¶ Intro / Opening
Welcome to Travel Buddy,
¶ Meet the Team and Role of AI at Switchfly
presented by Switchfly. In this podcast, we talk about all things travel, rewards, and loyalty. Let's get to it.
Hello and welcome back to another episode of The Travel Buddy podcast presented by Switch Fly. I have with me as always the wonderful Rachel Satow and Ian Andersen. Welcome back to the show, and we also have a special guest back for his second time, Rob Neat Gman, who is basically head of AI at Switch Fly. you know all the things that there is to know about tech and ai, and loyalty programs at Switch Fly and all that, and you're leading, engineering teams in that regard.
So tell us a little bit about what you guys are doing at Switch Fly.
first of all, Brandon. Thanks for having me again. pleasure to be here. my title is Head of Data Science and Machine Learning. So essentially I hit two things. one is. try to drive data driven decisions across the company. and the other side is AI or more machine learning, wherein I try to help with, improving efficiencies or operations side of things using, machine learning as well as customer facing side, more personalizing the whole shopping experience to the degree possible.
Amazing.
That's, that, that's at a high level, what I do.
So when it comes to to
it's also an extremely understated,
what I'm getting at.
explan. Yeah. Yeah.
Yes, exactly. yeah. So when it comes to ai, you are, you are one that is. Literally thinking at a very macro level and a very micro level where we're seeing these trends, we're seeing things develop across the way that companies are using ai, particularly in the travel industry, particularly for loyalty programs. And then you're actually implementing those models and those teams and building product to serve those kinds of trends.
So that's what I want to get across because generative AI has been a huge topic trend. For years. It's a buzzword. essentially everybody's an AI company at this point, but you guys at Switch Fly are actually doing pretty amazing things with AI and the way that you're rolling that into the product and the customer experience. And so since AI is such a buzzword, I wanted to get really practical, like what are the ways that you guys are actually incorporating AI into your product?
How's that impacting customers? How's that impacting travel loyalty programs in general? And then we'll talk a little bit like future state and trends. So first question, Rodney, what are the ways that you guys are actually using AI at Switchfly in real tangible ways where you're bringing in these LLMs into the product, into the customer journey? What does that look like? What, are things are you guys developing there?
Sure. I think ever since, l LMS became very
¶ Real-World Applications of Generative AI
popular, my, my take has been, there are three ways in which Gene AI helps. One is productivity improvements, which applies to every single, or almost every single person who uses a computer. And second way is more. second and third are more time to market. So if, I were to build a traditional or machine learning model using traditional approach, the lifecycle is find the data, label it, build a model, deploy it, where gen ai, that labeling exercise can be done very quickly.
and so labeling and automation, The, time to market that has sped up quite a bit. And the last use case I like to think of Gene AI is, more prompting or prompt engineering, wherein the model itself is the large language models itself are so great. You can give it some specific instructions, and sometimes those instructions could be 50 to a hundred lines long or maybe even longer, but.
Really complex instructions and it can do specific tasks for you, or you can fine tune a large language model to solve a specific problem for you. so at a high level, three, three things. And, of course when I talk about the first one, productivity improvement, if I were to think from engineering point of view, it helps us write code faster. it helps us automate things like say, say writing unit test cases.
if, I were a finance person, it can help me do some data analysis and forecasting, I could, upload a small Excel and ask it questions on, on on predicting future trends or identify some gaps in the data. O one of my favorite ones on productivity is, almost having a PhD level expert available to you that you can ask any questions and I like to almost brainstorm ideas and try to use that special specialist lens to examine ideas from different perspectives.
Hmm.
But I think overall, I think productivity improvement is, valid across, any industry. not just travel, but those are high level use cases where we use, GenAI every day.
Awesome. That's helpful.
Brandon, something I was thinking about when we were getting ready for this, I was, I went back and looked at the podcast we did a year ago, last July, with NY and,
This is episode 13, by the way, last July, and we, it is called AI and Machine learning and Travel. So go take a listen to that.
and it's, really fascinating to see, I think at the time you and I especially, we were still struggling to get our heads around what exactly, this kind of new AI world is like and what it can be used for. rev need, I think had, clearly a much, much firmer, hold on, what it's been. And I think over the last year, what's. Really dawned on me is that I was so much in a, thinking about it from the user perspective, from the end user perspective, what can I go do in chat GPT or Claude or whatever.
Where I didn't fully grasp what it, the ramifications on the sort of enterprise side of things. and I think that's where RevNet has been, really focused on is that before it even gets to the end user. there's so many different touch points that AI can help speed things up and, sharpen, sharpen the, accuracy
¶ Challenges in Data Integration and AI Accuracy
and, productivity. So is that, fair? Neet? Am I, starting to finally grasp where you might've been 10 years ago with this understanding in there?
I, I, think, fairly accurate. I think when we look at the whole business lifecycle, there are just so many pieces that go together. what Gene doesn't solve yet is knowing where those pieces are and building that, data integration pipeline. So that's still a manual task. hopefully in, maybe not near future, but some state and future. those large language models would be smart enough to. And know how, to find the right information.
but yeah, starting from operations all the way to user experience, there is still that, dirty data problem that needs to be solved. Finding the right data set, giving it to the right model and surfacing it to the user.
Because it, really is just garbage and garbage out, right?
yeah. De definitely, o over the past year, or two models have definitely become faster. They're, they have become more accurate. they've become cheaper, fewer hallucinations. But some of the problems, when we talk about production grade applications, we don't want someone to think they're booking a certain hotel. And, but they're actually booking a hotel across the street. so the accuracy
worst.
for sure. so accuracy is, or importance of a production grade application. I think user impact is paramount and that's what drives what features we feel confident about rolling out to users.
So what I'm hearing is AI is not going to immediately eliminate all jobs for white collar workers. fairly accurate?
Knock on
I, do, think it is a few steps away, until.
Okay.
we talked a lot last time too about, and you've helped us out with, we've written some, blog articles and some other, stuff on. Like user data within the system and privacy and, transparency of how we're using ai. one, one question I've had, and, Rachel, I'm sorry I'm totally monopolizing all this, but one question I've is, regulation globally still seems to be an issue, right? Everybody's struggling with where this is going and how do we.
Put guardrails around it and how are you thinking about the privacy piece and the transparency and that aspect of it when so much of that is still up in the air?
For sure. I think at the heart of all we build at Switch fly is,
¶ Privacy, Explainability, and Regulation
I try to put privacy and explainability first, as in. Any feature we build. So for example, we have this feature called AI Destination Recommender, where a person can say, find me cities that have a beach, or cities known for culture and history. When we make a recommendation, we give an explanation of why we are recommending something. if it's the first ranked city, why is that better than the second or third? So we provide explanations.
similarly if, explainability goes beyond Gen ai, so any recommendation to a user, even things like, we have this algorithm that finds hotel deals. When we preference a deal, we explain it to the user. This is a price drop that has happened for the same check and checkout dates in the last 30 days. This is the lowest price, which is why we are recommending. Something. So that explanation goes a long way in building trust and having people keep coming back to the platform.
As we have seen through data, about 20% of our users for certain clients are repeat users. So we want people to have trust in the system, in not just the data, but but a system that values their privacy and preferences in. In an inclusive way.
on that note, from a marketer standpoint, we think about things like GDPR CAN-SPAM etc all the time. And I think when we are talking more about the, software side of things and the engineering applications there, we have to be cognizant that there's a very fine line between being helpful and being creepy. and for us, when we think about that user-first interface or that user-first mentality, that means being like super context aware and to Raven's point.
There's, being very transparent and explaining why certain things are being served or being, very upfront from a marketing perspective, from Ian and, my side pers we need to make sure that we are being open with sharing how we are using GenAI or how certain features function, and. We want to ensure and instill that it's based on behavior. this is something that we're learning from users actually utilizing the platform and not just from overall surveillance or, that creepy side.
For sure. We, don't tap into any data brokers to get additional insights about users. it's. the, data we use, our systems are fully compliant, G-D-P-R-C-C-P-A, and and we look at user behavior in an anon anonymized way. So if a lot of people click on certain hotels, maybe that is a popular hotel for a particular season in that city. So worth, recommending that to someone we know nothing about.
So does this, get into where the line between. AI and
¶ Distinctions between AI, Machine Learning, and GenAI
machine learning is as far as ai, the way I understand it, please, tell me if I'm wrong, is that, you have these large language models that synthesize just ungodly amounts of data to, then, come up with, variations on whatever the, prompting is. But machine learning is the system actively. Learning about you, And where you're clicking and what you're doing. I know those terms are used so interchangeably, but, there really is a difference, right?
For sure. yeah. I think in the past few years now, gen AI has become equivalent to AI and machine learning is ai. So at a high level, the way I describe is. AI is this superset or umbrella, that, that includes systems or processes that are intelligent. They could be rule based, with simple if the else conditions, if the weather is sunny, it's not going to rain. even those simple, ideas. So AI encompasses all of that, but machine learning is a subset within.
AI that becomes more tied to pattern matching or pattern recognition using a large data set or a data set that it can draw those patterns from. And it uses mathematics and statistics to draw or identify those patterns. gen AI or or large language models is a subset of machine learning itself. It's not a branch as such, but it's a type of. Machine learning, in a way. and a lot of it is based on transformers.
If we were to get technical, that came out in 2017, of course a lot more advances have happened since. And, and now we use LLM Gen AI Machine learning and AI interchangeably. But there is some technical difference when we. When we get down to the nuts and bolts.
I got, I was, reading something about. What, always strikes me, and I, know I, I specifically remember us talking about this last time of like how old this, like the technology or the, at least the sort of mathematics and theories behind the technology is that there was, a guy that built a. very simple computer in like 1952 that taught itself to play checkers, just by repeating and learning the rules and of, those before my parents were born and we were getting into machine learning.
and is it just that like we have now finally come to. This point with, computer processing that we can, that, that we've seen this explosion. Is that kind of what's going on?
yeah, you're exactly right. a lot of the systems and algorithms used today are very old, and for, the longest time, there wasn't either enough data or. Enough compute to apply those old techniques on a larger scale to make them seem intelligent enough or super human. level intelligence. I remember there is this researcher that I don't know how long she spent, I, vaguely recall about two or three years of effort in labeling images.
the data set is called ImageNet, which is millions of labeled images, whether an image has a, human or a cat or a dog, and like labeled to the details. And, her effort was creating that label dataset, but it took 20 years from the point she created that dataset to. Someone using a lot of compute and efficient, systems to create a image recognition model that could identify images better than humans could. So, it, techniques are old.
and of course, now, I don't know if you guys have heard this, that we are running out of data again in that, When someone talks about using all the internet to train the data, a lot goes into what I just said, but let's say all that data is utilized. So then where do G PT five or the next version of the model get the data from? So there is this side of generate synthetic data, but also can we improve,
That, seems really scary. It just
Yeah. That's wild
gonna make fake data just to keep this thing going. Yeah.
that's crazy.
Yeah. because I think one limitation is at least the data that's available in the public domain has already been consumed by these companies, or, the companies that lead frontier research models. and so either it's synthetic data or enterprises letting their data be used in some controlled way. but at cer certain point, it becomes efficiency of algorithms and being able to work with less data, then looking for more data.
So I wonder, It makes me think of something that's been on the periphery of the news lately as far as, where AI is going. You hear so much about the power requirements and obviously if it continues to ramp, in this exponential pattern that it has been the past few years, the power requirements are gonna pretty quickly. Overtake over, overcome what we, can do. so there's been talk of in the future of, is generative AI gonna be more generalized?
And we'll see that kind of the ag, the what is the a GI will it become more specialized and focused, to have kind of fewer power requirements. where do you see that going?
I, I personally see it, it has to be a balance at some point. As in, we can only, or mankind or humankind can only produce so much energy given the infrastructure that's available. So at some point it's a balance of what's the energy. Available. And then do we make our processors or chips faster, at the same time reduce their energy consumption? and then comes the next aspect, which I hope to see in my lifetime, artificial general intelligence. A lot of research is going on in that space.
I don't know if you you've seen, one of the documentaries from, Google DeepMind researcher DE has, he, won the Nobel Prize recently, for solving the protein, structured problem or protein folding problem.
Hmm.
So, there are a few labs across the world that are working towards a GI. But it's, it's hard to, assess whether is it a compute problem for now or given enough compute, does it become a data problem or does it become just the algorithmic efficiency or power problem? I think those are just different parameters these research labs are playing with.
think we can figure that out on this call, right?
yeah, We
is.
Yeah, for
I was about to say, this is all barring a third party extraterrestrial tesser act coming into play to power.
Yeah, I was saying, I was thinking in my mind like, I'm really excited to build my cabin in the woods, powered by, some solar and I have one lamp, and I just read books. no, I wanna, I wanna shift gears a little bit and think more, Practically in terms of there's a lot of research going on, there's a lot of trends. There's energy usage. There's privacy and data and, regulation, compliance around these issues.
But I also wanna mention and bring to bear that these things actually do these systems that people using them, this whole, this whole world we're talking about. It does impact business in real ways, and it impacts and drives conversions for loyalty programs and things like that. And so I'm curious from the data that you've seen, and maybe even anecdotally, things you've heard or things that you've read.
What are some ways that you're seeing AI have a tangible business impact, particularly in the travel industry? even if it's just your own data, you're seeing, X percent increase in conversions and this and that on the platforms. Is there anything like that you can give us some insight into, like how, these big abstract concepts or what feels to a novice like myself, an abstract concept, but now I want to implement it or I want to invest in it and I wanna make a business decision?
On these kind of concepts, like how do I even begin to think about that? What are, you seeing to help me make that decision?
I, I'm not a business first person, but my perspective is, those
¶ Tangible Business Impact in Travel
business problems are still the same. People want to find the right information in as few steps as possible and be able to trust that it's reliable. So now the workflow that we have behind it, we can surface new features. for example, if you look at a hotel search results page where people, or I, would look for a hotel, I would put filters on a certain star rating. I would put filters on certain prices or certain amenities, to, find exactly the hotels.
That I'm inter, that I'm interested in, and then I may review those details manually, but surfacing features, like one of the things we are working on is give a natural language, text option to user where they can ask questions or type some text and find hotels that match exactly that criteria. For example, if a person is interested in laundry or laundromat services.
I haven't seen, plat other platforms offer or any platform offer a amenity filter saying you can check a box and it would give you hotels that have that service.
Mm-hmm.
but now with Gen ai, we could create this natural language text feature. A person can say that and find exactly the hotels, that match their criteria. So it basically makes. Unstructured data or natural language text, searchable in more variations than we can imagine.
So for example. Let's say I'm planning a trip to Paris. We talk about Paris on this show a lot. I like Paris. but let's say I'm going to Paris and I'm thinking, okay, and I'm typing into a chat bot maybe. And I'm just saying Hey, I'm looking for something in the first Aron des month and I want to be near a restaurant like this. And I have this in mind for the actual hotel. I wanna make sure there's a pool. I wanna keep it under this, price point. I'm gonna be there for five days.
You can, say all those things in like a chat. it would surface what it's, interpreting that information. It doesn't have to be like a dropdown. Filters, is what I'm hearing. Is that, kind of how it works?
I, I, yeah, exactly. So we are not making that big a shift yet where users, Just interact with the bot to make their booking, just yet. But we want to keep some consistency in how people were using, or entering a destination dates, hitting a search, and then looking at the results. But now they can interact with the results using a natural language text box. but hopefully in the future as we see users get more comfortable with using natural language text.
We could shift to either a voice activated or, text-based, chat bot
Yeah. That's awesome. I like that. That's cool. Are you seeing any impact on like loyalty programs specifically and how they might be interacting with these platforms?
for sure. I think, I think, overall, if, you look at again, the same business metrics, That we measured before. so in terms of customer engagement, we do see the moment we make our platform better, either even in if it were give such results to users quicker. We see people doing more searches, we see people engaging with more content and almost it is a statistically significant difference, for all the AB testing we have done. And, so we do see improvements in. Conversion.
We do see improvements in search engagement rates, the number of hotels, or average number of hotels a person clicks, in a day.
So going back to the top of the conversation where you were like, there's productivity improvements and there's obviously like technology improvements. You're really seeing in hard statistical data, those kind of productivity improvements on the platform. And the way that AI is filtering and searching and labeling, there's real business impact. More conversions, more clicks, more hotels searched That's pretty amazing.
Yeah. And just to chime in here, Revit since we chatted about this at earlier this year, with the launch of Neighborhood Insights, which is powered by our models. The, quick stats that we had chatted about was an increase in, users at a 4% increase in users searching. So going back to what Nee had mentioned, we're seeing more activity, and engagement with actual search. then, from those viewing destination pages, and then a 1.6% rise in total conversion.
So actual booking from those destination pages, all with the implementation of, of our models.
That's amazing.
yeah, that, that was a cool feature as well where, I haven't seen any other platform do that yet. Wherein, when a person starts exploring a city, I think one of the first questions I ask myself is, which neighborhood should I stay in that city?
Yeah.
because cities could, or different neighborhoods have different vibes, and so we created this feature where we could make neighborhood based recommendations within a city. And explain why we are recommending a certain neighborhood. For example, a neighborhood is known for its monuments and history, so we recommend that neighborhood and hotels under or within that neighborhood, and that led to some statistically significant improvements across the board.
That's
And just, go ahead again.
Oh, no, I was looking at, I cannot, I, just looked up, because I couldn't remember the exact statistics, but. I cannot believe this was two years ago that we were talking about this, the similar hotels, thing you, you put together over two years ago now. and even, just the, short time we were doing the, use case, examples, we saw 20% increase on, conversion rates, on booking rates, was that 30% increase on, searches? just, it was, startling how drastic it was in such a short period of time.
this was a matter of weeks that, that we looked at this, it wasn't, a whole giant data set. So I can't imagine it's just gotten even, better since.
Yeah. Anecdotally, the neighborhood insights has become, quickly become one of my favorite features. so I'm actually planning a trip to New York in January to see a Broadway show, and I grew up. hours from the city. So I am familiar enough, but it's obviously changed since I've moved out of state.
And, I found myself like really leaning into neighborhood insights to try and refresh what may have changed since I've been gone, and to try and find like the perfect hotel spot for, me and a couple of friends to go see with this show because, There are some people who wanna stay in the middle of Times Square, and there are some people who wanna stay outside of all of the hustle and bustle. so I found myself like leaning into that feature quite a bit.
That's great. No, I love that feature because it's almost like vibe check, okay, I'm going to, let's, I'm just picking Paris again, but say we're gonna Paris and I want to, I want a quieter neighborhood, or I want a neighborhood of more shops, or I want a neighborhood that's got a lot of, let's say Michelin star restaurants, or a lot of museums. Like, where do I, go? And to be able To To put some language to that.
'cause if I'm not that familiar with the city especially, that's such a fantastic feature.
And, one thing to highlight of just bringing it back to how quickly this all moves is, a couple years ago when we were really first talking about. Ai, getting integrated in with switch, it required a little bit more from the user as far as, specificity, right? Like the similar hotels feature. It. you had to pick a hotel before you got some sort of specificity, right? and it's just gotten. More and more, I don't know, what's the word I'm looking for?
It's, predictive as far as leading you rather than, being reactive. It's being more proactive. it is maybe a subtle distinction, when you're looking at it side by side. But as far as usability, that's, it's. It's, game changing, right? Being able to go in and not have to explain necessarily what I'm looking for entirely to give very subtle kind of interactions and let the, program really tease that out of you, and get you to that point. I think's a big deal.
Yep, for sure. I think, more than making huge changes that changes or gives a new look and feel to how people search, it's these, consistency of the features that people are used to. How can we do some minor adjustments? A person has already. I chosen a hotel that they're reading about, so why not recommend some other similar hotels so the person doesn't have to go back in the shopping flow and find another hotel.
So it's these smaller incremental improvements that are making a big difference in delivering value.
That, that makes sense. Just from a, a, i, don't think I've thought about that, but the, If you say you booked on Switch fly a month ago and then you came back and it's completely different, no matter all of the like upgraded functionality, it's probably not as impactful than if you come back and it's pretty similar, but there have been some subtle upgrades and you're like, oh, hey, last time I couldn't do this, or last time, whatever.
Like it, even if it's not as like broad and sweeping, it probably is a little more. Impactful per user.
Yep. Yep. and which is why when I was talking about the natural language search. We want to keep existing UI or UX and slowly move users to, a natural language search, but at the same time learn about, sometimes I wonder the way people use a platform, it's more of a symptom of how the platform is designed, which is consistent with every other platform before we start thinking of a personalized concierge. 'cause practically how many people. use a concierge today.
and the other part of that is, the data that they're willing to share. So it's a, trade off and, we want to make sure we take the baby steps and, deliver value where users see.
Which will get them more comfortable with sharing more data. Like it just is a natural kind of progression that Yeah, it feels right.
To me, the listening to you guys talk about this, it's, Rodney, you use this language, a moment ago where it's like changing the relationship to how we're searching for travel. And it makes me think of, I've had this thought on my head, Ian, as a history nerd, you'll appreciate this, but it's like the, printing press, how it changed, the relationship.
We had to text and information and, then, The, our relationship between church and state and education and all kinds of different things that we won't get into, but literally everything. but what I find fascinating is that's where we're going, is more closely to how the human mind works, which obviously is the underpinning of a, large language model and that it's mapping a neural network between our brains and information, things like that.
But what's interesting is Ian, you mentioned like you, when you're searching for something, you just are, you don't know exactly what you're looking for, and it's the same kind of thing when you are speaking. As I'm speaking now, I don't know exactly what I'm thinking, but I'm, searching it out as I'm speaking and a conversation, a dialectic or writing itself is thinking. And so I love that you guys are, this is very meta.
But you're just going in this direction of like how people are interacting with travel itself and this kind of they're just like vibing their way into the trip that they want and using AI to get there. Is that, ki, am I directionally correct in how you guys are thinking about this? I.
Yeah. this was something that, Reni you and I had talked about way back, but it was really about the consolidation of the effort of planning your journey. your. The way we search. so. a lot of the intention behind some of the features that are going into the Switch fly platform are with the end goal of making it simpler and re removing friction points so that you're not having to go to five different other websites to find out this information. And you can all do it all within.
One platform that you can then eventually book from. So it reni, correct me if I'm wrong, but yes, it is, all with that goal of being able to, streamline the whole traveler journey.
that, that's, that's on point. Rachel, I think overall there are so many travel platforms, consumer facing or reward. Loyalty, rewards focused, and each, external platform has their own special features. So rather than having people do research and discovery outside and come to our platform just to make a booking, we have these features where we want people to be able to stay within our platform right from the point they where they decide to travel and.
Find the right destination, the right neighborhood, the right hotel activities, or whatever the itinerary looks like, and be able to do it in one single place.
That's cool. I love that. Okay. Last question for you, just in the last two minutes or so, what are, ways that you're thinking about the future of AI and travel? So we talked a little bit about vibing your way maybe into, travel and the way that you're searching, but what are some other maybe technologies or trends that you're paying attention to that will really impact your work in the next 12 months? Because we can't predict any further than that.
Like literally every month AI is totally different, is what it feels like. But So the next 12 months though, where are you guys pointing your ship?
A big focus is, we have done, some experiments over the last six to 12 months. A lot of it is focused on operationalizing those. our Northstar is our business metrics. so what drives conversion? What drives customer engagement and the features, that help move that Northstar, we are focused on that. so a lot of experimentation, AB testing, new features and making decisions. How far do we want to go on a certain feature, where it drives.
Business value, but at the same time, it's useful for the users. so essentially that's the driver. And then
¶ Future Focus and Responsible AI Innovation
tying it back to gen ai, more productivity improvements. how do we, improve the quality of our deliverables? How do we identify issues earlier? How do we maybe automate code reviews or write better test cases? so. a lot of productivity improvements and business value driving features, both for users and the business.
That's great. That's a great place to start.
So I do have one final one, Brandon.
Okay.
it's triggered by your, meta wanderings is, ny from a, data scientist perspective, what is intelligence? Is there a way for a machine to be intelligent the same way a human is?
Oh,
in 60 seconds, go.
I want bullet points.
that's a great question. I personally like to think of, when I see my 3-year-old son, since he was born. I'm seeing him more like a machine in training for that time in that he, ma he makes an action or says something, and then my wife and I, we nudge him in a certain direction supervising him. And his neural network, which is maybe far sophisticated than a machine network, but his neural network is in training.
And so at least in theory, if there is right dataset, right algorithms and enough compute, then a machine should be able to ex exceed human intelligence. The question is not if it's a question of when, in my mind, and. Purely speculative, that when could be five years from now, it could be 10, 20, or 30. but it's more of a question of when, to me.
Scary, hopeful. I can't tell. but if, you have any training data on my eight week old to get him to go to sleep, I would love to just plug that one right in 'cause I'm very tired. No, that's great. That's a great analogy. Awesome. team, thanks so much. I love being on the show with you guys 'cause there's so much wisdom and insight and just fun. And I enjoy it. so Ravini, thank you again for your expertise.
I am, super excited in the way that you guys are thinking about AI generally, conceptually abstractly, but then how it really impacts businesses and, makes travelers lives better. that's the whole point. And so as a, abstract user on your platform, I appreciate you guys and what you're doing.
Yeah, but, if you haven't yet, this is to you listeners, if you haven't yet listened to episode 13 AI and Machine Learning with Rodney the first time, please go back and listen to that because it'll get you up to speed on the, philosophy or the approach that, you guys at Switch Fly.
Kind of, just think through AI and machine learning in general and then this, obviously the show we talked a little bit more practicality and kind of future facing, but, and it's really interesting to see it just in the last 12 months. How this kind of technology and thinking has developed and, how you guys are approaching it. So please go listen to that and with that we'll see you on the next show. Thanks
Thanks Brandon.
Thanks Brandon.
Oh, thanks for having me.