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Can we really make a chatbot that understands (rather than just replies to) questions based on the database/options of replies that it has? I mean, can it come up with correct/non-stupid replies/communications that don't exist in its database?

For example, can we make it understand the words but, if and so on? So whenever it gets a question/order it understands it based on "understanding". Like the movie "her" if you have watched it.

And all of this without using too much of code, just the basics to "wake it up" and let it learn from YouTube videos and Reddit comments and other data source like that.

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    $\begingroup$ Define "understanding". $\endgroup$ – Oliver Mason Aug 2 '18 at 15:40
  • $\begingroup$ @OliverMason I added the "soft-question" tag as this may be a suitable naive question (ideally with answers linking to the basic concepts:) Pointing out the need to define understanding in this context is a good start! $\endgroup$ – DukeZhou Aug 7 '18 at 18:57
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This question has been studied academically for decades, and is really an extension of the work on Philosophy of Mind that was done in the two or three centuries before that.

A good resource is Mind Design II, though it's getting a little bit old now.

The modern schools of thought are:

  1. Cognitivism. This is in decline, but was extremely popular in the 70's and 80's, and still fairly widespread in one form or another in the AI research community. It says that human brains really are just computers. If they're just computers, and that they're probably running a sort of symbolic reasoning algorithm like unification (although I think it's hard to find anyone who really thinks it's unification anymore). This is the idea underpinning work like SOAR. The main bottleneck, as Drayfus pointed out in the 1970's, is that you need to write down all the facts about something for a machine to "understand" it. "All the facts" turns out to rapidly turn into an infinite number for anything more complex than the smallest "microworlds" that you could deploy an AI program in. Searle also proposed his Chinese Room argument in response to this group, but it holds for Connectionist approaches as well (more on that later...).

  2. Connectionism The connectionists hold that the complexity of our brains comes from massively parallel computation consisting of messages passed between billions of neurons in our heads. They think the correct approach to general AI is likely to involve simulations of similar architectures. It turns out that many of the things that are incredibly hard for Cognitivist projects (e.g. vision), are easy to solve with these approaches. The main criticisms from Cognitivists are that we don't have a very good idea of what these things are doing, and so the claim that they help us understand intelligence is false, and using them to solve practical problems might be dangerous. These are both somewhat fair, in my view. Older Cognitivist arguments, put forth most elegantly by Jerry Fodor, have now been discredited. Fodor argued that properties like language could never be understood as statistical artifacts of parallel computation, but he was wrong: all the best computational systems for language are now connectionist, and no one's ever made a cognitivist one that's even half as convincing. This is the dominant paradigm behind most of the modern advances in the field. Hinton's work forms the basis of most of the recent advances.

  3. Dynamics Searle's argument was rooted in the idea that mapping inputs to outputs couldn't be what's happening in our heads, and that such a system couldn't be called intelligent. This also seems to be an implicit assumption in your question. The Dynamicists believe a variety of things, but I'd characterize them as collectively rejecting this idea. Authors like Paul Churchland argue that Searle's argument is rooted in a sort of pre-enlightenment "folk psychology". It's a bit like the theories that predated modern chemistry. Everyone was sure that fire was a substance that lived inside wood. If you heated up the wood properly, it could get out of the wood, making more heat. On the surface this seems pretty reasonable, but of course, it's wrong: The fire is actually a mixture of the wood with oxygen in the air, forming a new gas. There's no fire inside the wood. Similarly, Churchland would argue that there's no "Consciousness" inside us, allowing us to control our actions in the way we popularly imagine. Subjective experience is more likely to be "along for the ride", and entirely or mostly separate from the intelligent behaviors we observe. Some researchers think it could be described by a sub-system that maps observations of what the rest of the brain does into "stories" for the rest of the brain to receive as a sort of summary digest. Active research in this area tends to focus on things like the insect metaphor, and the interaction of the machine with its environment. It was fairly popular in the 1990's, but the phenominal success of connectionist approaches in the 2000's has led to its decline. Probably the best known experiments were the work of Rodney Brooks.

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Defining what it means to understand something is a complex philosophical question, with answers that can split the AI community into different camps.

Clearly an algorithm that associates the ASCII characters of word like "if" with a set of numbers based on statistics of where it appears in a corpus of reference texts is missing the essence of subjective experience that you or I might feel when reading it.

The related terms you should explore are https://en.m.wikipedia.org/wiki/Qualia and https://en.m.wikipedia.org/wiki/Chinese_room which explore subjective experience and whether an artificial system can possess it

With current knowledge of how our own minds create understanding, it is very hard to tell what is required. It may just be multi modal learning, so that words are associated with sensory experience. Experiments with virtual or real robots that experience an environment and need to communicate about it are one way to explore the subject.

In short, what it means to understand something, whether it is possible to replicate artificially, and whether it is an important trait of an AGI, are all open questions at the cutting edge of AI research.

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Of course you can (read through to the end). You just need to teach it how it is taught to a baby. But first, you need to create the baby's brain. So you need to build a brain that learns from videos, poll videos, and not just understands but practices and understands other people's reactions.

Sorry, but that's not enough. You would have the same work as God (if it really does exist and took this job). You would have to raise a baby so he could grow up. If we can raise a baby in code, it will learn much faster than a human.

I've been studying and looking since creating this "baby." I've made some babies, but none of them have been enough. But it's what I chose to do in my life (one of things). So I'm still raising a baby, from time to time it's getting smarter.

It's easy to build a robot that goes into reddit to read and extract feeling from what people write. You can watch YouTube videos and differentiate objects, humans, colors, etc. But that is what we would codify for this "baby" to do.

Perhaps the first step would be to rebuild a brain through code. We are already creating some pieces, we are already studying for many years synapses, neural networks, etc. But there is still a whole brain that we still do not understand. And I'm talking about the human brain (biological).

When I say you can, it's an incentive. I told myself I can. I'm going down this road. If I really can, I do not know.

One tip I give you: Google is far from successful. But you are trying and reaching for it. That's enough, right?

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