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
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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
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>>
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