Dream Vacations Made Easier: How Airbnb Delivers on Hospitality with World-Class AI

AI continues to advance rapidly, but still faces hard challenges when it comes to real-world applications. The right data partner can provide thorough and unbiased data to prevent the misunderstandings leading churn at this stage of the customer journey. Join us for the first session of our new series, Bound to Succeed: AI Tech Talk with Wilson Pang. Host, Appen Chief Technology Officer, Wilson Pang will discuss the challenges of AI in hospitality and how to overcome them with guest, Airbnb. Discuss the problem of users lacking trust in bots and how rephrase, questions mining, and question answer models are employed to mitigate trust issues. In This Webinar We Will:
  • Uncover challenges and gaps in applying advanced AI in customer support products
  • Identify why AI models sometimes fail and how we build AI models and systems to overcome the challenges
  • Discuss the problem of users lacking trust in bots and how rephrase, questions mining, and question answer models are employed to mitigate trust issues
  • Explore the long tail problem of ontology and how to overcome it with AI ontology mining
  • Showcase how large-scale pre-training and transfer learning are utilized to overcome multi-lingual challenges and make our models more adaptive to the travel domain
Learn how modern AI models, training schemes and training data acquisition techniques or even product design can be reshaped to achieve more successes in real world applications. There’s a good reason overcoming complex operational challenges using AI/ML with content can lead to stickiness, audience, and revenue growth.  A lot of factors requiring high quality data go into custom feeds to ensure that what users see will be engaging. Don’t miss out on what’s bound to be an enlightening conversation! Wilson Pang, Appen Chief Technology Officer  With over 17 years’ experience in software engineering and data science, he co-authored the book Real World AI: A Practical Guide to Responsible Machine Learning which hit the bestseller list on Amazon in March 2021.
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