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Language is noisy. People often say things that have little to do with what they mean and context is really important.

EX: "How long do stars last?" Means something very different in a science class than a tabloid headline. Is that tabloid talking divorce or obscurity? Notice how three sentences in I am clarifying last.

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.

Sorry, I've edited my original comment to be clearer. What I really meant is that there is wide tolerance of noise in those domains. "How long does stars last" has a completely different meaning than "How long do stars last" - not tolerant of noise.

If an 6th grader asks their science teacher "How long does stars last?" / "How long stars last?" /"How long do stars last?" / "How old do stars get?" / "Stars, how old can they get?" / ...

In similar context they probably end up parsed to the same question assuming correct inflection, posture, etc. Spoken conversations are messy, but they also have redundancy and pseudo checksum's. Written language tends to be more formal because it's a much narrower channel and you don't get as much feedback.

PS: It's also really common for someone to ask a question when they don't have enough context to understand what question they should be asking.

All those sentences sound (mentally) a lot differently. Some of those sentences give you the impression the speaker is an idiot, for example.

I'd suggest "How long do these stars last?" and "How long do these stairs last?" might be a better example. Human language has more redundancy than computer languages and in a real context it would probably still be clear what was meant even if the wrong word was used, but it's still a much spikier landscape with regard to small changes than images are.

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