Comment by nopinsight | Hacker News Reader

Comment by nopinsight | Hacker News Reader

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Consider this simple sentence:

"Jane grew up in an idyllic rural area."

No current AI implementation, to my knowledge, can understand such a sentence nearly as well as humans do. A competent chatbot judge could suggest a novel situation, say a broken-winged black Pegasus appeared in Jane's hometown when she was seven, and ask pointed questions to find out if the interlocutor is a human or a bot.

The issue with almost all current approaches to AI is that it is either purely symbolic or sub-symbolic. The current symbolic approaches cannot completely capture preconceptual experience human use to make sense of the world. When we hear "idyllic rural area", humans use our mental imagery and sensory experiences to help us understand the sentence much more deeply than the list of words suggests.

The subsymbolic approach could potentially solve this issue, but it raises the problem of integrating all those complex, interacting parts, vision, auditory, motor control, conceptual thoughts, etc. into a unifying whole. More importantly, would we be able to control and direct the beast sufficiently well once it becomes reality?

There is now some AGI (Artificial General Intelligence) research on integrating the two paradigms. If anyone is interested, a presentation is available here: http://ieet.org/index.php/IEET/more/goertzel20130531

To quote a great scene from the movie "Waking Life" [1] by Linklater:

"When I say 'love', the sound comes out of my mouth and it hits the other person's ear, travels through this byzantine conduit in their brain, you know, through their memories of love, or lack of love, and they register what I'm saying and they say 'Yes, I understand'..but how do I know they understand because words are inert. They're just symbols. They're dead. You know, and so much of our experience is intangible. So much of what we perceive cannot be expressed, it's unspeakable. And yet, when we communicate with one another, and we feel we have connected and we think that we're understood, I think that we have a feeling of almost spiritual communion, and that feeling might be transient, but I think that's what we live for".

[1] https://www.youtube.com/watch?v=pvnQu30kQ2c

Jane grew up in an idyllic rural area.

I'm currently working on a system for understanding natural language and you might be surprised by how many assumptions one must make in order to understand just this simple sentence. The only part of this sentence that nearly all humans could understand is the concept of growing up. Everything else would have to be inferred inductively from our personal expectations and experiences. For example, from this sentence, you wouldn't know the gender, race, or even species of Jane except from your experiences of people saying similar things about people about of whom you already knew the race, gender or species.

Consider the difficulty of modeling that sentence in a computer system. If all you had were examples of texts to go by, such as the scenario in the grandparent, how would you determine that Jane was a female? Within that vast body of text, somewhere, it has to contain the statement that "Jane" is a female name. Or Jane can only appear in sentences as the referent of a feminine pronoun, like "she" or "her". Or in that vast database of images and video, all human beings that were identified as Jane have to have female characteristics.

From an even deeper philosophical perspective, how do you know which Jane this is referring to? Is this Jane supposed to be an actual person with hidden but unknown state (such as the name of her parents) or is this a purely fictional creation, for whom it would be meaningless to ask who her parents were? How do you teach a computer the concept of a fictional character? The interesting about fictional characters that they do not describe what something is, but instead, describe what something is not. In order to create a computer that could pass the Turing test, the computer would have to be capable of modeling both fictional and concrete things at a minimum. It would have to know when someone was talking about something fictional or something that is supposed to represent an actual object. If we do not destroy ourselves first, the day will come when computers will be able to make this distinction, but I think the design of such a computer will have to be evolved rather than architected from the top down. I think the problem is just too hard.

Why is it relevant if AI "understands"? The post above doesn't claim it would understand, it just claims that for all the practical purposes it would pass Turing AI test.

Because you couldn't pass a Turing test, even for practical purposes with that approach. Passing the Turing test is a problem that cannot be solved by big data alone. You have to model not just language patterns and word sequences but the prior causes for those word sequences. The prior causes for the words (and images, and videos in the original example) are ultimately desires and experiences of real human beings and truths about the universe.

In other words, a computer has to actually model the world and the changes to the state of the world as the conversation goes on in order to pass a Turing test. I didn't read anything in the original description to suggest that was happening.

Is there evidence for preconceptual experience? It seems to me that there is no experience without concepts.

This is very well and succinctly put!

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