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From the article, it doesn't sound like any actual artificial intelligence or machine learning techniques are being applied here, just a collection of algorithm

From the article, it doesn't sound like any actual artificial intelligence or machine learning techniques are being applied here, just a collection of algorithms. Does anyone know technical details?

jrockway 12y ago
They said it's a genetic algorithm, which just means they're looking for an approximate solution to an NP complete problem. My guess is that they have a list of tasks and dependencies, and then try to do the tasks in an order that minimizes conflicting use of dependencies. An example being track section A requires signal work and track work. section B requires track work. Do the signal work on section A and the track work on section B, then do the track work on section B. This is better than doing the track work on section A and letting section B remain idle. Of course, with hundreds of miles of track and thousands of tasks to perform, the algorithm does a better job than people. Though people are not particularly bad at this task.
- lifeisstillgood 12y ago
  I also suspect this is a "defensive" move. A good experienced co-ordinate will give a ML system a run for its money on "gut" plus be able to factor in the efficiency of certain teams, morale and even how hard the "track work" is - but he gets it wrong and it was "gut" and his boss cannot justify it. Put in an ML system and his boss now gets to say "the system decided" Both ways there is an expected % of sub optimal decisions but only one can the boss blame someone else.
  - PeterGriffin 12y ago
    Well, "gut" is far from obsolete, but not everything requires "gut". The article claims the program performs better when compared to data from manual management. It'll be saving them $800k/year alone. Also since the program will be evolving over time, you can assume there are still people monitoring its performance and feeding that back into the loop. Similar scheduling algorithms are already in use all over the world. If you're a student, chances are your program is made by a computer "boss" too. Scheduling is not the kind of chore humans are best at. We don't have to be cynical and suspect ulterior motives. After all, we created computers to be our assistants, and here we go, they are. And it's good. Every win for computers is a win for people.
  - TeMPOraL 12y ago
    Thing is, computers are really better at such tasks when the number of constraints grow too much for human to handle (i.e. beyond a few). It's because in time the human expert is able to articulate the solution he just thought of, the computer will search 100 000 other points in solution space and likely find a better one.
    - lifeisstillgood 12y ago
      But ... Only if a programmer has coded the other solution into the algorithm OR if the ML is able to distinguish between a correlation and a causation. Before Looking for Black Swans look for Xmas Turkey's.
      - TeMPOraL 12y ago
        > if the ML is able to distinguish between a correlation and a causation Of course it is. You can get the causation structure out of a correlative dataset with some basic Bayesian math. http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...
GFK_of_xmaspast 12y ago
'machine learning techniques' are just algorithms that you can get extra money for

From the article, it doesn't sound like any actual artificial intelligence or machine learning techniques are being applied here, just a collection of algorithms. Does anyone know technical details?

jrockway 12y ago

They said it's a genetic algorithm, which just means they're looking for an approximate solution to an NP complete problem. My guess is that they have a list of tasks and dependencies, and then try to do the tasks in an order that minimizes conflicting use of dependencies. An example being track section A requires signal work and track work. section B requires track work. Do the signal work on section A and the track work on section B, then do the track work on section B. This is better than doing the track work on section A and letting section B remain idle. Of course, with hundreds of miles of track and thousands of tasks to perform, the algorithm does a better job than people. Though people are not particularly bad at this task.

lifeisstillgood 12y ago
I also suspect this is a "defensive" move. A good experienced co-ordinate will give a ML system a run for its money on "gut" plus be able to factor in the efficiency of certain teams, morale and even how hard the "track work" is - but he gets it wrong and it was "gut" and his boss cannot justify it. Put in an ML system and his boss now gets to say "the system decided" Both ways there is an expected % of sub optimal decisions but only one can the boss blame someone else.
- PeterGriffin 12y ago
  Well, "gut" is far from obsolete, but not everything requires "gut". The article claims the program performs better when compared to data from manual management. It'll be saving them $800k/year alone. Also since the program will be evolving over time, you can assume there are still people monitoring its performance and feeding that back into the loop. Similar scheduling algorithms are already in use all over the world. If you're a student, chances are your program is made by a computer "boss" too. Scheduling is not the kind of chore humans are best at. We don't have to be cynical and suspect ulterior motives. After all, we created computers to be our assistants, and here we go, they are. And it's good. Every win for computers is a win for people.
- TeMPOraL 12y ago
  Thing is, computers are really better at such tasks when the number of constraints grow too much for human to handle (i.e. beyond a few). It's because in time the human expert is able to articulate the solution he just thought of, the computer will search 100 000 other points in solution space and likely find a better one.
  - lifeisstillgood 12y ago
    But ... Only if a programmer has coded the other solution into the algorithm OR if the ML is able to distinguish between a correlation and a causation. Before Looking for Black Swans look for Xmas Turkey's.
    - TeMPOraL 12y ago
      > if the ML is able to distinguish between a correlation and a causation Of course it is. You can get the causation structure out of a correlative dataset with some basic Bayesian math. http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...

GFK_of_xmaspast 12y ago

'machine learning techniques' are just algorithms that you can get extra money for