IUI Example – Google Flights

IUI Example – Google Flights

Google just rolled out a new feature to Google Flights, that is pretty cool – they are now predicting if your flight is going to be delayed.  As I’ve mentioned previously, I love to travel and really dig any innovation that will improve my travel experience.

As I read about this, I couldn’t help but recall a presentation I saw by Aparna Chennapragada, VP of Product Google, during an O’Reilly AI conference.

All systems imperfect — there will be a precision / recall tradeoff in almost any system that you rely on. But what you want to pay attention to, as a practitioner, is the cost of getting it wrong. Let me give you an example. Let’s say that you’re building a search system and you return a slightly less relevant article in a search result… it’s not the end of the world. But then let’s say that you build a local search product, where you inform the person searching that, yes, Home Depot is open, you should go now. Then, the person gets in the car, goes to Home Depot, and it’s closed, and they say “What the Hell?”. The cost of doing that, the cost of getting that wrong is higher.

She then gives the example of when they were building Google Assistant…

When we were working on the Google Assistant, and we say, hey, you’re flight is on time, you can leave right now and it takes 45 minutes to get to the airport and then you go to the airport and you miss the damn flight and can’t speak at the conference, then the “What the Hell” is much higher.

There are a number reasons a flight can be delayed or cancelled:

• Mechanical Issue with the plane
• Weather (at both the departure as well as the destination airport)
• Late inbound aircraft
• Crew
• Etc.

What Google seems to be doing is simply tracking the inbound aircraft, either by gate numbers – if a flight to say New York  is departing at supposed to depart at 8:21 PM and the incoming flight to that gate is delayed, there is a great chance that the New York flight will be delayed. I’m sure they are doing more than that, they probably have tons of historical data and some good algorithms that take things a little further.

As a side note, each one of these is well known, and airlines have operational departments to deal with issues as they arise. I even read a great book a few years ago – ‘A New Approach to Disruption Management For Airline Operations Control’ that went into detail about a proposed multi-agent, intelligent system to improve operations. I also talked about Smart Airport system in a recent post.

The big takeaway here is that when you’re building things like this, it’s really critical to understand the costs of being wrong and what it means to the person using it!

What do you think?