Natalie Osborn

Artificial Intelligence:
What’s in It for Hospitality?

Mention AI during a dinner conversation and often the topic turns to a debate on whether it is helpful or hurtful. While many may think artificial intelligence means a bleak future of robots turning against humans and running amok in the streets, it has many more practical applications for businesses, even today. Artificial intelligence makes it possible for machines to learn from their experiences to the point where they can perform human-like tasks. Most artificial intelligence that has application in today’s world, such as self-driving cars or intelligent chatbots, relies on the analytic foundations of deep learning and natural language processing. In this article, we will explore the analytic foundations of artificial intelligence, look at how that may be applied in a hospitality context and even more importantly, prepare you for your next debate on artificial intelligence!

Deep Learning

Let’s start with deep learning. Deep learning is a type of machine learning that enables a computer to perform human-like tasks, such as recognizing speech, identifying images, detecting objects or even making predictions. Traditional computing involves running organized data through predefined equations. In contrast, deep learning sets up basic parameters about the data and trains the machine to learn using layers of processing to recognize patterns. Deep learning techniques have improved our ability to classify, recognize, detect and describe – in one word, understand.

Deep learning continues to advance due to developments such as algorithmic improvements that boost the performance of deep learning methods. Models have become more accurate as new machine learning approaches have developed. Access to more data, including streaming data from the internet of things, text data from social media, notes and transcripts allows neural networks to be built that have many more layers. Increasingly powerful computing capabilities including distributed computing and graphic processing abilities have enabled further training of algorithms for deep learning. When you add to that the evolution in human interfaces with machines, including the ability to interpret voice, gesture, swipe and touch, you start to open up a multitude of new possibilities. We are starting to see these possibilities today with technologies such as Siri, Alexa and Cortana.

Deep learning has several practical applications in the business world today, and many of these are applicable to hospitality. These include:

Speech recognition. Deep learning technologies are being employed to help systems recognize human speech and voice patterns. For hospitality, that can enhance our ability to receive guest requests, it can make automated voice attendant interactions far more intuitive and allow a guest to control in-room functions and order services using their voice.

Image recognition. With image recognition comes the ability to automatically caption and describe scenes from images. Self-driving cars use 360-degree cameras to help benefit from image recognition. Thousands of photos submitted by bystanders of crime scenes can be processed to determine how and where the criminal activity occurred. For hospitality organizations, I like to think that image recognition can help us with guest experience, whether it is the automatic recognition of guests on arrival, or even as an enhancement to manual inspection takes in housekeeping or food service.

Recommendation systems. Some retailers have already popularized the experience of a recommendation system by providing suggestions as to what you might be interested in based on past behavior. The same experience can be used during the booking experience for guests who need to find a suitable hotel or casino experience. With deep learning, recommendations can be enhanced with the full complexity of accommodation options as well as the depth of guest preferences. Deep learning can provide recommendations that can be much more intuitive than what we have today.

Natural Language Processing

One of the other important foundations of artificial intelligence is natural language processing. This technique allows computers to understand, interpret and manipulate human speech. Natural language processing helps computers communicate with us in our own language, to hear speech, read text and interpret that information. This technique also allows computers to determine sentiment and which parts of text or our speech is important.

Natural language processing is important because of the large volumes of text data. It is already possible for a machine to analyze more language-based data than humans can. And machines can do it more consistently without the pesky human tendencies such as bias and fatigue. Natural language processing is filling the gap between human communication and machine understanding.

If you have ever attempted to learn just one other language, you can certainly empathize with the fact that human speech and language is complex and diverse. There are many languages and dialects with minor variations on these languages. And when we speak and write, we do not do so with absolute consistency. As humans we abbreviate, we misspell words and we leave out punctuation when we write. When we speak, we have strange accents (I’m guilty of this one!), we stumble through grammatically incorrect sentences and we borrow from other languages (guilty again!). Natural language processing helps resolve the ambiguities in language and allows speech and text data to be used by many downstream applications.

Natural language processing, along with text analytics, can be used for the following:

Investigative discovery. Identify patterns and clues in text data to help detect problems. This technique has been used in multiple industries to detect and solve crimes such as fraud. This can also be used to understand systemic problems in hospitality operations that may manifest themselves in customer complaints, survey data, ratings and reviews or even operations logs.

Subject-matter expertise. Classify content into meaningful topics so that you can take action and discover trends. Let’s say that investigative discovery, using natural language processing, highlighted problems around your room service delivery process. This would enable your room service manager access to information about the problems, already identified and organized, without reading through all the detailed content. With content classification, you can do just that. Imagine being able to organize volumes of review data into actionable information for your properties, without the impact on team resources. How fast could these kinds of issues be resolved?

Social media analytics. Track awareness and sentiment around key topics and identify key influencers. Now, we not only know what guests are saying about our properties, but how they feel about it. With this technique, you can understand what features of your hotel guests are talking about, as well as if they impact the guest in a positive or negative way.

Natural language understanding. This subfield of natural language processing allows computers to generate well-formed human language on their own. These algorithms can understand the meaning and nuance of human language in many contexts. We may see applications in hospitality that include generating quality content for webpages, or even help with making sense within specific areas of hospitality, such as contracts and law.

Artificial intelligence can be a fun topic for debate at the dinner table, but it also has the potential to drive some important developments in the hospitality industry. And while many may feel that they will soon be replaced with a robot, human understanding is still required to set up these systems and ask the right questions to achieve the right results. Besides – couldn’t we as an industry benefit from automating some of the manual and error-prone tasks that consume massive amounts of our human resources? Perhaps then we can concentrate on situations where human-to-human interactions matter the most and drive toward a more consistent and enriching guest experience. I’m not sure what you are having for dinner while debating AI, but that is certainly food for thought.

High-level performance in Macau padded Wynn’s overall profitability atop continued success at Wynn’s domestic properties. Wynn Resorts’ adjusted EBITDA company-wide was $473.0 million in the third quarter, up $167.5 million (+54.8 percent) from the $305.4 million reported in the third quarter of 2016.

In Macau, Wynn properties reported adjusted EBITDA of $321.4 million, an 82.1 percent increase from the $176.5 million reported in the third quarter of 2016. Additionally, Wynn’s adjusted EBITDA for its Las Vegas properties totaled $459.6 through the third quarter, an increase of 7.6 percent from $427.1 posted a year ago.

Wynn Resorts is looking to service continued demand in the domestic market with the construction of Wynn Boston Harbor in Everett, Massachusetts, which will be located adjacent to the Mystic River just north of the Boston city center. Once complete, the new property will contain a hotel, waterfront boardwalk, meeting and convention space, a casino, spa and numerous retail offerings. The $2.4 billion project is slated for completion in mid-2019.

Wynn Resorts recently announced plans to begin construction on their $1.5 billion carnival-themed Paradise Park on January 3, 2018, to be located behind the existing Wynn and Encore properties on the Las Vegas Strip.

The property will boast a 47-story, 1,500-room hotel complete with convention space, casino, numerous restaurants and a lagoon.

Natalie Osborn is senior industry consultant for SAS Institute’s Hospitality and Travel practice, and an 18+ year veteran of hospitality and hospitality technology solutions development, specializing in analytics and revenue management. Prior to joining SAS, Natalie was the director, product marketing for Minneapolis-based IDeaS Revenue Solutions, where she worked from 2000 to 2011. She is a frequent contributor to industry publications, speaker at industry conferences and is co-author of the SAS and Cornell Center for Hospitality Research blog, “The Analytic Hospitality Executive.”