4 ms·

That's pretty impressive! Maybe this isn't as big a concern as I think, but the ease of these commercial demos relies on a crucial assumption: that I am nearly

by ljoshua 10y ago

That's pretty impressive! Maybe this isn't as big a concern as I think, but the ease of these commercial demos relies on a crucial assumption: that I am nearly completely price insensitive. I'm not a cheapskate, but I don't usually purchase the first product a vendor puts up because there's usually something better and slightly less expensive if you put in an additional minute or two into the search. But with the demos given (ordering flowers, booking hotels, etc.) it's usually a sample of 2-3 different options, and from what I've seen, usually higher end items. Will such assistants still maintain their usefulness when I don't have the monetary pleasure and freedom of just saying "Yes" to the first option offered?

kilre 10y ago
I guess this would still work if you can just say: give me a hotel in the prices range of... with a good rating. Surely the result wont be as good as manually digging out not yet discovered great hotels, but it might still be satisfactory
derefr 10y ago
I always imagine that these services are supposed to serve as the "automation put me out of a job" of the full-time personal assistants had by the wealthy. In that role, it's usually the assistant's job to present a selection that's sorted by optimality on axes of both quality and price (and arrival time, reliability, packaging options, etc.) Properly done, this means that you should almost always be "defaulting" to the first option presented; rarely switching to the second or third (and there only really needs to be two or three) when there's something in particular that stands out about them despite them optimizing lower. When described this way, it's clear that this is a much harder job than just doing fancy language recognition. An AI personal assistant has to know what you care about—what weights you put on various optimization criteria—without you having to explicitly specify them. Presumably it would be an online learning system, and screw up a bit at first. I think the key in such systems is how they'd behave before being trained by lots of individual preference data over time, though. Hopefully it could "guess" at some initial preferences in a good way. (Maybe by taking all the preferences of every other user, putting your sparse preference-set as a point in that preference space, and then seeing how you cluster: effectively "stereotyping" you based on its other "employers.") On another tangent, one thing I haven't seen built into any such system yet is the "active academy model" mentioned here (https://scifiinterfaces.wordpress.com/2014/06/24/course-optimal/ https://scifiinterfaces.wordpress.com/2014/06/24/course-opti...). One of the criteria you'd expect a PA to optimize for in their employer is patience. Rather than staying with an initial optimization-problem solution, if the answer is not time-sensitive, one can continue to search the solution-space for new, better solutions that might crop up (involving things that maybe didn't even exist at the time the question was first asked.) If given two weeks to book a hotel room, there might be a good hotel room on the market now, but a better one on the market later. There are very complex questions involving the costs of reserving and then cancelling reservations to re-book, or holding off on reserving and maybe never finding anything better and running out of time. For physical goods, there is even the possibility of an "indefinite optimization problem"—if I tell my PA to "buy me a top-of-the-line computer", then presumably I want my computer to continue being top-of-the-line unless otherwise noted, and this will require constant weighings of various components' or configurations' depreciation-rate, market-liquidity, employer's opportunity-cost for maintenance time, etc. All the questions a major corporate IT "buyer" employee considers, behind the facade of a single innocent request.
daveguy 10y ago
Given the open decision process (with a visual representation of the exact information and logic that went into a decision) it seems like someone could fairly easily add a "reverse sort by price" or other more complex model. Of course that is assuming a single "price-sort" function could be applied across multiple domains. If each price-sort algorithm has to be implemented and delivered as a separate model it would take a lot longer and be a lot more tedious for both developers and users. I think the real question is -- how easy is this development process? A gui representation of the program is great, but a lot of times it is easier to deal with text if you have a well designed library. They mentioned it being like an app store, so what is in it for the developers? Selling apps, directing through your own store / affiliate links? It does look pretty cool, but when there are at least 5 established competitors implementation is everything. EDIT: I would like to add that this looks like a great combination of the logical analysis of soundhound, the mobility of siri, the ability to extend of facebook and alexa. It could definitely be a breakthrough. Still -- implementation. EDIT2: Follow-up questions starting at 16 min. Launching at the end of the year, working with select developer partners toward the end of the year. "What makes it something that makes our lives better rather than makes us talk to things we don't want to talk to." A: "Developers motivated by self interest to make things work smoothly". What is that self-interested motivation? They get to sell you stuff? What do they want to sell you? The most expensive thing that fits your query -- or maybe the thing that's $5 more expensive that should sell for $20 but that will sell for $25, or $30... I agree, based on that response, it sounds like even with developer interest in the platform it won't be developer interest to give you the best product, but developer interest to give them the most money.
- deleted 10y ago
  [deleted]
- thesimpsons1022 10y ago
  yeah but things can be more complex than that. If ordering flowers for my girlfriend I will never buy the cheapest one or the most expensive. Hotels are even more complex. Location matters as well as the specific room type and a lot of other things. No one wants to stay at a crummy motel. How do you handle, "I am willing to pay up to 30 a night more if I'm closer to the beach... etc"
tedmiston 10y ago
In an ideal world, it will maintain your price preferences across sessions. Something like, for example, knowing my preferred seat on a flight is "frontmost available (non-premium) aisle seat unless the layover is very tight, in which case, frontmost seat period"... that would be amazing and truly useful.
- zippergz 10y ago
  I don't think my price preferences (for flights or really much of anything else) can be boiled down to an algorithm. There are so many factors. What other travel do I have planned around the same time? Have I had a slew of crappy flights lately and want to treat myself? What am I going to be doing the day I arrive and depart (e.g. do I need to be rested enough to go right into meetings, or do I have a while to get settled?)? Am I flying on business or for pleasure? I'm not sure I can even articulate all of the factors that go into my decision on what flight to purchase, but I wouldn't be surprised if there are hundreds of them over the course of a year...
  - lazaroclapp 10y ago
    > I don't think my price preferences (for flights or really much of anything else) can be boiled down to an algorithm You'd be surprised how many people are trying, see e.g.: https://www.kaggle.com/c/expedia-hotel-recommendations https://www.kaggle.com/c/expedia-hotel-recommendations You could capture one or more ML features for every one of your questions above, plus hundreds more and add the features that define who you are within their customer population. Then the challenge becomes getting the right model and enough data to train it so that 98% of the time it chooses what you would have chosen. Just keep in mind that, if I have enough information to predict which flight you will take, I have enough information to predict who you are voting for, when you are joining a protest, which is the lowest salary you are likely to accept, hell maybe even enough to predict which opportunities to commit a crime you are likely to take... so, here is to hoping that figuring out which flight we want to take remains unreliable.

by ljoshua 10y ago

kilre 10y ago

I guess this would still work if you can just say: give me a hotel in the prices range of... with a good rating. Surely the result wont be as good as manually digging out not yet discovered great hotels, but it might still be satisfactory

derefr 10y ago

I always imagine that these services are supposed to serve as the "automation put me out of a job" of the full-time personal assistants had by the wealthy. In that role, it's usually the assistant's job to present a selection that's sorted by optimality on axes of both quality and price (and arrival time, reliability, packaging options, etc.) Properly done, this means that you should almost always be "defaulting" to the first option presented; rarely switching to the second or third (and there only really needs to be two or three) when there's something in particular that stands out about them despite them optimizing lower. When described this way, it's clear that this is a much harder job than just doing fancy language recognition. An AI personal assistant has to know what you care about—what weights you put on various optimization criteria—without you having to explicitly specify them. Presumably it would be an online learning system, and screw up a bit at first. I think the key in such systems is how they'd behave before being trained by lots of individual preference data over time, though. Hopefully it could "guess" at some initial preferences in a good way. (Maybe by taking all the preferences of every other user, putting your sparse preference-set as a point in that preference space, and then seeing how you cluster: effectively "stereotyping" you based on its other "employers.") On another tangent, one thing I haven't seen built into any such system yet is the "active academy model" mentioned here (https://scifiinterfaces.wordpress.com/2014/06/24/course-optimal/ https://scifiinterfaces.wordpress.com/2014/06/24/course-opti...). One of the criteria you'd expect a PA to optimize for in their employer is patience. Rather than staying with an initial optimization-problem solution, if the answer is not time-sensitive, one can continue to search the solution-space for new, better solutions that might crop up (involving things that maybe didn't even exist at the time the question was first asked.) If given two weeks to book a hotel room, there might be a good hotel room on the market now, but a better one on the market later. There are very complex questions involving the costs of reserving and then cancelling reservations to re-book, or holding off on reserving and maybe never finding anything better and running out of time. For physical goods, there is even the possibility of an "indefinite optimization problem"—if I tell my PA to "buy me a top-of-the-line computer", then presumably I want my computer to continue being top-of-the-line unless otherwise noted, and this will require constant weighings of various components' or configurations' depreciation-rate, market-liquidity, employer's opportunity-cost for maintenance time, etc. All the questions a major corporate IT "buyer" employee considers, behind the facade of a single innocent request.

daveguy 10y ago

Given the open decision process (with a visual representation of the exact information and logic that went into a decision) it seems like someone could fairly easily add a "reverse sort by price" or other more complex model. Of course that is assuming a single "price-sort" function could be applied across multiple domains. If each price-sort algorithm has to be implemented and delivered as a separate model it would take a lot longer and be a lot more tedious for both developers and users. I think the real question is -- how easy is this development process? A gui representation of the program is great, but a lot of times it is easier to deal with text if you have a well designed library. They mentioned it being like an app store, so what is in it for the developers? Selling apps, directing through your own store / affiliate links? It does look pretty cool, but when there are at least 5 established competitors implementation is everything. EDIT: I would like to add that this looks like a great combination of the logical analysis of soundhound, the mobility of siri, the ability to extend of facebook and alexa. It could definitely be a breakthrough. Still -- implementation. EDIT2: Follow-up questions starting at 16 min. Launching at the end of the year, working with select developer partners toward the end of the year. "What makes it something that makes our lives better rather than makes us talk to things we don't want to talk to." A: "Developers motivated by self interest to make things work smoothly". What is that self-interested motivation? They get to sell you stuff? What do they want to sell you? The most expensive thing that fits your query -- or maybe the thing that's $5 more expensive that should sell for $20 but that will sell for $25, or $30... I agree, based on that response, it sounds like even with developer interest in the platform it won't be developer interest to give you the best product, but developer interest to give them the most money.

deleted 10y ago
[deleted]
thesimpsons1022 10y ago
yeah but things can be more complex than that. If ordering flowers for my girlfriend I will never buy the cheapest one or the most expensive. Hotels are even more complex. Location matters as well as the specific room type and a lot of other things. No one wants to stay at a crummy motel. How do you handle, "I am willing to pay up to 30 a night more if I'm closer to the beach... etc"

tedmiston 10y ago

In an ideal world, it will maintain your price preferences across sessions. Something like, for example, knowing my preferred seat on a flight is "frontmost available (non-premium) aisle seat unless the layover is very tight, in which case, frontmost seat period"... that would be amazing and truly useful.

zippergz 10y ago
I don't think my price preferences (for flights or really much of anything else) can be boiled down to an algorithm. There are so many factors. What other travel do I have planned around the same time? Have I had a slew of crappy flights lately and want to treat myself? What am I going to be doing the day I arrive and depart (e.g. do I need to be rested enough to go right into meetings, or do I have a while to get settled?)? Am I flying on business or for pleasure? I'm not sure I can even articulate all of the factors that go into my decision on what flight to purchase, but I wouldn't be surprised if there are hundreds of them over the course of a year...
- lazaroclapp 10y ago
  > I don't think my price preferences (for flights or really much of anything else) can be boiled down to an algorithm You'd be surprised how many people are trying, see e.g.: https://www.kaggle.com/c/expedia-hotel-recommendations https://www.kaggle.com/c/expedia-hotel-recommendations You could capture one or more ML features for every one of your questions above, plus hundreds more and add the features that define who you are within their customer population. Then the challenge becomes getting the right model and enough data to train it so that 98% of the time it chooses what you would have chosen. Just keep in mind that, if I have enough information to predict which flight you will take, I have enough information to predict who you are voting for, when you are joining a protest, which is the lowest salary you are likely to accept, hell maybe even enough to predict which opportunities to commit a crime you are likely to take... so, here is to hoping that figuring out which flight we want to take remains unreliable.