Comment by idunning | Hacker News Reader

Comment by idunning | Hacker News Reader

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This seems like a pretty standard application of scheduling/optimization/operations research. Other applications include:

- Crew scheduling for airlines (e.g. how to satisfy union rules/minimize time-on-ground/get people back home)

- Container flow at ports

- Railway yard scheduling (e.g. allocating cars to engines)

For another example of this particular kind of work (allocating maintenance staff to projects), see a colleague's paper on work for a large gas/electric utility: http://stuff.mit.edu/people/uichanco/scheduling.html

Calling this AI is really a stretch, although the definition of AI is fairly loose I suppose. All the "smarts" are entered by humans in the form of constraints (rules, in this article), the computer's role is essentially just very efficiently searching a combinatorial search space for the best solution. Integer linear programming is a common approach for getting optimal solutions, but can require deep expertise to make fast enough. It seems in this case they have settled for heuristic solutions from a genetic algorithm - perfectly reasonable approach.

The real story for any system like this is actually getting humans to buy into it, see e.g. the many articles about the UPS ORION system for more coverage on this. The last paragraph hints at this but doesn't talk about how they got buy-in, which is something that would be really interesting.

In many ways our relationship with AI is reminiscent of the one we used to have with gods and spirits. Thanks to the AI Effect, one day, we will be able to point to each aspect of intelligence and find that actually, it's just some banal algorithm used by Walmart operations departments. After which, we will be forced to conclude that Intelligence does not exist or worse, face something fearsome indeed.

More seriously, your point is common and valid. My own belief however, is that whatever it is that underlies our ability; it will be some mish of deduction, abduction, optimization and search. The application of search/optimization towards the fulfillment of some goal should count as intelligence. But I understand why this view might not (yet) be widely held. http://en.wikipedia.org/wiki/AI_effect

> conclude that Intelligence does not exist

You are going about it backwards. Intelligence obviously does exist, yet when we try to figure out what makes it tick each step seems banal.

Your conclusion is that intelligence might not exist, mine is that defining intelligence by the parts that make it up is incorrect. Intelligence is some sort of super-process that is not so easy to break down.

It's like how we share 98% of DNA with an ape - so clearly there is only a 2% difference. Except not. There is a huge difference, an unmeasurable difference, which means that classifying DNA by percent similarity in base pairs is an incorrect approach.

I see this as an extrapolation of an expert system, which is an AI. It seems the more you know about how they work, the less they seem like artificial intelligence and more like some simple optimization problem. I suppose this is what the Chinese Room (http://en.wikipedia.org/wiki/Chinese_room) is all about.

I don't think it truly qualifies as an expert system because, to the best I can determine from this limited article, it doesn't make any inferences from a base set of rules. Rather, it is provided with a comprehensive set of all rules, and attempts to find the best solution that satisfy those rules (or, in the language of operations research, constraints). The value provided then is that, because it can consider the space of solutions efficiently, it may discover solutions which are not obvious to human operators.

The way these systems are made, and as the article mentions, is that the obvious rules are added, and solutions evaluated. Experience humans will then usually discover something about the solution that is impossible/impractical, but was not included in the provided rules. The new rules/constraints, and the process repeats until humans are comfortable with the feasibility of the solution/schedule.

That's a good point. This system is more of a constraint solver than an expert system. It's not really answering questions than optimizing one (big and dynamic) scheduling problem.

I feel the same way about machine learning algorithms and even deep learning. The more you learn about how the algorithms work, the more it seems like just simple averaging of numbers. I think this is a good thing though, because it removes the mysticism behind it.

The term of ai is missleading. There are a lotts of different concepts people associate with ai. Classical ai is just some search for the optimal solution in the state space, often represented as a tree. However ai in common sense refers to a system that figures stuff out by it self using supervised, unsupervised or reinforcement learning.

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