Can AI Alleviate The Stress of Your Summer Holiday?

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Who doesn’t love a summer holiday? Whether it’s a staycation, or you’re packing up the whole family to jet off for some summer sun, it’s something we look forward to all year. Until you remember that it’s actually a bit of a logistical nightmare.

Where are you going? Have you booked? How are you going to get there? Got some music to listen to on the way? Summer wardrobe ready? Got the camera ready to go?

Doesn’t sound that relaxing!

...unless you’ve got the right tools to help you out. From Skyscanner to Flickr, and Netflix to ASOS, there are an abundance of companies using AI to help alleviate the stresses of travelling, so we’re taking a look at the ways that these platforms can help you out from planning your trip to creating the best memories.

In the spirit of summer, RE•WORK are running the Summer Special Promotion, offering 25% off on any event in 2018.*


First things first - where are you heading?

When planning a trip everyone has their own criteria - and first and foremost we need to pick a location. Back in 2013 Skyscanner launched it’s ‘Everywhere’ search field, allowing you to search for the cheapest destinations and tailor a timeframe that suits you.


Skyscanner have also recently added the machine learning ‘Explore’ feature that provides recommended destinations tailored to each user. At the Machine Intelligence Summit in Amsterdam, Neal Lathia, Senior Data Scientist at Skyscanner spoke about the how they have bootstrapped a destination recommender system using the rich implicit data generated by their millions of users. He explained how by using simple algorithmic approaches and experiments that gauge how localised and personalised recommendation affects user engagement.

Think Skyscanner can pick your next destination for you? Watch Neal’s presentation here to see how’s it’s done.

Destination, check. Now for accommodation.

There are literally thousands of options when it comes to booking a place to stay. Are you after a hotel, a guest house, a villa? Do you need a 4 bedroom apartment with a pool near the sea, or is a studio flat in the local town perfect? So many decisions.

Accommodation search engines such as Trivago and Airbnb serve millions of queries every day. With such a high volume of traffic, Airbnb understand the importance of addressing customer issues in the shortest amount of time whilst maintaining customer satisfaction. Back at the Machine Intelligence Summit in New York we heard from Avaneesh Saluja, Machine Learning Scientist, who explained how Airbnb are extracting and categorising these potential improvements from vast amounts of service tickets. We spoke to Avaneesh and he gave us some insights on their use of natural language processing to help overcome their problems, and you can read more here.


The Deep Learning Summit will be returning to San Francisco next January 25 & 26. Industry leaders and experts already confirmed to present their latest work include: Yves Raimond, Director of Machine Learning, Netflix; Eli David, CTO, Deep Instinct; Ian Goodfellow, Staff Research Scientist, Google Brain and many more.

Use the code SUMMER when you register, and save 25% on any of our San Francisco Summits.

Trivago are also keen to provide a smooth user experience, and their ability to predict the intention of users’ queries to provide relevant suggestions does exactly this. The machine and deep learning driven methods they are using is able to learn from previous searches to recommend additional keyword searches, so for example if you search ‘hotels in Munich’, they may want to recommend ‘Oktoberfest’. Recent progressions in ML have shown great performance in natural language processing (NLP) and have enabled Trivago to create more relevant suggestions and results. 


Hear Rami Al-Salman, Data Scientist and Machine Learning Engineer at Trivago explain the applications of ML in hotel search problems here

Flights booked, accommodation sorted.

When you land, all you want to do is get to your destination as quickly as possible. No stressing about navigating the railway system or getting on the right bus. Thanks to the global popularity of apps like Lyft and Uber, it shouldn’t be too challenging to find or pre book yourself a ride. Uber AI Labs are working to make your journey even more painless, and are applying machine learning to their research to bring self drive cars, urban aviation, optimised cities, and passenger safety to travellers to improve efficiency and save money.

At the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam in June, we heard from experts applying ML to driverless cars, and you can sign up to watch exclusive content from the event on our video hub here.

What’s next on your holiday preparation list?
Check back next week, or sign up to our mailing list to hear how AI can make your summer trip easier.

To learn more about AI applications across multiple industries, view the full calendar of RE•WORK events and save 25% on all 2018 passes with the code SUMMER.*

*The discount is only available at the time of booking and cannot be applied post-purchase. This offer excludes RE•WORK dinners.

Deep Learning Machine Learning A I Smart Transport Deep Learning Summit Machine Intelligence Summit Machine Intelligence Deep Learning in Retail & Advertising Summit Autonomous Vehicles

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