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By reinforcement learning, I don't mean the class of machine learning algorithms such as DeepQ, etc. I have in mind the general concept of learning based on rewards and punishment.

Is it possible to create a Strong AI that does not rely on learning by reinforcement, or is reinforcement learning a requirement for artificial intelligence?

The existence of rewards and punishment implies the existence of favorable and unfavorable world-states. Must intelligence in general and artificial intelligence, in particular, have a way of classifying world-states as favorable or unfavorable?

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  • $\begingroup$ Your last question asks a different question, "... have a value system?" which is a lot different from reinforcement learning. $\endgroup$
    – Pimgd
    Aug 9 '16 at 14:27
  • $\begingroup$ Here is a related/similar question. $\endgroup$
    – nbro
    Dec 7 '21 at 10:05
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It's impossible to give a definitive 'yes' answer to your question, since that would require proving that alternatives cannot exist.

More philosophically, it depends on what you mean by "preference over world states":

However counter-intuitive it might seem, it is conceivably possible to create Strong AI purely from local condition-action rules, in which there is no global concept of 'preference value' and/or no integrated notion of 'world state'.

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Simply put, we don't know how to create Strong Artificial Intelligence yet, so we don't know what is or isn't required to create it. At best we can engage in "informed speculation", in which case I'd say that the answer is more likely "yes" than "no". But that's basically just a hunch.

If you're interested in a pretty good overview of what "pieces" might be required to create Strong AI, and if you haven't read it yet, Pedro Domingos' book The Master Algorithm might be of interest.

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Simply yes, but it can lead to over fixing of the NN.

Humans favour not dying, which is only realised once a consequence is defined for the system to realise that death is an unfavorable result. Which can be train vai observation. Allow your system to observe between 2 or more separate people/systems. Then allow opportunity to test in a safe environment with the pre existing info of the consequences that may follow, provind that if the system makes a mistake in the test/safe environment it will be saved unknownly and then informed that it made a mistake, the place system in an unsafe world in same conditions, informing it that if something happens it will die. That is the way humans grow up, and we've lasted very long with this technic.

I'm an AI Researcher and Software Engineer for the past 7 years.

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  • $\begingroup$ Thanks for your answer. I don't doubt the power of reinforcement learning. Could you elaborate on AI that doesn't use reinforcement learning? $\endgroup$
    – bpachev
    Aug 12 '16 at 17:41
  • $\begingroup$ Learning is a trial and error application. Unless you provide an acurate dataset for the AI, and the applicable environment for it to apply this information. This would be a closed system and would allow the AI not to in anyway harm itself, a good example is a user tracking AI or a chat bot, neither can be damaged, if provided with enough information that learning would not be needed anymore, it could just understand and implement/act to this new data but not learn from it as it has enough to exist as a closed system $\endgroup$ Aug 12 '16 at 20:25
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Introspect! Do you need to know what's good/bad, pleasurable/painful or so, in order to understand and/or learn?

I am a human, hence a general intelligence, and so are you. So know thyself! I can tell for myself that I have different ways to understand and learn; some may be similar to reinforcement learning. Esp. the ~automatic ~innate ~unconcious ones, like motor movement, remembering tasty food and many other primitive functions.

But I can also understand things through ~intentional ~analytical ~logical thought; which some may call pure reason (Immanuel Kant).

Yet, you don't need to hear all that, since have it already in your own mind.

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