What You Can Learn From The Burgeoning Health, Wellness And Nutrition Scene In Israel.

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Hey folks, it's Ben Greenfield welcoming you to this very special episode of the Ben Greenfield Fitness Podcast. Straight to you from Tel Aviv, Israel, where I am on a wellness and nutrition blogger tour. Now, let me preface this entire audio by telling you that I have created an article that I think you're going to find fascinating that I'll talk about in just a second. But in the meantime, that article is over@BenGreenfieldFitness.com Israel. If you don't know how to spell Israel, you could probably just google that. Now let's jump into the meat of today's podcast episode. Like I mentioned, I just wrote a fantastic article for you over@BenGreenfieldFitness.com Israel where I talk about what you can learn from the burgeoning health, wellness and nutrition scene in Israel. And it really is pretty crazy over here. A few of the things that I've got in that article for you are first, some vegan food recipes. Yes, that's right. Ben Greenfield, the man who hunts and fishes and kills animals and eats animals, also eats vegan. And I've been eating vegan actually almost the entire time that I've been here and I've found some stuff that's really, really dang tasty. I give you one of the recipes that I found at this restaurant that I ate at called Metze, same restaurant Natalie Portman eats at. So there you go.

It's gotta be good.

Metze cooks this vegetarian food and the guy who is the chef there is a vegetarian and a wild plant expert. His name is Gal. He's been a wild plant expert since he was 13. So they usually don't publish their recipes. I twisted his arm. I got him to hook us up with his custom recipe for moussaka which is like melt in your mouth. Eggplant, layered with tomatoes, layered with potatoes. There's like tahini in, it's brushed with olive oil. It is one of the most fantastic things that you will ever eat. And yes, it does not have meat in it, but you can check that out. I put a recipe for that over in the show notes. I also did quite a bit of time over at the Dead Sea. I did a mud wrap in the Dead Sea. I bathed in the mineral rich waters of the Dead Sea. I posted a rather entertaining video of what it looks like to be floating in Dead Sea water, which is crazy. You can barely even get your head underneath the water. It is so buoyant. But you can also check that out over there. And one of the interesting things is that the spa that we went to actually oxygenates the air in the spa. And I discovered this method that you can use to oxygenate the air in your home. And it basically uses some ferns and plants that are known to boost the oxygen level in your home. They've been studied by NASA, things like the Japanese royal fern. There's another one called the snake plant, the spider plant, and also one called the Chinese evergreen. I give you some links and information to some of these plants over in the show notes. But what you can do is plant these in your home and they literally purify and boost the oxygen level of the air in your home while you're sleeping at night or while you're sitting at home working. I've got some pretty entertaining photos of me in the spa, also covered in Dead Sea mud and date mud as well, a few other things. Cocktails. Been eating, drinking rather a lot of very interesting cocktails while I've been over here in Israel. One of the cocktails in particular uses coconut milk. Now, I gave you a recipe for a bunch of my favorites because I do tend to drink a little bit more when I travel and I pick the healthy stuff. I found a bunch of cocktails with fresh herbs, teas, fresh local ingredients in them, and muddled plants and fruits and lots of goodies thrown in. But one of them was called a pundu cherry. And I this at a restaurant called Taizo, which is an Asian fusion restaurant here in Tel Aviv. And the ingredients of it are vodka, vermouth, coconut milk, lemongrass, and herbal berries. Now, as far as herbal berries are concerned, I'm not quite sure which they used. I couldn't really figure it out or get a taste for which herbal berries were in there. But you could probably even throw like blueberries or goji berries or something like that into a cocktail like this and it would taste fantastic. I'll also put a link in there to the olive oil martini that I make and also a photo of all of us bloggers over here enjoying our own cocktails. I've been working out since I got here every day, hitting the gym. Okay, so one of the things that happens when you're drinking alcohol and sitting in the Dead sea and eating plenty of tasty vegan food is you got to turn that into muscle. And I've been using one of my favorite five by five workouts where I pop into the gym and I choose five big lifts. I'm going to give you this exact routine over in the show notes@BenGreenfieldFitness.com Israel but I do five sets of five reps of a bench press, five sets of five reps of a back squat, five sets of five reps of A power clean, five sets of five reps of an deadlift and five sets of five reps of a push press. And during my approximately 90 second rest periods in between every single one of those sets I do mobility work. So I snag a foam roller or I'll even use a kettlebell or a dumbbell. If a foam roller isn't around to dig into muscle tissue. I'll do yoga moves like sun salutations, a little bit of ab hollowing work and crunches. But basically I use that time wisely as I'm resting in between each of those five by fives. It's one of those workouts that when you're traveling, you're jet lagged. Whatever. You can get up at 5am in the morning, hit the gym, do it, get it out of the way and it's kind of a no brainer. Of course you do need to warm up well because these are heavy lifts, but it's also a great way to ensure that you are partitioning many of the calories that you when traveling into muscle tissue rather than into fat. I'm also going to in the show notes for this podcast over@BenGreenfieldFitness.com Israel give you my hotel room workout that I do when I don't want to get to the gym or don't have time to get to the gym. Comprised of burpees, lunge jumps, mountain climbers and even rows with the suspension strap that I travel with, as well as a picture of me working out on the beach here in Tel Aviv where they have little gyms sprinkled along the boardwalk. Quite convenient. And finally, the most entertaining and educational part of this episode comes now. One of the things that I'm doing while I'm over here is I'm meeting with a lot of nutritional and healthy living startups here in Israel. And one of the startups that I met with yesterday is one of the top Kickstarters ever to be on Kickstarter. It's called the sio. It's a molecular analyzer that uses something called a spectrometer to analyze anything around you. So you can like see how ripe an avocado is through the peel. You can find out the quality of your cooking oil, you can know the well being or even the type of your house plants, you can analyze soil, you can authenticate supplements. And you'll hear about exactly how we do this in the interview that you're about to listen to. You can pretty much upload and tag the spectrum of any material on planet Earth to the SCIO database. And with this tiny little handheld device which I'm holding in the photo that I have uploaded over on the website, you can analyze this stuff. It's amazing. And I'm going to of course put some photos and some links to their Kickstarter project over in the show notes. So before we move on and you get to tune in to that amazing SciYo interview where I'm basically gathered around a table in a little startup facility with a bunch of the other bloggers who are here on the trip with me, know that I will be back to regular podcast programming next week and also be sure to visit the sponsor for today's episode, Onnit, over@onnit.com BenGreenfield where you can save 10%. And finally, everything that I just talked about, from the vegan moussaka recipe to the plants that you can use to oxygenate your home that have been proven by NASA to do so, to the coconut milk infused cocktail recipes, to every workout that I do when I'm traveling, and finally a link over to the world's first portable molecular sensor, the SIO. I've got all that over@bengreenfieldfitness.com Israel so thanks for listening to this rather abbreviated episode of the Ben Greenfield Fitness Podcast. And now onto today's interview with the CEO of scio. His name is Dror. Enjoy.

The underlying science of spectroscopy is the science that describes light matter interaction, how light interacts with physical stuff. For example, it describes why you see different colors. That's kind of one way that light interacts with matter. Another interaction that light has with matter is the underlying describes the underlying chemistry which is what we're doing. It is an infrared light which means it's lights that we cannot see. And it also a very sensitive technology or very sensitive sensors. So these are used in labs all over the world. The kind of high end spectroscopy market today is about $10 billion of them. For infrared spectroscopy it's about $3 billion. These are large capital equipment, capital intensive machines about this big cost you anywhere between, you know, something that looks like this is 20,000 to $450,000. Something that looks like this can go 100,000 for a lab to, you name it. If you want to do the RD facility, ultra high expensive, very, very, very accurate. And what we set out to do is to see if we can leverage the fact that every smartphone has a camera, has an Internet connection and has basically a supercomputer at the palm of your hand. And we wanted to see if we can leverage these things to create a experience similar to what you just saw. Kind of enable all of us to kind of Google everything. But you can't google the stuff around you. So how can we google the stuff around this kind of, this cheese? Can you actually google this specific cheese, not only read the barcode, but know what this is made out of? If you go to a farmer's market in Vancouver when you know, they tell you this is the low fat cheese, ma', am, don't worry, this come out of my, you know, goats. And you're like, I can't believe it tastes so good and it's so fat free, right? And you'll be able to tell immediately. So, so can you do that or you know, you, you have a protein shake, you mix it up and it's very sweet. You're like, there's no way they use artificial sweeten in this. No way. It's zero calories, only proteins, right? There's just doesn't make any sense. What can you scan it and then know if it's sugar or not? You. So there are many specific use cases in the kind of the world around us. And this is only food. Can you imagine that? This sensor is used in industries as diverse as food, agricultural, pharmaceuticals, petrochemicals, cosmetics, healthcare. One day you can actually imagine this being used enough. So there's many, many potential applications for the platform, but we were very focused on getting a consumer experience, not a high end lab analytical experience because we knew that was a, a non starter. I mean there's a reason why these things are so high, big and expensive. And second, really we don't need to know that much. I mean, you know, you really could care less if it's 3.7 or 3.3% fat in your, in your milk or in your cheese, or you could care less if it's, you know, 1% sugar or 1.5% sugar in that drink you're about to consume. What you wanted to know is, okay, I got the shake, how many calories do I get for, you know, for what I'm about to drink? And it's just the high sugar or the notes, zero sugar type of drink. That's really what you, what you want to know. And took us a long time just both to think of the applications, think of the sensor. And we've accomplished that as you're going to see in a second. The team that accomplished this is very, very diverse. So we have people with backgrounds in food biochemistry, they have masters in food biochemistry spectroscopy and this is what they did. And they used to be kind of chief technology officers of large dairy farms or of olive making presses. We have folks that have PhDs in molecular chemistry, folks that have masters in spectroscopy, just regular physics. So that's kind of on the applications end. So what happens when you shine light and materials all the way down to cloud based. So it's a cloud based engine. What you're going to see, you're going to see a sensor. This is the sensor. I'm going to scan the cheese. It's going to take me less than two seconds. That thing created creates a fingerprint that is the spectrum, right, that correlates to the underlying chemistry. It is Centaur cloud, which is basically the Google material that we are slowly, slowly, slowly building. Now. It's small and it's going to over time expand, is analyzed then using an algorithm. The algorithm is selected using the application that you choose here. And once that is done, the answer is sent back to your smartphone. So I said we know people of light matter interaction. We then have people that understand software from the big data algorithms to databases, to APIs, to an entire back end software that we need to build all the way to the web services, front end applications on the smartphone. And then it goes further into the electronics, the firmware, real time algorithms that run on this electronics and then even further we have folks that have expertise in hardware building these electronics, understanding the optics, building. So it's a really kind of a humongous, a big team, very multidisciplinary talk, a lot of different languages. So I hope I bored you enough with the technology to this point. But just to tell you there's a lot of cool technology that goes into it. The bottom line is it's supposed to create a magical user experience that every day you can go out and use it and explore the world around you, know more about things that you wouldn't know otherwise. And hopefully as the database expands more and more and more, we'll be able to know more and more about the stuff that's just around us.

In terms of the actual information that.

You'Re gathering from this device, is it.

A list of the actual chemicals that.

Make up that particular object that you're scanning? Is it the molecules? What do you see on the screen?

So that's a great question. Let's do a demo. That kind of calls for a demo. So what I have in my hand is a. This is for generation. So what I have in my hand is smartphone with two very basic apps. There are more here, but two very basic apps. One is kind of nutritional values of fruit and cheese. This is the one that we've got built for this demo. Very rudimentary user experience. What we're going to ship in a few months is going to look much better. And we have a thousand developers on the platform. So you can imagine people, you know, who want to take it only for the recipe side. You know, they'll build an entire app or embed our API into their app, and it'll be part of just the way you scan, or if it's somebody who really cares about healthcare, whatever it is that they want to do. This is very basic UI just for demo purposes. This is the scanner, right. It communicates via Bluetooth to this smartphone. This smartphone, using a data connection, connects our cloud and it'll return it. And who wants to try this first?

I'll go for it.

Okay, so you can. You'll see here. I'll hold it for. You're right. Or lefty.

I'm right.

Okay. So the way you hold this is with your thumb like this.

So I hold the device in my.

Right hand, thumb on the finger. Now press it once just so you get a feel for it. And release. Okay, Release. You see, there's a blue light coming out. Okay.

And now the spectrometers that I've used in labs before, they're like the size of encyclopedias.

Right. So. So I chose the kind of fruit and cheese app for now. And what we're going to do is we're going to take the scanner and put it about half an inch. So pretty snug. So something about. But yay close to the apple. You're going to press and release until the blue light comes off and then you can put it down.

I'm going to find out how toxic.

This apple really is.

Okay, so at least now.

Yeah. So you got the fingerprint. Send it to the.

That was fast.

And I don't know, did it show a result already or no?

Okay, so, yeah, it's showing the result on the screen.

Yeah, it's telling your carbs and your calories.

Exactly.

So it shows the amount of carbs.

In the apple and the amount of.

Calories in the apple.

Correct. And this is per 100 grams. It's not per apple, it's per portion size. So you still need to input the size.

Amazing.

What else will.

So, and it's. This is a pretty sweet apple. It has about 15% sugar. Right.

So this is what literally, like, test different varieties of apples and see which.

One might cause blood sugar response. For example. For example. So this is. So we have this for many fruits and vegetables, and now somebody else can do the same for cheese, for example. Yes.

So is it taking the whole. Like this entire Apple specifically is 70 calories?

No. So what it does. Very good question. So, as you saw, the scanning piece is about an inch squared, right?

Yeah.

This is the light, the illumination light. So it takes whatever is underneath this illumination, scans it. I mean. What do you mean, scan it? Illuminates it. See, the molecules vibrate, creates a fingerprint in this little scanner, sends a fingerprint with the app to the cloud. Our cloud is already populated with many, many fingerprints of many, many fruits and vegetables. The app asks it how many carbs, how many calories per 100 gram. This fingerprint correlates to. Sends the result back to the app.

Okay, so it's matching up the fingerprint of that app.

It doesn't match. It uses big data algorithms, so it's not really matching. But you can think of it in a way that we've never seen this specific apple. We just got it from the store. So as you saw. So it doesn't match it up. But it. But because we have enough fingerprints and give you an accurate enough response, if.

You scan something that has never been in the database before.

Excellent. You'll get. So not in this one.

I'm in a forest and I scan some random leaf that has never been in the database.

Excellent, Excellent. So you will get a information about what we can tell you from this fingerprint. It might look very similar to other leaves that we've scanned, or it might, you know, maybe the closest thing, if we've never scanned leaves looks like. Actually looks like a book because, you know, book is cellulose and this looks very similar to cellulose. And tell you, hey, you know, it actually looks like a book. You'll get kind of a similarity score to other things in our database. And it will also probably tell you, look, doesn't seem like we have anything good enough to tell you about this thing yet. Do you mind sharing with the community so we can crowdsource the database as well?

Will it tell me that it has cellulose like, well like will it say cloud for example on an app or on the website like this contains say cellulose starch structure.

So the fundamental thing that happens here, there's no kind of analytical chemistry that is involved kind of in the wet chemistry lab. What happens is very similar to a big data algorithm. So we look at all the huge databases that we have and we try to give you an answer that's as close as we can from the database that we have. Very similar to. If you ever use Google Translate to translate foreign websites that is not that good but it's good enough for you to understand what goes on in the website and the way Google Translate uses it or is done is that they've scanned tons of books in many, many different languages and assuming they know the same sentences, the same.

So when I scan that leaf that's never been scanned before in the forest.

And I get that data about it.

I can then go in and ask if I want to share this and I can say this is the leaf I found.

Take a photo. Exactly, blah, blah, blah. And maybe somebody else will actually look at your photo and say, you know the tags that I don't know your name is. So actually Ben said that it's Ivy. It's actually not Ivy, it's this, it's this plant and suddenly the metadata might change. So the next time somebody will scan a leaf actually it will look like.

Much better in that sense. In a sense that there's some quality control but it's crowd based.

Yeah. So I mean we will, so some of the database will have a data integrity team that will, you know, obviously make sure we don't contaminate the big database. But for some of the things like we're going to do pills in a second. Pills are very, very, you know, they're, they're manufactured in a very coherent way. So you can actually know if you're a supplement. For example. For example. For example.

So let's steroids in it.

So potentially, I mean, I don't know how. So this, this thing has limitations. You were asking question one second.

Oh, I was, yeah. So like, like product control and stuff like that. Like you know, like back in the day they, China had shipped a bunch of toys over the US So could people use that for product management?

Potential. So potential. But going back to the supplement question. So as you remember we, there is, there are limitations to this technology. For example, some things do not reflect light at all. They absorb light at like very blackish materials that are black, not only to our eyes but to just light in general. You know, they don't reflect anything. So we don't get anything. Some things are very transparent, you know, we can't see them. So we can get them. Metals, for example, are highly reflective. They don't interact with light at all. Can't sense metal. So there's, there are some things, salts, for example, they are also. There's different materials that we can't do much about. But sometimes you can actually measure the constituent cheese constituencies of materials by proxy. So even if we can measure salt once it's in water or it's with other cellulose, maybe we can measure salt. That's kind of one thing. There are definitely limitations of technology. It doesn't do volumes. Only does I just measured here. And also the level of detection changes between applications. So for example, there was a huge scare about melamine and baby milk powder in China at some point like three, four years ago. So some of those melamine would have been able to catch some which are very low level kind of tree detection allergies, for example, in, in foods we won't be able to do off the cuff because it is such a small portion. And usually you're only getting that. Exactly. So. So there are, there's definite applications although. And there are obviously different. A lot of things that you can do with it. So anybody wants to try the cheese? How much.

I'm scared.

Is it American cheese?

No, this is the cheese that you name it. What's the name? So yeah, press it once.

So this is an Israeli cheese?

Yeah, it's an Israeli cheese.

Maybe we're gonna find out.

So this is a nutritional values. So I don't know what it says, but I'll read you, I'll read to you what it says here. So it says number of calories per 100 per 100g. One second, one second. Calories is 300. I don't know what it says.

Pretty close.

Says 290 on the app.

And by the way, the package is plus or minus 20%. So if it says 320 between 240 and 360.

So the package is accurate on calories.

Yeah. What's next?

Carbs or.

Yeah, carbs.

Carbs says 0.2g. Okay. So it's kind of low on grams. Protein 26, 26.9, 25.

Yeah, that's close. Fats 22.

So it says 22 it's pretty close. So this is within the range that you would actually buy this in the supermarket, right? I have a question. Yes.

Well, you guys have sort of like a conversion chart. So if somebody was like, okay, I'm only going to eat like one. Say they're like, I'm going to have one slice of cheese in the next.

So how much?

48 grams.

So let's say it's so over here you just enter the little built in counter there. So we have a built in counter. So let's say it's 41 gram. 40 grams yourself. Done. Okay. And it automatically calculates how many grams you actually.

So for example, I'm going to put this audio, I'm recording our conversation and this is going to come up in to a lot of American listeners.

For example, right.

Is there going to be a non metric version?

You'll be able to change it. I mean, I know.

So which one?

Yes.

Which information is more correct about that cheese?

So I can tell you from kind of measuring stuff that I've done it. I usually trust this more than this label because this is printed once and these are actually made in batches.

Right.

And this is, you know, pretty processed cheese, what I have in my hand. So, you know, I'm not really sure. But if you buy a slice of Parmigiano, I can tell you for a fact, whatever it says on the label, it doesn't really matter because it depends. You know, Parmigiano has like these huge boxes and if it came from the center or if it came from the end, very big differences. So I scan it and I can tell you it changes from 25% fat to 35% fat and wow, it doesn't really matter.

Cheese is cheese.

But I want to know about medications.

Will this kill me?

It can kind of mess you up if it's not what it says.

So what I have here is in my hand I have a, what I call the light concentrator. And we're going to ship in a small excess so that you can actually drop pills in there. The reason I have the slide concentrator is you, as you can appreciate it illuminates a pretty sizable portion of the food. And here I have some pain medication. And you know, if I take a pill. Yeah. So if I take a pill and I illuminate it. Yeah.

Oh, that is.

So I actually illuminate my fingers as well. So that doesn't help.

You can't just hold.

So it's got this special container that you put. Did you say Sio or co Sio.

It comes from science. I mean that's the root of science, which in scio and Latin is to know. Okay, so that's where it came from. So what we're going to do now is we're going to drop the pill in there. Just put it in there and we can actually activate the screen from here. So you just press the screen and it doesn't do image recognition actually senses the underlying chemistry of this specific specific. So for example this it couldn't identify. Let's try to. Well I don't know why put another.

One in there now if it couldn't identify it that means either the it wasn't database maybe just didn't pick it up.

I don't know. We'll see. And sometimes what, what we need to do is to calibrate the. Because this is a very high end application. Okay, so this is a ibuprofen, right?

Yeah.

Okay, so this is the ibuprofen and this is an Adderall that is probably a little bit more generic.

If you find like a pill line on your carpet for example at your house.

And this is the Advili gel, you.

Could just like put it in there.

And so this is an Adviliquid gel.

That's pretty cool.

So I just dropped an Advil liquid gel in there.

So can you tell the difference between a liquid gel and a regular pill?

So does it tell you anything about them? Not yet, but as you can imagine just a matter of what we exterminated from the database. And it's. And it's as you can tell it's sensitive enough to know the difference differences between different formulation because the. Even though it's the same active pharmaceutical ingredient, the different non active ingredients, the formulation is very different. So we can actually tell the difference between a generic and a non generic definitely for added food additives. Assuming you know what you supposed to get and it's supposed to come, you know these. Yeah, this is supposed to be this.

For capsules and pills that look a lot alike. Like for example when I travel I'll take like a probiotic and a digestive enzyme. Right. And sometimes I don't remember which is.

This is the thing for you. Yes. Or if you, I mean that's the good case because you know what you got. But imagine that you got a headache, you're going, you travel to you know, to a foreign country, maybe in a third world country, you know, this is your summer trip and you need to go and you get an ad. The pharmacist give you. Please take this.

You know, you're like, I don't know what that is.

For example, food additives, etc. And do one of the.

All I'd love to do is pulse. Now I'm way away just to deal with.

Now. Now I have to sleep on my back. Okay, so you guys took me there. So. So I dropped another pill in there and let's see what it says.

I really like your graphics, by the way.

So this is aspirin, for example. So it's a totally different pain reliever. It's in our database so we can actually detect it. So this is kind of the little show and tell demo that I have for you. We will also launch an app for analyzing plants. So hopefully so. So the way we thought of the first apps was which materials do all of us have a visceral connection with? So food is one. You guys know this. Yeah. You blog about this is kind of because of you're into this, but definitely pharmaceuticals is another because I just want to pop the pill and I want to know what's in there. Yeah. And another one, as it happens, is gardening. So as we were talking to folks, we realized that A, gardening is the number one hobby in the United States and BE&B, people that care about plants, really care about the plants. There's a segment of the population and we're a startup, we're only starting, so you can imagine people. So the first thing is analyzing the health of plants, kind of how, when you need to water it so you won't kill them. And we think that potentially at one point in time, we'll actually be able to analyze the actual materials inside the plants, if they have any kind of active ingredients, etc. So that's another one of those things that we're working on. Yes.

What if you use that tool to just scan like a homemade recipe, is it going to. I mean, like a recipe that isn't in your database, is it going to be able to decipher what's in it?

So. So this is the way the big data algorithms come to play. So let's say you made a cheese dressing that you made. Okay. Now if it comes close to what looks like cheese in terms of the percent carbohydrates, percent proteins, percent fats that are in our database. And it's close enough. Even if we have never scanned something like this before, we will try to estimate as close as we can because we scanned something that looks similar to it. So we'll at least give you something similar and usually, again, the question that people have is how many calories per 100 gram. And it doesn't need to be super active, just needs to be there. And this super fat or not fat, you know, this is what people usually care about. And we hope that even for these cooked foods, we'll be able to.

Do you plan to incorporate any sort of scale?

So we talked to some of the scale, kind of there's smart scale manufacturers out there.

That would be cool. Yeah.

So we're talking.

So could you potentially scan each person?

So that's so potentially this. These are what we call healthcare applications. So this is still. This is not a medical device. This is kind of what we say, but definitely we've had a lot of interest from medical centers around the world in terms of taking this and, you know, the entire digit. Digital health movement is something that is happening big time. I'm sure you guys know, quantified Salesforce would, you know, would love to use this. So it's just a matter of regulatory issues that you need to face. And we're not there yet. But I think that over time, you know, we're into startup, we're not.

Yeah.

So we need to make sure that we start with the basic apps, make it work, make it scalable. This is a hardware company and the hardware company is, as they say, is hard. So you need to actually scale the manufacturing, make sure that we can ship them, all the good stuff. But there's definitely a bright future for what would happen if you one day can turn this. So by the way, this is the scanner that you just saw. This is the size of the scanner, and it's something about, you know, half an inch by half an inch by half an inch. And this is for comparison, an iPhone 4 camera.

Okay.

So you can imagine this being one size and this is the end product that, you know, eventually we can get this to. This is what we feel we can get this to. So this is pretty tiny and we think that this can be going inside smartphones, wearable devices, whether looking out or inside your body, Internet enabled devices. So smart scales, smart whatever in your kitchen, smart whatever in your. If it's cosmetics in your bathroom, there's many, many things that you can use. Smart applications, kind of a coffee maker that will tell you pop up a red light if the milk is, you know, sour because you forgot it outside in the fridge. Outside of the fridge. So it just won't give you that. You won't notice it. Only when you're drinking it, you'll notice it before. Exactly right. You know, 6:30am you're all kind of.

So like, what potentially in the future, what can you scan someone's body for and find out?

That's a good question that I have somewhat of an answer to. So. But body composition is definitely probably something that we can do. There are more things that we can probably do. Just not talking about this yet. Top secret, not top secret. I want to give answers that I know the answer to. Some of them we just don't know the answer to yet.

Thanks for showing us.

And it's only we're in the early beginnings of this company.

Visit BenGreenfieldFitness.com for even more cutting edge fitness, nutrition and performance advice.