How AI Learns to Smell with Alex Wiltschko - #771

23:13Sam Charrington:And when you're in this part of the process where you're manufacturing these molecules, like, is there, are there known toxicity screens that you can? Oh, yeah. You have to go through a very rigorous process in Europe and the U.S. and worldwide. And there's a binder of tests you have to submit. And they're really thorough and they're the right test, right? So, you know, is this safe on your skin? Is it safe to breathe in? Is it safe for your eyes? Is it safe for fish? Because you might flush some of it down the toilet or in the shower drain right after you wash yourself with a shampoo. The question that I'm curious about is, can you derive that from molecular structure or do you have to do it empirically?

23:55You can predict it, which is super important for how we're so efficient in what we do. But then you have to test it physically. It's just the law. And it's the right thing to do. Like you just check, right? Do the experiment. And we do over and over for all the products that we're taking through regulatory review.

24:13Sam Charrington:This is maybe a digression from the kind of technical conversation I want to have is like the business side of this thing. It's like, are you validating that, you know, the scent is what the client wants and then you license it to them and they find someone to manufacture it? Are you like making the sense at scale or like? Where we do most of our business is we actually blend molecules and ingredients that already exist and are already approved. And so like if somebody comes to us and they want to launch an air freshener or a shampoo or a fine fragrance, we need to, in order to get that out quickly, we need to be able to blend molecules together that we can get.

24:50And we actually stock many of these ingredients. And we've taught olfactory intelligence, which is really a fleet of different models, how to convert like a customer's request. Like, I want a scent that smells really fresh and clean and, you know, is going to be useful or liked by Gen Z men. Like, that's enough of a specification to kind of begin to make a scent against that. And then we have master perfumers and perfumers.

25:15Sam Charrington:Two-thirds acts. Just mix them together. I mean, you're not too far from the truth. Like, generally new scents are like existing scents, right? Because, you know, scent is art and, like, art evolves. It doesn't, like, take these crazy jumps, right? So you can always tell what the lineage of a scent is. And like, yeah, Axe is one of those, right? Axe is actually really famous because it was the first time that fine fragrance perfumes were actually brought to that mass market price point. So some of the scents in Axe body spray had never been like accessible to the average person because those...

25:50Sam Charrington:I never associated Axe with fine anything. So yeah, exactly. It's like it's built its own rep over the years. But like the way those scents were actually chosen in design were super interesting from like a marketing and kind of scent development perspective. But we've taught OI to collaborate with perfumers and also just work directly with our customers where you basically prompt it. And you're like, hey, I'd like to design a scent. Our perfumers use it as inspiration and our customers sometimes design their own scents for themselves. Right. In fact, I had a conversation earlier today where I sent somebody an OI design scent and they loved it.

26:21They picked it. They're going to launch it. Right. like just right out of like our software system. So you can think of that as like a Canva, right? Or a Figma for CENT where, you know, you and me can make CENT that wins in the market. And so that's what we've put it all together with.

26:37Sam Charrington:So the foundation of this is the, you know, this embedding space that you've framed. And I'm envisioning the task that you described as being akin a little bit to the Word2Vec embedding space math, like king minus, I always get the formulation wrong. Yeah, it's like king minus man is equal to queen minus woman or something. There's arithmetic, right, in the text space. Yeah, exactly. So, no, it's really similar, right? So the core business for us is, you know, you want to launch a brand that has some smell in it, like a shampoo or a fine fragrance. You got to get the scent made. So we actually have a factory where we make it.

27:16But the core algorithmic pipeline is we take your text or image or audio description and then we embed it into a perceptual space. And there's lots of steps involved in getting that right and having that be commercially viable and acceptable. But then we then have to decode from that space back to a formula. And that formula is a set of instructions for a formulation team in the factory behind me and a big robot that's the size of a school bus that can make a new fragrance every 100 seconds. Um, and, uh, that's basically how all this is strung together. And the algorithms are of course driven by data.

27:52And every single time we make a new scent for a customer, like, yes, they're happy. We're happy because we get paid. They're happy because their brand is working. Uh, but we also get data, right? So we're always sniffing those, those scents. We're always putting them through our chemical sensors. And so it's, it creates this really nice, like self-perpetuating machine where we found a business where we can serve the world and like actually help people be happy. and like become wealthy and grow their businesses. But also like it helps us on our mission, right? So the last thing that we do is not going to be fragrance design, but it is certainly the first.

28:27And so it's teaching us and it's also funding us. And I think that's a really powerful, like I'm glad we found it because I think it's really, really powerful for what we're trying to do.

28:36Sam Charrington:But there are a couple of things in there that I wanted to dig into. You know, essentially the encode and decode parts. It's like you mentioned multimodal and that's like super interesting. So I can describe this thing, but also say like, I want, you know, I want this scent to evoke this image, like or this image to evoke the scent, whatever the directionality is like. And so the first question is like, how did you go from like this kind of molecule map thing to this multimodal embedding space? right and then on the decode side okay you know you found the neighborhood of some you know some thing if it's if we're still like in the regime where this embedding space is fundamentally embedding like representations of molecules like how do you get from that from that like a single point to well that's a mix of these five things is that also ai or is that more deterministic or experience-based thing?

29:43Great questions. There's like a few things buried in there, right? So the first is the map that we discovered, like we've evolved the system a lot. So what I'm going to say is like the gist, but the implementation and engineering details have kind of diverged from what, like, the simplest way to describe it. But like, here goes. That map, that roughly 300-dimensional principle order map, the first model that we trained does indeed take single molecules as inputs. But there's no reason that you can't target that embedding with other inputs, right? There's no reason you can't target it with a...

30:16So like, what about a chemical sensor? Could you just like chop off the head of that neural network and like, you know, put it on another neural network that took in like chemical sensor readings? And the answer is like, yeah, you can for sure do that. So the question is not so much like, how do you crunch everything down to the original input space? It's more like, how do you map new inputs into that same space? And we've been able to do that. And then there's a question of, How do you deal with mixtures? And like that is the holy grail. And we've spent a ton of energy and time and science on how to do that.

30:48And I'm super proud of what we've done, what we're doing, what's still ahead. But like the special sauce is really there. It's like how do you reason about how these molecules interact? And that's a good question, but I'm not going to get the answer. Not a detailed answer. Yeah. Come work at Osmo and push back the frontiers. It's like it is the special sauce. ultimately like truly like the data that drives it is actually the the moat and so i think we have the largest olfactory data sets in the world ever but more importantly the rate at which we're generating data just far outstrips anybody right like even the companies have been around for 100 years like they say they have data but they have is a lot of excel spreadsheets that don't actually kind of add up to much and so everything that we do from the very beginning has been designed to be collected for analysis and for model training.

31:37And that makes all the difference. Yeah.

31:40Sam Charrington:So data and model training suggest that that mapping is indeed a learned thing. It's another model as opposed to, you know, the relationship's not that complex or it's business rules or definitely not business rules. I mean, like, you know, as with any like AIML project, you always start with the dumbest models first and see how far it gets you um and then uh in the most interesting cases like you're like that's actually not millennial equals x exactly exactly yeah just you know keep keep pushing the same sense like still polo blue it's still abercrombie and fitch fierce um and like that's not that's not that wrong right it's just you'd like to be a little bit more nuanced with it um and those are great sensitives to the test of time truly um but uh uh it's yeah it's really an exercise in collecting very very large data sets tailored for ai and then what you do algorithmically on top of that like when we started the company like whatever almost four years ago knowing how to build specific kinds of models on this data modality was super important and that's actually like been an accelerant for us um but really like a lot if you just have great data, a lot of stuff kind of falls out from it.

32:54But you have to be careful with every step of the way and have great, great people who are thinking about every step too.

33:00Sam Charrington:And does graph models still figure significantly into what you're doing or have you evolved to transformers? In the right applications, I mean, we're really not dogmatic about the modeling approach that we take. We're really dogmatic about data size and data quality. Does the implication then that you spin up new models for individual projects as opposed to what I envisioned was that like you had like the olfactory foundation model and that thing changes infrequently and you just use it for a new task? So when we say olfactory intelligence, we kind of mean like the suite or the fleet of predictive models on all aspects of smell.

33:39And there's dozens. There's dozens of models. And if you go look at like Neolabs or life sciences companies, some of them are pretty upfront that like their core model or foundation model is actually a fleet of models. And that's how self-driving cars work too. Self-driving cars are driven by like a fleet of models that all connect together along a spine to coordinate sensing with action, with planning and all that stuff. So I think the way we think about it is closer to autonomous vehicles, which is actually where my CTO's kind of background is. So Rich ran the data architecture for NVIDIA's autonomous vehicles program.

34:13We met at Twitter when we were working there, and then he went to Spotify and did Brexys.

34:17Sam Charrington:but you know that in and of itself is interesting and opens up this question which is like in AV there's this tension that I've explored quite a bit through interviews between models that you know are somehow faithful to the way we understand at least the physical world or the physical process or like classical control in the case of AV versus end-to-end I don't care about any of that stuff. I'm not going to like human tinker subsystems, whatever. I'm just going to end and train on data. It sounds like you're more in the, like we've got this physical understanding of, you know, olfactory systems or some process for developing these scents.

34:59And we're going to like have sub components

35:02Sam Charrington:or sub models. Yeah, I would say one part of that's true and one's not. So the one that is not true is like, we don't necessarily strive for physical understanding. We strive for predictive accuracy and like helping our customers and like, you know, building like being able to predict the right thing to like keep the organization going. And it doesn't sometimes we use physics like that's just a modeling choice is like using a physics based model. And I would say we actually cannot do one fully unified model because like there's regulatory work that has to be done. And so like you kind of need one prediction for is this safe in this specific way.

35:41So you got to have like at least, you know, it could be one model with like 12 heads, but you do have to have those specific outputs. And there are specific data sets that need to be used to like train those. And they effectively become independent, you know, models in that regime.

35:55Sam Charrington:Yeah. Got it. So the, the sub models are like, is it fair to say the core is predicting a smell, but then, you know, you've got these other heads or attributes that you're also trying to predict, which are toxicity, maybe manufacturability, maybe cost to manufacture, maybe whatever. Regulatory safety, you know, all that stuff. And like, you can't really skip any steps, right? They're all required. But yeah, the core is like, what does it smell like? And does it smell good? And does it smell strong enough? Like those are kind of the core things that you need. And then everything else you can figure out.

36:36Sam Charrington:Does it smell good? Is that a derived property of what does it smell like? Or do you also predict that independent of what it smells like? It's a derived property of the scent tupled with the target consumer. So, you know, an example is like you could either choose Parmesan cheese or kimchi or strawberry. Like, depending on who you show it to, they may or may not like it. And in the case of strawberry, actually, the kind of strawberries that people think of or want in Japan actually are pretty different from the ones that we think of or want in the U.S. and so well those are the like the gift ones but just like the the flavor right of them it's like it's a different sweetness profile it's like almost borderline different fruit in terms of how you construct it in a product um but you know like what you like is heavily heavily driven by what you've been exposed to before and like and what what did you feel like when you got exposed to it.

37:39And, you know, if you're in a Korean household, being exposed to kimchi is like, you do that under like warm family conditions. And if you're exposed to kimchi, it could be like literally whatever. If you're, you know, a non-Korean person who's never experienced before, you're like, what did I just open? I happen to love kimchi, but it was an acquired taste. And similarly, like from my culture, there's all kinds of like, you know, horseradish and filter fish and noodle kugel and all this stuff. So like, I love those things, but that's like my people's food. Like that's what we eat and not everybody likes it, but I'm into it.

38:13Sam Charrington:Which raises a question about, um, taste, like, which is kind of, I guess, you know, it's more adjacent to smell than it is to physically. It's pretty close. Yeah. And so does that, um, you know, is that something that you dabble in? Is it far enough a way that you don't you know think about it we think about it a lot we don't work in taste today because we're really really focused on our kind of first vertical and like we gotta we gotta focus um so there's kind of three aspects to this so there's smell which is for sure just like what comes in your nose but also like you can smell things that go the other way it's called retronasal olfaction um so when you're eating something you're actually creating this chimney effect that like kind of has scent basically vent the the reverse way um and so you this is how flavor is produced right so if you ever eat a jelly bean and hold your nose you actually can't it just tastes sweet you can't tell if it's like lemon or lime or grapefruit or whatever um so flavor is 90 smell 10 taste and taste is just what happens on your tongue like sweet savory sour salty umami all that um and bitter uh but it's in terms of dimensionality and richness again like i'm sure the taste neuroscientists would kill me for saying this but like it's just not as complex like there's just there's less diverse they've only got six we've got sorry you can have a taste person on your podcast next and they can defend themselves but like they just there's fewer channels of information and it's and for sure the experience of flavor is like destroyed if you cannot smell.

39:48Just ask people who lost their sense of smell in COVID or who've ever tried the experiment of just like holding your nose when you, you know, eat a bite of anything. So all that to say that like flavor is super related to fragrance and it's kind of there waiting for us when we're ready, as it were. So we've talked a lot about kind of where this is all going and

40:10Sam Charrington:what's possible, but any additional thoughts on that? Like I want to give computers a sense of smell and that means reading and mapping and writing smell and you know we've been on our journey super pragmatic about like building the scientific capabilities which is the first two years of the company and then finding a great business to go put this to use in which is the fragrance industry but like we like we're going to go further than that um and in fact we're talking with a number of organizations like i think the thing that's needed now for not just design of scent but the detection of scent is exactly what you're talking about from before which is we have to build a foundation model.

40:47And the thing that's been missing and that we've been building is you have to collect a whole bunch of data, right? And like, you know, for instance, if you wanted to do the very, very noble work of smelling someone with cancer early, detecting their disease early, or if you wanted to detect early infection with malaria or another infectious disease that claims the lives of mostly kids.

41:16Sam Charrington:That's super interesting thinking about those as data collection problems. But so here's the deal. You can go directly after that and like collect scent data from people with or without those conditions and try to build models. You're never going to get enough people to build a great model, right? And like there's just, it's hard to go get that much data. Meaning because there's not enough signal and the correlation between scent and disease or some other factor you speak? Just low numbers. It's just from a pure statistics and machine learning problem. You know, I think basically there's not an obvious, obvious, obvious signal that says this person has, you know, a disease and then doesn't.

41:57There are subtle changes across like many, you know, hundreds or potentially thousands of molecular signals. We just don't know. But we know dogs can do it. Like animals can actually detect these patterns. So there's something.

42:08Sam Charrington:Right, that's what I was thinking of when you raised the example. It's for sure there. It's for sure there. um but we can get computers to do it we just need to go get a ton of data right like we need to like band together and build a huge effort where we just like let's go sniff 10 000 people i don't care if they're healthy or sick i don't care how old they are i don't care i mean let's record all that information of course but like let's just go get a ton of data let's go to the grocery store and get cucumbers and bananas flowers and like steaks and whatever um and let's just go smell everything and then build a huge olfactory data set of what the world smells like and if we can do that then then it sounds a lot harder than scraping reddit one more time a whole lot harder than scraping reddit but it's worth it because literally nobody's gonna do it unless we pull pull ourselves together so like but this it's like worth doing right like you know this is the thing that maybe isn't a sign but like i have a lot of friends in the world of ai and ml and you know i came in through this as a biologist and as a biologist like you go do experiments and it's like hard and like you got to go to the bench and get your own data and you know it's gritty um and in the world of ai like you kind of just assume the data is out there and if the data is not out there you kind of assume you can give somebody a credit card to create the data um and like i think that's amazing but like the amount of infrastructure that you have access to is just astoundingly huge and like you just have to understand that if you want to like continue to expand the realm of like what AI can do and compute can do, like it might get hard.

43:38Like you might reach a point where there's not a vendor that can handle everything for you and, you know, shine your shoes or whatever. Like you might have to go to the work yourself. And so we're very much in that regime. And I love it. Like it's, it's so hard. It's just, it's beyond brutally hard. But I love it because nobody else is doing it. Right. And like, that's very much my preference for how I want to spend my life is I want to do weird things that nobody else is doing that matter, right? And so that's kind of my selection criteria. Like there's plenty of people that can go build, you know, AI voice agents for customer service.

44:11I think we're going to need that to like make commerce better. Like I'm not the guy to do that. Like I want to do this weird thing, right? I want to give computers a sense of smell. And so, you know, I think where we're going is like we are collecting huge amounts of data, but importantly, like we have this platform that allows us to store all this data, organize it, like massive efficiencies of scale digitally and then increasingly physically we can make a huge number of unique sense like we have all this infrastructure that we've built painstakingly over over a few years and now like let's go let's go get all the smell data right like and let's build this chemical um like slice of reality uh into ai which likes building a true olfaction uh foundation model and but but the data comes first right like we need to go get all the data and then like you know when whenever these models come out like opus 47 or when mythos comes out or codex 55 they talk about uh the model card in terms of all these benchmarks and so what i what i imagine for the future is that we'll collect all this foundational data and malaria detection will be a benchmark all right and cancer detection will be benchmark and you know building a great um market product for a new shampoo launch in Malaysia will be a benchmark.

45:27Sam Charrington:But... Or even the idea that the generic, this is where I thought you were going, that the generic foundation model that you are using, by generic, I mean kind of general purpose, part of a better term, is not just text and, you know, multi kind of the common modalities, but is now has, there's some kind of benchmark or model card statement around, you know, smell and taste and... Yeah, it's like, let's add these weird new benchmarks, which is like, how well can we predict, you know, what something smells like or how some smell that we detect in the world, which is just a combination of molecules, is predictive of something that we care about.

46:06You know, like one other way I like to think about this work is like foundation models for text and for images, they are approaching in some cases exceeding human intelligence. Certainly mine in certain regimes where I've been pushing it, I'm like, holy crap, this is smarter than me now. it's human intelligence. And that's because it's trained on human intellectual output. But 99 % of species on this planet can only speak with chemistry, right? Thinking of bacteria and fungi and plants and insects, like they just only can talk with molecules. And I think that it's really worth adding other kind of alien forms of intelligence to our AI models.

46:50And the way to do that is to train it on the intellectual output of those other intellects, which is, that's chemistry. That's the sense that's in the air. They're produced by living things for reasons to talk to each other. and uh you know terry tau who's this famous mathematician he has this one paragraph in a paper he just wrote where you know he's he's you know reflecting on these new forms of ai and the kind of weird mathematics that they can do and he says you know i think increasingly we need to have a copernican view of intelligence meaning for a long time you know in astronomy we had the earth at the center but then we had to dislodge it to actually face facts um and earth's a great place to be, but it's not the center of the universe.

47:30Right. And similarly, we've placed human intelligence at the center of the debate about artificial intelligence and like pretty cool to be human. It's awesome. I love it. But like, why should we be the center? Like what other forms of intellect are there? And so when he, when he, when I read what he wrote, I've kind of instantly, like it instantly resonated with me. And I've very much taken on this view that like, we should have a Copernican view of intelligence. There's so many forms of it. And there's so much richness out there of things that are intelligent, some of which we might not even recognize as such.

48:03And I think if we're building AI for our species and for the planet going forward, like we should have those forms of intelligence built in.

48:13Sam Charrington:Thinking about all the progress that we've been making in broadly physical AI, robots, autonomous vehicles, all that, and thinking as well about how good our sensors are for this approximation of visual stimuli. like do you have a sense for i'm imagining you do like where are we with like the you know olfactory cmos or whatever that uh you know knows on a chip i would say the devices that we use to detect chemistry are truly phenomenal but they're they've been very specialized to live in a laboratory so think about computation in like the 1970s big mainframes right stuck in a back office or like a data center, right?

49:04So we're still in the data center era of chemical sensing and of olfactory intelligence. So like our scent printer is the size of a school bus. It's just not, we're not gonna, that's not gonna leaf. That's bolted into the concrete, right? Yeah. But eventually all the - We just need those wheels on the school bus. Not wheeling that out, I promise you. But all this should fit in our phone, right? Over time. And that's the story of technology is something that works, that's valuable. And now we just like work really hard to make it small. So that will come.

49:34Sam Charrington:The cancer example is a good one. What are other examples of, like, if we had the auxiliary olfactory apparatus chip on the phone, like, what would it let us do that our noses don't do for us? Super excited by is just, like, the creativity that I think would come if we opened that up to everybody, right? So, like, how could we have predicted all the apps on the App Store, all the uses of camera phones and all that stuff? Like, it's just incredible. um i think that there's definitely things we can contemplate everything that a dog's nose does right i'm thinking of the app that you download that tells you who actually dealt it exactly all right well the old factory fingerprint is definitely sam today um i've got data to prove it um so they'll definitely be fart apps for sure in the future i just like it's human nature like i've you i worked at twitter and we clean up like i just i know human nature it's just like you know we've got a fascination with many things including the scatological but um i i i i think that um anything that a dog's nose can do and there's like 80 things more or less that you can train a dog to do like detects so tracking for example on your phone exactly that's an amazing spoiled produce spoiled meat and these are these are things you want in your fridge right you want to know can i eat this should i eat this right is it done is it cooked that's useful for a robot kitchens um there's so many things that the human noses and dogs noses can do none of the examples alone i think are so far that we've seen are huge businesses or necessarily accessible businesses smelling cancer is important but like the business there is really rough right medical diagnostics are like not a great unfortunately not a great business to be in um and uh it's just figuring out how to actually like, like not only build it, but then make it self-sustainable.

51:28Like that's one of the harder questions that I, I try to wrestle with. And I don't have the answer, frankly.

51:33Sam Charrington:Do you feel like that, the difficulty of that question is, I mean, clearly like it's suppressing the development of the technology, but do you think that it's like, if the business model was there, we could easily figure out the nose on a chip or is it just really, really hard? And therefore the, the bar, the economic bar is really, really high. I think it's a blend. So if the business model was figured out, I think we would have been sprinting at this for the last like four years exclusively as opposed to building a really great business in the fragrance industry, which seems unrelated. But when you really get down to the, you know, the science and the technology, it's super related.

52:13But we have devices that you can bring people to or that you can wheel around that can do this, right? um it's just a matter of uh how do you throw an appropriate amount of resources behind like scaling that miniaturizing that very possible right i can easily easily envision a device that's the size of like what do we call this like i don't know like an 80s cell phone so bigger than the cell phone but like kind of a bigger box right i can imagine a box that's like you know about that big, like, I don't know, a small baby shoe shoe box that can smell as well as a human nose. In fact, we're pretty close.

52:54Ours is about the size of two shoe boxes. We have a deployable sensor that's as good as our nose as you and me. And it's the size of two shoe boxes. It works. So getting that smaller, very straightforward engineering, not easy, but like straightforward, going smaller than that and like putting it into something the size of this phone and then putting it into a chip the size of this phone that's going to require some creativity and some work and i actually i couldn't tell you exactly how that's going to happen i can tell you that we have proof it lives between our eyes and our nose right like we're walking proof but um yeah there's a lot there's work to be done to actually like make that uh affordable and pervasive yeah

53:33Sam Charrington:yeah something you said maybe think about you know is there some inherent limit in the application of MLAI or the way you've done it and that like what's important isn't really mapping to like you know single label scents like you know cucumber or cinnamon whatever but like mapping to emotion which is a much richer thing but certainly if you're like you must be already doing that that's what people are trying to get at yeah we think about that a lot yeah so I I think there's there's first of all there's more labels than just cinnamon and cucumber uh no no i don't i don't mean like we didn't name enough i mean like there's other ways of labeling like these two are really similar or they're dissimilar and here's a scale so the richness of of sensory information once you start to not use human language and use like numeric scales which you can do it just takes some some fine tuning you actually you learn a lot about the chemical world when you start to label things more carefully that way um i mean in some sense that's the original observation from the embedding work at google is that proximity actually means perceptual so things that are nearby on the map smell similar right yeah exactly um and so then that like I don't know how you would do this, but I'm envisioning like jointly embedding emotional somethings, uh, emotional valences with, uh, you know, olfactory information.

55:09Sam Charrington:And I think that's very, very much possible. Um, I think the challenge, so there, there's a trend now called neurosense, which is largely bullshit if I may be frank. Um, and you know, people use EEGs. I used to use EEGs in my training. And also, I know people that run EEG companies, and they're very upfront. EEGs are useful for telling if you're asleep or if you're having a seizure. And it's very valuable to quantify sleep and quantify seizures. But it's not going to tell you if you're feeling up or low, right? It's just, it's not, can't do that. There's no signal there. And so there's a lot of crap out there.

55:46And so we've not waded into that space yet because I just, I have to do it right. Like I can't live with myself if I, if we don't do this right. But there's so many examples of this being true of sense, really being able to unlock emotions in one way or the other. And, um, we will get there and I think we will do it right. Um, but we're going to have to be very careful about how we quantify emotion, um, uh, which is its own thorny problem. But, uh, this is something I used to, I used to work on too. I love the topic.

56:16Sam Charrington:um and uh i'm imagining the the real estate agent going and spraying fresh baked chocolate chip cookie in the open house they already do that right i know they bake them is there an actual can oh yeah there's candles for sure yeah ah um a new car smell is like invented right um it's a thing right right but uh no a lot of this is also association right so same with the kimchi example it's like, well, that might be good or it might be the hearth, right? It's like, well, have you ever been in a home where a fire was lit? Like, does it smell like a burning building or does it smell like comfort and like, you know, marshmallows and hot chocolate, right?

56:54It just depends on what you're exposed to. But for sure, like the evidence I think is pretty clear that there are some scents that do things that are positive to your mood and it's almost physiological and can increase your focus or can increase your awareness or reduce your anxiety. Like, Like it's pretty clear to me, but I think we need to be really, really. Like aromatherapy? Yeah, I think aromatherapy has elements of really deep truth in it. And same with Ayurveda. Like I think that these are really old traditions. First of all, they're really appealing and they're beautiful to experience.

57:26It just smells nice. But like in kind of reading the research, there's nothing that's super clear cut. But like I just am of the conviction that there's some kernel of deep truth there. in the same way that like acupuncture now has this western equivalent called dry needling that's like actually therapeutic like acupuncture got there way earlier they just talk about it differently it's the same stuff and maybe some ways there's still things that haven't even been appreciated in in the western adaptations of it um but like i think that that correspondence will also happen for ayurveda and for aromatherapy and then also probably for the um the aromatic aspects of of herbal chinese medicine as well there's like you know plants are like our medicine and poison factories they make all the molecules that do good and bad things and all of our drugs like many of our drugs have some natural origin to them or they were inspired by a natural origin so like there's there's got to be some i just believe it i think there's got to be something there where scent has has real like powerful harnessable impact to uplift our mood and to make us feel better or perform better or whatever our desire might be that's actually, you know, realizable.

58:38And we just have to be, we have to be serious about it and we have to be thorough. That's all.

58:42Sam Charrington:Well, Alex, it's been great catching up with you and getting the download on Olfactory Intelligence. Super fun to talk about it. It's obviously wide ranging, but it's like we're pushing back the frontiers. And so like all the things you normally take for granted, like we You have to think all the way through. So I'm just thrilled to talk about this stuff all the time because I live it and breathe it. But it's fun to share with you, Sam. Thanks for having me on.