what's going on guys welcome to a new video so in this one i'm going to be answering the top 11 frequently asked questions i get asked about dropshipping i get messages every day questions every day across all the different platforms youtube facebook instagram and it's brilliant i love talking to you guys it's great to hear from people who want to change their lives for the better and do that by starting a dropshipping business however it's understandable starting a business it is a risk it is a financial investment people want to make sure they have the answers to as many questions as possible before they start that business so what i've done is i've collated i've put together the top 11 questions i get asked and i'm going to answer them all in one go in this video so next time somebody asks me one of these questions i can just redirect them to this video thanks for tuning in hope you guys enjoy it and let's jump straight into question number one so one of the most popular questions i get asked is what address should i use what address should i put on facebook what address should i put on my Shopify store because they don't want to put their home address out there on the internet for the whole world to potentially see now it can feel like that i felt like that in the beginning but to be honest it's nothing too much to worry about when i first started by furry first drop shipping business back in 2016 i was exactly the same i was really hesitant about putting my home address on my website online in case people would come to my house and knock on my door and complain about a product or whatever it may be at that time i was living at home with my parents as well so that just kind of added to the to the pressure or worry however i was so skint at the time i truly was on a low budget for starting my drop shipping business but i hated my job on the other hand i hated my job so much it was a small risk to take given the potential reward or my potential goal at that time which was always ultimately to be able to quit my job and do drop shipping full time so i thought what the hell this is worth the risk and i did it anyway i used my home address for about four months um it took me about three or four months before i made a single penny in drop shipping so if you're on your first one or two months and you still haven't made any money don't worry you don't need to worry it's perfectly normal at the end of the day if drop shipping was as easy as it looked on youtube everybody would be millionaires by this point anyway going back to the whole address thing so i had it on my website for the first three months or so um i didn't have a single customer come to my front door um which will be pleased to hear about it was very rarely used it was only used for returns which i'll get onto in a second when we address that question there are a couple of workarounds so if you have the money to do it there's services out there which will give you a virtual office what a virtual office is basically a big office building where people will rent physical offices it will have a reception it will have a concierge and they'll offer the service of being able to use their address for your business even though your business isn't based there or isn't stationed there so for example before i had this whole office here which i rent to run my businesses from i used to have a virtual office address so it was company name at mercury house so on and so forth it looked really professional nobody could trace the business back to my personal home address we were all good to go if you're looking for a similar service in the uk go to i believe it's called ukpostboxservice.com or if you search for uk virtual office services you'll be able to find people that will give you that same service for about 12 pounds per month a lot of them will come with mail forwarding at an extra cost too so you could be based in the north of scotland have an address that says your business is based in london and they'll forward any returns or letters that you receive to your business straight to your home address the other option is a po box however i don't know why anybody would choose this option because it's a lot more expensive and a po box is not as professional as your business at mercury house london may fair some of them will give you partial addresses like that which is pretty number two people ask me all the time because they're worried that when the parcel arrives at the customer's door because obviously it's going direct to there won't the customer see the invoice know how much we've paid and then be annoyed or kick off whatever it may be so the easiest work around is get it shipped to yourself and then turn it around yourself and forward it on obviously it's going to add on a couple of extra days plus an added postage cost and when you get to 50 orders a day you won't be able to keep on top of that sort of thing so in the beginning you could do that however like number one with the whole addressing it's not a big issue i've been dropshipping now for six and a half years and i can count on both hands maybe even one hand the amount of people have actually questioned the paperwork or what's actually on the parcel when it arrives to the customer i'll try and dig out an image i'm sure i'll have a return knocking about somewhere here so i'll take a picture and i'll put it on the screen now so this is essentially what your customer will see and like i said it's nothing to worry about you can also ask your suppliers at the time of fulfillment to not include any invoices or promotions or anything like that so again there's no trace back to a chinese supplier number three should i sell to the uk or the us this is definitely a popular one on youtube because the majority of information the bigger channels are americans as people in the us all the screenshots all the sales numbers that sort of thing is typically in dollars so people just assume dropshipping equals united states however my advice would be the ultimate thing you can do is test lots of different countries see which one is where you get the traction and then optimize your store but ultimately make sure that anybody who comes on to your Shopify store can shop in their local currency otherwise he's going to put them off and it's going to lead to your business failing so take it from your own personal standpoint of you whatever country you're based in watching this now if you're based in the uk and you're used to shopping in british pounds if you went onto a website and had to pay in australian dollars it's probably going to put you off you're probably going to leave and try and find that product here in the uk locally so ultimately i would always stick to local first because it's also going to come into play with question number four which is how do i deal with returns if you're in the uk selling to australia and you've got an australian customer that wants to return that item they're going to kick off they're not going to be happy about having to return it back to the uk so in this instance this is what you would do or here's the different outcomes number one is a customer says their product is damaged you ask for an image or a video of this if it's within the first 14 days and or 30 days whatever your returns guarantee is and it looks like the item hasn't been mistreated then you're obliged to give that person a refund however the item is broken so i don't want it the customer doesn't want it tell them to put it in the bin and refund their money scenario number two is they just want to return the item for every reason it's not broken however they just want to return it again if it's within that period then you're obliged to do so in which case i'll get it i'll get the item sent back to myself to my office that way later on down the line or over time you'll build up kind of like an in-house stock a certain quantity of the item should there be any issues further down the line with existing customers or people who reach out and contact you they place an order they get that order confirmation that says it's going to be two weeks or a week depending what the item is of course and they say actually i need this within three days i'll check stock here and if we've got it we can send it out uk locally from our office here number five how do i start drop shipping business it's a very simple three-step plan i could perhaps do a more in-depth detailed video on this one number one is you need to find a product number two is you need to build your Shopify store around that product so it's branded and then number three is you need an ad creative to then use on your marketing platforms so first of all establish what products you're going to sell make sure it's a product that's safe to sell it's got the profit room it's got the margins for profit in there it's trending it's in demand and you know for a fact people are buying it number two build your branded Shopify store around that product make it professional get a good logo color schemes fonts everything and then number three get some user generated content around that product so you have a few different ad creatives to test question number six do i need to register as a business before i start selling so from personal experience this is exactly what i did whether you follow this is completely up to you or not ultimately you should always seek your own advice when it comes to things like taxes i was young and naive at the time when i started i didn't care about legalities i didn't care about taxes i just started selling i wanted to make money so i could quit my job fast forward four or five months i was making over 20k a month and i thought i should probably get an accountant at this point to help me deal with things like taxes income expenses and that sort of thing so it was at that point in which we registered our company and from that point onwards and since then i've been with the same accountant he's taken care of everything to my knowledge however this is kind of like the textbook process that people should go by number one is they should notify this is if you're in the uk by the way so number one is you should register for self assessment this is if you're going to be a sole trader you're basically telling the government that you're starting a business so that at the end of every single tax year you have to submit what's called a self assessment it's basically a breakdown of your income and expenses so they can work out how much profit they've made and from that they can work out how much to tax you however i do believe you don't need to declare any income until you've made your first one thousand pounds that is to my knowledge what you should always do is go to the government website just search self-assessment or self-employed taxes or self-employed income or when to register for self-assessment and all the latest up-to-date information will be there on the dot gov website number seven how much money do i need um as much as possible is probably the most accurate answer but as a very minimum this is a very very very minimum is 200 pounds with 200 pounds if you get everything bang on perfect from the very beginning you can build a business a drop shipping business with 200 pounds from the ground up but you will be reinvesting every single penny for the first few weeks or even months to build up that cash reserve before you can start taking money out of the business preferably i would say somewhere between sort of a thousand pounds and two thousand pounds ideally unless you have a mentor or course to follow this going to put you down the right path save you from testing dozens and wrong incorrect creatives and shows you how to properly build and design a Shopify store so just as a recap then 200 pounds if you're an experienced professional 500 pounds if you have a mentor showing you along the way every step and then about a thousand pounds if you have a course that actually works to show you but hey how long does it take to get your first sale so again this is going to vary based on how well experienced you are when it comes to drop shipping if you're really good at drop shipping and know what you're doing you've got a decent store good product ad creative marketing campaign you can get your first sale on your first day a lot of the people i work with this is the case they will see their very first sale within hours of running their very first advertisements instead of asking how long does it take though a better question is probably how much do you have to spend because ultimately this is how you gauge the results of your ads as a referral or thumb if you've spent the same amount as the cost of your product so if you're selling the products for 40 pounds if you spent 40 pounds on an ad set on facebook and you haven't seen a sale by then something is wrong and you should switch it off you should definitely see at least one sale before you reach that point of ad spend number nine how do i test a product on facebook i'm going to be producing a video later this week on this i'm not going to say too much but in the beginning really small spends put out to lots of different audiences with a couple of different creatives run it for two three days max see what happens use the breakdowns to find out where the interest the clicks the purchases are coming from then take a very few hand selected ad sets the best performers to then duplicate into your scaling campaigns and this is where you double down on those significantly increase your budgets get those ad sets out of the learning phase and producing optimal results question number 10 is how to deal with long delivery times number one steer clear of alex so we'll go from an ali express perspective first number one steer clear of any supplies that don't offer guaranteed 15 day delivery otherwise unfortunately it could be anywhere from two three or four weeks also make sure you're crystal clear about your shipping policy your shipping rate make sure the customer knows exactly realistically when they're going to expect their product even if it is going to take two weeks tell them it's going to take two weeks and if they like the product enough they'll be willing to wait you can also use people like cj drop shipping who have warehouses all across the globe they may even have the products that you want to drop ship in the stock in a fulfillment center in the country that you want to sell to in which case you'll be able to offer competitive shipping times against any local business another option is also drop shipping agents these are people who specifically specialize in fulfilling drop shipping orders they'll know all of the best shipping methods and have all of the right contracts with the right delivery companies and shipping companies and they should be able to get your items delivered in less than two weeks if somebody can't guarantee you that i will try and find somebody else they can do not try and sell an item that's going to take over two weeks to get to the customer that's too long and it will only result in a headache in customer service complaints that you'll have to deal with charge backs from pay power and credit card companies and also negative comments on your facebook page and posts which is detrimental significantly detrimental to the success of your business number 11 last but not least should you advertise on facebook or tiktok the best thing you could do is test both see which one you get the most success on and then double down on it me i'm pretty biased with this question because all of my experiences on facebook i've been advertising on facebook since 2016 i've spent a lot of money on facebook so i know it pretty well so i'm a big fan of facebook despite the hate that it gets ultimately though from a from an unbiased and an honest answer both platforms can work it's just a matter of testing both and seeing which one works for your business and so with that being something guys that is the top 11 frequently asked questions about drop shipping i hope you've enjoyed the video if you're stuck with me this long make sure you subscribe to see more of my content i'd really appreciate that make sure you drop me a like as well so i know what sort of content you guys enjoy so i can make more of it all the best if you're starting a business this q4 make sure you come back at any point and ask me any questions thanks for watching and i'll see you in the next one


So, first let us start with the little characters. A regular expression matches a broad or a specific text pattern and is read from left to right which we already know that. The first thing to recognize when using a regular expression is that everything is essentially a character and we are writing a regex to match a specific sequence of characters. Regular expressions can contain both special and literal characters. Literal characters like uppercase characters from A to Z which simply match themselves like this. A lowercase character from A to Z which simply match themselves like this. And the numbers 0 to 9 which simply match themselves like this. So they are the simplest regular expression. In reality there will be more appropriate functions available to do just that. Ok, now let us move ahead. You may now think that what about the matching control characters like newline, carriage return or tab. Well, you can just add those to your regular expression in the same way as literals. Let us see some control characters like slash n which matches a newline if exist in the input string slash r which matches a carriage return slash t which matches a tab slash a which matches a bell character slash b which matches a back space slash f which matches a form field. Now let us see one very simple demo for a newline character. Ok, so this is another a good online regular expression testing tool. So first let us define the input text over here. Say for example test one. Now here I am hitting the enter key which is a newline. Now in the next line let us define another word. In the next line another word. Now here in the regular expression what happen if I write a slash n. So as you can see here that this slash n will match the newline in this input text. Alright now let us move ahead to the next concept. A character class is a group of one or multiple characters. With a character class you can tell the regex engine to match only one out of several characters. Simply place the characters you want to match between the square brackets. For example the pattern a b c d to h will match any single character from a to h. Here we may have a special characters or rather characters which have special meaning within a regular expression. In this module we will go through each of the meta characters to see exactly what they do and how you can use them to build effective regular expression. As you can see that you can add individual characters to a character class or you can use the dash character to indicate a range of characters. Using a character class is basically a way to say any of this character is considered a match. So basically this pattern says match any single characters from a to h same as this previous one. You can also combine several number ranges and literals in one character class. Okay another great feature is that we can invert the behavior of character class by negating them. So we do this by adding a character as the first character in the class like this. The class now means match anything except the characters contained in the class. Let's perform one demo to understand this concept. Alright so here I have defined the input text like this. Now let's say I want to match the word which ends with this AT character. So here I want to match the word like bad, cad and say that. Alright so now let us write the regular expression. So first let us define the character class. Here let us define the range. Say for example a to f and then a to t. So as you can see here that this rejects now started matching the word bad, cat, dead. Now what happened if I write the word f a t. So yes this word will also match because this word is full within this character range and also satisfied this condition. Now what happened if I write the word mat m and same way the word n a t. So why this word is not highlighting because this word is not full within this range. Alright now what happened if I write only b here. Okay so as you can see that now this time this rejects will match only this bad word and what happened if I add the kerexine over here. Let us see. So now the result is opposite. So now this time this rejects matching all the words and with at except this bad word. Let's take another example. Now this time say we have a price list from one real estate website and now we want to match the prices from this given list. It means we want to match the numbers and this dollar sign. So for that I need to write the rejects like this. Let us define the character class and then dollar. Now let us define the number range 029. So now here as you can see that this rejects now started matching only the numbers with dollar sign. Now what happened if I negating the result means what happened if I add the kerexine over here. Oh the result is totally inverted. Now it's matching any character except within this character class. Easy right. Now let us move on.


Okay, good afternoon folks, Larry Pesavento with the update for today, which is February the 12th, and I want to show the chart of the E-mini S&P. You can see the large ABCD pattern that is forming here up around 5080. We're trading at 5063 today, so we're getting very, very close to that spot. On my show coming up, I'm going to be talking about the dot com bubble and how well did ABCD perform during the dot com bubble? That was very interesting that year because the dot com bubble, the Dow Jones topped in January and NASDAQ didn't top until March, so there was two different markets. So that's why you've got to watch each of them separately. But we've got a gold market that's coming down to where we think will be a great buying opportunity down about another 50 or 60 dollars if it gets to that level. And then also, we're looking at the bond market because the bond market is not rallying. If the interest rates are supposed to be dropping, bonds should be rallying and they're not. So when the market is doing something, then what other than it should with the news, one should pay attention to that. It's trading at 119, the high end bid 125 and a half. So this is not good action in the Treasury bonds. Now, I would like to mention one other thing is our guest today is going to be Mike Moore, Moore Analytics always has some really good stuff about crude oil. And we're coming up against some really strong resistance. It's crude oil up here at this $78 a barrel. Again, that's anticipated resistance. At any time it could explode, do what's going on to the Middle East, even though it's not in the, well, it's in the news every day, but not 24 hours a day like it usually is. So those are the things that we're going to be following here this morning. We're going to look at the trades that we're in, some of the trades that we'll be looking at and we'll answer any questions that you might have, if I can answer them for you. That's mainly what I'm here for. So hopefully we'll have some fun. So if you have any questions, it's<PHONE_NUMBER>. And I'll be happy to answer them the best I can. Now I see that my time is 46 seconds too short, which is what I usually do. But I did want to mention, I'll bring up this chart here, just to show you where I'm going to start here. I want to show you the 1974 stock market bottom, why it was so important in my life and the lives of my friends. We'll be right back,<PHONE_NUMBER>.


Boom-t-i-um-i-um-um Welcome back, I did it-i-um-um South Shoe I said, I said it-i-um-um-um I said South Shoe People at home like I said it's show, gettin' it new Letting y'all know He comin' back, bro Call it a show, gettin' it new He comin' back, yo Letting y'all know Hey, man, welcome back! This is history. You know what? What?  We was just talkin' about this Me and my girl were just talkin' about this I was like, you know what? Me and Carlos have done the show together And Carlos have done the show without me But I have never done it Uh-oh By myself Without Carlos Uh-oh That's true Uh-oh We are makin' history Fuck! He didn't realize somethin' The legacy goin' somebody Come on! Help me, somebody! It's lit! I feel good Cause I know Loser's gettin' that dough And right now I'm hangin' with my bros and my sisters Tyler! Black! Come on! Come on now! Hey, Brady Hey!   He said He gave me that madder than that My boy at a time You know the hurricane You hearin' that nigga Had the flight job They turnin' in the hall Ready to action Right Nah, man, it's lit, though It's good to have my brothers and my sisters with me Yeah, and the fans need you, too, man 85, I miss you, man What's up, 85?          Yes, I do I still got it Oh, you still working with it?    Yes, she was skinny. Did she got fat?  Oh, I know but she didn't like that.  No, she ain't but she look good now.  Hey He lost it to her But is it like technically like he lost it to her Rating Okay, but then was the ratings going to her Oh, they just saying look Steve takes the time off never gonna let Kelly come in and see what's up Shit like that. I understand. They say hey Steve chill out.  I don't More ratings though I don't oh Listen You can save all the demographic talk are you telling me Steve got to sit down and let Kelly come take a time slot. Yeah Hey, hey, listen, don't make the shit feel good because I didn't got other work Yeah, is they let it they tell it Steve to sit it down to tell Kelly was not so much sit down It's just like move out the way. Okay. Okay. Okay. We need this show to stop So Kelly can do her shit during your time Her show So that's what I'm saying they didn't say like Kelly's coming to take your place They were like your show stopping. All right. We got another time that slot is that hour is needed to be filled So that's what I'm saying. Yeah, they didn't so what so what time? So what time Kelly was on? She's new right? So she didn't have a show.  We're not doing that We're not doing that. I don't know who they talk to that talk to a nigga. I'm gonna break it down Break it down. I need to know the truth. So you're telling me Kelly don't even have a show, right? No, you try to tell my summer.  I'm booze. Come on man. Open up like skin brother So so so Steve had a time slot correct. This is Steve. Let's hear them. Hi. Let's be dog Steve like I gotta go work Not a day before you do that. Not a day before you go to work between five 56 Can we can we let Kelly just just say hey to the people? You know saying we're not saying We're not saying anything right now. We just want to come talk and let Kelly go in there talk to the people Five six right quick right quick. No, no, no, that's how they start right quick. And before you know, they like Steve I mean Kelly did real good People like her Got five of the jobs Mr. Wrong answer. Come on. Come on. You're not gonna trip on old Kelly. I'm the high towel. I'm buddy Pal Don't trip on Kelly. It's a first show. I love her up.  I'm about right Especially if it's before the end of this upset contract, but he got to stay how long he probably didn't want to be doing Steve Yeah Yeah, it's rough. I know it had to be a time on that show break. He just wanted to break down be like It's some things I don't know I know I got a lot of shows on TV, but I just work hard.    It's way. Hold on.      You're like, okay, right?  All right.  I? Won't eat it, but I Lick cross it Oh lady, I like cross it. I Wanted in my mouth now talking about this earlier.  Yeah, like, you know It's a lie right there. Ain't no meat. It's a lie. You skinny D. C. There is there is me Hey, man, you already know what time it is your brother and you know what I'm playing yes The 2k 19 all day every day why my girlfriend is cooking me some food Yeah, man, you know what time it is all up on the west side of ATL I gotta make sure when I'm coming off the road. I am playing 2k 19 You know it purchase the Xbox one NBA 2k 19 bundle today.  You're trippin man, cuz I got all You know what you can do?  If you ain't got it go get it online, baby Man Hey, it's a little slabby skin right there, right? She be down there.  I'm not nasty.    Yes.  Absolutely.  Best believe we need a bit He'll bring some wings from the club You know that a lot a lot.   So Tyler what you got coming up man anything you want to eat for to know what you got going on Oh, man, once again, I'm on we TV Wilding out. Um, I got a tour with me Ronnie Jordan big baby Davis.  I also did a movie with Cat Williams and Jay Farrell called two minutes of fame. Come on, man That's new to me. Yep. I also did a movie with Nick Cannon. She bald come on, man That's coming out soon. I also did a movie with Mike Gibson Come on, man. That's coming out soon.  We just seen that on demand last night. Um Oh, yeah, I'll be forget little real side I just went over there shot another episode of that while now coming back on come on, man Oh, we did did another season You got it. Come on, man. Come on, man.  Uh, 85 sounds so wild. Uh, Come on. Come on. Oh I just shot another pilot with uh, ti. We're gonna try to uh, film it We we're gonna get a shot. It's called going viral. So come on, man. He'll be at that right quick Oh, my music. Uh, go get her music first and foremost nigga been bumping that appreciate it Trap soul coming out hold on time out.  I appreciate rap Oh I like when you do up I like it. I had to take the church. I meant to I ain't playing no game now And I got I got the trap soul coming out. Uh, come on, man The way that my turn that's going to be the most anticipated album that I might secretly drop another mixtape on Why not I did that's how you do it though. That's how you do it. That'll anticipate I might drop it just just for the fans and then I got one for the ladies. Love is painful. So come on, man We go we work it man. You know what I mean?  Give me like a month We ain't we ain't playing. I'm not playing. I gotta I gotta I can't say a crack no more You know, Kate can't say a came and I appreciate the fans.  November 18. What's going on? We gonna be in Chicago Look at this November 18th, we're gonna be in the big theater 85 south show live on baby. Come on now Old who We gonna be there Chicago come see us Tickets on the website 85 south show Bring some Harris chicken. I really want a breast and some mouth sauce With the fries and an extra piece of bread make sure I got three with the extra tub of mouth sauce for me Coming to Chicago coming coming. Yeah, I hope they got some of them shoes left to on baby When you be in there late, they're gonna be doing shit, bro. We're working and smoking nigga. That's When I take you I was I was on the set of rail I was uh, we would just start when I take you we were just starting filming for that Appreciate that bro. Appreciate that man. Be working.  Mm-hmm.  Thanks, bro. Yo, you so you was somebody else this time you were No, I was holding layers. I just I I didn't see it.  They probably I know you probably roasted in there for about an hour, but they put a I didn't couple off. Yeah They got the good ones on there. I add them dude. I add it to it though.  But then you popped up. You must like No, I'm I'm I'm I'm like it's um The love brother kind of like big friends. I got you. I ain't got a block.  Shit. I'm new to the goddamn instagram. I don't know nobody don't goddamn instagram On the gram. I know it's some cocaine My son gonna be mad.   That's what he better be glad I don't do Pounds at my house the my pounds niggas Y'all remember freaking each other a lot of back in the day Mrs. All up and down screaming at harry coochie.  Yeah, baby. I was your watch the color. That's a fun.  Come on, right? Yeah, you're right You don't hear my light no more No, no You don't let it ring ring Shout out to jamaica for that color. You don't have to lose daytime talk show slot after this dude. He lost it What do you mean? Like you know what? No more speed kelly carston took it.  Mic check one two one two one two Anybody open up? Uh, what mic is that one? What's up, bro, huh? What do you want me to say? Just something anything you say you landed noon. Oh, you see All right. Oh, yeah, you did. Okay. Yeah, say over. Yeah. I'm here.   I'll be at the grill Wait, what y'all call this one? This is the trap. This is the trap. I'm back at the trap today tonight They be like, Chuck, what's going on black man?    You let me Now mr. Christopher. Yeah. Yeah here for all y'all Tell him I said I put it up.   Oh, it's in my favorite piece So I'm aware that I ain't wearing the rolling rolling for the special purposes He's got one.  That's what y'all doing 85 That's how I do it This water being in the car It's car water. My name is you're gonna do me on some car water boy That's what y'all doing. I ain't been in this show forever.  It's in the headphone. Keep going. Yeah. Yeah. Yeah. Give me right. Yeah. Okay. Cool Bam new student government association prezatz the 2018 homecoming comedy show featuring 85 south the hilarious Carlos Miller winner of nbc's last comic standing And fam you alumnus Clayton English mtv while announced B Simone tuesday october 2nd and the al lawson center doors open at 6 30 p.m For more info contact a fam you sga at 850 599 36 24 tickets free for students non students Get your tickets at ticketmaster.com or the fam you box office


This research has developed a graphene-based memrister with a lateral structure that consumes less energy than traditional memristers. The memrister is capable of emulating typical synaptic behavior and is used to create a reservoir computing network with a high accuracy of 95.74%. This work could provide a cost-effective solution for mass production of artificial synapse hardware platforms, which would greatly accelerate the development of neural network computing hardware platforms. This article was authored by Chin Men, Wang Jingfen, Dong Hao, and others.


When designing motor control solutions, you might struggle to find the right compromise between size and power. Frequently engineers need to select and combine several different components. This can make layout design challenging and varies a constant pressure for continuous innovation and fast time to market. We at ST simplify your life, offering highly integrated solutions for a wide range of applications. For example, here at PCIM we are presenting a dual brushless motor driver based on ST Spin32 G4 and ST Dry 101. The ST Spin32 G4 is a system in package providing all the key elements for driving one brushless motor using field-oriented control. On top of which, its embedded microcontroller is so powerful that it can implement a precise positioning control of two brushless motors at the same time. And here is where the ST Dry 101 helps to drive the second motor. This compact and powerful get driver can drive a complete inverter also providing dual overcurrent protection strategy. One is based on shunt current sensing and the other on monitoring voltage drops of each MOSFETs. The ST Dry 101 and ST Spin32 G4 combo with low voltage STRIP F7 MOSFETs enables the easy implementation of compact, robust and cost effective solutions. Here I'm talking about dual axis applications like textile machines, robotics and pick and place machines. For more information you can visit the ST Spin32 G4 and ST Dry 101 product pages on ST.com.


Welcome, welcome. Hello, Kronan. Good to have you here. Hi, Nicos. Good to be here. Good to see you again. This is Nicos Manuselis. I have the pleasure and honor to be hosting a series of fireside chats on AI and food safety. I think about the most relaxed conversations, the type of conversation that I would have with you over a glass of wine, over a cup of coffee or tea, trying to demystify, uncover what is behind artificial intelligence, AI, and its applications in food safety and food risk prevention. I have to admit that you, Kronan, was, I think, number one in my list. The top number one person that I wanted to invite in this conversation. So, thank you for being here. Kronan McNamara is the founder CEO of CREM Global, extremely widely known as, I will use my words and then you will give me the proper intro, AI modellers that protect everyone's health. And what I really like about your work, Kronan, at CREM is that it seems that you're working on local problems that have global impact or global problems that have local impact. It's amazing, at least in the way that I understand it. So, let's start by who you are and what the company is doing. Okay. Thanks, Nicos. And thanks for pronouncing the company's name correctly, CREM Global. So, my background is physics, actually, and maths and computing. So, I got involved in food safety research during kind of post master's postdoc research in Trinity College. And I found it interesting to kind of apply the mathematical modeling and Monte Carlo type simulation into a new area, which was kind of interesting with lots of data, lots of complexity and interesting challenges. So, we got involved in food data science, food science modeling back in the early 2000s. And I founded the company CREM Global in 2005. And we got started working with some industry data and regulators and industry on both sides, looking at different aspects of food intake, food exposure, and then moving into more machine learning and predictive modeling as we went along. So, it'd be interesting to talk to you about that a little bit today. So, when you started, it was something like 20 years ago? Yeah, nearly. Okay. It was all about mathematical modeling and predictive modeling. And how did the name come? Oh, that's the most frequently asked question. So, I'm happy to explain it today. So, when we were in Trinity College, there was a research project and it was named by Professor Mike Gibney, who sadly passed away last week, actually. So, very sad news on that front. He was an inspiration and great at bringing consortia together to do research. And he was a professor of public health and nutrition in Trinity College at the time. And he came up with this project called the Center for Research in Exposure Modeling Estimates, which was an acronym for CREM. So, we worked on that project in Trinity College and it followed on from an EU project called Monte Carlo. And as we went along and started to engage with industry and government, they got to know the name CREM. So, when we formed the company, we thought it would be sensible to keep the name CREM because it would already have a little bit of recognition out there, but we dropped the acronym. So, that's a most frequently asked question. We tried to come up with new acronyms, but eventually we just left it and just called it CREM Global. And you started working on exposure modeling problems. What do you do today? Well, yeah, exposure modeling is still a big part of the work we do. And we work in food safety, agriculture, chemicals in cosmetics. Those are probably our three big domains. I always liked building platforms and products. So, we invested a lot of time and energy in developing our own cloud-based platform, which is a secure way of aggregating and gathering data, which we can talk about a little bit more. So, we help industry collaborate by sharing data in a secure and anonymized way. And then that data can be visualized or modeled using predictive analytics or machine learning type methods. And at the same time, it can also be put through more, I suppose, mathematical models that we've created over the years, which really just look at exposure. And that can be combining consumers' habits and practice data, which we get from various sources, either public or through purchasing data. And then combining that with ingredient and formulation data. So, then it can kind of look at a very detailed level of what ingredients are being consumed or being used in cosmetics or being consumed in food and adding all that up. So, it's a simple enough model of adding up lots of stuff. But the complexity comes in the uncertainties. So, we use what's called Monte Carlo simulation to model all of the uncertainties. So, any number or input in the model can be represented by, you know, probabilistic kind of range or distribution to help simulate that uncertainty and variability. And then give good predictions of exposure or intake with looking at the high consumers or the average consumer and creating those kind of that type of information for government and for industry. Okay, now you will help me understand this a little bit better. Because I hear you describing different things. I will park for a while the conversation around data sharing and private data sharing, which I find fascinating and it's essential. It's one of the topics that I really wanted to talk about with you. But you said that you started working on mathematical modules and then in a very sophisticated way you incorporated in this mathematical modeling, also the parts that have uncertainty. Yeah, exactly. Parts that cannot be determined in a certain way. So, at which point of this journey did AI come to play? And how? Good question. And a bit later in the journey, because our core offering when we started the company was were these types of exposure, mathematical models built from first principles, using scientific knowledge around intakes and different factors that are important like absorption factors and things like that, that we can model. Because you're never sure of those numbers, you have to use a probabilistic approach. And these mathematical methods I was using in college, I actually did a bit of work in the financial industry as well, valuing complex derivative options, again, using probabilistic methods where you don't know the future really, you have to only know the present, but you know, the future will diverge in various ways within known limits, I suppose, are reasonably well known limits. The financial industry didn't always get that correct, as we know. And they overestimated their confidence in those kind of limits and things that can go wrong. But yeah, exactly. So any uncertainty can be, you can model it using these Monte Carlo methods. And that's something I found very interesting in college. And I've enjoyed implying that into food safety risk assessments or exposure assessments these days. And what you're saying is that the starting point is a number of set first principles as you call them, which are the input factors or variables that might affect the outcome of the model. And that at least in traditional mathematical modeling in my basic understanding, they were given as something that is for granted by the scientists in its field. They say, guys, we know from our research that these factors correlate with an outcome that you should expect. Yeah, exactly. And when did AI come to play? And how did this change your perception of the problems? Yeah, so that's exactly right. So these are from scientific first principles, we would collaborate with toxicologists and with nutritionists to understand the key issues. And all of these parameters do affect the intake and the exposure estimates. But the question is how much do they impact it? What are the key drivers and how confident can we know about that? So those are those are one category of models you can use, which are scientific models or mathematical models from first principles as you call them, which is correct. And then, you know, I've always had an eye on the machine learning aspects as well, always fascinated by that approach, and never had a cause to apply them in those exposure type projects, because, you know, these first principle models were working very well, and they're well understood, kind of more transparent, I guess, as well than an AI model, which kind of comes up with predictions without necessarily any scientist saying, you know, programming in the different relationships. But as we went through a number of different projects where we were gathering new data and asking different questions, essentially, not just exposure questions, but other types of questions like, for example, food fraud prediction, or outbreaks of a pathogen in a manufacturing environment or in a agricultural, you know, in region, you know, these are different questions and harder to model from first principles, because we don't know the first principles a lot of the time. And therefore, these machine learning methods are really interesting to use to just discover correlations and categorizations of different things within the data that white would not be obvious, you know, to humans, but just by looking at visualization, for example. So different challenges require different models, I guess. And that's where we when we had different challenges, we moved into the more machine learning AI type models. And there will be the types of problem short challenges that require the machine to build the model as it goes. And as it gets new data to generate this mathematical model on its own. Do I get it right? Yeah, exactly. So even just training it on a lot of historic data, obviously, you have once you get there, you have a model that you've kind of trained and tested and you're reasonably happy with, or historic data or more new data comes in, you can retrain the model and update it if necessary. Yeah. Or keep testing it as you go and see if you need to retrain the model. Yeah. You have two scenarios, one that still works extremely well when you apply the traditional mathematical modeling approaches and one, but where you see machine learning approaches working best as simple examples to understand the types of challenges that fit better to each one of the approaches. Well, exactly. Like being, I suppose, a physicist as a background, you know, some of the physics we know the maths of we can just use models. And similarly, in some of our intake or exposure modeling problems, for example, we might be asked to look at the exposure of a population to a pesticide, we have a lot of data on food consumption, we have data on agricultural conversions from food to the raw commodities, we have data on the pesticide monitoring program. So these are all three different distinct data sets that were never designed to be used as one one calculation in the model. But by using like first principles, we eat food, we measure that we know the person's information about the person, maybe their age, their body weight, and we know the amount of food they're eating from these kind of studies that come from government or, for example, the NHANES database, we know the information of the commodities we know the pesticides really just have to, you know, we really just have to kind of combine those in a sensible way, you know, and that will create a first principles model that works. You could train an AI model to do that eventually, if you did enough traditional studies and knew the answer and said therefore, you know, new pesticides has been developed here some data on it, the AI could learn those patterns, but it's not really necessary because the first principle approach works well and is explainable. And but in other questions, then, where you need to go into AI and machine learning, for example, are much more I suppose, they're sort of different questions in science where we don't know the cause of everything and it could be human factors that apply to things like, for example, food fraud is one interesting one, you know, when climate changes, when prices of commodities change, when, you know, different political things happen around the world, you know, this stuff as well, you know, you're looking at these questions yourself, you know, there's no real first principle model that says, well, when the political instability goes up, therefore, the risk of fraud goes this way, you know, there's no physical reason for that, you know, scientific kind of principle you could apply there. So therefore, you have to measure that using data and, and, and, you know, in the old days, we use statistics and just said, well, this looks like there's a correlation there. And that's really what machine learning is doing, right? It's correlation testing on steroids, all the time changing parameters and trying to find fitting of these many different parameters. And that's the way I see machine learning is really just automated statistics on steroids, just finding patterns and optimizing things. And therefore, when you finish that process, you end up with a model that can actually, on new data, make a prediction, right? So the final outcome or the final IP, what you're generating, and is your, is the company's knowledge IP, it's the model percent, right? It's a trained model percent in both scenarios, right? Yes, the IP of the of the company Kramglobal is the data and the model, I guess, or whatever curated data, let's say, our access to data is a really important part. And then the model itself, I guess, is part of the, you know, the trained model and how you go about that is important. We also have the, you know, we also think of our cloud infrastructure as IP as well, I suppose, because we've engineered a system that can host models, host visualizations, and gather and help organizations manage and gather data in a collaborative way. So different aspects of that, I suppose, would be considered IP. So what I hear you describing is that technology per se, which we would expect, being from the technology component world, is an IP on its own data. Let's talk about data, but it can be our data, or it can be data that is provided by third parties. But the model per se, and the trained model that deliver reliable predictions is something that is being developed by a company like Kramglobal. Yeah, let's talk about data. Where is the data that you're working with coming from? Yeah, there's different sources of data. And there are public data sets that we can access and governments publish these. For example, the NHANES, the CDC in the USA, would publish an NHANES database, which is very rich and has lots of information in it and is useful. Other data sets like the Agricultural Research Service at the USDA and other PESA side programs, monitoring programs, and all of these data sets can be published. And other European and Asian and South American countries have similar data sets. So that I always like to kind of start from the public data and see what's there and what you can access. Sometimes you have challenges around accessing the data and seeing what permissions are associated with that data and trying to negotiate that process. So that's a good start. But the thing is everybody has that data, right? So for a company, it's not necessarily a competitive advantage to have that data. You can spend time and effort curating it and making it useful, but there's some value in that. But then the real interesting part comes when you have industry data or somehow working with industry clients or even government clients to gather private data, be that around the products that they're creating, the formulations or the monitoring programs that they're participating in, in private industry as an individual entity. And there's certain value in their own data. But when they start to share that data as an industry group, the real value emerges because you get a bigger picture of what's going on in the environment, in their sector. And you get enough data to train a model. As we know, machine learning models are hungry for data. So the more data you have, the better you can train a model. So it's great when organizations start to pool data that can be used for training machine learning models. So one source of data is coming from the public sector world. And this is not, although it is something that everyone can access or try to access and process and use. It's not as easy as it originally sounds. Exactly. And then the second source that I hear you describing is a customer's data. An organization is coming and is sharing private data with you because they want to build something to address one of their use cases. Yeah. But you do highlight, again, it's, I think the second or third time that you, you highlight this in our conversation, the value of getting more than one organization pooling together their private data. How open are they in doing something like this? Yeah, that's the challenge for many reasons. And it's always a slow start to these kind of initiatives. So some industries have done this really well. And actually at the SOT conference coming up, we're going to do a little joint presentation with RIFM, the Research Industry for Fragrance Materials, on how they got it right. How they've done this for many years and really shown a very successful case study of industry sharing data. Now, these are fragrance formulations, which are very, very highly secretive. These are the secret sauce of perfume, fancy brands and perfume, that they don't want people to know what, how they formulate those mixtures to make these expensive perfumes, and then they sell those onto other product manufacturers like shower gels and shampoos and body lotions to use those same fragrances. So very proprietary information. They would never share that data with their customer, but RIFM, through good science and good trust building over the years, convinced them to start sharing that data with RIFM, the Research Institute for Fragrance Materials. So now they have done, they've gathered data of over 2,800 fragrance ingredients, they've got the most comprehensive database of formulations used in the whole cosmetics and personal care sector. When the government has a question, they go to RIFM, when industry has a question, they go to RIFM, and we work with RIFM to help them gather that data, and we've put together the model of exposure and risk that's used then to set safe limits for all of those fragrance ingredients in collaboration with RIFM. So it's great when you see success stories like that and you can use it to motivate other sectors to try and do something similar, but they're always slow to get started. But when they do get started, as I think you know, in other projects, they can really build up a strong trust and collaborative community there, that actually tends to really work well together in the medium term. And what I hear you describing again is that the real value is in creating a resource that is of value and supports and delivers value to everyone involved, all the stakeholders that are involved in sharing data. In your experience, what is the aha moment where they say, OK, now we get it? Yeah, hopefully as you can deliver that aha moment early in the process so that they kind of get small wins or even early wins. They call it low hanging fruit, sometimes at the initial stages of a project, because if they don't see it and they're putting a lot of effort into data collection, and usually there is some effort involved, internal to their internal infrastructure, internal administration and trying to organize this data. And also then they're worried about sharing the data in the first instance. So if they don't see results reasonably early, they can really lose momentum. So it could be from the first visualization that you do on the aggregated data and they're comparing their own industry or sorry, their own company with the aggregate. That's a win. You know, oh, we're doing well here. We're not doing so well here. We're better than average on this. We're not we're worse than average on that. So that's that can be just a simple visualization. And sometimes that's impactful, you know, dashboard that can they can interrogate and see. And that's that's a nice way to start. So building up a nice bit of data visualizing it. And then as you have enough data, you can start to start to try to train a model. But I wouldn't go straight to trying to try to train a model. I'd go straight to a visualization initially just to give them some value quickly because training a model, you know, they'll be like there's nothing there for them to see initially unless you can visualize the model and the results of the model. And will they really trust the model? Not really. Probably not initially. So that that's a time it takes time to trust the model. But they can trust seeing the data and transparency of the data to us to as much of a level as you can do in the aggregate worlds. You can't give away, you know, information that could be giving away private information of one of the participants. So you have to be careful with the aggregate visualizations. But then you can give them their own private visualization of their data, which is is more detailed. And that's that's a nice way to start these kind of data projects. So even pulling together the data, the aggregate ones or even a better version, a more curated and high quality version of their own internal data and developing some initial visualizations that can help them understand what they see in their own data and benchmark themselves. Yeah. Against others is a strong argument. Yeah, this is what you said. And you also said, as far as the model is concerned, and I thought that you would say when they see the results of the model, they get crazy and the bite, you said, no, it's difficult for them to trust the model. Why is it difficult for them to trust the model? What do you think? Well, there's a few reasons, I think one is they're already experts in this business they've been in for many decades, right? And they know they kind of know what's going on anyway, you know, in terms of food safety. Other industries, maybe there's, you know, there may be more trusting because they don't have the intuition, maybe food fraud is one of those markets where they might trust a model better because they don't really know what's going on.  So that's what I think I spoke is one of the reasons that they're already experts, right? And they and if they're clever, they're going to be a bit skeptical and they should be skeptical of a new technology until it proves itself. Do you get do you get this kind of skepticism? Well, yeah, definitely. I think, you know, oh, that works great. Like, you know, you can tell the client, oh, you've tested the model on like thousands of records and you're getting 95 percent, whatever confidence and accuracy. And they're and then they, you know, they're like great, but I'm going to test it out for a few months before I trust them. And you're like, well, that's what we just did using your historic data, but they don't necessarily, they want to test it themselves as well, right? And what you're describing is creating this trust and creating this environment where people are sharing organizations, it's even more difficult, are sharing data with with each other in a controlled environment in a group that has agreed on data sharing principles. Yeah, what would what would it take? I wonder to continue this journey and making such insights available for all? How far are we from a future where this valuable knowledge that you see and generate, especially when you're looking at predictions, where we can make it something useful for the rest of the world? What do you think? Well, yeah, that's a really interesting part. And in some of the projects that's already possible, that they're not saying that only people who participate in the data sharing can use the model. Actually, some of them are saying anyone can use the model. Now there is a cost involved in using the model just because it maintaining the model and all of that. But some of the projects we're involved in already have gone that step to say this can be used by government, this can be used by other sectors of other companies that haven't necessarily been a part of the project in the sharing of the data. So definitely that's possible. It depends on, I suppose, how sensitive they are to the data. And maybe in some of the other projects, we could look at sharing a subset or a more even more anonymized version of the data to give people some insights. But there's always a risk with that where some NGO or other organization with an agenda will just pick up some one or two points in the data that look bad and, you know, make a story out of it and cause a lot of trouble for an industry. So why would they want to sort of take that risk where they don't have to? What's the benefit to them of doing that? So there's a challenge, obviously, there to try and overcome. But maybe I think I really like the spirit of scientific sharing of knowledge. So writing maybe scientific papers or things like that around around the results that have come from the data could be very useful to share the knowledge and learnings that could be translated to other regions or other industries, right? This makes sense. Yeah. And do you have a problem or a challenge or an area that you haven't touched yet, but you are eager to look at? Do you have a dream problem that you'd like to attack? That's a good question. I hadn't thought about it, but yeah, lots of ideas. I think I think nutrition is an interesting area and causes a lot of health consequences, probably even more than some of the food safety things that are far more high profile. So malnutrition, either over nutrition or under nutrition, I think is a very interesting scientific area that could have huge impact on health and people's well-being. And I'd love to do more in that. We did a study many years ago on personalized nutrition called Food for Me and recently on a new programme that took a twin study in Stanford and looked at the genetic impacts and things like that. It was it was interesting to see the study that they did with twins, right? So the genetics and the impact of genetics on people's diet and on their lifestyle as well as other factors. I thought it was fascinating. So I'd love to do more in that space. So this would be the space and the the area where you would like to get more data. Yeah, yeah, have access to the data and to build the models and try to see what you can predict in terms of expected outcomes. Yeah, real science into it. Like there's better tools now like everybody's wearing some kind of fitness tracker, like a watch. I've got the Apple Watch. It's got great data, monitoring my health, all these different metrics. You can get, you know, more genetic data quite easily these days, cheaper microbiome information, all this stuff. So I suppose it's just an interesting challenge with all that complexity and lack of understanding of the interaction between these things of how to live a healthy life and optimise your lifestyle under under the various conditions of people. So if we have this conversation in five years or ten years from now, what would you like to have done, have achieved and be very, very proud of? I think these I'm not sure if we'll get into that that area in the next five or ten years. Maybe we will. But I think we'll continue on our journey around all the other aspects of food safety and chemical exposure and nutrition. We are doing nutrition work, but that's a very ambitious grand challenge, right? And maybe we will try and set up something like that, maybe as an EU project or as an even bigger worldwide project. I'd love to do an EU project someday where I kind of came up with like a grand challenge like that and could have the time and space to try and really work on something like that for five years. I find a lot of the EU research projects are, you know, there's some kind of science behind them, but nearly it's already known at the start what's going to happen at the end. And you do your bit and it's a bit formulaic sometimes. But I'd love to do a more ambitious research project with some good resources behind it if I had time and the resources to do it. Yeah. So you would like to have spent five years already or more devoted on shortly one of the grand challenges and having the time and resources to do it. I really wish that you tell me such a story when we have this conversation. Yeah, maybe we will make maybe I'll have to wait till I retire to do that. Okay. So if you would like our audience to keep one key message from our conversation today, what would that be? On the topic of AI really are generally or whatever else we thought stop on today. Yeah. Well, I always try to emphasize like really good scientific thinking and being critically critically evaluating, you know, data and using strong mathematical methods, you know, so I think that'll always be important, you know, we see these days, the emergence of AI systems like the chat GBT and ML, LLM models, large language models that are becoming very, very, very powerful. And people worry about, well, where does that leave us in the work we have to do? And for now anyway, we still need to be very good critical thinkers in order to interact and evaluate the output of these models, like I was saying earlier, you know, if you provide a predictive model, even to your customer, they're going to be skeptical and they're going to think about it critically and see if it makes sense and take time to trust it. So I think being able to evaluate things like that, and being good scientists, good, good mathematicians and will be really important in every industry really going forward. So I think that's a good takeaway for your takeaway. So let's stay good, critical thinkers and evaluate what such technologies can offer with critical and scientific. And you said with a mathematical eye as well. Yeah, that's a powerful message. Yeah, thank you so much. Mathematics is probably the language of science really, isn't it? At the basics, the language of science. That's a punchline that I will keep and use to promote our conversation. You can use that as the headlight for this. Thank you so much. It was a pleasure to have you here with me. We've talked about amazing things and we covered lots of different aspects. Thanks for the time and the energy and the openness. Oh, pleasure. Thank you, Nick. Us. It's been a really interesting shot very enjoyable afternoon.