How would you use an accurate Natural Language Understanding (NLU) API?

Example commands I wish I could do (some would require me hosting a server to communicate with my home, but I'm okay with this):

"Lock my doors." "I'm going home." (Notify people, set AC, bring up a map, etc.) "What's the weather tonight?" "Write 'happy birthday!' on my friends' Facebook walls each time it's their birthday." "Ask my girlfriend what's for dinner when she gets on Skype." ...and so on...

That said, I'm super excited about this and would love to see Maluuba's work accessible on all devices so I can just IM the service instead of using a phone when I'm at home.

I can't think of many uses cases for natural language as an interface to a traditional website. If there's something that's more convenient to access with text/speech, the user can just use set commands.

On the topic of building a personal assistant app, from what I've seen the real value lies in the act of interactive scheduling. You can't say "schedule a meeting with george" without checking with george's schedule to make sure he's available, and potentially re-scheduling the event at a time of mutual availability. Of course this goes far beyond the scope of understanding language, but I think this is a big part of what will differentiate a good personal assistant app from Siri.

EDIT: Someone on the thread brought up customer service, which is also a great opportunity. Interacting with a company's service representatives is a huge time sink for people everywhere, especially on the phone. Anything that lets people spend less time on hold is a huge win for mankind.

But I upvoted because I really liked to discover this company, what it does, and more importantly how it does it. Thanks for the clear examples and output provided !

I guess everybody agrees, nowadays entering commands to any kind of machine is still much more accurate and quicker if you use your hands for doing so.

This said, i think the technologies is in particular useful in situations in which you are not able to use your hands. This could be for example because you are busy working or you are handicapped. So one idea which comes to my mind would be an assistant for handicapped people, e.g. a device which can be used to control an electric wheelchair.

This isn't too far from saying that you have a search engine that "knows" how to answer questions, because you have hard-coded it to omit question words and transform question phrases into something declarative in hopes of finding the best match (e.g. "What is the largest mountain in Asia?" --> "The largest mountain in Asia", and then list the top search result from Google).

There's this tremendous amount of pragmatism that goes into some of the example questions, too. If I ask someone if they can recommend a good sushi place, personally, I'm probably more interested in the best sushi hit they've ever had than whether then place I can walk to from work is decent. You're going to have to make a lot of assumptions that you cannot adequately contextualize to take a broad question like that and say "Well, here are two within two blocks of you." I might actually be ranking proximity lower than, say, quality, popularity, or memorability. By hard-coding such assumptions into your reasoning, you're not in the trade of understanding natural language; you're in the trade of doing exactly what Bing does now, at a smaller scale, with a few more buckets.

If you're really into building NLU, I have a few somewhat snarky suggestions for you: 1) Swing by a cryogenics facility and check back in with us in 20-30 years. Repeat until cognitive scientists have figured out a way to transform human thought into something that can be quantified and transformed into words.

2) Do some reading in cognitive science to understand the immensely difficult problem it is to quantify meaning, both within the context of a discourse as well as discourse-agnostic (archetypal meaning versus contextualized meaning). Look at some charts, and realize that word meaning is not only relative to other words in the same discourse, but other words in shared classes.

3) Read up on Grice, and understand that, despite his philosophical approach to linguistics, his points on implicature and the value of what goes unsaid in a conversation lend as much to context as what goes said. Then try to sit back and figure out how to program and analyze what's going unsaid in a search query.

What I'm trying to say is that I would do nothing with this API because I can't trust it to deliver on its very lofty promises. Natural language understanding is essentially a pipe dream in the current state of computer science and bioinformatics. Word meaning is so idiosyncratic that it is a miracle that humans can generally agree on what a word represents; most of these are extremely fuzzy, and cannot be divorced from the contexts in which they are uttered. It's far too difficult to contextualize even a sentence outside of a pure information retrieval standpoint.

I appreciate what you're aiming at here, but I doubt you'll ever be able to succeed at it. That being said, if you rephrased it into something like a decision engine, I probably wouldn't be so irritated by it, and might even give it a shot.

There are other applications for NLU that I'd use daily were they available. A prime example is taking text and producing a tl;dr version for me.

Can you elaborate on the calendar domain?