
>> -Perhaps also the sound of a human voice is pleasurable.
>> -Other things may be pleasurable depending on what we initially want
>> the AI to enjoy doing.
>>
>> So, the definition if "good" is highly probabilistic, and the system's
>> inferences about goodness will depend on its experiences; but pleasure
>> can be directly observed, and the pleasure-mechanisms remain fixed.
>>
>> On Wed, Aug 27, 2008 at 12:32 PM, Mark Waser <[EMAIL PROTECTED]> wrote:
>>>>
>>>> But, how does your description not correspond to giving the AGI the
>>>> goals of being helpful and not harmful? In other words, what more does
>>>> it do than simply try for these? Does it pick goals randomly such that
>>>> they conflict only minimally with these?
>>>
>>> Actually, my description gave the AGI four goals: be helpful, don't be
>>> harmful, learn, and keep moving.
>>>
>>> Learn, all by itself, is going to generate an infinite number of
>>> subgoals.
>>> Learning subgoals will be picked based upon what is most likely to learn
>>> the
>>> most while not being harmful.
>>>
>>> (and, by the way, be helpful and learn should both generate a
>>> self-protection sub-goal in short order with procreation following
>>> immediately behind)
>>>
>>> Arguably, be helpful would generate all three of the other goals but
>>> learning and not being harmful without being helpful is a *much* better
>>> goal-set for a novice AI to prevent "accidents" when the AI thinks it is
>>> being helpful. In fact, I've been tempted at times to entirely drop the
>>> be
>>> helpful since the other two will eventually generate it with a lessened
>>> probability of trying-to-be-helpful accidents.
>>>
>>> Don't be harmful by itself will just turn the AI off.
>>>
>>> The trick is that there needs to be a balance between goals. Any single
>>> goal intelligence is likely to be lethal even if that goal is to help
>>> humanity.
>>>
>>> Learn, do no harm, help. Can anyone come up with a better set of goals?
>>> (and, once again, note that learn does *not* override the other two --
>>> there
>>> is meant to be a balance between the three).
>>>
>>> ----- Original Message ----- From: "Abram Demski" <[EMAIL PROTECTED]>
>>> To: <agi@v2.listbox.com>
>>> Sent: Wednesday, August 27, 2008 11:52 AM
>>> Subject: **SPAM** Re: AGI goals (was Re: Information theoretic approaches
>>> to
>>> AGI (was Re: [agi] The Necessity of Embodiment))
>>>
>>>
>>>> Mark,
>>>>
>>>> I agree that we are mired 5 steps before that; after all, AGI is not
>>>> "solved" yet, and it is awfully hard to design prefab concepts in a
>>>> knowledge representation we know nothing about!
>>>>
>>>> But, how does your description not correspond to giving the AGI the
>>>> goals of being helpful and not harmful? In other words, what more does
>>>> it do than simply try for these? Does it pick goals randomly such that
>>>> they conflict only minimally with these?
>>>>
>>>> --Abram
>>>>
>>>> On Wed, Aug 27, 2008 at 11:09 AM, Mark Waser <[EMAIL PROTECTED]>
>>>> wrote:
>>>>>>>
>>>>>>> It is up to humans to define the goals of an AGI, so that it will do
>>>>>>> what
>>>>>>> we want it to do.
>>>>>
>>>>> Why must we define the goals of an AGI? What would be wrong with
>>>>> setting
>>>>> it
>>>>> off with strong incentives to be helpful, even stronger incentives to
>>>>> not
>>>>> be
>>>>> harmful, and let it chart it's own course based upon the vagaries of
>>>>> the
>>>>> world? Let it's only hard-coded goal be to keep it's satisfaction
>>>>> above
>>>>> a
>>>>> certain level with helpful actions increasing satisfaction, harmful
>>>>> actions
>>>>> heavily decreasing satisfaction; learning increasing satisfaction, and
>>>>> satisfaction naturally decaying over time so as to promote action . . .
>>>>> .
>>>>>
>>>>> Seems to me that humans are pretty much coded that way (with
>>>>> evolution's
>>>>> additional incentives of self-defense and procreation). The real trick
>>>>> of
>>>>> the matter is defining helpful and harmful clearly but everyone is
>>>>> still
>>>>> mired five steps before that.
>>>>>
>>>>>
>>>>> ----- Original Message -----
>>>>> From: Matt Mahoney
>>>>> To: agi@v2.listbox.com
>>>>> Sent: Wednesday, August 27, 2008 10:52 AM
>>>>> Subject: AGI goals (was Re: Information theoretic approaches to AGI
>>>>> (was
>>>>> Re:
>>>>> [agi] The Necessity of Embodiment))
>>>>> An AGI will not design its goals. It is up to humans to define the
>>>>> goals
>>>>> of
>>>>> an AGI, so that it will do what we want it to do.
>>>>>
>>>>> Unfortunately, this is a problem. We may or may not be successful in
>>>>> programming the goals of AGI to satisfy human goals. If we are not
>>>>> successful, then AGI will be useless at best and dangerous at worst. If
>>>>> we
>>>>> are successful, then we are doomed because human goals evolved in a
>>>>> primitive environment to maximize reproductive success and not in an
>>>>> environment where advanced technology can give us whatever we want. AGI
>>>>> will
>>>>> allow us to connect our brains to simulated worlds with magic genies,
>>>>> or
>>>>> worse, allow us to directly reprogram our brains to alter our memories,
>>>>> goals, and thought processes. All rational goal-seeking agents must
>>>>> have
>>>>> a
>>>>> mental state of maximum utility where any thought or perception would
>>>>> be
>>>>> unpleasant because it would result in a different state.
>>>>>
>>>>> -- Matt Mahoney, [EMAIL PROTECTED]
>>>>>
>>>>> ----- Original Message ----
>>>>> From: Valentina Poletti <[EMAIL PROTECTED]>
>>>>> To: agi@v2.listbox.com
>>>>> Sent: Tuesday, August 26, 2008 11:34:56 AM
>>>>> Subject: Re: Information theoretic approaches to AGI (was Re: [agi] The
>>>>> Necessity of Embodiment)
>>>>>
>>>>> Thanks very much for the info. I found those articles very interesting.
>>>>> Actually though this is not quite what I had in mind with the term
>>>>> information-theoretic approach. I wasn't very specific, my bad. What I
>>>>> am
>>>>> looking for is a a theory behind the actual R itself. These approaches
>>>>> (correnct me if I'm wrong) give an r-function for granted and work from
>>>>> that. In real life that is not the case though. What I'm looking for is
>>>>> how
>>>>> the AGI will create that function. Because the AGI is created by
>>>>> humans,
>>>>> some sort of direction will be given by the humans creating them. What
>>>>> kind
>>>>> of direction, in mathematical terms, is my question. In other words I'm
>>>>> looking for a way to mathematically define how the AGI will
>>>>> mathematically
>>>>> define its goals.
>>>>>
>>>>> Valentina
>>>>>
>>>>>
>>>>> On 8/23/08, Matt Mahoney <[EMAIL PROTECTED]> wrote:
>>>>>>
>>>>>> Valentina Poletti <[EMAIL PROTECTED]> wrote:
>>>>>> > I was wondering why no-one had brought up the information-theoretic
>>>>>> > aspect of this yet.
>>>>>>
>>>>>> It has been studied. For example, Hutter proved that the optimal
>>>>>> strategy
>>>>>> of a rational goal seeking agent in an unknown computable environment
>>>>>> is
>>>>>> AIXI: to guess that the environment is simulated by the shortest
>>>>>> program
>>>>>> consistent with observation so far [1]. Legg and Hutter also propose
>>>>>> as
>>>>>> a
>>>>>> measure of universal intelligence the expected reward over a
>>>>>> Solomonoff
>>>>>> distribution of environments [2].
>>>>>>
>>>>>> These have profound impacts on AGI design. First, AIXI is (provably)
>>>>>> not
>>>>>> computable, which means there is no easy shortcut to AGI. Second,
>>>>>> universal
>>>>>> intelligence is not computable because it requires testing in an
>>>>>> infinite
>>>>>> number of environments. Since there is no other well accepted test of
>>>>>> intelligence above human level, it casts doubt on the main premise of
>>>>>> the
>>>>>> singularity: that if humans can create agents with greater than human
>>>>>> intelligence, then so can they.
>>>>>>
>>>>>> Prediction is central to intelligence, as I argue in [3]. Legg proved
>>>>>> in
>>>>>> [4] that there is no elegant theory of prediction. Predicting all
>>>>>> environments up to a given level of Kolmogorov complexity requires a
>>>>>> predictor with at least the same level of complexity. Furthermore,
>>>>>> above
>>>>>> a
>>>>>> small level of complexity, such predictors cannot be proven because of
>>>>>> Godel
>>>>>> incompleteness. Prediction must therefore be an experimental science.
>>>>>>
>>>>>> There is currently no software or mathematical model of
>>>>>> non-evolutionary
>>>>>> recursive self improvement, even for very restricted or simple
>>>>>> definitions
>>>>>> of intelligence. Without a model you don't have friendly AI; you have
>>>>>> accelerated evolution with AIs competing for resources.
>>>>>>
>>>>>> References
>>>>>>
>>>>>> 1. Hutter, Marcus (2003), "A Gentle Introduction to The Universal
>>>>>> Algorithmic Agent {AIXI}",
>>>>>> in Artificial General Intelligence, B. Goertzel and C. Pennachin eds.,
>>>>>> Springer. http://www.idsia.ch/~marcus/ai/aixigentle.htm
>>>>>>
>>>>>> 2. Legg, Shane, and Marcus Hutter (2006),
>>>>>> A Formal Measure of Machine Intelligence, Proc. Annual machine
>>>>>> learning conference of Belgium and The Netherlands (Benelearn-2006).
>>>>>> Ghent, 2006. http://www.vetta.org/documents/ui_benelearn.pdf
>>>>>>
>>>>>> 3. http://cs.fit.edu/~mmahoney/compression/rationale.html
>>>>>>
>>>>>> 4. Legg, Shane, (2006), Is There an Elegant Universal Theory of
>>>>>> Prediction?,
>>>>>> Technical Report IDSIA-12-06, IDSIA / USI-SUPSI,
>>>>>> Dalle Molle Institute for Artificial Intelligence, Galleria 2, 6928
>>>>>> Manno,
>>>>>> Switzerland.
>>>>>> http://www.vetta.org/documents/IDSIA-12-06-1.pdf
>>>>>>
>>>>>> -- Matt Mahoney, [EMAIL PROTECTED]
>>>>>>
>>>>>>
>>>>>> -------------------------------------------
>>>>>> agi
>>>>>> Archives: https://www.listbox.com/member/archive/303/=now
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>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> A true friend stabs you in the front. - O. Wilde
>>>>>
>>>>> Einstein once thought he was wrong; then he discovered he was wrong.
>>>>>
>>>>> For every complex problem, there is an answer which is short, simple
>>>>> and
>>>>> wrong. - H.L. Mencken
>>>>> ________________________________
>>>>> agi | Archives | Modify Your Subscription
>>>>> ________________________________
>>>>> agi | Archives | Modify Your Subscription
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>>>>> ________________________________
>>>>> agi | Archives | Modify Your Subscription
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>>>>
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>>>
>>>
>>>
>>>
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