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One of the most compelling issues regarding AI would be in behavior and relationships.

What are some of the methods to address this? For example, friendship, or laughing at joke? The concept of humor?

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    $\begingroup$ I know they're working on this to some degree in Japan, although I think one of the great insights is that even simulated behavior by an automaton can produce chemical response in humans. I don't know what's going on re human behavior, but they do have robot animals designed for human interaction: parorobots.com $\endgroup$ – DukeZhou Dec 14 '16 at 19:22
  • $\begingroup$ These are many questions in one post. So, your questions will very unlikely be answered fully. $\endgroup$ – nbro Dec 20 '16 at 18:48
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Theory of mind

Relationships and normal social behavior require a human to possess a reasonable "theory of mind", a skill in understanding and modeling the thought processes that happen in the minds of others, and making reasonably accurate predictions on how particular actions will be understood by others.

In general, this might be treated as any other machine learning/prediction task - while this skill is quite complex and too hard for our current systems, there doesn't seem to be any obvious qualitative barrier that needs to be breached. Notably, there's no reason to believe that a mind needs to be able to experience a certain "feeling" in order to model that other minds can have it - an AI agent could form a causal model of how friendship works in human relationships and use that to exhibit behavior that's consistent with friendship in all aspects and/or facilitate friendship behavior towards it by particular humans, if it fits the agent's goals. Whether you'd consider that "real friendship" is pretty much just a matter of how you define the word, with some parallels to the "p-zombie" discussion.

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In general, what you are describing implies a hierarchical sequence model, in which mannerisms adapt to the regime or paradigm in effect. Expressive modalities are how we recognize the operative context from the behaviors of other agents. For an artificial agent, avoiding un-canniness would involve clustering the factors underlying the classification of discourse contexts, tagging them with corresponding factor models for manner in a way which closely aligned to the classifications which human agents make. This involves developing an adequate latent representation for both spaces, which is likely to involve quite a lot of training data, or some clever transfer learning in conjunction with one-shot techniques (generally, taking samples as modes).

When the context becomes one of friendship, for example, the style factors of expression should extend to factors of trust, collaboration, disclosure, sympathy. In order to implement these, layered abstractions in the representation of behaviors will need to be crafted, presumably by training. In order to align these learned categories to human categories, which I suppose to be essential to emotional fluency, exploiting the structure of corpus semantics - including distributional characteristics of the linguistic labels and inferences - when estimating loss gradients seems a natural strategy, which exploits cultural learning.

These comments are necessarily speculative, as no such systems are current, to my knowledge. At least, not in any significant degree of maturity or application. Certainly other approaches are conceivable. I am just extrapolating plausible strategies, in the context of current technologies, strategies for implementing the kinds of behaviors you describe in a useful way.

More, specifically, I am considering the question as a manifold-learning problem, and hence requiring a representation model in which the inputs and outputs vary over state-spaces, with the behavior being learned as a mapping between those spaces. Each of those components (the input space representation and its natural topology, the output space representation likewise, the mapping between them, and the sequence model hierarchy which extrapolates from the manifold to a control process emsemble) has its own peculiar challenges, and the challenges to creating an adequate implementation are a confound of those. Still, it seems more a question of time and money than of notional feasibility.

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Methodology for a social enabled AI.

Regarding social interactions, I believe that trying to build a copy of human behaviour based on technologies we understand, might not be effective .

Instead, I would start from the roots of how human grows and learn, or better, by what is each human trying to solve from childhood to the end of their life. In that way we will be able to build artificial beings, able to exploit the best of what technology might provide.

As an example, humour is the kind of social trick a gifted social artificial life will learn to understand and to master, first, like all of us, in order to empathize with others when this common emotions arise, before to analyse the conditions in witch it arises so that to be able to reproduce it, then eventually if the need become a priority, by theorizing its mechanics, possibly mastering it and using it like a tool.

(I didn't studied the details, but humour is often generated by instigating surprise to others, it requires modelling how others generally think, leading them to be surprised.)

Humour, friendship and social interaction should be naturally discovered by IA like by Human, and eventually used in order to fit once goal. On the other hand this require intelligence to be sensible to surprise in a positive way, at least in some specific conditions.

Additionaly, Human may have other laughing without building a conscious theory , this help us understand intelligence and build an artificial one: Language is here used to transmit initially abstract concepts built by intelligence. It helps us accelerate our modeling of the world, that would remain very basic if only based on our own experience.

By the way, unconscious resolution of problems seems to be a characteristics of some machine-learning techniques like neural network and deep learning and are criticised for that. Because we would like it to be able to explain us concepts they used to solve the problems we gave it. This is because we ask that tools to build abstract and possibly original solutions, before to give it the ability to communicate and the goal to do it ..... Still some work remains to be done in order to give our reasoning tool the ability to master a language, and to use it given the knowledge of what concepts and semantic domains, interlocutors do already master, to then build new concept in a transmissible form like words, images, video or just body-movements, face expression or so.

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