On Wed, Aug 27, 2014 at 1:53 PM, Terren Suydam <terren.suy...@gmail.com>
wrote:

>
> On Wed, Aug 27, 2014 at 6:31 AM, Telmo Menezes <te...@telmomenezes.com>
> wrote:
>
>>
>>
>> On Wed, Aug 27, 2014 at 1:14 AM, Terren Suydam <terren.suy...@gmail.com>
>> wrote:
>>
>>> Hi Telmo,
>>>
>>> I think if it were as simple as you make it seem, relative to what we
>>> have today, we'd have engineered systems like that already.
>>>
>>
>> It wasn't my intention to make it look simple. What I claim is that we
>> already have a treasure trove of very interesting algorithms. None of them
>> is AGI, but what they can do becomes more impressive with more computing
>> power and access to data.
>>
>
> I agree that can be made to do impressive things. Watson definitely
> impressed me.
>
> Take google translator. It's far from perfect, but way ahead anything we
>> had a decade ago. As far as I can tell, this was achieved with algorithms
>> that had been known for a long time, but that now can operate on the
>> gigantic dataset and computer farm available to google.
>>
>> Imagine what a simple minimax search tree could do with immense computing
>> power and data access.
>>
>
> The space of possibilities quickly scales beyond the wildest imaginings of
> computing power. Chess AIs are already better than humans, because they
> more or less implement this approach, and it turns out you "only" need to
> computer a few hundred million positions per second to do that. Obviously
> that's a toy environment... the possibilities inherent in the real world
> are even be enumerable according to some predefined ontology (i.e. that
> would be required to specify in a minimax type AI).
>

Ok, but of course minimax was also a toy example. Several algorithms that
already exist could be combined: deep learning, bayesian belief networks,
genetic programming and so on. A clever combination of algorithms plus the
still ongoing exponential growth in available computational power could
soon unleash something impressive. Of course I am just challenging your
intuition, mostly because it's a fun topic :) Who knows who's right...

Another interesting/scary scenario to think about is the possibility of a
self-mutating computer program proliferating under our noses until it's too
late (and exploiting the Internet to create a very powerful meta-computer
by stealing a few cpu cycles from everyone).


>
>
>>
>>
>>>  You're talking about an AI that arrives at novel solutions, which
>>> requires the ability to invent/simulate/act on new models in new domains
>>> (AGI).
>>>
>>
>> Evolutionary computation already achieves novelty and invention, to a
>> degree. I concur that it is still not AGI. But it could already be a
>> threat, given enough computational resources.
>>
>
> AGI is a threat because it's utility function would necessarily be
> sufficiently "meta" that it could create novel sub-goals. We would not
> necessarily be able to control whether it chose a goal that was compatible
> with ours.
>
> It comes down to how the utility function is defined. For Google Car, the
> utility function probably tests actions along the lines of "get from A to B
> safely, as quickly as possible". If a Google Car is engineered with
> evolutionary methods to generate novel solutions (would be overkill but
> bear with me), the novelty generated is contained within the utility
> function. It might generate a novel route that conventional map algorithms
> wouldn't find, but it would be impossible for it to find a solution like
> "helicopter the car past this traffic jam".
>

What prevents the car from transforming into an helicopter and flying is
not the utility function but the set of available actions. I have been
playing with evolutionary computation for some time now, and one thing I
learned is to not trust my intuition on the real constraints implied by
such set of actions.


>
>
>>
>>
>>> I'm not saying this is impossible, in fact I see this as inevitable on a
>>> longer timescale. I'm saying that I doubt that the military is committing
>>> any significant resources into that kind of research when easier approaches
>>> are much more likely to bear fruit... but I really have no idea what the
>>> military is researching, so it's just a hunch.
>>>
>>
>> Why does it matter if it's the military that does this? To a sufficiently
>> advanced AI, we are just monkeys throwing rocks at each other. It will
>> surely figure out a way to take control of our resources, including
>> weaponry.
>>
>>
>
> I think the thread started with a focus on killing machines. But your
> point is taken.
>
>
>>
>>> What I would wager on is that the military is developing drones along
>>> the same lines as what Google has achieved with its self-driving cars.
>>> Highly competent, autonomous drones that excel in very specific
>>> environments. The utility functions involved would be specified explicitly
>>> in terms of "hard-coded" representations of stimuli. For AGI they would
>>> need to be equipped to invent new models of the world, articulate those
>>> models with respect to self and with respect to existing goal structures,
>>> simulate them, and act on them. I think we are a long way from those kinds
>>> of AIs. The only researcher I see making inroads towards that kind of AI is
>>> Steve Grand.
>>>
>>
>> But again, a reasonable fear is that a sufficiently powerful conventional
>> AI is already a threat (due to increasing autonomy and data access + our
>> possible inability to cover all the loopholes in utility functions).
>>
>>
> The threats involved with AIs are contained within the scope of their
> utility functions. As it turns out, the moment you widen the utility
> function beyond a very narrow (and specifiable) domain, AI gets much, much
> harder.
>

As above...

Telmo.


>
> Terren
>
>
>> Cheers
>> Telmo.
>>
>>
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