Pod Insights The Vergecast Teaching AI to taste and smell could help the future of product design About this episode For the next four Tuesdays, Verge senior reporter Ashley Carman will explore how artificial intelligence and machine learning are shaping the future of a variety of industries.
In this episode, Ashley explores how AI can be used for product design, but more specifically, for creating fragrances and flavors.
Guests include founder of ScentTronix Fredrick Duerinck, computer scientist at Cold Spring Harbor Laboratory Saket Navalkha, and Michael Spranger from Sony AI
This podcast was made by producer Liam James, senior audio director Andru Marino, senior reporter James Vincent, and senior reporter Ashley Carman.
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Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 This episode is brought to you by On Investing, an original podcast from Charles Schwab. Each week, hosts Lizanne Saunders, Schwab's Chief Investment Strategist, and Colin Martin, Head of Fixed Income Research and Strategy for the Schwab Center for Financial Research, bring you fresh insights on what's happening in the markets and why, and what the implications might be for your portfolio. Join Colin and Lizanne as they explore questions like, how do you evaluate corporate bonds that look interesting? And what sectors are on the move right now? Download the latest episode and subscribe at schwab.com/oninvesting, or wherever you get your podcasts.
0:45 Nilay Patel? Hey Vergecast listeners, it's Neelai. For the past few Tuesdays, we've been running a mini-series we made about the many uses of artificial intelligence and machine learning all over the place. It's been hosted by Verge senior reporter Ashley Carmen. Today's the last episode. Hey Ashley, how's it going?
0:59 Speaker 02 It's a sad day, last episode day.
1:01 Nilay Patel? But like a happy day, because our AI future is here.
1:04 Speaker 02 It's true, we figured it out.
1:06 Nilay Patel? So we've done voice synthesis, AI in video, robots writing books with authors in text. What's this week's episode?
1:14 Speaker 02 Today, we are talking about how AI could help us create new flavors and new smells.
1:20 Nilay Patel? That's, this is like, it's like over, now we're like through the looking glass AI taste. What's an AI taste?
1:27 Speaker 02 Well, I haven't tasted an AI taste, Neelai. This is all, you know, in the works. This is all happening as we speak.
1:32 Nilay Patel? I see.
1:33 Speaker 02 So basically, like, I thought, this is crazy, like, AI can't smell. But then I realized I just have to think of like, my nose and my little nose hairs as sensors. And then it all made sense. I was like, oh, I'm a computer.
1:45 Nilay Patel? Very deep, very existential.
1:47 Speaker 02 And my brain is running an algorithm. This is why you have to start thinking about these things, when you think about smells and tastes. It's like, oh, we're just running the algorithm.
1:55 Nilay Patel? I like what's happened over the past four episodes here, is you've started to consider yourself more and more as an AI.
2:00 Speaker 02 It's true, I'm replaceable.
2:02 Nilay Patel? It's been an interesting month. All right. Check out the rest of the episodes if you haven't listened already. They're all very good. But here's episode four, the Vergecast AI series. Enjoy.
2:17 Speaker 02 When I cook a meal, I typically take stock of what's in my fridge and either Google a potential recipe or wing it. I'm definitely not doing anything fancy to figure out what could maximize my meal or flavor. But I'm a mere human, and if multiple tech companies have their way, that could change in the future with AI. In today's episode, we're talking about AI and how it relates to product design. But more specifically, how people create fragrances and flavors, smells and tastes. It sounds wild, I know, because, well, at least as far as I know, AI isn't capable of smelling or tasting. But that's what we're going to investigate today. Can AI personalize fragrances, concoct new recipe ideas, and maybe even develop a flavor
3:01 Speaker 02 we've never tasted before?
3:06 Speaker 03 It's extremely complex what we're... It's insanely complex what we're trying to do.
3:10 Speaker 02 That's Frederick Dunrick. He's the founder of a company called Scenttronics that's part software company, part hardware. Through their algorithmic perfumery platform, he and his team are trying to make the process of developing a personalized scent as easy as answering a few questions and pressing a button.
3:26 Speaker 03 Algorithmic perfumery is, on one hand, it's a machine. And it's, on the other hand, it's a very big software piece. And how it works is that people go to a website and then they fill in a questionnaire. And the questionnaire can be partly psychology questions or physiological questions or cultural background questions. And then based on the input they gave, the answers they gave, a unique perfume formula is created by the AI. And then people get a code, and then they can go to the machine, and then they fill in the code. And then the miracle of creation is happening, and then the bottle travels, and then the unique perfume is created for them.
4:01 Speaker 02 The questions might be as predictable as something like, "What scents do you already like?" Or more probing, like, "How do you typically dress? What you do for work? And even where you grew up?" Frederick says this is all essential data to build a scent people like.
4:13 Speaker 03 What is important to understand on scent that it is very cultural sensitive. So it means that the context you are in as a person and what you've smelled very often before is often a thing you're very accustomed to. Like, for instance, if you're from a different region, for instance, in what we have seen, because we have done tests in art festivals in Asia, we see that there are people, for instance, they don't like woody stuff. They really don't like woody stuff. So whenever the AI concocts something with X amount of percentage or over X amount of percentage of wood or woody ingredients, basically, people, they don't like it.
4:52 Speaker 02 As Frederick mentions, he and the team are not only trying to code software and algorithms that can accurately produce a scent you'd enjoy, but also pair it with a machine that mixes that smell on demand. They're building their dataset from scratch, meaning that whenever they create a smell, they ask for feedback on it, all in the hopes of improving their software.
5:12 Speaker 02 At this point, you, like me, might be wondering, why do we need AI to do this? Frederick says it's a highly complex field and one with many possible combinations that only machine learning could handle.
5:25 Speaker 03 We have three machines, actually. One machine has 38 accords. An accord consists of between 5 and 20 ingredients.
5:34 Speaker 02 I'm going to interrupt Frederick for a second and let you know that these accords he's referencing are scents made up of several perfume notes or ingredients.
5:42 Speaker 03 They are like mini compositions, mini building blocks where you can work with. And then a perfume typically is built out of 1,000 parts. So you have 1,000 small drops make a perfume. So then you can start understanding that the amount of possibilities that we have is spectacular. Like, it's really gigantic, what we can create and the variety we can create.
6:06 Speaker 02 Every person receives three scents from the machine, and Frederic admits they're not always a perfect match.
6:12 Speaker 03 Once every X amount of time, one of the three fragrances that you get is a random perfume. So basically, that sets our benchmark, because basically it tells us, okay, if we would give out random scents, how much would people like it then? And how much is contributed by the story? Or how much is contributed by the setting of the machine? Or how people interact with it online? You know, we really validate that effect. You know, sometimes it's super bad. We almost don't beat random. But right now, in the last, I would say, two quarters of a year, it varies between, you know, we go above 12% difference, or sometimes even to 20% difference. So that's significant. And then basically, we continue to optimize like with the best performing algorithm.
6:57 Speaker 03 So yeah, no, we are making progress.
6:59 Speaker 02 But successfully making these on-demand personalized scents is just part one of Centronix's loftier goal.
7:05 Speaker 03 The ultimate goal that we have is, we really want to give you something that allows you to feel in a certain way that you want. So for instance, if you would say, hey, you know, I had a bad day and I want to relax a little bit, can you give me something that really physiologically helps you to relax? So that's an extremely complex, because then you need to understand like, okay, what is your physiological response? But as well, what do you perceive? And if you say, relax, we need to interpret that. So there is basically a lot of parameters to be able to do that one. The other thing is, we want to be able to give this to as many people as possible. And we want it to be unique. So I really want to give you something which is uniquely yours. And bless them,
7:50 Speaker 03 there are a lot of companies that you do a questionnaire with, and then in the end you get a scent. But then those scents are not really customized. They're basically, you know, standard-made scents, and they're not uniquely yours. And so for us, it was really the thing, no, I want to give you something which is really for you, uniquely made for you.
8:08 Speaker 02 And this gets extra complicated, Frederick says, because of those cultural associations he mentioned earlier. Just because mint is relaxing to me doesn't mean it's relaxing to you. Instead, a key component to measuring how you like a scent is monitoring your physiological response. Of course, Frederick is thinking about that, too.
8:28 Speaker 03 We're setting up a research project which is really about, okay, what physiological response do you get? So then we're going to measure, for instance, amongst others is your arousal state, like your skin conductancy. What is your heart rate doing? What is your respiration pace doing? And then you really can go to the physiological level. Like, because that's the beauty of scent. Like scent, it's processed in the oldest part of our brain. It's extremely powerful. You know, it can get you in your flight or fight mode without you being able to control it. Like, if you would smell a little bit, the smell of burning plastic, but subconsciously, just below your awareness level, you could already get, you know, more sweaty hands. Your heart rate could go up.
9:09 Speaker 02 That physiological response is going to be key to getting us to Frederick's dream world.
9:14 Speaker 03 If you go further down the line, we're working on a very small device that basically you can wear, that you can connect to your phone. And basically, if your smartwatch detects, like, hey, you're stressed, it can give you a small puff. Because basically, you know, you kind of optimize it in such a way that you're able to control your mood a little bit. So that's the end goal. But to get there, there are so many hurdles that we need to take. And we've only taken, I would say, you know, a very small piece of it.
9:42 Speaker 02 That certainly sounds ambitious and likely a long way off. But once he and the team understand ingredients and the combinations people like, along with why they like them, they can start moving towards this hyper-personalized, AI-driven scent world.
9:59 Speaker 02 This is where I started to wonder if the AI itself could actually perceive a smell like humans do and make a judgment about it. So that's why I called Sackett Navalka.
10:09 Speaker 04 I'm a computer scientist working at the Simon Center for Quantitative Biology at Cold Spring Harbor Laboratory. And I'm interested generally in this field called algorithms in nature, which is this idea that biological systems have evolved interesting solutions to solve what are, you know, fundamentally engineering and computational problems and that there's a lot we can learn from studying these types of systems.
10:33 Speaker 02 Sackett studied fruit flies specifically to try and learn how they process smells and use that research to develop new algorithms.
10:42 Speaker 04 So the idea was how do fruit flies perform similarity searches? Okay, so the idea is that a fly, let's say, learns that odor A is something good and odor B is something bad and to be avoided. But the next time the fly experiences odor A, it's not going to experience it in the exact same way. It's going to experience, let's say, odor A' just because of noise and, you know, other things that are happening in the environment. But somehow, when it experiences this odor A', it needs to realize that A and A' are similar enough to each other that it should go and maybe, you know, eat this thing that it's smelling. Okay, so how is it doing that? How is it like finding these similarity relationships between odors?
11:24 Speaker 04 And then this is a problem that computer scientists face all the time.
11:28 Speaker 02 To be clear, Sackett is not necessarily pursuing this research in order to help computers smell or to improve the state of the art in AI product design. Really, he's looking for ways to make searching data sets more computationally efficient, to allow systems to quickly sort and compare information, the same way that fruit flies can quickly sort and compare different odors in nature.
11:51 Speaker 04 So for example, you know, when you go to Amazon, you'll be buying a product, and they'll suggest similar products to that that you might also be interested in. Or, you know, on YouTube, you'll be watching a video, and they'll have this sidebar of similar videos to the one you're watching. So this basic idea of, you know, you have this huge database of things that you've seen in the past, songs that you've heard, videos that you've seen, and then you get this query from the environment in real time, and you have to quickly determine, you know, what is similar to this that I've seen before, so that I can now know, you know, how to behave in response. It's really a basic computational problem that comes up in, you know, a lot of cases.
12:30 Speaker 02 This could help create a bridge between the world of organic senses and the digital world of data processing, including AI. If you understand how brains, even fruit fly's brains, react to smells, you can maybe help a computer understand that response.
12:45 Speaker 04 So now you have this smell. How is a person going to respond to that smell? Or how is it going to be perceived? What is it going to be perceived as similar to? Because it's not just chemical structure that tells you how similar two odors are. You can add a carbon atom to a molecule, and it'll be completely different, a completely different smell. So, understanding the relationships between these and predicting the relationships, I think, is an important way to get at this question of how it's going to be perceived. And I think that these kinds of algorithms that we're developing are going to be useful for that kind of classification.
13:20 Speaker 02 These algorithms, Saket says, could have other use cases as well. For example, they could help pick up on very faint scents in high-stakes scenarios, like in airports, where scent can be used to detect security threats. But software is only one part of the problem. And this is what Frederick was getting at, too. We need to solve for not just creating the scent, but knowing how people will process it, and then determining whether they'll like it. Maybe the way to do this is by building an electronic nose.
13:50 Speaker 04 I think there's actually two aspects to actually making a successful product like this. So one is getting good sensors by themselves. So that's, you know, having, you know, in our nose, we have receptor neurons that bind to different types of chemicals and they're really good at sort of sampling from the space of all possible molecules or odor compounds. And so if you don't have a good early representation or a good sensory measurements of what's going on, I mean, no matter what your algorithm is downstream, you're pretty much screwed. So I think there's sort of two classes of problems that people are thinking about. One is how do we develop good sensors to sort of sample from the olfactory space? And then two is, okay, now that we have good sensors and a good representation,
14:35 Speaker 04 how do we then, you know, do the classification, the novelty detection, the background subtraction, and, you know, all the other more complicated things downstream? So we're sort of more focused on the second problem. The first is, you know, lots of other people are working on that.
14:50 Speaker 02 As Sackett points out, if computers are going to start processing data about the sensory world, they're going to need to have actual sensors that can take in information. Basically, if you're using AI to design new scents and flavors, it needs to be able to accurately detect how things smell and taste.
15:08 Speaker 02 That's where our next guest from Sony comes in.
15:10 Speaker 05 So my name is Michael Springer. I work for Sony AI. I'm based here in Tokyo, Japan. My background is in AI research. And one of the key things I think is missing or that we don't understand well enough yet is this question of human creativity and how we can build AI systems that help in our endeavors for human creativity.
15:29 Speaker 02 The company is indeed working on its electronic nose, or E nose, and also E taste.
15:35 Speaker 05 It's almost like image sensing, right? So you're trying to get digital representations of food related to how the food might taste and smell, but that's of course still different than human perception. And so then you get into this issue of essentially trying out different things, measuring how people respond to food. And there is sort of different initiatives in this direction. So obviously, large food companies have a bit of commercial interest in understanding how people might react to their products or their potential products that they're developing. So there's lots of data in that direction that typically resides in large food companies.
16:07 Speaker 02 But separately from this initiative, Sony is also working on AI that might be able to assist in recipe creation, particularly for high-level chefs.
16:16 Speaker 05 We had really interesting conversations about their view, the chef's view, on technology and the potential for AI to solve and impact some of their processes and the problems that they might have in recipe creation. And more often than not, some of the answers we're getting is really in this direction of, "Oh, I'd really love to know more about where certain ingredients are coming from, or I'd love to know more about the seasonality of ingredients, or I'd love to know more about the health aspects and sustainability aspects." And I think that's really, that goes to the core of why I think AI is important in this field is, really, recipe creation or any kind of creative endeavor, I think it's really a process of constraint optimization.
16:57 Speaker 02 As with the use of AI in fragrances, the idea is that these people are working with extremely complex data sets, whether for smell or taste, that machine learning sorts through to find hidden relationships. In this case, that might mean finding the connection between the composition of a dish and the response of the person who eats it. Do they like this dish?
17:17 Speaker 05 Very, very quickly, the problem becomes very, very complex with different sources of information, continuous measurements of how food impacts human perception, all the way to the seasonality, these large signals and changes that impact food and the performance of food. But also the sustainability aspects, like when is it good to harvest certain food? All the way down to how we create food for consumption, more broadly speaking, like how do we control the agriculture of the food in order to produce it and get the best possible performance essentially out of food as an optimization problem.
17:57 Speaker 02 Recipe creation in cooking is a situation Michael sees as ripe for AI.
18:02 Speaker 05 A lot of the information that you find in recipes is really implicit, and recipe descriptions are very coarse. But somehow, to us, they make this enormous amount of sense, and it's like, we can almost visualize the steps that are necessary to take in order to fulfill certain recipes. But if you look at the actual information that's transported in recipes, like literally what it says, like what are the lines and what are the things that are not being said? And you, as a person, while you're cooking, you're filling in some of this detail. And so I think that's a really interesting challenge for computers, or more broadly speaking, also for AI, of course.
18:37 Speaker 02 None of the Sony technologies in public use yet, so we haven't tried an AI-created dish, unfortunately. But Michael says that they might have more to say about this soon. Ultimately, though, this entire conversation leads us back to a question we asked in prior episodes of the show. What role does AI play? For food, will it design recipes entirely or inspire them? Is AI writing the menu, or is it just the sous chef?
19:04 Speaker 05 I think AI is going to play a role in prediction. And I think that's maybe more important in gastronomy space than it is in the image space, because of course, in the image space, you can just look at an image. But in the gastronomy space, right? If you have a recipe, what you really have is sort of a textual representation of a potential taste. But that doesn't mean that you, by looking at the recipe, you can feel it on your tongue, or you can envision the smell that this thing is going to make. And so you're going to have to actually go through the process of making the dish and then trying it out. And my vision for the future would be, my hope, my aspiration would be that AI can help in some of these processes by predicting what a specific recipe might look like, or whether it fits a specific creative vision.
19:51 Speaker 02 That's a lot to take in, and to be honest, I'm hungry now. So we're going to take a break, and when we get back, James Vincent, The Verge's AI and machine learning senior reporter, will be here. We're going to chat all things flavor, taste, smell, and AI, plus a little more on product design. We'll be right back.
20:18 Speaker 00 This episode is brought to you by On Investing, an original podcast from Charles Schwab. Each week, hosts Lizanne Saunders, Schwab's chief investment strategist, and Colin Martin, head of fixed income research and strategy for the Schwab Center for Financial Research, bring you fresh insights on what's happening in the markets and why, and what the implications might be for your portfolio. Join Colin and Lizanne as they explore questions like, how do you evaluate corporate bonds that look interesting? And what sectors are on the move right now? Download the latest episode and subscribe at schwab.com/oninvesting, or wherever you get your podcasts.
21:07 Speaker 02 All right, we are back and with us is James Vincent, The Verge's senior reporter who specializes in AI and machine learning. Hello, James.
21:16 Speaker 06 Hello, Ashley. It is lovely to be back again and chatting machine learning with you. How are you doing today?
21:22 Speaker 02 I am great.
21:24 Speaker 06 Good.
21:24 Speaker 02 So you've heard throughout this episode, we're talking about product design as it relates to flavors and smells. And as I always say, I need to get your take. What are your thoughts here? What are your impressions?
21:37 Speaker 06 This is a really interesting topic for me, and I feel like there's something about it which is, it's kind of difficult to talk about, right? I've written about some of these sorts of creative endeavors in the past. And the thing is that they are inherently less, I don't know, measurable, shall we say, right? If we're talking about what text can an AI output that a human can't, you can just type that in there, you see what it comes up with, and you can judge that. But when you're integrating AI into these sorts of product design processes, I'm a little bit more skeptical because I think it's actually very hard to tell what contribution the machine is making and whether that contribution is necessary. Now, I don't want to come across as too skeptical, and it's been really interesting
22:22 Speaker 06 to listen to, you know, these people talk about how they're using it. But I do worry that sometimes AI is used as a bit of a marketing flourish in these areas. And it's saying, oh, we had to use machine learning because it's just so incredibly complex. Whereas I feel like, well, incredible complexity is what humans are used to dealing with when it comes to sensations and tastes and smells and sights and this more aesthetic realm. I wondered, you know, how did you feel about talking to these people? Were you convinced that AI was a necessary part of the equation?
22:56 Speaker 02 You know, it was tricky because in some ways, I felt like, okay, I've never made a perfume, for example. There is probably so much that goes into it that I'm just deeply not aware of. So in that way, I was like, I'm gonna have to take your word for it. This does seem complex. But at the same time, like you were kind of mentioning, it was hard for me to fully grasp why we needed AI to do these things. Like, for example, Talking about having a puff of a certain smell delivered to us based on the readings happening in our wearable, like that sounds cool, it sounds cool, no doubt about it. But I also think of like my mom, who has a diffuser that I bought her and is like, "Today I'm in the mood for lavender."
23:35 Speaker 02 So in that way, it is kind of like, I don't know if this is for me, a problem that needs to be solved necessarily. But you're right, that it is just harder to like qualify because with our previous episodes, you can see the work being removed from video editing and things like that. You can, you can hear our voice clones, you can read the text and also hear it in this form, hear the text. And you're like, okay, that's really interesting. I cannot believe a computer did this.
24:05 Speaker 06 Yeah.
24:05 Speaker 02 Whereas here, you're like, I don't know.
24:07 Speaker 06 Did a computer do this?
24:09 Speaker 02 Yeah.
24:10 Speaker 06 Yeah, I don't want to, as I say, I don't want to express too much skepticism about this because I, I do think that there is potential for AI to be used in these complex data sets. Like what AI is good at is finding hidden relationships, it's finding connections, as you said, within these vast data sets. And I feel that there definitely is a place where that can be used. And I think AI can be really useful at sort of personalizing stuff to you and trying to create something that is unique. So I think what AI does, it creates abundance, right? And then it is up to humans to try and choose what is the best of its output. And I think that's sort of like what we talked about in the text episode, right? Is that you get these text generation machines and part of the good thing is that they can
24:55 Speaker 06 come out with a ton of content, but you do, do still need a human element to winnow that down. One thing that I've sort of tested that is similar to this is an app called Endel, E-N-D-E-L, which creates these sorts of infinite loops of music. And they're supposed to be used for working or for working out or for going to sleep or for helping you focus. They're, they're sort of generated on the fly. It's not pre-recorded stuff. And they're supposed to personalize it to your location, the time of day, and maybe if you want to add in some information about how you're feeling, they'll sort of tailor To that now, the degree of tailoring, I think, is probably really simple. I think it's probably just a lot of if this then that functions, which is not really machine learning.
25:40 Speaker 06 It's just basic programming. But the abundance that the thing can come up with is a factor of machine learning, I think. And I think there is something really appealing about this idea of having something that is unique and personal to you. It may not be tailored exactly to your needs. It may not be like this perfect match of your desires, but it is still unique. I think that's kind of cool.
26:00 Speaker 02 Do you think, and this obviously came up a lot in the episode, we talked a lot about just the fact that there's this whole part of trying to personalize things that maybe we can't even access, which is this data around how we physically respond to things.
26:14 Speaker 06 Yeah.
26:14 Speaker 02 Specifically in the sense world, but maybe also in other areas as well. I just wonder if you think that this sort of personalized future is a far ways off until we have Apple Watches that can like do more intrusive, I guess, monitoring of our like blood levels or anything else. I have no clue, but like, yeah, if you just think that that part of the technology needs to catch up before we enter sort of the true world where AI can really help personalize everything.
26:42 Speaker 06 Yeah, I think that's a fascinating question because I think that's like a sort of a core question of the digital age, which is what can we capture in digital systems? And can we capture every facet of human experience and emotion? Because I think if we're talking about smells and tastes and those sorts of aesthetic experiences, they are just, they're really hard to pin down. They're hard to describe if you're the one experiencing them. And I am a little bit skeptical about this stuff. I think there is knowledge in the world that is hard to digitize, to capture, to record, however you want to term it. And I think there's always going to, not always, but I think there is for the foreseeable future,
27:24 Speaker 06 going to be a limit in how well computers can interpret this stuff. And this is why I mentioned confidence earlier, because I think it's, a lot of it is about confidence. If you think that the machine is clever enough to read your mind, then you sort of let it. Do you ever feel like this yourself? Like when you, you're getting something delivered that's tailored to you. I don't know what it might be. Maybe it's like a workout or something like this, or clothes that are being recommended to you. Do you ever feel that actually, wow, the algorithm saw through to my, what I needed in this moment? Or do you feel that it's just getting lucky?
27:54 Speaker 02 Well, I think the only place I can think of where I really see this is less in physical goods, but more algorithms tailored to me, like my Instagram feed.
28:03 Speaker 06 Oh, of course, yeah.
28:04 Speaker 02 And in some ways, that is, I guess, a physical good because I'm thinking of the shopping feature where I'm just like, why are these stores not in real life? Everything is so good. And so I guess I'm predictable. I mean, to some extent, that's more of like aesthetic versus sort of the senses and things like that, but.
28:23 Speaker 06 Very similar. It's the same realm, right? It's taste.
28:25 Speaker 02 Yeah, I can imagine also, though, if I shopped at a specific candle company and I was buying a specific thing all the time, they might be able to be like, you know, we see you really like this. Try this. We think you might like this one, too.
28:38 Speaker 06 Yeah.
28:38 Speaker 02 So I feel like I can't, it can be in that way. I can be seen.
28:42 Speaker 06 Yeah, I, I, I feel that's a really interesting comparison, the shopping and the products, because like, that is something that like, is very personal, but it also follows these macro trends that can be mapped in an easier way. And for me, that is more legible as data than what I enjoyed at a meal in the restaurant. I feel that comes down to what smell I like. Like, the smells you like are so damn personal. Like, what smells you associate with, you know, people you love, you know, with your parents, with loved ones in your life? Like, is it because you like the smell? Or is it because you like the person? Like, I, I, I don't know. I do feel that there's this realm that I'm skeptical about AI being able to map fully
29:24 Speaker 06 because that, yeah, it's based on those interpersonal experiences.
29:28 Speaker 02 We're just complicated beings.
29:29 Speaker 06 We are complicated beings.
29:32 Speaker 02 We have a lot of baggage we carry around with us. The AI will never know our baggage.
29:37 Speaker 06 Yeah, machines aren't always going to be able to help us deal with this stuff.
29:41 Speaker 02 I like this as a place to sort of end on because I feel like that was the point of this series, was looking at how AI intersects with these various industries and where we see them in use now.
29:52 Speaker 06 Yeah.
29:52 Speaker 02 And we've seen where they're in use now, but it sounds like to me, the broader conclusion we've come to is that, one, maybe we're replaceable at some point, but this human touch is kind of really needed right now. And these AI use cases are putting AI more in a position to be an inspirer, an A, assistant, things like that, where it's not fully driving the car, if that makes sense.
30:14 Speaker 06 Yeah, I know. I, I, I think that's completely right. I, I, I feel that like. We often get quite scared about the, what we see as the intrusion of these non-human systems into what we think of as human domains, whether that is writing, whether that is cooking. And we get scared even when it's only a little bit of our territory that's being impinged on. And it's just, it's just novelty, you know. And I, I don't think that machines are gonna end up destroying the ability of, of humans to be independent, creative entities in the world. I don't think that's how, I don't think that's how these things work. I think it, it just seems like a bigger danger than it is because it's so new and it's unusual to us. I mean, you've been, you've been doing all these interviews. Do
30:57 Speaker 06 you feel saddened by the rise of the machines in the creative industries? You don't sound like you do. You sound like you feel maybe reassured.
31:06 Speaker 02 Yeah, I was gonna say, wow, I'm so happy we are getting to this point where I'm like, wait, I'm not anxious for once. But what I will also say is, going into this somewhat blind and really learning about AI through this podcast series, I'm coming out of it being like, wow, like AI actually does a lot more than I thought, simultaneously does a lot more than I thought, but also a lot less. Which I feel like is a really good place to be. Like, okay, it could do some really cool stuff.
31:32 Speaker 06 Yeah, I, I always get to that point in my reporting where like, half of me is thinking, these machines, they're really up to some interesting stuff here. And the other part of me is thinking, these machines are so dumb. Yeah, you, you just, you flip between those two extremes because it's new territory and you're trying to work out what the context is. And it's always very difficult. So yeah, no, I, I'm completely with you on that.
31:54 Speaker 02 And it'll be interesting if in three or four years, maybe even less, but I'm thinking three or four years, we did the same series and checked in where things were then. I imagine it's going to be wildly different.
32:05 Speaker 06 Yeah, yeah, yeah. I feel like a lot of our conversations about where the future is gonna go for this, there has been always loads to talk about and loads of like, loads of possibilities. So yeah, at three or four years from now, we'll be seeing even more. I have no doubt about it.
32:18 Speaker 02 I'll ping you on Slack for our reunion special.
32:20 Speaker 06 Let's put it in the Gcal now. Let's, let's get a date. We'll, we'll, we'll schedule it out now. Exactly. Surely there's an AI assistant that can help us do that.
32:28 Speaker 02 Probably.
32:30 Speaker 02 Awesome. Well, thank you so much, James. This was amazing. I'm beyond happy we got to collaborate on this show. You're the best. I hope everyone enjoyed hearing from James. And if you're listening, follow his work on theverge.com. Find his Twitter. What's your Twitter, James?
32:44 Speaker 06 My Twitter is JJVincent. And thank you so much for having me, Ashley. It's been, yeah, it's been amazing fun to work with you, to talk with you. Really enjoyed it.
33:00 Speaker 02 Thanks again for listening to this Vergecast AI mini-series. This podcast is made by producer Liam James, senior audio director Andru Marino, senior reporter James Vincent, and me, senior reporter Ashley Carmen. Talk soon.
33:22 Speaker 07 Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing in a wearable. So your vitals app, now with heart rate variability, can tell you when something is off. And your readiness score can let you know when to rest and when to push. Hear the story in every heartbeat with Apple Watch Series 12. The features described are for wellness purposes only and not for medical use. iPhone 11 or later required. Based on Apple Conducted Study of Heart Rate Accuracy, August 2026. Visit apple.com slash Apple Watch Series 12.
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