6 ms·

https://ifttt.com/ https://ifttt.com/ "Computer, create a rule for my car." "OK. Which car? Do you mean Red Toyota Purchased 1997?" "Yes." "OK." "Computer,

https://ifttt.com/ https://ifttt.com/ "Computer, create a rule for my car." "OK. Which car? Do you mean Red Toyota Purchased 1997?" "Yes." "OK." "Computer, when car is at location House, then boil kettle." "Do you mean Kettle in Kitchen of 48 My Road?" "Yes." "OK. Is that all?" "No. Computer, this rule is only valid on weekdays." "OK. So when car Red Toyota Purchased 1997 is at location 48 My Road on Monday, Tuesday, Wednesday, Thursday, Friday, boil Kettle in Kitchen of 48 My Road?" "Yes, but only if my iPhone was not inside the car." "Do you mean Jemima's Apple iPhone 4Gs?" "Yes." "OK." ...

seanmcdirmid 13y ago
I'm a PL researcher who has also worked a bit on dialogue systems. Getting those rules encoded for even a small domain would be a huge task, primarily because machine learning hasn't helped us in this area yet. We will get there, but maybe in 5 or 10 years.
- oblique63 13y ago
  Exactly. I think OP's argument was that we should devote more resources to these sorts of problems instead of bickering about low-level stuff so much (if they aren't directly helping that goal). I don't see how that view implies ignorance of current technological limitations.
  - seanmcdirmid 13y ago
    The OP wrote: >Computing is in a sad state today, but not because of fucking callbacks. It's in a sad state today because we're still thinking about problems on this level at all. He seems to imply that it's sad today is today and not 20 years from now. So what? People have dreamed about replacing "programming" with "dialogue systems" since at least the 60s; they are like flying cars, always 10 or 20 years away. We might be closer now, but it's not like we were not dreaming about this since before I was born. In the meantime, we have code to write, maybe it's worth doing something about this callback thingy problem just in case the singularity is delayed a bit.
    - oblique63 13y ago
      That's more of an orthogonal point than a counter point though. Just because we haven't accomplished something yet doesn't mean we shouldn't invest any more effort into it. I was responding to your insinuation that OP must not be knowledgeable about language implementation to hold the views he/she does. In this context, technological limitations are irrelevant because that was not the point; the point was an opinion about the relative effort/attention these problems receive. I'm admittedly not really invested in this area myself so I don't really care, but it's disingenuous to try and discredit OP's views like that. At least this response is more of a direct counter-opinion.
    - tluyben2 13y ago
      There are not that many researchers (or other people) working on 'less code'. And the response from the community is always; don't try it, it's been done and it won't work (NoFlo's Kickstarter responses here on HN for instance). Instead I see these new languages/frameworks which have quite steep learning curves and replace the original implementation with the same amount of code or even more. As long as 99% of 'real coders' keep saying that (more) code is the way to go, we're not on the right track imho. I have no clue what exactly would happen if you throw Google's core AI team, IBM Watson's core AI team and a few forward thinking programming language researchers (Kay, Edwards, Katayama, ...) in a room for a few years, but I assume we would have something working. Even if nothing working would come out, we need the research to be done. With the current state we are stuck in this loop of rewriting things into other things, maybe marginally better to the author of the framework/lib/lang and a handful of fans resulting into millions of lines of not very reusable code. Only 'reusable' if you add more glue code than the original code in the first place adding only to the problem.
      - seanmcdirmid 13y ago
        Nothing was new with NoFlo, it was just trying the same thing that failed in the past without any new invention or innovation. The outcome will be the same. Trust me, the PhBs would love to get rid of coders, we are hard to hire and retain. This was at the very heart of the "5th gen computing movement" in the 80s and it's massive failure in large part led to the following second "AI winter." > I have no clue what exactly would happen if you throw Google's core AI team, IBM Watson's core AI team and a few forward thinking programming language researchers (Kay, Edwards, Katayama, ...) in a room for a few years, but I assume we would have something working. What do you think these teams work on? Toys? My colleague in MSRA is on the bleeding edge of using DNNs in speech recognition, we discuss this all the time (if you want to put me in the forward thinking PL bucket) over lunch...almost daily in fact. There are many more steps between here and there, as with most research. So you are unhappy with the current state of PL research, I am too, but going back and trying the same big leap that the Japanese tried to do in the 80s is not the answer. There are many cool PL fields we can develop before we get to fully intelligent dialogue systems and singularity. But if you think otherwise, go straight to the "deep learning" field and ignore the PL stuff, if your hunch is right, we won't be relevant anyways. But bring a jacket just in case it gets cold again.
- georgemcbay 13y ago
  Indeed! Intelligent machine learning is 5 to 10 years off, just as it has been since the early 1980s.
  - ulisesrmzroche 13y ago
    Well put. I wasn't even born then, but things have changed so little that the first thing thing that comes to mind is that I really need to see Tron someday. I doubt computers will ever learn to talk humna, rather, as we all see more and more every day, humans are going to have to learn to talk computer.
    - seanmcdirmid 13y ago
      We will get there. It is just taking a lot longer than initially anticipated. I for one am not looking forward to trans-humanity, but I see it as inevitable.
    - georgemcbay 13y ago
      I was born in 1973 and by the 1980s a lot of people were convinced intelligent machines and nearly general purpose AI was just around the corner. There was tons of university and military funding going into projects to achieve this. Of course, now the idea of that sort of blue-sky research is almost universally dead and the most advanced machine learning will likely be developed at Google as a side effect of much effort put into placing more relevant ads in front of us, which is kind of hilarious.
    - michaelmior 13y ago
      If most humans eventually learn to talk computer, what's the difference between "human" and "computer" language?
- KirinDave 13y ago
  The primary challenge is the stated example is speech and NLP. It's not building the decision trees. We can do that today with off the shelf technology. Hell, it's the kind of thing people love to hack together with their Arduinos. Microsoft even has a pretty slick thing called on{X} that does some amazing tricks with rules reacting to network events and geolocation tools. Although I confess that commercializing a system that lets you asynchronously start potentially unmonitored exothermic reactions in your home? Probably hard. :D
  - hyc_symas 13y ago
    There are these wonderful inventions called automatic electric kettles, you should look into them. They seem to be commonplace in Europe, not so much in the US. But seriously, all it takes is a bimetallic strip to turn the kettle off when it reaches the desired temperature. Eliza ran in 16KB of RAM. The logic flow is easy, all you need is a sufficiently large dictionary.
randallsquared 13y ago
The distinction between "is at" and "arrives at" is going to result in a high power or house gas bill. :)
stormbrew 13y ago
This just demonstrates that the problem, for laypeople, isn't so much the programming as the debugging. What does "your mom" do when she realizes the kettle is on for nearly 18 hours every day because that's how long her car is there? Being able to communicate effectively, in a bidirectional manner, with a computer is a skill. It's a skill people will probably always have to learn, one way or another. It's more likely that more people will learn these skills than that we'll devise a way for computers to be the smart ones in the conversation any time soon.

seanmcdirmid 13y ago

I'm a PL researcher who has also worked a bit on dialogue systems. Getting those rules encoded for even a small domain would be a huge task, primarily because machine learning hasn't helped us in this area yet. We will get there, but maybe in 5 or 10 years.

oblique63 13y ago
Exactly. I think OP's argument was that we should devote more resources to these sorts of problems instead of bickering about low-level stuff so much (if they aren't directly helping that goal). I don't see how that view implies ignorance of current technological limitations.
- seanmcdirmid 13y ago
  The OP wrote: >Computing is in a sad state today, but not because of fucking callbacks. It's in a sad state today because we're still thinking about problems on this level at all. He seems to imply that it's sad today is today and not 20 years from now. So what? People have dreamed about replacing "programming" with "dialogue systems" since at least the 60s; they are like flying cars, always 10 or 20 years away. We might be closer now, but it's not like we were not dreaming about this since before I was born. In the meantime, we have code to write, maybe it's worth doing something about this callback thingy problem just in case the singularity is delayed a bit.
  - oblique63 13y ago
    That's more of an orthogonal point than a counter point though. Just because we haven't accomplished something yet doesn't mean we shouldn't invest any more effort into it. I was responding to your insinuation that OP must not be knowledgeable about language implementation to hold the views he/she does. In this context, technological limitations are irrelevant because that was not the point; the point was an opinion about the relative effort/attention these problems receive. I'm admittedly not really invested in this area myself so I don't really care, but it's disingenuous to try and discredit OP's views like that. At least this response is more of a direct counter-opinion.
  - tluyben2 13y ago
    There are not that many researchers (or other people) working on 'less code'. And the response from the community is always; don't try it, it's been done and it won't work (NoFlo's Kickstarter responses here on HN for instance). Instead I see these new languages/frameworks which have quite steep learning curves and replace the original implementation with the same amount of code or even more. As long as 99% of 'real coders' keep saying that (more) code is the way to go, we're not on the right track imho. I have no clue what exactly would happen if you throw Google's core AI team, IBM Watson's core AI team and a few forward thinking programming language researchers (Kay, Edwards, Katayama, ...) in a room for a few years, but I assume we would have something working. Even if nothing working would come out, we need the research to be done. With the current state we are stuck in this loop of rewriting things into other things, maybe marginally better to the author of the framework/lib/lang and a handful of fans resulting into millions of lines of not very reusable code. Only 'reusable' if you add more glue code than the original code in the first place adding only to the problem.
    - seanmcdirmid 13y ago
      Nothing was new with NoFlo, it was just trying the same thing that failed in the past without any new invention or innovation. The outcome will be the same. Trust me, the PhBs would love to get rid of coders, we are hard to hire and retain. This was at the very heart of the "5th gen computing movement" in the 80s and it's massive failure in large part led to the following second "AI winter." > I have no clue what exactly would happen if you throw Google's core AI team, IBM Watson's core AI team and a few forward thinking programming language researchers (Kay, Edwards, Katayama, ...) in a room for a few years, but I assume we would have something working. What do you think these teams work on? Toys? My colleague in MSRA is on the bleeding edge of using DNNs in speech recognition, we discuss this all the time (if you want to put me in the forward thinking PL bucket) over lunch...almost daily in fact. There are many more steps between here and there, as with most research. So you are unhappy with the current state of PL research, I am too, but going back and trying the same big leap that the Japanese tried to do in the 80s is not the answer. There are many cool PL fields we can develop before we get to fully intelligent dialogue systems and singularity. But if you think otherwise, go straight to the "deep learning" field and ignore the PL stuff, if your hunch is right, we won't be relevant anyways. But bring a jacket just in case it gets cold again.
georgemcbay 13y ago
Indeed! Intelligent machine learning is 5 to 10 years off, just as it has been since the early 1980s.
- ulisesrmzroche 13y ago
  Well put. I wasn't even born then, but things have changed so little that the first thing thing that comes to mind is that I really need to see Tron someday. I doubt computers will ever learn to talk humna, rather, as we all see more and more every day, humans are going to have to learn to talk computer.
  - seanmcdirmid 13y ago
    We will get there. It is just taking a lot longer than initially anticipated. I for one am not looking forward to trans-humanity, but I see it as inevitable.
  - georgemcbay 13y ago
    I was born in 1973 and by the 1980s a lot of people were convinced intelligent machines and nearly general purpose AI was just around the corner. There was tons of university and military funding going into projects to achieve this. Of course, now the idea of that sort of blue-sky research is almost universally dead and the most advanced machine learning will likely be developed at Google as a side effect of much effort put into placing more relevant ads in front of us, which is kind of hilarious.
  - michaelmior 13y ago
    If most humans eventually learn to talk computer, what's the difference between "human" and "computer" language?
KirinDave 13y ago
The primary challenge is the stated example is speech and NLP. It's not building the decision trees. We can do that today with off the shelf technology. Hell, it's the kind of thing people love to hack together with their Arduinos. Microsoft even has a pretty slick thing called on{X} that does some amazing tricks with rules reacting to network events and geolocation tools. Although I confess that commercializing a system that lets you asynchronously start potentially unmonitored exothermic reactions in your home? Probably hard. :D
- hyc_symas 13y ago
  There are these wonderful inventions called automatic electric kettles, you should look into them. They seem to be commonplace in Europe, not so much in the US. But seriously, all it takes is a bimetallic strip to turn the kettle off when it reaches the desired temperature. Eliza ran in 16KB of RAM. The logic flow is easy, all you need is a sufficiently large dictionary.

randallsquared 13y ago

The distinction between "is at" and "arrives at" is going to result in a high power or house gas bill. :)

stormbrew 13y ago

This just demonstrates that the problem, for laypeople, isn't so much the programming as the debugging. What does "your mom" do when she realizes the kettle is on for nearly 18 hours every day because that's how long her car is there? Being able to communicate effectively, in a bidirectional manner, with a computer is a skill. It's a skill people will probably always have to learn, one way or another. It's more likely that more people will learn these skills than that we'll devise a way for computers to be the smart ones in the conversation any time soon.