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Yep. The problem is that it's _so_ noisy, that the encryption, as it were, might be too strong to crack with statistical methods. You might need the key; i.e., something like a human brain.

EDIT: a combination of noise, I should say, and paucity of information.

Well, we also get things wrong all the time. We regularly either ask for further information to decide what they mean, or expect that it's OK to get the interpretation wrong but be corrected.

Asking a computer to solve all the ambiguity in human language perfectly is asking it to solve it far better than any human can.

No, you only need context. Context in the form of knowledge about the place, company and history that the statement is spoken in. Wikipedia will serve well for a lot of that.

Representation of relationships without representation of qualia gives you brittle nonsense - a content-free wireframe of word distributions.

For human-level NLP, you need to model the mechanism by which the relationship network is generated, and ground it in a set of experiences - or some digital analogue of experiences.

Naive statistical methods are not a good way to approach that problem.

So no, Wikipedia will not provide enough context, for all kinds of reasons - not least of which is the fact that human communications include multiple layers of meaning, some of which are contradictory, while others are metaphorical, and all of the above can rely on unstated implication.

Vector arithmetic is not a useful model for that level of verbal reasoning.

But that's the thing with AI. We make the context. In the case of AlphaGo, IBM's Watson, Self driving cars, we set the goal. There are different heuristics, but we always need to define what is "right" or what the "goal" is.

For AI to determine their own goals, well now you get into awareness ... consciousness. At a fundamental mathematical level, we still have no idea how these work.

We can see electrical signals in the brain using tools and know it's a combination of chemicals and pulses that somehow make us do what we do ... but we are still a long way from understanding how that process really works.

> we still have no idea how these work.

I'd actually just say that we've not really defined these very well, and so arguing about how far along the path we are to them isn't that productive.

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