Luka

A.I. recommending restaurants in SF in a chat interface

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Eugenia Kuyda — Luka
Luka’s conversational technology can be applied to other verticals (restaurants are the first step). Consumer AI will be huge, and chat as a UX is intuitive. Luka is the first startup that is building practical consumer AI based on a chat interface now. We're waiting for an update to be approved by the AppStore this week - Luka will get rid of Facebook authorisation (phew) and will be able to reserve tables.
Stewart Rogers — Journalist/Analyst/Speaker - VentureBeat
@ekuyda Any plans for an Android version?
Gillian Morris — CEO, Hitlist
@ekuyda This concept is huge in Asia - WeChat and others already allow consumer AI, and Path's TalkTo functionality also does this somewhat (@tomlimongello could say more on that, I think). And of course Siri was supposed to do all this, though via voice rather than chat. (@libovness's piece linked below has a good analysis of why chat is more promising than voice, at least for now: http://whoo.ps/2015/02/23/future...)

But Siri has never been very good, and I'm excited to see another entrant in the space, especially one in my favorite industry, travel. It's worth considering the limits of what AI will be able to parse, though. There's a great discussion of the limits of chat-based AI in Brian Christian's book, The Most Human Human: http://www.amazon.com/The-Most-H.... Essentially, we can probably get to 90% of use cases, but it takes a book to even start explaining why cracking the final 10% will be so hard.

On another note, there are distinct similarities to what @xdotai does for email scheduling. I love things like this that don't require another app download or making you learn a new UI.
Jonathan Howard — Cofounder, Emissary
@ekuyda I really wanted this to work, but it went the same way Chat AI always seems to: awkward uncanny valley conversation and/or outright failure to get the thing I want.

@rrhoover suggested I share this funny exchange I had with Luka on my first try :) Trying to keep an open mind, so let me know I'm "doing it wrong" somehow!


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Eugenia Kuyda — Luka
@tomlimongello @libovness @xdotai @gillianim Thanks, Gillian! WeChat is definitely the best example of what a messenger can be - not only for consumer AI but also for offering so many services in a chat interface (it's the biggest mobile wallet in the world for now).
The Most Human Human is perhaps my favourite book on the subject, gives a very clear perspective on what it really means to be 'human' in a conversation.
I believe there are limits at this particular point of history of what AI can do in regards of conducting a human-like conversation, but I strongly believe in assisted AI, where the most difficult cases would be solved by humans (the last 10%), while technology becomes better and better to finally take humans out of the equation.
Next week we're launching as a text number - so no need to download the app anymore :)
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Eugenia Kuyda — Luka
@rrhoover @staringispolite @rrhoover @staringispolite Hi Jonathan and thanks a lot for your comment - these things make Luka better. We're aiming at a 90% quality meaning that for every 100 replies Luka will have adequate and relevant responses in at least 90 cases. However there are still 10% of mistakes (and right now for Luka it's a little more, around 16%, but it's getting better every day), and those could make for a bad experience for the user. We fixed the issue that we found in your conversation (the place you were looking for is called Red Door Coffee, not Red Door Cafe, which is another place in Bush street - we're learning how to handle all those different ways that people spell restaurants and cafes, and we're getting better with all those typos and disambiguations). The more you talk to it, the better it becomes, and hopefully we can make it so that no users get the uncanny valley conversation like you unfortunately had. Thanks a lot for trying the app out, I would be very happy to see it working for you!

Jonathan Howard — Cofounder, Emissary
@rrhoover @ekuyda Thanks for the reply! Coffee -> Cafe was actually a voice-to-text error I didn't catch until I took the Yelp screenshot above. Weird that it pretended to know the incorrect Red Door Cafe first, then progressively forget more and more context from the conversation. For what it's worth, this uncanny valley conversation was what made me feel like quitting, not so much the result of finding/not finding any one query.

I think I would've kept going if Luka had replied with any of the following instead:
1) Did you mean Red Door Cafe in Pac Heights?
2) Did you mean Red Door Coffee in SoMa?
3) "I didn't find that listing in Soma"
4) "I can't find that - did you mean one of these? [UI list to choose from]")

Also note that Yelp handles the incorrect spelling just fine. If the AI were just searching Yelp and guessing the top result that's in SoMa, it would've worked.

Keep on fighting the good fight!
ilan kasan  — Product and Business Leadership
@ekuyda nice product and great UX. Take a look at www.commersational.com which enables merchants to chat eable their ecommerce catalog and transact via text
💥
Ryan Hoover — Product Hunt
I just gave it a test run (made with Tailor):



Very impressive. I see you have integration with Yelp (and probably other 3rd party services) but how are you deciding which restaurants to recommend and keeping your database up-to-date?
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Eugenia Kuyda — Luka
@rrhoover Thanks! We collect all data automatically from more than 15 sources - Luka parses data from user reviews (Yelp, Foursquare, TripAdvisor etc) and professional reviews and blogs (from SF Chronicle to Eater) and guidebooks (Michelin, Zagat). We plug all that data into our data model, which is pretty elaborate (we have more than 300 data points for each restaurant, plus menus and photos) and also use some relevant quotes to show our users.
The recommender system is based on a knowledge-based recommender system. Luka's knowledge graph stores all the connections users may have with any possible entities (whether you only eat gluten-free food, or like only the places that are walking distance, or hate chinese) and matches that with all the data we collect about the restaurants (that are part of the graph too) - that makes recommendations hyper personalized and relevant.
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