The AI Boss That Deploys Hong Kong's Subway Engineers
Taco Cowboy writes The subway system in Hong Kong has one of the best uptimes: 99.9%, which beats London's tube or NYC's sub hands down. In an average week as many as 10,000 people would be carrying out 2,600 engineering works across the system — from grinding down rough rails to replacing tracks to checking for damages. While human workers might be the ones carrying out the work, the one deciding which task is to be worked on, however, isn't a human being at all. Each and every engineering task to be worked on and the scheduling of all those tasks is being handled by an algorithm. Andy Chun of Hong Kong's City University, who designed the AI system, says, "Before AI, they would have a planning session with experts from five or six different areas. It was pretty chaotic. Now they just reveal the plan on a huge screen." Chun's AI program works with a simulated model of the entire system to find the best schedule for necessary engineering works. From its omniscient view it can see chances to combine work and share resources that no human could. However, in order to provide an added layer of security, the schedule generated by the AI is still subject to human approval — Urgent, unexpected repairs can be added manually, and the system would reschedule less important tasks. It also checks the maintenance it plans for compliance with local regulations. Chun's team encoded into machine readable language 200 rules that the engineers must follow when working at night, such as keeping noise below a certain level in residential areas. The main difference between normal software and Hong Kong's AI is that it contains human knowledge that takes years to acquire through experience, says Chun. "We asked the experts what they consider when making a decision, then formulated that into rules – we basically extracted expertise from different areas about engineering works," he says.
In other words, this is basically Drools, plus a ton of billable consulting hours?
Everything currently run by committee should ideally be run by an AI with limited human oversight in the future. Groups of humans suck at the two things AIs are great at: remembering things and making decisions.
Laws, paperwork, unions, paperwork, regulations and paperwork wouldn't allow this to happen.
Get free satoshi (Bitcoin) and Dogecoins
I dunno. If a corporation smells a profit in it, then I think they'll find a way.
Tic-Tac-Toe, Global Thermonuclear War, and relationships all have the same winning move.
This is a perfect example of an Expert System.
Expert Systems have been one of the most successful and longest used AI models in industry. FPGA routing and layout programs have relied on this form of AI since the early/mid 90's.
What we see now are the first steps towards making a big chunk of management obsolete. Expert systems are well on their way to out-compete managers who in many situations cannot make decisions of the same quality as an AI. Or to put it differently: An AI can make better decisions than a human in many areas. And in these areas humans (managers) will not be able to compete.
"People get scared when you talk to them about AI,"
Team Leader, please report to the debriefing room ASAP.
Riiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiight. It's those unions. Those ones whose membership has been steadily and measurably been decreasing for 30 years(almost exactly at the same rate as wage stagnation occurs, as a complete coincidence).
How small does Snowball's organization have to get before you stop believing he's behind everything?
Is it called Manna?
(T>t && O(n)--) == sqrt(666)
The article was posted by someone who does not appear to have been around computers in industrial applications. Computers have been used for at least 4 decades for maintenance planning in large facilities as well as other areas such as transportation routing, product blending, production scheduling, etc. The maintenance activities for the London tube or the NYC subway are likely also being planned and scheduled using some sort of similar system even if the uptime result is not as good as Hong Kong.
So, naturally, the next step is to fire all those people who would no longer have something to contribute. As a purely added bonus all these people fresh out of things to contribute happen to be with years and years of experience, which means seniority and high pay.
The mid level bean counter would think, "well, I should be able to fire at least 20 of them. Savings of 2 million on pay, another million in benefits, almost 10 mill over three years. Even if I have to let the SOBs CEO and CFO grab a mill each, I should be able to get at least 250 K for myself. Time to fire up power point, 'Work Force Optimization due to the increased Efficiency achieved by the AI system. By Gottah Avemyb Onus, Sr Vice President, Hatchet Division'"
sed -e 's/Chuck Norris/Rajnikant/g' joke > fact
Not to mention that high-performing metro systems worldwide are highly unionized.
Of course not for somthing as critical as maintenance planning and scheduling. But its OK for air traffic control functions. Where the consequences of a bad rule set are not nearly as serious.
Have gnu, will travel.
The article says that they're using a genetic algorithm. I'm no expert at AI, but my understanding is that an ordinary expert system doesn't use a genetic algorithm; an expert system just involves percolating propositions through a bunch of human-specified if/then statements.
I'd hazard a guess that the system described here is using the human-specified rules as part of the fitness function for the genetic algorithm. That's one way a system could use human-specified rules, but I think it's different from how an ordinary expert system uses them.
If you can call this an "expert system", then at a minimum, it looks like it's pushing the boundaries of the definition of "expert system".