← Harvard Data Science Review Podcast
Harvard Data Science Review Podcast
Better Data Science and AI Technologies for Better Vine and Wine?
00:45:39
Loading summary
Transcript32 lines
- [00:02]
A
Hi everyone and welcome to HTSR Podcast. I'm Shao Lehman, the founding editing chief of Harvard Data Science Review. This episode is full about wine and it's a part of our ongoing coverage of the Wine to Mind conference which we started about a year ago. It's a conference about the use of AI and data for for the wine industry. Before we dive in, I want to give a big congratulations to my usual co host, Liberty Vitter. Liberty has a new roommate. After waiting for nine months, she's taking some well deserved time off to be with her family and we were sending her lots of love. And I also know Liberty is waiting for the moment to drink again. In the meantime, I'm thrilled to be joined by our guest co host, Magill Paredes. Magill is a partner at Carney, where he helps companies develop AI strategies and create real business value. He is also a wine lover and a co editor for the column on active industry learning for Harvard Data Science Review. We're excited to be joined by two amazing guests. First is Katerina Axelson, founder and CEO of Tastree. Her company literally taught a computer how to taste. Then we have Kia Pania, co founder and CEO of Scout, which use camera technologies like your phone or webcam to monitor vine health, track performance and predict yields. He's also the CEO and co founder of Neotemple Wines and I can personally vouch their wines are really fantastic. We'll hear from both of them about how their companies are helping the wine industry to improve wine quality, boost production and connect people with wines they will love. Let's get into it. Well, first of all, thank you all for joining this podcast. I'm going to start with Kia Kia. First, I want to thank you again for being a keynote for the wine to mind 2025. It was a fascinating talk. I'm also deeply grateful to you for hosting me at your winery. Enjoy your wine. But I'd like to start with you to ask a little bit about your adventure recently. What your company does and how do you use data and AI? Just give a broad introduction to the audience about what you do.
- [02:35]
B
Thank you so much Shali. So my journey really has three parts. The first part, I spent 25 years in my career really help modernize and digitize a lot of different industries and bringing operational efficiency through the use of data. I was one of the early people at Tivoli Systems, Marimba, BMC Software and then Splunk. My last journey and what I realized is once you put powerful data in front of experts in their field. They know what to do with the data. My wife and I also love wine, and we bought a vineyard and started our wine journey about 14 years ago. As we got into it, one of the first things I wanted to do was imagine the vineyard of the future. You know, how do I get the right set of data for our winemaking team and our viticulture team so that they could do the same? And what I found was a big gap. I took a course at UC Davis, my alumni called the Wine Exec Program, met my co founder, Mason Earls, who taught a course called the Vineyard of the Future. We started chatting about what's the best sensor in the vineyard. And he said, kia, the answer is easy. It's the farmer's eye. Because the farmer sees problems, remembers what last year was, understands what's different. The only problem is that doesn't scale. And that really became the inspiration for Scout. So what we have built is a solution that uses regular cameras, much of which you can find in webcams. And it's incredible how cheap and powerful they've become. We attach that to regular smartphones, so no proprietary hardware, no expensive hardware for farmers. And that's important because we think the solution and this solution can expand to all vineyards and all farms, quite frankly, around the globe. And then the power of AI. So AI becomes the computational system to marry computer vision with the location of the vines. We take 20 photos of every single plant. We use the 20 photos to effectively create not only a picture, but also a medical record of each and every vine and capture the vital statistics, whether those are canopy and vigor growth, how many clusters, how many shoots. It's like an X ray, basically, going very deep, using computer vision and labels with the power AI to count things, measure things, and build a database basically of what you have.
- [05:14]
C
Kia, incredible work you're doing. Thank you so much for sharing. We also have Katerina. Katarina. There was this really interesting CNN that the title was the tech startup that taught a computer to taste wine. We'd love to learn a little more about yourself, your journey and tell us a little about Tastry and the problem you're solving.
- [05:35]
D
Thanks, Miguel. So before Tastry existed, I was a chemist. A very young in my career chemist, still going through college and paying my way through college by working in the wine industry. And I was always odd in that I was obsessively reading research papers and because of the work I was doing behind the scenes in the wine industry and my exposure to it and the fact that Cal Poly, where I Went was offering winemaking classes. I got interested in sensory science. And as I was reading these papers on all the focus groups that we do, all the ways we understand consumers, consumers and how they perceive chemistry, I started to feel unsatisfied and like there was a lot of gaps in understanding how consumers actually perceive, you know, flavors and fragrances and things like that. And so, long story short, I had this thesis that the reason that we cannot have a consensus between predicting how much consumers would like something or what words they use to describe the experience. Experience is because machines don't look at chemistry the way humans do. We run panels, but we as humans are experiencing all these chemistries in different concentrations at one time as this complex chemical soup. And in wine, it's even more so because it's an agricultural product with a lot of nuances involved. And so the end result of that was about two years of R and D, where we ended up creating an analytical chemistry methodology that serves as our data set for our AI that actually grabs all the understanding of analytes in a solution at once. And then it took some time after that to, you know, figure out the commercial application for the wine industry. But because we were able to acquire this unique data, we tied it to consumers and we focused on training our algorithms to predict the likability of products as opposed to what words they would use to describe their experience. And we can do that with 93% accuracy today. And our company tagline is we taught an AI how to taste and smell. And maybe for the more technical people out there, what we're essentially doing is, is we're creating digital twins of chemistry, right? Whether or not that chemistry exists in the cloud. And we're creating digital twins of consumers, and we're running simulations to predict what is the best possible outcome or opportunity or white space for oat wine. And so that way, depending on who you're talking to across the supply chain, we're really acting as a matchmaker between brands, retailers, distributors, consumers. So there have been many, many applications. And our challenge was figuring out where the most compelling use cases were.
- [08:51]
A
That's fascinating that I want to follow up later on your mention about Digital Twin, because Harvard Data Science Review actually just launched a special call for special issues on Digital Twin. But for now, I want to kind of dive into probably the most fundamental questions anytime we we talk about data science, and for both of you is the data collection and the data quality that drives everything, I think, for both of you. So I like to hear from each of you, what is your Data collection process. How do you guarantee the quality? I know it's easy for me. I'm kind of a special expert on talking about data quality, writing articles about the series. But practice is a different story. How do you deploy things? How do you make sure people follow what you tell them? So I'd like to hear from both of you how you ensure the data collection, data quality in your practice. Maybe go with Ikea first.
- [09:48]
B
Yeah, absolutely. So we understood data quality was very much a top priority. And part of what we're trying to do with our data is make it more accurate than a human walking around with a tape measure trying to measure and count and remember what they saw and write it down on a piece of paper. By the way, the name scout for our company is inspired by scouting, which is actually something that people do in the vineyards. Usually they're interns. They're given the task that nobody likes in the wine industry, which is go out for eight hours and count things instead of drinking wine. So in the beginning, we started calibrating ourselves on what is the human based error rate and then how do we understand what the current AS is? First thing we did with our customers is we started asking them for their maps and their existing data. And I remember the early days, I was very nervous because 100% of our data was different than the customers. And what we found over the first year, 100% of our customers had. But this is the good news, Charlie. Their data was wrong. And that was shocking to me. So what we found was it's a target rich environment. We're comparing things that. And by the way, it wasn't the intern's fault. You know, I think in some cases people tried to shortcut and assume that a vineyard is square, therefore it has the same number of plants. And what we found is that's not how farming works. People will not have the exact distance between the plants. And sometimes two or five sneak into a row that are different than the neighborhood row. And so not everything's a metric. A lot of people think of these as grids with the same number, and they're not. So we feel right now we've kind of seen this, we have 98 to 99% accuracy. The 1% or 1 1/2% is really distinguishing between a young vine and a weak vine. Right. And that's a human problem as well. Most. But in terms of understanding whether something's in the ground, is it rootstock or is it a mature plant, is it producing? Those are areas that at this point, I would say we don't have a data quality problem. How we instituted this is within the data pipeline. And in fact, we have now a data czar, if you will, completely separate from the engineering team whose job it is to literally look at data quality as a metric end to end. And that helps each of the pieces in the pipeline understand what their quality measures are. And how do we assign a risk score to the data as it comes up so that the data itself in some ways is self describing. So if I took a photo on a rainy day that was blurry, that should be rated as a lower quality, higher risk for error. So we're trying to ingrain this data quality concept not just in a person or a team, but literally throughout the whole system. So we're actually using AI itself to also identify high risk, potential blind spots so that we can stay away from it. The area in quality right now, data quality right now, that's very challenging. And I don't, and this is physics and biology, is that obviously grapes could be behind a leaf, so you can't see visibly all the grapes. This becomes an interesting problem, especially for sparkling wine, where people don't like to thin any leaves and they'd like to leave it there so that it naturally comes out. And you'd be very happy to know that we're using a sampling algorithm to be able to now direct humans to go to certain spots and count the actual fruit and then use that to calibrate the AI models from that set. So in ground, sampling still is a very important component of our solution. And humans are always in the middle either helping us calibrate our model better or, you know, humans who are doing the labeling or doing some of the other, you know, elements that right now cannot be done by AI.
- [14:16]
D
Yeah, so the, the core foundation of our company Tastree, is built off the notion that our new invention, our means by which we acquire data that is unique, is what allows us to answer questions that traditional technologies were unable to answer. So from the very beginning, the company started because of this invention that allowed us to generate our own data in house. So there's really two data sets that we have to generate in house. I don't have historical data sets on past consumer purchase history or the Nielsen top 2000 wines sold in the U.S. that's all showing what happened in the past. What our data allows us to do is in a much better way, anticipate the future. And so one data set is the chemistry. It's the analytical chemistry methodology. We have a lab based in The Central Coast. It's a third party certified lab. It is compared against other labs in the country for accuracy and reproducibility and things and like that. But the key distinction is our methodology is much more efficient and again can grab all the compounds at once. And the method is proprietary. So the lab is data set number one. Data set number two, which allows us to understand chemistry relative to consumers, is we have an experimental design where we first run a traditional focus group where we have consumers who would taste and rate the products like wine in the focus group and we would ask them questions about how much they like those wines. Those wines we have the chemistry for. And once we have that data, consumers rating certain number of products, we introduce a third data set which is we ask them questions about their preferences for foods and flavors that may or may not be correlated with wine. And I'll give you an example. You may have seen some gimmicky looking quizzes on some wine websites in the early 2010s that asked you things like, do you like dark chocolate or black coffee or licorice or bell peppers? And the reason we introduced that data set is because we know that consumer preference is strongly tied to culture, cultural upbringing and what you grew up eating. And so once we have that data set, we can eliminate the need to have a focus group in the future just by understanding what that person's general palate is. It's not a one to one relationship. Right. So I'm not saying, hey, if you hate bell peppers, then we're not. We're going to say you hate wines with pyrazines. And it's more complicated than that. But it does give us analogs that give us a much more efficient, much more scalable way to generate then synthetic data and extrapolate what the other palates in the country are like, even if we don't have tasting data on them. So it's really the interplay of those three data sets that allow us to make predictions.
- [17:42]
C
I want to ask you guys how you guys are using AI outside of Scout and Tastree. And in Katarina's case, Tastry started with wine. I know Katarina, you're working on other spaces now. Would love to hear from you on that. And then Kia, you have a project that's close to your heart, which is Neotempo. I would love for you to talk to the audience about that. So maybe we can start with Katharina, maybe you can share how you're using this technology in other spaces and then we can go to Kia, Sure.
- [18:11]
D
So when we developed the IP intellectual property, we had validated that our methodology applies to anything sensory based. So that could be coffee, it could be soft drinks, it could be laundry detergent, it could be fragrance. But, you know, this was my first company and I was just a scientist when I started this. I was not a CEO. I am now. I just wear a lab coat for photo shoots now. But back then I was actually wearing a lab coat. And so we said, look, we need to focus on the vertical we were born in, which is wine. Prove it out in this vertical and then we'll expand and into other industries. And to be honest, growing in the wine vertical took so much longer than I expected. And we quickly realized we picked the hardest vertical from a technological perspective and from integrating AI into the workflow perspective. For example, the wine industry has what, 160,000 SKUs coming out every year or that are registered with the ttb. That's not how many ketchup labels there are. Second, it's an agricultural product. So we're picking up on nuances in terroir and all these things in the oak that could be influencing the seasonal effects on that. And then all the available chemistries and complexities of wine required a very large investment to acquire that data. And then thirdly, wine, as you know, is a very unique industry. It's fragmented. It's not the fastest adopter of technology. You know, there's a lot of artisanal practices there, and it's cyclical in the sense that during different parts of the season, you're dealing with different problems, right? You're dealing with harvest, then you're dealing with blending and all these things. So we were in the wine vertical only exclusively the wine vertical, much longer than we expected. And we've done a really good job. We've worked with a lot of amazing winemakers, some of the most prolific winemakers in the industry. We've worked with some of the best brands, some of the most iconic, smaller brands. But I just wanted to say we will continue to do that. But when I pop my head back up, and this has only been happening in really the past year or so, and I started to talk to CPG companies outside of wine, I realized how much easier it is to implement this technology in other industries so much faster, and that the value propositions are a little different. So if I'm talking to, I don't know, a fragrance company or a Coca Cola or someone like that, I'm telling them, hey, I can cut your focus group costs by 80% and help you bring a product to market in four months instead of 12 and flip the failure rate of that product from 80% to 20%. That's a pretty easy pitch. When I'm talking to the wine industry, it's a lot more nuanced than that. And depending on the kind of winery it is, the value propositions are completely different. What a small boutique cult winery needs is completely different than what a large production that is nationally distributed needs. Their problems are completely different. So we do continue to expand, but it has been very interesting to learn just how unique the wine industry really is.
- [21:50]
A
Yeah. This is amazing.
- [21:51]
C
Thank you, Katarina. Kia, tell us a little about Neotempo and how you're applying AI and as they would say, drinking your own champagne or eating your own dog food.
- [22:03]
B
Yeah, I think starting our own brand and really focusing on farming first. For 10 years, we grew grapes and sold the grapes into some of the best brands in Napa Valley. Shaffer and Dariush and other brands really gave us a perspective around what does it take to make high quality grapes. And then in 2020, right before the pandemic was when we made a decision to move ahead with creating our own label and our own brand called Neo Tempo. We called it Neotempo because we wanted to be very proud to be a modern, contemporary wine brand, not afraid of applying innovation from dirt to glass. So everything in our project is different. Almost every function is data driven and technology enabled. So we're the first to use Monarch tractors for entire vintage. We're on our third vintage of having electric tractors. We did the first Autonomy demo of a tractor in Napa Valley. In the back of our vineyard we have a ecological sustainable model that is unprecedented in terms of capturing rainfall and recharging the well. We really kind of pushed sustainability and organic farming on one end, but then married it with technology innovation in terms of irrigation sensors and having kind of automated strength stress based, basically water as opposed to regular water usage. We actually cut down on our usage of water by having more intelligence directed. And in fact, in the 2022 horrible heat waves, that technology saved our vineyard and saved the vintage because we irrigated for seven hours from 4am till 11am before the peak heat came. In the afternoon, the vines were hydrated and that's one of my favorite, personal favorite vintages in a very difficult vintage for other growers. We also are unique in that we share all our experiences. I have an open source mindset, Smart Farm. AI is a website we created. We put everything, the list of all the technologies. And quite frankly, Scout was born out of that experience. We've also taken the precision concept from not just in farming, but to winemaking. I was just mentioning to Catriona. I was one of her biggest fans of what she's doing in Tastry, we've applied Tastry. We find a lot of value in really comparing our wines to our wines. One of the things we've promised our consumers is that we make wines that are unique. And when you say that, they have to be unique even across a family. So what's very interesting, I'm sure Xiao Li actually tasted, tasted two wines that came from one foot apart in a vineyard that chemically they're very different. And Catriana's software can actually show that. In Tastry, we can actually show the chemical differences, even though it's the same farming methods, same winemaker, same barrels. So we love showcasing small parcels and the power of exposing that terroir into the flavors. I couldn't agree more that the wine industry is very complex. I think with Scout, what we focused on is the number one cogs item in any winery, which is farming. And then how do we help them reduce this variability and complexity by at least being able to separate out the different parts of their vineyard and manage them differently, rather than having everything kind of go into a soup that then you're trying to accommodate different pallets and different flavors. So through the combination of AI and data, we can actually create three SKUs from one vineyard that tastes very differently, farm them organically, use precision to make them very pure and, and, and quite frankly make them also profitable. Because instead of having one wine that is lesser than the three, we have three wines, all of which can basically perform on from both the quality and price level. Other functions I use AI just, just, you know, my wife and I are the only, you know, full time employees on Neotempo. We use AI everywhere in our tasting room for translation of tasting notes to 12 different languages, all the way to music pairings, food pairing suggestions. One of the things we love is just using the concept AI to recommend ideas. I have it almost as an advisor for the business. I have it give us ideas around placement. I was in Hawaii on a trip and literally went through a long prompt to honestly look at distributors and restaurants and suggestions on restaurant components. It's incredible what you can do. As long as it knows your brand and knows your preferences, it can be really used to do a lot of good.
- [27:11]
A
Since this is a data science podcast, I'm going to go back to A little bit of nitty gritty in terms of the next step. You guys talk about collecting data and the data quality. I want to ask both of you, after you have the data, there's lots of database management, there's a data analysis. Can you speak a little bit about what do you do? Are you using traditional regression methods or you're doing deep learning or something fancier? And it's a combination of both. And how do you make sure your database is working properly when things get changed? Different versions. I want to be slightly nitty gritty, but for this audience, like in practice, how do you deal with all these seemingly mundane. But they could also change your conclusions?
- [27:59]
B
Yeah, absolutely. So we collect all of our data in a time series so that we can. First of all, we've kind of thought about this as something that over time provides a new data set. The changes. Because we're looking at the same vineyards season after season, at different times of the season. The data set that says what has changed is also very, very important and in effect becomes a new data set. We use a number of, at this point, pretty standard techniques around regression, anomaly detection. Some of the forecasting and prediction is very interesting. So, for example, in yield forecasting, we asked clients to upload their historical yields. If they have those, those become some boundaries and some hints, if you will, for the model to know whether it's in line or if there's massive discrepancies. And if there's discrepancies, there better be a reason. So we almost kind of use that as another guardrail for data quality to give us what normalized limits are. I think one of the big challenges that we have is a lot of the data that exists today and historical data are manually captured or in Excel spreadsheets. And we've now began to ask customers to just give us their historical Excel spreadsheets and using RAG and other techniques to kind of bring that data set in and store it in a more structured fashion. And then the last part that's also important on the AI side for us is that we introduced a new product called Via earlier this month. Via stands for the Vineyard Intelligent Assistant. It's effectively a LLM extension. It's a chatbot that plugs right into ChatGPT or Gemini or Claude, and it has a database of about 1,000 articles. So if you think about traditional Scout, as we're taking private data and indexing it for the vineyards VIA indexes, the public trusted reference data, that effectively would be the equivalent of a massive library on viticulture best practices and field notes. What I'm excited about, and this is on our roadmap in the future, is bringing both of those data sets together. Because then imagine a world that you're capturing these photos and the metrics and the vital signs and at the same time you're layering on top of that all the best practices that allows us to become very proactive in telling people things that we're seeing without having to wait for the user to ask us. So that's kind of a little bit of the detail. I can, you know, we're on Google Cloud, I can go into, you know, kind of the different layers of the stack. But, but we try to the degree possible use horizontal generic technology that's proven and then invest our core IP on viticulture or agriculture specific models and domains.
- [30:53]
A
Katerina, same question for you.
- [30:56]
D
Yes, and I will be speaking as a chemist and on behalf of my PhD data scientists and mathematicians. So just bear with me. So there's two things. One is there was AI or machine learning model, I should say, that had to be developed in house to work with our data. It was an invention in its own right. There was not a term for the kind of model it was, I think when we did it, I think the closest thing that exists out there today is maybe like a liquid neural net, maybe is one way to kind of think about it intuitively. But, but essentially we had to find a unique way to associate disparate data sets in a multi dimensional space, just to give you an idea. And so that's really cool piece of our secret sauce. Now there are many other data science techniques and AIs we use beyond that to extrapolate information that we gather. You know, one example is when we're trying to create a heat map of real and synthetic data on consumer preferences in the US we have to do like a Bayesian ridge regression, right. Or some Bayesian ridge statistics to extrapolate what is the distribution of pallets on a store, local, regional level. So there's all those other techniques that we have and that's kind of like a living breathing thing that has updated continuous continuously, right? Because we have new products coming out every year, new palettes coming in every month. And so you can see like the drift of preference versus products over time. But you could always go back and see what it was or try to go forward and see what it will be.
- [32:54]
C
So Katarina and Kia, this has been a great conversation so far. There's a lot of anxiety around AI and rightly so that we're hearing layoffs, we're hearing a lot of changes. A lot of the layoffs might not be related to technology or AI, but still some might. And there's people who think that AI is going to take away jobs. There's people that say that no, there might be jobs taken away, but jobs will be created as well. Others think that it's not really the jobs that will will be taken away. It's more like certain tasks within roles will start being outsourced to AI. But at the same time, there's a lot of opportunity and hope and a lot of people talking about the wonders and how AI can help solve things. What are the biggest opportunities and what are the biggest risks that you guys see that AI has? Maybe you guys can comment on that. Kia and then Katarina.
- [33:47]
B
Yeah. So the first opportunity I see is unfortunately the wine business is one of the most inefficient industries. It's a multi billion dollar industry. It's under pressure on margins. So I think without AI there'll be a lot of layoffs because it's not a well running, efficient business. And I think there's hope with AI that some of the big problems that are nobody's fault, by the way, some of the efficiency, it's not like people get out of their bed saying, hey, let's go work at an inefficient business. Let me give you an example. It's the only multibillion dollar business that I know of or industry that I know of that doesn't have a forecasting model until the fruit is literally picked very late in the season. That means every winery in the world has to make guesstimates on how many bottles and how many packages and how many corks and how many labels they need to order without having the actual data. And so if we're targeting yield forecasting at a block level to be accurate within 9%, the current rate when we survey our customers is somewhere between 20 to 30% inaccuracy. That's unimaginable. Can you imagine a car industry where you would buy a bunch of tires and not know how many cars you would manufacture and then you'd pay for a bunch of warehouses where these tires would be stored for the next season. It so I'm very optimistic around AI. I think what it will do is cut waste, cut tasks that nobody signed up for. As I, as I said before, if you're an intern and you join a winery, you didn't do that to go do manual vine counts. I think it will bring young people in. We've seen that with technology shifts. I want to remind everybody, we went from horses to tractors. Tractors did the job much faster. We went from landlines to cell phones. And again, nobody's complaining other than nostalgia about landlines. And one thing we know is we can't stop technology because in this type of world, the people will learn how to make it work. Humans will learn how to work with technology. It absolutely has negative consequences. I'm not in the dial on that, but I think if we're pragmatic and we don't fear monger, and we don't make it sound like, you know, everything's fine without technology, we will get through it. And maybe on the other side, what we will have is a more efficient, better performing industry with a workforce that is trained on data and knowledge. And we let the AI do the things that, quite frankly, humans are not great at, which is computational models that are four dimensional. I mean, the reason at the beginning of this podcast we talked about wine is wine is one of the most complex subjects that exist. There's so many variabilities. You know, if you look at the cardinality of all the things that can contribute to what ultimately is in your taste, it goes from soil to biological matter. Rootstocks, clones, you know, weather, temperature, picking time, you know, bricks, you know, phenology, chemistry. So I think AI is phenomenal for industries that were underserved by old tech. None of my clients have a large research budget and internal developers and data scientists that are sitting around building apps, so they can kind of almost skip that entire generation of tech that required expensive developers and expensive data scientists, and maybe adopt technologies that were built on AI, that are smarter, that they give the end experience that they need without having to spend millions of dollars building this in house.
- [37:39]
D
I 100% agree with everything Kia just said. So I would speak from my personal experience. You know, trying to sell a technology company into the wine industry took a lot of annealing the market, if you will. And when we first launched, the number one kind of joking kind of feedback I would get is winemakers would say, oh, so you're going to replace my job? Hahaha. And it's ironic because it actually turned out to be the opposite. It actually saved a lot of jobs because the reason they took a chance to use us initially was to solve emergencies and disasters that were happening in the production process. Like when this fires, we had the crazy smoke tank season and we had to figure out how to ameliorate tens of millions of dollars of crops or wine or you were going to lose it. We were able to work with the teams to solve those emergencies that would not have manually been possible to do fast enough the way it's traditionally done. So that's actually how we got our foot in the door, is solving problems the winemakers just didn't have the time or resources to do. And that established a lot of trust. And I think once they were able to get their hands on the platform, you could see that it's really the human that is guiding and in charge of the AI. It is, it's your slave, not the other way around. You still need context, like you still need inputs. And it's really like a paintbrush for the artist to be able to evaluate options more efficiently. And I just don't see for AI like this, I don't see the future doesn't have a human in the loop. I don't see that getting adopted anytime soon. It's really not AI that's replacing humans, honestly. I see it in data science and software development and design. It's really humans using AI that are replacing humans.
- [39:50]
C
Yeah, that's great. Thank you, Katarina Choli.
- [39:53]
A
Clearly this conversation can go on for a long time. And Kiya, you're absolutely correct. That's a really main reason that we choose wine as a feature topic for Harvard Data Science Review. Other than. Of course I love wine, but there are so many factors. We haven't even talked about all the other factors, how the wine was served, how long it was decanted, who are you drinking with. All these factors all affect your enjoyment. But we do need to wrap up this time because it's getting to a time I'd like to have a glass. So I like to complete this wonderful podcast, but we always complete with a magic wand question. The magic wand question for both of you is that if you can wave your magic wand, what is the new AI technology you like to have for what you do?
- [40:43]
D
For a tastry?
- [40:45]
A
For testing. Yeah, for anything you do. Like, what would be something you don't have but you wish you have?
- [40:51]
D
I would love to expand into other verticals and have the resources to do that. So I would love to move into the other categories that are interesting to consumers. Right. There's so many other. You know, I know cannabis or CBD beverages are of interest. Non alcoholic beverages, the ready to drink category is exploding and I'd like to see how that correlates to those particular consumers wine preferences and how you can guide them down a chain of different products. So more data. More data in different verticals is going to have to be my answer.
- [41:33]
A
That's absolutely important. But what would be the new AI technology you would need for doing that, or the current. The existing ones can serve your purpose?
- [41:43]
D
Well, it would be. I'm still speaking a little bit as a scientist, but the aspiration for what the new technology would be was to start correlating one single consumer's preferences across many other verticals. And what I mean by that is, if I know that you like these oaky, buttery chardonnays, for example, does that mean that I can predict that you're going to like the butter pecan ice cream on the shelf, which is a completely different product?
- [42:15]
A
Thank you, Kia.
- [42:18]
B
Yeah. So I would say if I could have a magic wand and had the data set that we're building, I would love to be able to get to a forecasting model that could combine forecasting and what we see on the ground with weather. And this is something we've been looking at very closely, so it might not be obvious, but grapes shrink during heat, unlike apples and oranges, and that has a massive impact in terms of weight. So you could have a heat wave that you lose 20% of your water weight. And being able to marry these two very interesting time series data that potentially happens in the future and being able to build some calibration between them so that there's a correlation. We know, for example, that at 20 bricks, grape shrink, that's the maximum peak. And then they start going down certain varietals. Again, a lot of this is research, but in the world of AI, you take that raw basic principles and you start building models and ways that you can calibrate it. Because we don't know what the future holds. We know what the past holds. You know, one of my favorite sayings is taking hindsight and turning into foresight. And so we love looking back at the past as a indication of the future. In fact, one of the exciting new projects that my co founder, CTO Mason's working on is this notion that, like, literally on every day of a growing season, he's narrowing down, what are the scenarios, what are the previous vintages that were at? And then when you get to the latest part of the season, either it's a new season on its own, or you've reduced the variation down to the scenario. So it's kind of an interesting way of even thinking about AI, not just in having an answer today, but becoming a much better forecaster of what could happen. And that would be something that if we had that today with the data underneath it, it would make a huge difference.
- [44:29]
A
Well, thank you so much. That was just wonderful. And I want to thank all three of you for your great contribution. And thank you so much. Thank you.
- [44:39]
B
Thank you so much.
- [44:40]
D
Thank you.
- [44:45]
A
Thank you for listening to this month's episode of the Harvard Data Science Review podcast. If you are interested in the vine to Mind, next year's conference will be at UC Davis from May 18th to 21st. Please look out the website vine-mind.org for future announcement. To stay updated with all things HDSR, you can visit our website at hdsr.mitpress mit.com or follow us on Twitter or Instagram DSR A special thanks to our executive producer Rebecca McLeod and producer Tina Toby Mac. If you like this episode, please leave us review on Spotify, Apple or wherever you get your podcasts. This has been the Harvard Data Science Review Everything Data Science and Data Science for everyone.