How is AI transforming the food industry?Welcome to The Drip, where we keep your mind hydrated with some science, music, and a mantra.I'm your host, Zachary Cartwright.
As a lead food scientist at Aqua Lab, I'm constantly curious about how new technologies are shaping the future of the food industry.
And at a recent conference, I watched a presentation on how AI is revolutionizing the product development process by analyzing vast data sets, developing insights, aiding in ideation, and identifying optimal ingredient ratios to accelerate time to market.
Here to discuss the role of AI in the food industry is Ravi Karkara and Vinay Indraganti, who are co-founders of AI for Food Global Initiative. Thank you both so much for being here today.
I just want to divide this podcast up into some different sections, with this first section talking about the AI revolution in food.
And my first question to you is, how is AI transforming the food industry from farm to table, and what implications does this have for the future of our meals?
We all know that AI is a topic that everybody is interested in.It's become a copy table topic as well.
But what we're not aware, most of the public and quite a few people working in the industry itself, not aware of how prevalent AI is across the food ecosystem.So before we get into AI and the you know, what is it doing today in the food ecosystem?
If you look at how we get food on our table, so farm to port, if you really look at from a farm, you know that food is being cultivated on the farm and that food is being cultivated through seeds that have been in test farms, that have been, if they are genetically modified, if they're hybrid,
they have a whole process for the seed development itself.So there is an R&D happening today on the seed level.And now we're talking about purely farming, right?So we can get into other aspects of agriculture.
But right now we're looking at that and AI is very much in the heart of the genetic revolution.
So things that we're seeing, that we hear about CRISPR doing things that is prevalent in the medical industry is also being applied on the seeds that we have, that we cultivate, that is being developed today.So it all starts from there, right?
So some of that AI is being used to predict some of the outcomes of those genetic modifications.
The other place would be if we are working on hybrid, what kind of hybrid combinations of seed can provide a fruitful result from a seed development itself.So that's seed R&D, right?And right now on the farming side, so if you look at
seed R&D as a R&D part of agriculture.The actual production part, like you would say a factory is a production, but for that cycle, production is actually the farm.
So that R&D is then being transformed into a test farm and then being transformed into widespread production onto the farm.And when we get into production, those seeds are
being so, using a lot of robotics, and that robotics use a lot of computer vision, they use primarily vision-based AI today, and there's a lot of soil analysis, so IoT information that's being sent back.
So there's a lot of big data being used today, and that big data is then being used for predictive analytics and being used for other AI usage there as well, to be able to predict the soil conditions and so on.
So a combination of vision-based and IoT-based data collection and then AI algorithms on top of it.So that's on the farming side.Once you harvest, now you get into the whole logistics cycle of it.
So food being harvested, being sent to either warehouses or sent to ingredient companies, or being sent through those warehouses to our grocery stores, or being sent to food production companies.So there's four different paths of post-harvest.
Either go to warehouse for long-term frozen and come back to later on, or being sent to ingredient companies that makes ingredients from these powders and seasonings and flavors and such, goes to the grocery store directly to consumer, or can go to the
package food manufacturers.So potatoes will go either to a potato starch manufacturer ingredient or it'll go to a potato chip manufacturer or something else that goes in potatoes.So all of those things.So now there's a logistics piece to it.
So there is AI now in demand sensing in terms of where should this crop now go?Should it go to the warehouse and prolong storage because there isn't enough demand?
Should it go to the grocery store because there is higher demand depending on seasonality?Or should it go to the ingredient manufacturers or to the food manufacturers, right?So that sensing is being done.
So the AI creates that awareness or has that awareness and routes it using logistics. And then there is once it gets into either ingredient companies or food manufacturing companies, now they have a set of AI in R&D.
So there's ingredient development, new ingredient development formulation.There is similar to where we have R&D in seed development.Now there is R&D in ingredient development and R&D in product development.So that's an AI component to it.
It's very nascent, but it's really picking out.
So one of the things that we have seen at BCD is that this is an area of acute interest, especially in trying to figure out novel ingredients, discovering novel ingredients, looking at replacement ingredients, substitute ingredients at different costs and nutrition level and so on.
So there's a lot of work being done in this industry as a whole, and AI is becoming more and more prevalent there. So you take that R&D space and then you get into production.So once you finish your R&D, you're actually now producing.
AI is doing a lot of things on the factory floor.The most prevalent are the predictive analytics.So predictive analytics or predictive maintenance of equipment.So there's a lot of work that has been done.It's a very well-known use case.
The other area, especially in food and beverage, is quality that is based on computer vision.
So there's a lot of things that we've done with some of our customers where, you know, looking at the production line, we're able to see whether the chips that are coming out or the extruded stack that's coming out is actually the right shape, is the right size.
And there's a lot of vision-based QA that happens that is being based by computer vision.And there's some automation and robotics being done there. And then once it's being produced, now you get, again, you get back into logistics.
Do I store into a locked warehouses?Do I store into send a direction to my customers?What do I do?And then once it goes on the road or the sea, whichever mode of transport I have, what's the best mode of transport.If I can predict my demand,
I would rather send it by rail.It's the cheapest.Second is road.Third is air.And then C is, of course, if you have to do trans-ocean, it has to be C. So that's how it's being done.So there is a lot of AI algorithms in that sense as well.
And then there's a lot of algorithms on the retail side, kind of figuring out where is the demand, how do we So there is a lot of demand sensing there on the retail side.
Both food companies and retail stores use that, and they feed into the whole logistics demand.And then what do we do with where we are?So there's a lot of fear.
And then again, AI in terms of sustainability as well, in terms of how can we use AI to use less of our energy, our resources, to create a positive sustainability impact, lower our carbon footprint.So some algorithms working in that as well.
So yeah, I mean, there's a lot of work there now on the farm in terms of weather sensing, there's already AI there in a lot of areas for a very long time already.
Just to add, I think on the sustainability piece, 30% of the greenhouse gas emissions come from the food sector.A lot of focus has really been on the oil and gas, but we seldom realize just from that farm to all the way from supply as Vinay was
There's a lot of emission and how do you work with AI so that you make agriculture, just from crop production to livestock management and really the whole journey of the carbon emissions there to be corrected or maybe making the system more efficient.
I think the whole area of how to make land use, like, I mean, working with drones, looking at precision agriculture, et cetera, I think these are some important areas.
It's a huge wide area that we also talk about in the book, you know, that'll be out next week.
Yeah, thank you for that response.
A lot of times when I think of AI because of my work at Aqualab, I see it being used or wanting to use it in product development, but it's really interesting that every aspect from farm to table and beyond is going to be used and is starting to be used in many ways.
And I'm curious, how do you see it being used for flavor innovation?And maybe you can share some surprising ways that AI is being used to enhance flavors and different textures in food products.
And how does this compare to traditional culinary methods?
Yeah, let's look at what is possible with AI today and where are things more prevalent.So when we look at flavor development, one thing we have to realize is that we have to understand the nuance of flavor along with neuroscience.
So it's not just about the right flavor, it is the right flavor for me, for us.And there's a lot of work that has been done at Monell and other places that really have made this the primary focus of their research and their body of work, right?
So when we look at flavor development, there are two primary aspects we should look at.One is the molecular part of it, which is pure chemistry.
So when you look at flavor, usually we call it flavor chemistry because it is primarily chemistry in that sense.Now, how we get the flavor, the process could be biochemistry.
We could be using other things with enzymes and bioenzymes and so on and so forth.There are different ways of doing it, but the end result, most of the time, it's purely chemistry. So flavor chemistry comes into play.So there's molecular piece.
So the moment you talk about chemistry, you talk about molecular science.On the second side, we have the neuroscience. Now we're talking about our perception, our sensory olfactory system, and so on.Now we're talking about two different things.
When AI is now talked about in flavor chemistry, AI enters into both these areas.AI enters into the neuroscience area, through many different ways.
So neuroscience, there is modeling that's being done of the human olfactory system, understanding which amount of what chemical has what reaction and perception within our system, and now being able to predict based on changes in that or different combinations.
So now we're looking at the chemistry piece of it, not through molecular, but through perception, reception and change, how does it affect us?So we're creating models, AI models to mimic that, right?
So that if we give a specific molecule compound and place it in a certain place of our tongue or nose and combination of that, what does it do?So there is a lot of work being done on AI, on the neuroscience part of it.
An interesting work that we were involved with was salt reduction through umami and kakumi, and that's an area that's definitely of interest.But in order for us to do that, how do we create an AI model to be able to predict that by reducing sodium?
So that's one area that's on the neuroscience piece.
On the molecular science piece, so there is a lot of work that has already been done coming purely from labor chemistry to predict the reaction of molecules, different kind of molecules, different formulas.
What does that do and what kind of output would it have?So purely from a molecular perspective, we know that certain molecules, when they come together, the output is so-and-so, right?So to be able to use that into AI models, That's being done today.
Now, the overlap of molecular chemistry and neuroscience is the area that AI is now being able to fast track. How do we bring molecular chemistry and bring neuroscience and the reaction of that?
So we are maturing into a combinational AI model where molecular chemistry and neuroscience is now being brought into.
And then through those molecular compounds that we can create, new one, novel ones that we have never seen in the world, how would that react?So those kinds of models are now being done.
I would add, so when it comes to texture, a lot of texture is based on perception and feedback.So when we look at perception and feedback, there is a physical component to texture.
And texture could be visual texture, texture could be sensory, could be a mouth feel, could be certain different elements, right?So in that sense, when you talk about, let's take a very simple texture, which is mouth feel.
and mouthfeel if I want to have a mouthfeel, a full mouthfeel.The definition of different people of having a full mouthfeel is going to be different.And while they might be in the same
somewhat similar range, you might say, it feels full, it's somewhat full, not really full, but fullness is and somewhat might define fullness as different.These kind of texture, and when you look at crispiness, you know, how crispy is a cookie?
Okay, you can break a cookie and say, well, it looks crispy, but is it as crispy as the other person seeing it's crispy?So those kinds of things, you know, are very personal.
And translating that into a computer model, really now we're talking about a vocabulary that we've built to be able to do that kind of translation.So those kind of things are very unique.
So even if two companies want to make a crispy oatmeal raisin cookie, the crispiness of those cookies might be different.
Now, if you do some analytical testing on it and say, okay, here is the hardiness of it, here is the crunch of it, and here is the sensory, here is the sound of the crispiness when you break it.There's so many different variations one can say.
So texture is a very interesting part of flavor.When you talk about texture, how do you define it?So there are different ways we can do that.Now, once you define this, now we use what we call reinforcement learning to let the models learn on
how these parameters are now being perceived by AI, and we train the model through that.It's a little bit more challenging texture, but it's something that's being worked on right now as well.
That brings me to my next question, because I see that with AI, we're going to really be able to personalize the dining experience and be able to really customize for different consumers.
You gave an example, Vinay, at the American Association of Candy Technologists of being able to order exactly what type of Snickers you want and have specific flavor profiles or things in that type of product.
How is AI creating this personalized dining experience and how is this going to impact restaurant menus and customer satisfaction?
When we're looking at personalization, we're looking at personalization from many different angles.We're looking at it from a sensory taste angle.We're looking at it from a nutritional angle.We're looking at it from a
regulatory compliance angle and a holistic angle that includes all of this.And some of these is also sustainable.There's a lot of emphasis saying, you know, not only do I want all these things, I want it to be sustainable.
And can we forget, I want it to be affordable.So all of this putting together today, we're not able to create a product that is
very highly nutritious, extremely delicious, satisfying, very much sustainable, organic, clean label, but also very affordable.It's difficult to get something like this.If you look at it from a brand experience,
if you look at it from a food service, like a restaurant experience, right?We're all trying to make this happen.Today, the closest we see is menus.So a very interesting AI experiment that we've all probably been part of is what Coca-Cola did, right?
So you go to a vending machine, we can have all kinds of information there and The moment you say, I want a little bit of this flavor and a little bit of that flavor.
And, you know, you want to mix your orange and mix a lemonade or you mix a little bit of a cola there, if you want, you know, you can do whatever you want.
And that combination, even though it seems almost unlimited, those are limited combinations, you know, maybe too much for the human brain to handle at that particular point.But for AI, it's all being modulated at that point.It's got AI models in it.
Similar concept can now be brought into a restaurant.So when I'm there at a restaurant, I can look at a menu, I can say, I can make this menu a little bit more spicy.And we do that, right?
We ask the restaurant to say, give me more spicy, give me less spicy. And when we do that, essentially what we're doing is we're asking a change in the amount or replacement of ingredients.Okay, I don't want this particular flavor, add this instead.
So we're not swapping ingredients, we're changing the quantity of ingredients.So all of that thing that we ask, the predictiveness is limited to that particular chef on that line in that shift.
So the whole experience of a restaurant, when we want to change that and say, look, I want to make it more predictable, which some people might like it, some might not, but we say that is my benchmark.I want to make my food to a certain standard.
It's got to be there all the time.You have to rely on automation.We don't rely on automation. what you're doing is you're bringing in some amount of AI and some amount of robotics, which again comes to AI to do all of that.
So this way we're saying, okay, if I want to have a little bit of less salt and more spicy and less sugar and more of fiber and so on, you're telling AI on what the proportion of those ingredients should be.
And that translates into an AI model that says, okay, here is your recipe based on the customer.And the customer could be on a sliding scale, could be a number or whatever it is.It'll tell you this corresponds to this and so on.
So now we've created a formulation.Is the process still the same? We just changed the formulation, the ingredient mix, and so on.
Now that model now tells either the chef or the robotic arm at this point to say, okay, this is how much you're going to add.And there you go.Now you've created a repeatable experience to the customer in a restaurant.
And you could take the same concept and say, okay, can we apply this on a production line of sneakers where my sneakers is more sweeter than yours, or I added a little bit of salt in my sneakers to make it a little caramelized, whatever it is, if that parameter is available,
provided to me by the brand in some kind of an app.I do that, I make those changes, and out comes a sneaker that's customized to me.
My dream is to have something like this on a production line that Zack has your sneakers, my sneakers, and Ravi's sneakers, and all of those, and I would like to be able to do that one day and maybe create a micro factory out of it.
Ravi, what do you see as some of the biggest challenges that food companies face when integrating AI technologies?
we are right at the onset, right?
So we are still trying to understand how AI will really create food systems and production lines, supply lines that make the whole food system, or like Vinay said, more affordable and nutritious, at the same time sustainable.
And I think wearing a sustainability hat, I think one of the biggest things that many of these factories deal with is really the whole carbon footprint.
And I think there, I think AI can really help, I'm wearing a sustainable lens here, really help make the whole process of manufacturing, make it more green, really like from terms of energy efficiency, water use, really in terms of water recycle, waste reduction, looking at packaging material that many times you know that the food industry is criticized for overuse of plastic, for example.
What are the sustainable packaging materials?And then of course, it goes into the whole area of supply chain and distribution.That's another area of making it more efficient.So I think completely, if you ask me, where do you already see that going?
AI can really help detect those loopholes in the entire production and make it more efficient.
Second, I think also one of the things where BCD is really coming together is, because we're looking at the entire, from the beginning to the end, we think that we work with you to also make sure that the areas, the new areas of efficiency, the energy use, many of these big gigafactories run their machines using diesel, for example, right?
So how do you really move away from oil and gas into green fuel?That could be the future fuel, right?Biofuels, green hydrogen, and so on and so forth.But I think when it comes to specific areas, I think we need to really come back.
If you pick up the area of plant-based, for example, completely in its new stage, it gives a huge opportunity for us to really look at.
multiple challenges but I think Guneet probably will answer wearing a food expert's hat and I adding on my sustainability lens here, Guneet.
totally understand where Ravi is kind of focusing on that area.And if you look at the industry itself, I would say there are three distinct areas that I would call out.One is, what are the challenges for AI to be incorporated in the industry?
First and foremost, take out the fear of adoption of AI, right?So number one is, and the biggest, by far the biggest, roadblock is our understanding of how we can leverage AI and moving away from the fear of utilizing AI.
So there's a lot of negative connotations.And that's one of the reasons why Ravi and I and Michael, we wrote this book called the AI for Food Movement, is to showcase
what AI is doing today in the industry, what is the power of AI to do in the future, near future, kind of midterm future, so that people can understand that there is a lot of good in AI for food.
And so creating a positive spin, because when you think about AI, there's a lot of negative connotations to it.
And when you think about food and you think about processed food that's being produced in factories, there's a lot of negative connotations today coming to that, right?
With processed food, ultra-processed food, and ultra-ultra, and how many ultras they put in now.But there's a whole concept of, hey, if it's manufactured in a factory, is really good.
So now if you bring AI and food together, why it's like it's an explosion there.So the whole idea is how do we make it more palatable, more realistic and something that is actually a good thing.So that's on the consumer side, we have to make that.
And that's where the whole AI for Food global initiative is, where Ravi and I co-founded this initiative to talk about to make this awareness, right?So, awareness is number one.
From a consumer side, from an industry side, hey, you know, a lot of, you know, if you talk to people working in the food ecosystem and they're working in, let's say, logistics today, right?
They wouldn't really know where AI is being used in the farms or is AI being used in R&D of the seeds and how does that impact How does that particular thing impact their work in logistics?
A good example would be if you produce the round type of seed, it's taken a lot more water to really raise that seed, and somehow the moisture content is always high, even after X amount of months after you harvest it.
That water content, if it's high, It goes into higher tonnage in what we're transporting.So what are we doing exactly?We're transporting water.Do we want to transport water?Maybe it's a good thing.Maybe it's not a good thing.
So those kinds of awareness, once we understand how we're all interconnected, the second thing is data.Data is always something.When you talk about AI, the next thing you talk about is data.
Because you talk about AI, you talk about data science, which means you talk about a data scientist who works with all kinds of data and what we do.So data and AI, they are inseparable.
So the moment you talk about AI and you talk about, OK, perception, all right, I've overcome this barrier of making people comfortable with AI.The next immediate thing is, where is the data?So data is always a challenge.
And the larger the company, the more complex their data, more distributed the data, more diverse the data.And now we're getting into different kinds of data, structured data, unstructured data.
So how do I bring all this data together, which typically are in their own different silos, their own different buckets, their own different applications?
How do I bring all this information in to provide my model, my AI model, the information that it needs?So Now we're getting into the art of AI.So the science of AI is, where is my model?Which model?How do I develop my model?
And do I build a custom model?Do I take an open source model?So that's really a science of it and how. How do I bring the data?What kind of data should I bring?Where do I get the data from?How do I wrangle that data and bring that in?
So now we're getting a little bit of the art.And this is what really separates, in my opinion, a successful versus an unsuccessful project, an AI project, is how good are you at the art of AI, right?So awareness, data, and the third one would be
on the R&D side.So how do we bring in AI into formulation so that when we talk about personalization, we're talking about how do I make these formulations as real-time as possible?So that's been really a challenge today.And why do we make that?
Because the fourth one would be bringing AI into the manufacturing side.So the biggest impact AI can have in the food industry is on the manufacturing side.And if we are able to make our plants more efficient, what Ravi was saying, we actually
bring a lot of money towards other things, right?So you're actually affecting the bottom line.So you're saving a lot of money.That money that you're saving can be plowed back into other things.So how do we bring AI to make that possible?
If we're able to do these four things, awareness, data, do AI, bring it in formulation, and then on the factory.There's already a lot of AI being done today on logistics.
Of course, it's not nearly enough, but I know there's a lot of work that's being done there.So that's a given in the logistics side.So this is where I think our key challenges are.
You know, in our journey, we've also realized this whole thing about, you know, AI is going to take up its jobs.What does it really mean?
And I think for wearing, you know, completely another hat, and we had organized a global conference, right, in August on the app of food movement in Chicago, which was the first of its kind.
And I think we had one very interesting panel on AI skilling.And I think for us on this challenge where people say, we don't know what we don't know, but we know that AI is taking away my job.
But the reality is that AI powered industrial revolution, and especially when we're making these conversations about making America, you can only make an America when you make your factories more smart, whether dark factories or micro factories, which means you're making them more efficient.
You're having factories that run 24 hours with less humans, but more robots and machinery that can really create more efficient, nutritious, affordable, and sustainably produced food.Who will do it?We need that skill base.
One of the partnerships we got into was the Foundry Education Foundation of USA to really look at where are these students who will be skilled into having these factories that will be powered by AI professionals.
Similarly, the work we're doing with universities, whether it is CSI or Cornell, where we're looking at how young professionals, food scientists of the future will really use AI as an advantage
to create those food that is like, like when I said, you know, the whole, which is tasting well, it is nutritious.It is also more healthy for me.If I'm more healthy, that means there'll be less people falling sick.
And also the food, which is not contributing 30 to 40% of greenhouse gas emissions. And like you said, the challenge is there's a lot of, as it is in human revolution, there's a lot of unknown.So people don't know what really will happen.
But I think we need to learn, especially like podcasts like this, where we're creating education, working to create skills so that people can join me.And it's going to be a lot of co-creation as we move forward into this journey.
There's so many different topics we could get to, but we just have a few minutes left and I just wanted to briefly discuss some of the ethical concerns.
As AI becomes more prevalent in food production, what ethical considerations should companies keep in mind to ensure transparency and also keep consumer trust?
When you look at the public-private partnership, coming to play, government leadership coming to play.Now people might say otherwise, but really we need, this is an infrastructure, a baseline question about society, right?
So when we talk about societal boundaries and what should it look like, and now we're now talking about
okay, we're now going to the highest level to our foundational institutions, to the Congress, to the Senate, to the judiciary and say, okay, help us define.And some of those folks might not be the most knowledgeable in that.
And that's why they rely on experts and they rely on the industry.And that's where the public-private partnership comes into play.And if we get that dialogue going, and in fact, That was one of our channels in the AI for Food Global Summit.
We talked about what should that be.That's a good forum for us to find out.So our approach was that let us figure out, one, what can AI do today?What is it?I mean, what can it do?What is the work being done in AI that
we know publicly or otherwise, and how far can we see it go?So now we're creating a predictive chart of our own about the capabilities that we have.So if we create that kind of a graph, let's say, then we say, hey, we are here.
Our capability, our ability as human beings, our ability as a society to absorb this kind of change and disruption that's coming towards us.So maybe it is the full graph.Maybe we can consume all of that.We can absorb all of that.Or maybe we can't.
And that boundary, somebody has to put.If, as a society, we want to be able to thrive, as a society.What should that boundary be?Where should those lines be?
To be very honest, I don't think here we'll be able to get an answer in the podcast, but that's where it's a framework.We should have this kind of a framework to say, can we put these boundaries?
And once we put those boundaries, they have to be evaluated on a regular basis to see, are we doing good?Like any other plan, right?A plan is a plan as good as yesterday.So that we evaluate on a regular basis and see, you know, are things changing?
Do we need to do more?Can we bring in more?Can we bring in less?I think that's where the societal boundaries are.Now, that would address the questions you asked about.How ethical should it be?Is
truly replacing human roles in some areas, a good thing, is it a bad thing?And those kinds of things, and some of that will be driven by market as well.How much of that can be driven by market?Because now we're truly talking about societal changes.
If everything, let's take the complete scenario of everything in the world, every particular role, job is absolutely autonomous.I'm not talking about automation, autonomous, being done by robots and computers and AI and everything.
In that world, what will we humans do?What will we do?Are we in that path?Are we there 100 years?Are we there 300 years?Are we there in 50 years? Can we see that kind of line?
So now we're talking about societal impact and it's beyond just us here in the US, it's global.So these kinds of forums are needed for people to talk about this.And there is a forum, an area for us to talk about.
And that's what we thought when we put together the AI for Food Global Summit.That was one of the areas we believe that will be addressed through those dialogues in that space.
Yeah, I was just going to add, Vinay, that, you know, I mean, I was having lunch with somebody the other day, who's a vegetarian, right?And he was saying, oh, you guys work in the food industry.So here's a question.
He said, you know, tell me that if there is cow meat being grown in the land, and, you know, we Hindus do not believe in eating beef and also especially cow, but that meat is now being grown there, right?That steak.
Is it still meat and is that still cow? So here is a question for everybody here to really think, what does it really mean?
And I think the point that Vinay was saying that there's so many different dimensions, cultural aspects to it, and interpretation, it is so sensitive and linked to in some places, food is linked to religion, you know, whether it's a halal meat, whether it's, you know, people are vegetarian, plant-based.
And also you remember that the food industry and the farming industry and the agriculture industry, is also a very political industry.It is also linked to the political economy of this.
So I think the best thing to do is to really ask exactly what you did, to ask these questions.Our attempt in the Global AI for Food, Global Initiator, is really like we believe in the power of bringing and convening people together online or offline.
And that's why we have these global conferences, regional conferences, and national conferences to have these conversations because these need to, as we move forward, we cannot run away from them.It's better defined.
And second is really like the public-private partnership, really get governments together.Very, very few governments right now.
Australia is probably one of the few governments who's really pulling together and having a conversation of what is the ethical aspect of AI for
I think that's where we need America to really step in, the FDA and others to come and say, let's have a conversation.
Let's bring the food scientists, let's bring the academics, let's bring the industry, let's bring the consumers, let's bring the policy makers and really say, what does it really mean as we are?And you cannot run away from it.
We are, as we speak right now, are already in the AI-powered economic boom.And we've seen what it does.So let's make the best out of it.And as we said, our mantra is only one thing.
We want to make sure that food is affordable because we do a lot of work on food security.It is nutritious and healthy so that it's not, as we know, that we haven't touched upon food safety and AI today.So really making sure that happens.
and is also very clearly committed on our journey to sustainability is really based on principles of green growth and net zero.And that's what we are really aiming for with this initiative and this book and this journey.
Yeah.Thank you so much for that.And thank you both for being here today.There's so much more we could go into.I was hoping to
touch on the future of feud security, but I'm going to leave that as a little bit of a cliffhanger so that people go and they find your book and they learn more about all these things that we touched on today and even more.
I want to switch gear here just to wrap things up.I wanted to ask you both if you have a music recommendation or if there's a type of music or an artist or a song that you can't get out of your head, what what music have you brought with you today?
Oh, hard rock. my all-time favorite, Deep Purple.I think I've introduced Rock, my daughter, and she's now an ACDC fan, wears her t-shirt with pride, so.
I'm on the opposite spectrum here because I'm totally into healing and mantras.So for me, I start my day and end my night with a lot of Vedic mantras.And for me, working on food is really to do a lot with Anupama Sotram.
I'm happy to share that with you.Because for us, at least in the culture where I come from and where I come from, food is also seen as a god, a goddess that gives you food.So we worship food as an offering to the humankind from the gods.
So there is a sutra or a vedic verses put together to really celebrate the vibration of the food which without which none of us will be able to speak because that really is the fuel to the human body.
So that is the level of linkages and going back to the questions of ethics and you know that's how personal it can be and be a soul journey. As long as, I mean, I love rock and everything.
I love all that great music, but I'm a very, at this point in my life, I want to vibrate more with the Vedic hymns and the mantras.That's how I live my life.
That's great.And usually I ask my guests each for some music and mantra, but I love that you guys divided this up.And so I appreciate the music and the mantra recommendations from each of you. Thank you both so much for being here.
I know this is a long podcast, but this is such an important topic.And even though we didn't get to everything, there's so much here that I think will inspire our listeners to go out and find your book.
We'll make sure to link it in the podcast description so that our listeners can find it and learn more.But thank you so much, both of you, for your time.And I'm really looking forward to
where things go in the future on this topic and how your team is able to help with that evolution.So thank you guys. Today's episode is sponsored by Aqualab.In this episode, we discussed how AI is transforming the food industry.
Did you know that Aqualab is also transforming the food industry with our drying intelligence and automation system called Scala?
This system consists of a model-based control algorithm that allows for moisture sensing of products inside of a wide range of dryers, resulting in reduced variability, increased yields,
less operator error, and lower energy consumption with an average ROI of three months.A link to learn more about Scala for moisture control will be in the podcast description. Today's song recommendation is Places To Be by Fred Again.
This is just such a masterful blend of ambient electronica and delicate emotional depth.This track opens with a really gentle atmospheric synth that gradually builds into a hypnotic rhythm, offering an almost meditative vibe.
Fred Again excels at creating these soundscapes that feel both intimate and also expansive, and Places To Be is no exception.
I really appreciate this track's understated beauty and just the emotional resonance he's able to achieve through this really distinct production style.A link to this track will be in the podcast description.
To round out this episode, I'll be offering another mantra.This is something that you can say to yourself to express something you believe in or maybe motivate yourself or feel calm.
This episode's mantra is, when I struggle, I always learn something new. I'll repeat this three times and maybe you can say it to yourself or even out loud.Here we go.When I struggle, I always learn something new.
When I struggle, I always learn something new.When I struggle, I always learn something new.
As you keep this mantra in mind, I also challenge you to think about what you learned from the last time that you were really struggling and how you can use that the next time you are in a similar situation.
Thank you so much for listening to another episode.My name is Zachary Cartwright and this has been an episode of The Drip brought to you by Aqua Lab.Stay hydrated and see you next time.
The Drip by AQUALAB (formerly Water In Food)
