3 ms·

Yes, if you've programmed the scenario in. We might solve specific problems, sure. But how large is the problem space? How much of it have we mapped, and how we

Yes, if you've programmed the scenario in. We might solve specific problems, sure. But how large is the problem space? How much of it have we mapped, and how well does the machine cover it? If there is a long tail of obscure problems that the machine cannot handle because it was not taught how to, machines will remain shitty drivers because they can't respond with creative judgment to a new situation.

coldtea 10y ago
>Yes, if you've programmed the scenario in. Explicitly programming scenarios in is not how it really works. >How much of it have we mapped, and how well does the machine cover it? Enough for Google cars for example to have logged over 300K miles in actual conditions with no incidents. Also enough to have such things (besides Google's) in pilot operation in several cities the world other, for things ranging from cargo transport to mass transportation (self-driving busses). It's not like "programming the scenario in" is some huge switch/case statement that needs to cover all possible arrangements of things on the street -- it's machine learning algorithms with several rules and invariants to check and various corrective responses when those are off, and the smartness comes from the combinations of such rules. >machines will remain shitty drivers because they can't respond with creative judgment to a new situation. The think is, with the appropriate machine learning algorithms they can both add experience and respond with creative judgement to new situations -- they don't have to have hardcoded responses to them from the start.
- astazangasta 10y ago
  https://www.theguardian.com/technology/2016/feb/29/google-self-driving-car-accident-california https://www.theguardian.com/technology/2016/feb/29/google-se... Here is an example of the car failing for precisely the sorts of reasons I describe above.
  - coldtea 10y ago
    Note what it says: "“Our test driver, who had been watching the bus in the mirror, also expected the bus to slow or stop". So he made the same assumption as the car did. So what else could it do, magically guess? Self-driving cars are not infallible -- they model the same assumptions we do. But a benefit is that they can also react much faster than we do -- which won't always save the day of course: if some idiot driving against the traffic, for example, suddenly appears in front of such a car, they will still be an accident in all probability.
    - astazangasta 10y ago
      This thing drove into a bus, and, also, didn't learn and needed to be reprogrammed so it would drive into buses again. You are being a credulous fanatic, and I am done.
      - coldtea 10y ago
        >This thing drove into a bus As have thousands of actual human drivers -- and worse. The human driver also said that he thought the bus would do the same thing the car "brain" thought it would. >You are being a credulous fanatic, and I am done. Oh, the irony.

coldtea 10y ago

>Yes, if you've programmed the scenario in. Explicitly programming scenarios in is not how it really works. >How much of it have we mapped, and how well does the machine cover it? Enough for Google cars for example to have logged over 300K miles in actual conditions with no incidents. Also enough to have such things (besides Google's) in pilot operation in several cities the world other, for things ranging from cargo transport to mass transportation (self-driving busses). It's not like "programming the scenario in" is some huge switch/case statement that needs to cover all possible arrangements of things on the street -- it's machine learning algorithms with several rules and invariants to check and various corrective responses when those are off, and the smartness comes from the combinations of such rules. >machines will remain shitty drivers because they can't respond with creative judgment to a new situation. The think is, with the appropriate machine learning algorithms they can both add experience and respond with creative judgement to new situations -- they don't have to have hardcoded responses to them from the start.

astazangasta 10y ago
https://www.theguardian.com/technology/2016/feb/29/google-self-driving-car-accident-california https://www.theguardian.com/technology/2016/feb/29/google-se... Here is an example of the car failing for precisely the sorts of reasons I describe above.
- coldtea 10y ago
  Note what it says: "“Our test driver, who had been watching the bus in the mirror, also expected the bus to slow or stop". So he made the same assumption as the car did. So what else could it do, magically guess? Self-driving cars are not infallible -- they model the same assumptions we do. But a benefit is that they can also react much faster than we do -- which won't always save the day of course: if some idiot driving against the traffic, for example, suddenly appears in front of such a car, they will still be an accident in all probability.
  - astazangasta 10y ago
    This thing drove into a bus, and, also, didn't learn and needed to be reprogrammed so it would drive into buses again. You are being a credulous fanatic, and I am done.
    - coldtea 10y ago
      >This thing drove into a bus As have thousands of actual human drivers -- and worse. The human driver also said that he thought the bus would do the same thing the car "brain" thought it would. >You are being a credulous fanatic, and I am done. Oh, the irony.