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Isn't that a situation where a machine would excel? One challenge is the abundance of decisions with only one non-deadly option, the other challenge is lack of

by usrusr 7y ago

Isn't that a situation where a machine would excel? One challenge is the abundance of decisions with only one non-deadly option, the other challenge is lack of time. The first challenge is solved by well-defined decision trees and mental checklists, but those make the second challenge harder. Computers don't need to balance between checklist thoroughness and decision latency, they can look at all the data they have in practically zero time. Mandatory bad car analogy: a driver will often prefer a faster response over spending time to check the rear view mirror before slamming the brakes, a computer would never have reasons to do so, they can check without meaningful delay.

asdfman123 7y ago
There's probably a wide range of unusual, one-off edge cases that almost never happen which would be impossible or difficult to find. And maybe catching/handling them properly would involve more sensors, when people really just want a well-trained human who is able to improvise in the cockpit. It would be difficult code a machine to find a good landing spot in extremely variable terrain. For instance, I saw a video of a plane safely crash-landing landing on a four-lane highway with traffic. It would be impractical to code something to handle that very rare situation.
- usrusr 7y ago
  I'm not a pilot, but the by far most dangerous time to have an engine problem is the period that starts the moment you have accelerated too much to safely stop the plane on the ground and that ends with having sufficient speed and altitude to glide back to an emergency landing. There surely are defined procedures for reaching the least deadly outcome for every phase within that period (basically a lookup table based on accumulated energy, with different input options, with either time or airspeed and altitude is proxies for accumulated energy). Being well trained just means applying those rules with minimum hesitation. Even experience cannot be much of a factor on top of that, as very few pilots will ever experience a scenario like that outside the simulator. My layman's suspicion, honed in the MCAS debates, tells me that the only other thing experience and human "general intelligence and gut feeling" can add to the equation is educated guesswork regarding which data to distrust when things just don't add up consistently due to a malfunction.
  - throw0101a 7y ago
    Yup: > V_1 is the critical engine failure recognition speed or takeoff decision speed. It is the speed above which the takeoff will continue even if an engine fails or another problem occurs, such as a blown tire.[9] * https://en.wikipedia.org/wiki/V_speeds https://en.wikipedia.org/wiki/V_speeds The plane has to keep going until V_2 (or V_2_min).
  - tim333 7y ago
    >very few pilots will ever experience a scenario like that outside the simulator One thing that no doubt helped with Sullenberger was he was a hobby glider pilot where that kind of situation comes up regularly if in a much less dangerous way as gliders are slow and light.
l33tman 7y ago
Yes I agree.. As long as the system can simulate the degraded plane, and is fast enough, it can brute force through a huge number of decision trees and scenarios in milliseconds to find the optimal. A human would do something similar but will prune the tree much more heavily than the computer as the human is slow. This is analogous to any chess engine, the difference is that the plane flight requires a much more difficult simulation scenario compared to a chess board. The system has to understand or at least measure what has degraded as well which might be difficult. (I don't mean to make it sound as I think it's easy to solve, but the basic strategy to solving it is simple :)
- ssully 7y ago
  The basic strategy is simple, but implementing it is not. I think a perfect real world example is Tesla's recent roll out of their summon feature. The basic strategy of having your autonomous car pull out of a parking spot and pick you up at the door is simple, but the actual implementation shows how difficult that actually is.
nradov 7y ago
No this isn't a situation where a machine would excel because it can't handle unexpected failure modes for which is hasn't been programmed. Human pilots can often come up with a "good enough" solution based on understanding fundamental principles without having any prior experience. For example, the pilots on US Airways flight 1549 intentionally changed and disregarded parts of the checklist for handling a dual engine failure because it wasn't a perfect fit for the actual conditions at the time.

by usrusr 7y ago

asdfman123 7y ago

There's probably a wide range of unusual, one-off edge cases that almost never happen which would be impossible or difficult to find. And maybe catching/handling them properly would involve more sensors, when people really just want a well-trained human who is able to improvise in the cockpit. It would be difficult code a machine to find a good landing spot in extremely variable terrain. For instance, I saw a video of a plane safely crash-landing landing on a four-lane highway with traffic. It would be impractical to code something to handle that very rare situation.

usrusr 7y ago
I'm not a pilot, but the by far most dangerous time to have an engine problem is the period that starts the moment you have accelerated too much to safely stop the plane on the ground and that ends with having sufficient speed and altitude to glide back to an emergency landing. There surely are defined procedures for reaching the least deadly outcome for every phase within that period (basically a lookup table based on accumulated energy, with different input options, with either time or airspeed and altitude is proxies for accumulated energy). Being well trained just means applying those rules with minimum hesitation. Even experience cannot be much of a factor on top of that, as very few pilots will ever experience a scenario like that outside the simulator. My layman's suspicion, honed in the MCAS debates, tells me that the only other thing experience and human "general intelligence and gut feeling" can add to the equation is educated guesswork regarding which data to distrust when things just don't add up consistently due to a malfunction.
- throw0101a 7y ago
  Yup: > V_1 is the critical engine failure recognition speed or takeoff decision speed. It is the speed above which the takeoff will continue even if an engine fails or another problem occurs, such as a blown tire.[9] * https://en.wikipedia.org/wiki/V_speeds https://en.wikipedia.org/wiki/V_speeds The plane has to keep going until V_2 (or V_2_min).
- tim333 7y ago
  >very few pilots will ever experience a scenario like that outside the simulator One thing that no doubt helped with Sullenberger was he was a hobby glider pilot where that kind of situation comes up regularly if in a much less dangerous way as gliders are slow and light.

l33tman 7y ago

Yes I agree.. As long as the system can simulate the degraded plane, and is fast enough, it can brute force through a huge number of decision trees and scenarios in milliseconds to find the optimal. A human would do something similar but will prune the tree much more heavily than the computer as the human is slow. This is analogous to any chess engine, the difference is that the plane flight requires a much more difficult simulation scenario compared to a chess board. The system has to understand or at least measure what has degraded as well which might be difficult. (I don't mean to make it sound as I think it's easy to solve, but the basic strategy to solving it is simple :)

ssully 7y ago
The basic strategy is simple, but implementing it is not. I think a perfect real world example is Tesla's recent roll out of their summon feature. The basic strategy of having your autonomous car pull out of a parking spot and pick you up at the door is simple, but the actual implementation shows how difficult that actually is.

nradov 7y ago

No this isn't a situation where a machine would excel because it can't handle unexpected failure modes for which is hasn't been programmed. Human pilots can often come up with a "good enough" solution based on understanding fundamental principles without having any prior experience. For example, the pilots on US Airways flight 1549 intentionally changed and disregarded parts of the checklist for handling a dual engine failure because it wasn't a perfect fit for the actual conditions at the time.