Hello my friends and welcome to another episode of Running with Ryan today. I'm going jogging with a really interesting and inspiring character Maybe you've never heard of her. She's never been on any podiums. She's never gonna set a world record But she is gonna run over 35 marathons and ultra marathons this year She is a huge force in the body positive movement She is funny. She's a comedian my friends ladies and gentlemen, please give a warm welcome to Latoya Shantae Snell I'm gonna sneak up on her. Here. She is. What's up girl? Welcome to Boulder. Thank you. Thanks for coming all the way to Boulder for this thing I know, you know, it's really important for you to be on the show and you came all the way from Breckley And you're awesome. Yeah, how do you say we go running? Oh, yeah. Oh, yeah, let's do it First you admire this view. I mean look at that. Yeah, you have that in New York. Oh, no No, we have pollution and rats rats at pole dance. So no That is pretty incredible. All right, let's go have ourselves a wonderful day. What do you say? Yes? Yes? Yeah, we got some high altitude here, but you're gonna be just fine because the beautiful mountains are gonna power you first of all I like to get this out of the way. Yeah, you don't drive a car. I don't drive a car It's very rare that I meet somebody like this Why don't you drive? Because I'm a New York City resident and now we're kind of spoiled Yeah, I believe in trains and avoiding the five hundred dollar Insurance. Yeah, that makes a lot of sense. Could you drive a car if you got into a car right now? It'll be really really tragic tragic. Hey, that's exactly like me. My friends never let me drive How many races are you doing this year about 38? No big deal. No big deal. What are the distances of all these races five altars? 15 marathons And the rest of them are like I have sin Just little old half marathons. Yeah, you know doesn't run in the park And how did you get into running? I got tricked into this. You got tricked into this? Who would do such a thing? Man listen, I didn't run for anything more than the ice cream truck Which is a great thing to run for by the way. Yes it is and I still do. I love those bomb pops No, but I got into this about six years ago Like almost six years ago So now for my half my first half through a friend I'm meeting this year. He's out in the UK We've been friends on my space 16 years. Wow, and He signed up for a half marathon. I follow suit. It was a bucket list item and Somehow I fell in love with this sport. Yeah, it's not Just a running as a people it's a culture And that's like I want more of it. Yeah Yeah, how you doing? How's that the ones? Oh god. Oh god. Oh damn.  Personally, like what are you? What are you striving for every time you get to the starting line? Well this year's a challenge myself, so For the first six months. I wanted to see how far I can actually take it before I'm always like looking the ways to Face my fears. Sometimes our imaginations are like the worst people to listen to which is your head Yeah, and the dialogue and I realized I wasn't going to get past it unless I actually went through it. Yeah, so This year in particular is to see if I can get faster as a runner But I want other communities to get involved people who don't see themselves as an athlete We're all athletes. It's a matter of you know when we choose to pick out poison We live in a society, unfortunately where people are very judgmental and they'll look at somebody And whether they're their color or their shape or whatever and they have a preconceived notion already of that person Yeah, what are some of the things that you go through? With this kind of stuff Some of the things get really vicious and file. They'll attack my motherhood How could you feel comfortable, you know going out on these races abandoning your family? Yeah, to Someone actually attacking and poking fun at my son for being a type of diabetic It's like when you're short of line. Yeah, and you have the question Is it about you or is it something with them? Yeah, and it took years for me to get to that place. Yeah of understanding What do you say to mean people? How do you do you deal with them directly online? Or do you just kind of live your positive life and hope that that inspires people and you just deal with the haters be very honest both Like sometimes you want to embrace your petty say Jack petty white Yeah, and it feels good for about five seconds. Yeah For me It depends on the approach Some people are cry for help, but I came to a piece and understanding that You're not going to fix everybody and regardless of you have to ask yourself. Are these people actually receptive to what you have to say So there's times Where I actually go back and forth and then this sometimes where I know the conversation is already dead before it started Yeah, so what you can do is live your best life live your best life. I love it And you're like a super positive person you talk about how toward the end of races the backside of races You're the one pumping everybody out. How do you do that? What's your method man? You're at mouth 22 everything hurts all you want is a shower and it's hard When you're in a back-of-the-pack, you know things are already being torn down The cups sometimes are gone the stations are going the audience is gone You have to entertain yourself and sometimes you get to hang yourself with other runners. That's actually left out there That's feeling just as bad So instead of wallowing in that depression I choose to enjoy it, you know, so I find ways to do it through Instagram stories Talking to people that's out there in a course trying to figure out their life a little bit And we go hand-in-hand across finish line. I love it and those to me Those are some of the most inspirational moments at races. It's the fast guys and gals go fast and that's great Right. I'm proud of them, but it's the people towards the end that really have the stories Yeah, you know, they have gone through the pain cave times 1,000 to get to that finish line Right and those are the people to bring a tear to my eye. Like I mean, don't get me wrong I would never ever take from any one story, you know, sometimes when I listen to the back-of-the-pack and even the front-of-the-pack people just have this disconnect. Yeah, and If we start at listening to each other a little bit more You'll realize how much more we have in common. Yeah, the differences that separates us. Yeah, look at this beautiful scenery. Look at this She's good to learn a little oxygen break. I get a view of my beautiful mountains. You see, I'm a north boulder guy I don't come to south boulder very often. Oh really? Yeah, this place is huge And it's beautiful and I can understand why everybody's like into something like fitness like out here Yeah, like why why do you need a phone? Yeah, totally Well, you are not just a runner. No near near to my heart. You're also a cyclist. Yes, I am. All right. Let's talk about that Oh, man.  There you go. I'm gonna flash you real quick. Oh, yeah, we got to flash Everybody's flashing here in boulder. This is not New Orleans What do you love most about biking? What is it that, you know, besides the physical aspect and you know, burden calories, whatever Freedom freedom. That's what I'm talking about Like don't get me wrong. Like I love running The cycling You're able to just surrender let your hands go and just Really just focus on what's in front of you and what's around you just take a moment and just absorb and everything, you know And that's the reason why eventually I want to go into triathlons So I can get a little bit of the best of both worlds, but I have to get past this swimming Swimming's hard.  That's why I've never gotten to triathlons I feel like I'm fighting and I'm going nowhere in five minutes So when you started running Was it because you wanted to lose weight you wanted to be more fit you what was the impetus for actually running? Yeah, um fitness in general. Um, initially my story started out as a weight loss journey I know that's like a curse word to some people. Don't you talk about weight loss like this and sorry guys This is my real part of my journey, but um 2013 I was well over 265 pounds and um And it's not even so much to a number now. I guess he's not so much a number. It was to look Yeah And the looks that people give you because they treat you like a third classes Not even a second class like a third class It's like we know that you're real, but we're gonna either stare at you really weirdly or act like you don't exist And um, I didn't like the way I felt not even so much the way I looked And I hated the comments that came with it people telling me that I would be much better If I lost weight When I lost the weight I lost it in a year went down to 165 pounds. Wow and So 270 to 165. Yeah, wow and the crazy part is After I lost the weight I was more unhappy than ever was in my life and it wasn't the weight I realized I was using fitness and running As my placebo to do all the things that I enjoyed Yeah, I can do it any size. Yeah, true, you know So I gained a little bit of it back, but that wasn't until I actually went through a stent of anorexia Before I actually know in it and it's one of those hard conversations to really have with people because Usually they associate anorexia to have a look, you know, um, and I didn't fit that look I didn't I wasn't bone skinny I didn't have any like signs in my teeth but Unless someone talked me into eating 1200 calories a day By marathon training Wow 200 calories a day. No 1200 1200. That's still not much. Yeah, so essentially I was starving myself Long story short dead in winter 12 degrees outside.  Yeah, it's a process that I still fight through every day We have to remind myself to eat and you know, how long have you been running now? Almost six years six years and I imagine this is something that's going to be with you for the rest of your life Like you're hooked. Yeah, as long as I have my my mobility I'll find a way to move and even if I'm you know, like because I I do have um A series of health issues. Um, because I mean as much as I don't look the part of being an athlete with disabilities. I am Uh, I have sciatica Dis issues herniated disc Uh, I have endometriosis. You have weird orange hair. Something's wrong. Something's wrong. Oh my god. No, just kidding. The orange hair is awesome Oh, no, no, no. This is no. This is self-diagnosed Just that's just crazy new york syndrome but yeah, like um You know all these things you have a restriction on your mobility, but when you have those things taken away from you You realize how much it is a gift. Yeah to have it and to fight for it So my goal is to regardless of what I do Just keep moving. What do you say to people out there who might be watching who? Maybe they're like you and they're like, oh, I could never do that. I could never run a 5k What do you say to people like that? How do you get them psyched for it? Walk for a 5k walk There you go. Pro for a 5k. Break down the 5k to where it's only a mile. If a mile's too much Do just a walk down the block. You know, everything starts with a walk and starts with movement and Four motion is motion. So We have to stop overthinking it and stop looking at it. You know with this I have to be this type of body Or this type of shape or have everything or your ducks in a row. You're never ready. Yeah, so Face it. Let me all head on and don't let your fears kidnap your joy. Do we have a nature break real quick? Oh, yeah It's only illusion has bowled it here She's high on colorado not that kind of high I told myself I would never do 100k and I did it last year, you know A few americans who don't know what 100k is that's 61 miles. Oh my god. What do you think? I don't know drugs No, it's um, no, definitely the course had wonderful tequila. I mean the same course that we were just talking about Yeah, the javelina 100. It's a good one if you're gonna do 100k Recommend the javelina 100 because the eight stations are all parties and everybody's dressed up. It's halloween weekend God, like I had a funny story with um javelina.    I'd like to show off that we both have really bright colorful hoka speed goats Hoka and look at these socks. Look at those socks. Let's talk about fear fear holds back a lot of humans With not only athletics, but work projects relationships love How do you combat fear? You got to face it head on man Fear is our worst enemy And sometimes we make it scarier than it actually is, you know, when I think about it in relation to Running athletics, you know It's usually our mind now stop It's not even so much things that people will say to us those things contribute to it But it's our I saw it says this self defeat is 12 that we have I like to call myself a recovering self-defeat is For many years. I want to say for about 27 years of my life I talk myself out of almost every success that came my way I think I would have Been more successful in my life if I would have gave myself a chance Most of us are so terrified that once we hit a certain point How can I do better than last time? Part of doing better than last time means that you have to surrender to the fall You know, we think that we have to be perfect. Nobody's perfect. If you're perfect, you're not human You know, don't be afraid to screw up once in a while. It's supposed to happen There's growth and there's a learning experience by actually failing. Let's have a time out. We got horses here. Yeah time out. Hey guys Back back to the fear conversation Like um When I think about all of the things that I stopped myself for 27 years from doing It was always this doubt that was implanted for myself And with from others it was like the internal dialogue was are you worthy enough of doing X Y and Z that was in relation to work To fitness to health My darkest point. I literally was told by my doctor if I kept this up.  He's a really good guy and I remember seeing his face and he's like that's this He's like, you know, what are you going to do about this? And I was like, I guess I'll die That was my response, you know, and it's really dark, you know To tell somebody that but you have to understand the mindset of what get people to that place, you know, where You almost manifest failure and that's a job in itself to keep up And if we put in even a quarter of work Of trying to do the best that we possibly can and this doesn't have to be dynamic Sometimes we're looking at these goals Like it has to be something like grandeur. No reduce it down. Make it smaller. Hey, I want to run a marathon one day Cool, let's start off with a 5k. Oh, I'm doing this house of 5k program But it's not working out for me. It's telling me by this week. I should be running a straight mile with no brakes Screw that and on four more weeks Like like rules are meant to be broken. Yeah, exactly If you're following these Rules of how you're supposed to be How do you know how to actually improve the next time if you're never daring to take a chance on yourself or the program Challenge these things question these things ask yourself Why am I so hard on myself, you know, um, and That's why I love fitness so much because if you feel That struggle that's progress. Um, when you get to that level of comfort Is good But at the same time if you're dare for too long you're stagnant and then you get bored Then you're wondering why you're doing this every year The reasons why I run Change as I meet different people. I get to hear different people's conversations Uh, I remember meeting a follower. I won't say her name.    I'm like all those wonderful things you're saying about me You can't say one positive thing about yourself. Yeah and It's something that you can acknowledge and people's dialogue and what they don't say their body language can tell you a lot about a person Yeah And a lot of my body language changed as I became more confident in who I am Uh, what I looked like When all my rough days and I still have them because people get eluded like they get this delusion about me that Oh, you're an athlete.  So you don't have bad days. No I have them more. We all have yeah I have them more than people may know and The thing that gets me out of it is I intentionally force myself to do the things that I hate Which this is going to sound terrible.  No, I write three positive things about it I don't care how stupid it may sound at that moment It's three good things I can carry on for the rest of the day and I rinse wash repeat it Until I start to feel better about myself.  We're gonna run with it I like yellow We are gonna run with it We're gonna run with it like if we focus too much on these negatives We're never going to be able to grow. Yes. I'm not asking you to walk around this bubble and act like these things don't exist But ask yourself, how can you grow from it? Oh I just I just want to give you a hug. This is so wonderful. It is a beautiful day to be alive on planet earth I wish I could take like some more new yorkers out here Yeah, and see what I see like, you know the gift of traveling being a traveling athlete It's like if they experience different people with different cultures In the surroundings, uh, it reminds you How much more is around you and it makes everything so much smaller Like even the petty things it makes them so much smaller when you're able to look at things like this. Yeah I mean that is just awe inspiring. Yes. I have another question. Yes In the running world There aren't a lot of people of color. Yes, and there aren't a lot of plus-sized people Pink elephant. Pink. Yeah You show up to the start line. You don't look like most people. No, um, I stand out. Um, Everybody think i'm jamaican when I go to other parts of the world I was like, oh It looked beautiful and they'll say things like yeah, modern. I'm like, dude. That's like dude That's really really like wrong Just because you have dreads or something like yeah, it's the dreads in the beginning. It was really hard Because you never want to feel like you're alone.   Yeah.  There's a hurt. Yeah, but um I also think there's a barrier that's not only placed You know outside of the community or within the community. There's a fear again that word comes up a lot. Yeah There's a fear of the unknown if you don't see enough or something You don't take advantage of it. You don't you know, you don't want to be the first Now some people thrive off of it I'm person-like I don't I don't thrive off of it, but I don't also desire it either I feel like If you put yourself out there other people will come too and you have to make it accessible Yeah, and I think that's really The biggest thing that's the issue in our community when it comes down to people being the person of color and being a plus size athlete Is seeing it. So there are certain things. I think that race directors can actually do To get more people more diversity out there show more of our faces right us out there Tell us that hey, you'll want it.  We all have a plus size buddy or maybe a friend of color You don't realize how much more you have to put out there Just for being yourself and it makes and other people look at you as the outsider Um, and sometimes I experience that up races, but I show up Because I want more people to show up. I want people to know that yes, this is open for you too Like make space don't think that you're taking up space that you don't belong Or you're going to some imposter syndrome Just being there is a statement of his own. I can't thank you enough. I mean for doing this And for all you out there watching this, I'm gonna link some of the really awesome films that she's been a part of in articles You can get to know her better.  We will see you down the road and uh, oh wait, we gotta end this with uh Okay, let me get let me let's get this all set up here. We're gonna. This is the grand finale here Okay, here we go. Hold up. She's gonna pick me up. Yes. Oh my gosh. Look at this.


Hi everyone and thanks to Philip, Alina and Michael for organizing this nice online seminar. Feel free to interrupt with any questions. I should be able to finish within a lot of time, no worries. So I'd like to tell you today about efficient quantum factoring algorithms. So let's maybe, I'm not sure if it's needed for this audience, but let me remind you, factoring integers is all about. And if you have a number like 15, what you want to know is it's three times five, and then you knew that 21 is three times seven, 55 is five times 11. But as numbers become bigger, it's a bit more challenging already for 1843, you know, maybe some of you can guess or can figure it out and, you know, it would take a few seconds, but it's already a bit more challenging, right? 1843 is, I'll give you a second, but it's 97 times 90. What about 1851? Okay, that's even trickier. And there's actually a nice story about 1851. Let me show you the story. So Karl Pomerance shared this story, this experience he had from a high school math contest. So they asked him to factor 1851. And, well, he was fairly good arithmetic, right? And he was, he was sure he could easily try and divide it up to, you need to try to divide up to about 90.  But, you know, he wants to be clever. So let's try to figure out how to do it more actively without just trying to divide up to 90. And he spent a couple of minutes looking for the clever way to do it, but then started getting worried that he's wasting too much time. He did waste too much time and he missed the problem. So this fun story was probably quite traumatic for him. Later, he figured out how to do it. And the trick is this. You write 1851 as 8100 minus 49. And 8100 is the square of 90. 49 is the square of seven. So you have 90 square minus seven square. And that's just 90 minus seven times 90 plus seven. So it's 83 times 97. And that's actually the trick is actually what later led him to develop very fast factoring algorithms like the quadratic sieve and other algorithms, including the one we'll talk about today and short algorithm. I mean, he was a similar idea of what's basically happening here is we're finding a non-trivial square root of identity modulo 8051 in some sense. So you'll see it later coming up. This number, 81, we'll get back to it. So this is actually from a highly recommended enterprise award-winning article that he wrote in the notices of the AMS. So highly recommend reading that. This is from Karl Pomerance. Okay. Moving on. Factoring is a hard problem. Computers have trouble doing this. Factoring numbers. The current record is 250 decimal digits. I think it's 800 something binary digits. It took 2,700 computer years. Okay, this is a group. We did this a few years ago. You know, lots of computation power and that's currently the world record for factoring difficult numbers like the number here is an RSA number. It's a product of two big primes. Okay. So it's sitting in difficult. At least, you know, it seems so. It's not an NP-hard problem. Right. So it seems like it's a hard problem. The best known algorithm, the number field seems, you know, it's a beautiful algorithm. It runs in time exponentially in the cube root of number of digits. So it's, you know, it's highly nontrivial. The fact that something like that exists is amazing. But it's still, you know, it's still exponentially into the one third. So it's, you know, at some point, you know, it's still hits a wall and that sets around these numbers of 250 digits. And this is extremely important problem, right? We don't use it for any form of secure communication, you know, any form of encryption or signatures.  So it was a big shock and a big surprise when in 1994, Peter Shore was shown here, found that factoring is actually easy. It's easy for quantum computers. So he found the algorithm, the quantum algorithm that can very quickly, very easily factorize integers.  The minute we have quantum computers. That was a big shock. And, you know, isn't really the end of the world because of Shore's algorithm. And well, not so fast, right? So luckily, starting last summer, you know, there's a transition happening where people and networks and browsers and websites transition to post quantum cryptography. So that's post quantum means cryptography that's secure against quantum computers. So it's not based at all on things like factoring integers is based on things like geometry of lattices in high dimension. Okay. So that's something that's already started. There's another reason why maybe you're not terribly worried as that may be quantum computers do not exist yet. Okay, so those things are don't exist at least big enough computers don't exist, but there's constant progress. So this was early on 2017, you know, this was IBM had some, you know, machine like that and a few years later, another one from Google and another one. And basically now it seems like every week there's a bigger and better quantum computer. Whether they'll be big enough to actually factor as numbers, I don't know. I can show you some nice, you know, figure that I took from Sam Jack and so what's what I actually don't know what's going on in this figure. It sounds very interesting, but I don't ask me too many questions about the details here. But basically what he's showing is this is kind of where computers are. Now it is actually, I think there's been some progress or maybe somewhere here and here are a few other points. And this is kind of where you need to be to factorize numbers to use Shor's algorithm. And there's some other milestones that don't go away at surface codes. And I don't know much about this, but basically that's kind of what you hear you have maybe a hundred cubits, maybe a few hundred cubits now. And for to break up to factor as numbers like RSA, you need to be tens or hundreds of millions of cubits really to factorize them well, because these are really physical cubits and it's difficult. Okay, so what is the purpose of today's talk? It's not about how to build a quantum computer. I really want to show you this maybe maybe there is a way to improve Shor's algorithm and and be able to factorize, you know, with worse quantum computers in some way, maybe we can somehow optimize the algorithm and factorize in an easier way. That's the goal. Let's see how we get there. Again, feel free to interrupt if there are any questions. So let's read this carefully. Okay, so this is the main result. There is an algorithm. And it relies on some mild number theoretic assumption, even though this is a seminar about a number theory, I will not go into details of what exactly the assumption is. I can say if you want, I can say something about this. But there is some mild number theoretic assumption you'll see where it's coming up later. Under that assumption, there's an algorithm for factoring MVP integers. And it uses only something like n to the three halves gates. So it's a quantum circuit with only n to the 1.5 or three halves gates. It needs to apply this quantum circuit multiple times. It needs to apply it into the one half, so square root of n times roughly, independently. And then it takes all the outputs from those quantum circuits and perform some polynomial time classical post processing. That's very easy. And then it gives you the factorization. So for comparison, if you look at Shor's algorithm, it requires a bigger circuit. It requires a circuit with n squared gates. So if you look at this, you're kind of wondering what did we actually gain? I mean, this is n squared. And this is n three halves times n to the one half. So it's the same thing. Well, the thing is the quantum computers are very fragile. And it could be that you might have a quantum computer that can run for that many gates into the three halves and then basically decoheres and basically breaks. So if you have something like that, you can just run that computer just multiple times. So once you can do it, you can run it once, you can just run multiple times. That's not an issue, typically. It doesn't really burn or goes up in flames instead of destruct. You can just run the computer again multiple times. That's not an issue. The important thing is you don't have to keep the calculation coherent for too long. That's really the idea here. I only need to do n to the three halves gates and not n squared gates. That's really the idea. The fact has to do with multiple times is not a big deal. In theory, we can talk about the practice in a minute, but in theory, that's the idea, so that in principle, all else being equal, fewer gates should be easier to implement. Okay, so again, this is a good time for questions. Just like any other time during this talk, let me know if anything is not clear. There are many things I'm hiding, so it's good to ask some questions. Let me say also this was extended to discrete logarithms. I'll be talking about factorization, but you can also extend it and you can see this recent footprint from a Karen Gardner. Okay, also another main default note here. Those estimates assume that using fast integer multiplication in practice, you probably don't want to use those fast integer multiplication because they care big constant. There's a big overhead usually for small numbers, but the similar speedup is there no matter what multiplication I'll go into the news. Okay, so that's kind of the statement before moving on to talk about how it's done. Let me maybe talk about the practice. What does it actually mean? Does it mean we can factor as numbers using existing computers, existing quantum computers? Well, no, the answer is not yet. It's actually not even clear if this algorithm improves in practice on Schultz algorithm. When I say in practice, I mean for small numbers like numbers that we care about in practice, that 2000 bits, if I want to really factorize numbers that are used currently in cryptography like 2000 big numbers, the thing about Schultz algorithm is that it's really amenable to many, many optimizations. If you look at its actual implementation, the constants are very good because it's relatively compared to what I'll show you today. It's simpler. The constants are better. So all those constants, they really matter once you want to factorize number with only 2000 bits. Once you go to bigger numbers, then the advantages, the asymptotic advantages that I'll show you today, they can kick in. But for small numbers like 2000 bits, you might not still see the improvement. So again, Schur is generally highly optimized and it's hard to beat it, especially it's hard to beat it for small numbers like 2000 bits. And I should also say generally just the question is, what is better in practice? It's a difficult question because what is practice? There are actually many different architectures of quantum computers out there. People build some superconducting qubits. Others use neutral atoms. This is a new progress from Harvard and then company called QR. There are people using optics. So there are multiple different limitations and each one has its own limitations. Some can implement certain gates. Some have difficulty implementing some other gates. Some have issues with space, with number of qubits. So basically, there are many trade-offs and the trade-offs are very different depending on the architectures you have in mind. So there's no very easy answer to this question of whether this algorithm is useful in practice because practice is not yet there. We don't know what is the quantum computer that will be there in a year. Having said that, there is one clear issue that I should point out with this algorithm is the amount of space, the number of qubits that it needs. It's a big topic. It's a big issue because at least for superconducting qubits, for some architectures like the one Google uses, for instance, the number of qubits is a big bottleneck. So it's very difficult for them to add more qubits. And the advantage of shore, even though shore needs more gates, even though shore does many more gates, it's actually quite amazing. You could implement shore with very few qubits. So the amount of quantum memory you need is very it's only three times 10 logical qubits. And at least the water show today needs, in principle, many more. It needs to enter the three-halves qubits.  But luckily, this was recently improved to about 15n. This has worked by Raghavan and by Kuntanatem. So it's not quite as optimized as shore in terms of memory, but I think it's going to be getting there. And I should also say that some of the architectures that people use, like the one I mentioned, from the group of Luki and this company called Fuera, might not be as sensitive as other architectures and it might be easier for them to just get more qubits. It's not really the main bottleneck potentially for them. So in short, this is a difficult question and I'm not an expert in quantum architectures, physical architectures of quantum computers. I just want to show you some of the pros and cons. The screening advantage I'll show you today, a sympathetic advantage in terms of number of gates, but there are pros and cons there. So I mean, I'm running very fast. Any questions? I see some things in the chat. I don't know if it's for me. I can try to look into that. Okay. Oh, okay. So that's not for me, probably. Anything else? Any questions? Should I move on? Okay. So we want to see how the theorem is proven, but for that, I first have to tell you about Schor's algorithm. So this is Schor's algorithm. If you haven't seen it, it's okay. I won't assume you know anything about it because it's actually all here in this slide. So Schor's algorithm is all about finding the periodicity of the function and the function is this exponentiation function. So Schor takes this function that maps z to four to the power z. Okay. It doesn't have to be four, but let's take four, for example, and you take it modulo 8051. This function, this exponentiation function, or this I should say modular exponentiation function, has a period. And if you look at this, you might be able to see there are lots of colors. So what I do here, I kind of use color to represent the value four to the power z. So you know, this is initially, it's four to the power zero. So it's one, four, 16, 64. And then it goes on and on and on and on. At some point, you know, you start seeing there is periodicity, there's a cycle, and maybe you can already spot it. If you look carefully, you will see that kind of around this region, we start seeing the same thing we have here. So this is the point. It's actually 984 where the function starts repeating itself. So there's a period happening here. And I can show you that with this nice animation. So look carefully and I'm going to move this here and place it on top of the original one. I think you can see that it's identical. Okay. So this is, this is where the period happens in this function. So around this region here, let me show you again, you start, you start having a period. Now, once you have this period of 984, once you have this period of 984, you're basically done. I'll show you in the next slide how you exactly do that. Let me just say one more thing that Schwarz's algorithm actually needs to do more than computing this function. So what Schwarz's algorithm, what it does is computing this function actually in superposition. So you have to do it inside the quantum computer. That's important. And you do it in superposition over all the possible inputs up to some bound. Okay. So together kind of simultaneously in superposition, you compute all those values of 4 to the power z, motor to 8051. And then you use the big hammer called QFT, quantum Fourier transform. That's, that's, you know, the main idea in this algorithm and pretty much any other quantum algorithm that gives you exponential speedups. Quantum Fourier transform is very good at identifying periods. And it allows you to identify that 984 is the period of this function. Okay. Once you know that, once you know the period of this function, it's 984, then you're done. And this is really very basic number theory. So I'll go ahead and show you this, but it's probably well known to many of you. Having figured out that the period of 4 is 984. Okay. So once we know what the period is of this number 4 of this modular exponentiation, what it means is that 4 to the 984, right? It's like 4 to the 0. It's like 1. Okay. What it means is that if I take not 4, if I take 2, if I take 2 to the power 984, that number is a square root of 1, right? Because if I square this number, I get 4 to the 984 and that's 1. So that's why it shows 4, because 4 is a square. So it was convenient to work with the square number. So once I know the period of 4, I know that I have a power of 2, namely 2 to the 984. And in this case, it's 1163. And that's a square root of 1. Okay. So going back to Pomerance, you see that having a square root of 1 is very nice. Now it's not a trivial square root of 1. It's not like 1 or minus 1, right? It's 1163. So once I know I have this period, I'm basically done because I have the square root of 1. And once I have the square root of 1, I know that 1163 minus 1 times 1163 plus 1, you just expand it, you get 1163 square minus 1. That is 0, right? Because this is square root. Because 1163 square modulo 8051. And what does that mean?  Which means that 8051 must have a non-trivial common factor with one of them so I can easily extract it by running GCD. And that's really the end of Shaw's algorithm. I use GCD to compute the common factor of 8051. And one of this, maybe this one, 1162, that gives me 83. And I'm done because 8051 is 83 times 97. So again, the idea is to find the period of dysfunction, or I should say maybe it's the order, the multiplicative order of 4 modulo 8051. And from that, I can easily factorize 8051. So that's kind of very briefly, this is Shaw's algorithm. And again, the idea is to look at a function like this, like 4 to the power of z, and figure out at some point it as a period. In this case, around 984. So what's the issue? We're very happy with Shaw's algorithm, but as I was saying, it requires many gates. So let's try to understand how many gates it actually requires. And luckily, the number of gates is really just dominated by this classical part of computing exponentiation. Everything else, like initializing the circuit or doing the quantum Fourier transform, it's actually very fast. So it turns out this is really the only thing you have to worry about. It's a classical computation. Even in number z, you want to compute a to the power z. You have to compute it inside the quantum computer in superposition. That's why it's so expensive. On my laptop, on my phone, I can easily do that. But quantum computers don't have those billions of memory cells yet. Like our phones have. So how do we do that? Well, this is well known. We use a trick to compute this thing. And we have to compute, I should say, we have to compute this function for relatively large z, because we need to be able to get to see the period and the period can be actually quite far. So remember, 8051 is an example. But typically, the number is going to be the module, which is going to have maybe 2000 bits. And you probably have to go really far until you see the period.  That's the number of bits you have in the number you want to factor. So z is pretty big. It can go up to 2000 bit number in order to make sure you see the period, right? But duplicative order could be quite big. But once you're, you know, at this point, you'll surely see the period already. So, you know, that's kind of how, how you have to go with this exponent, 2 to the little n. How do you do that? Well, of course, you don't do just a times a times a times a, this will never finish. What you do is repeat a square, right? So this is a, this is a standard trick that you use to very quickly compute that exponent. Well, we'll try to improve with it today. I'll try to show you how to improve with this. But the trick, the way it works in Schwarz's algorithm at least, is that you basically do repeat a square. So you do a, a squared, a to the power of four, a to the power eight by squaring repeatedly. And if you just calculate and I show you the next slide, it takes n multiplications that you have to do basically each time you square, you have to do one multiplication. And each multiplication involves n bit numbers. And assuming we can multiply n bit numbers in roughly n times, this is, this is a fast integer multiplication algorithm. So we know that we can do it in time roughly n or n log n. In total, we get n squared gates in the quantum circuit. So this, this is where the n squared comes from in Schwarz's algorithm because you have to do n multiplications. And each multiplication involves multiplying n bit numbers. So in total, we have n squared gates. If you don't use integer, fast integer multiplication, if you use naive multiplication, you know, schoolbook, it will be n cubed. Okay. But, but let's, it doesn't matter. You can do it either way. But first, let's, let's be, for simplicity, let's focus on this and talk about n squared gates here. Let me show you exactly how repeated squaring works just to make sure we're on the same page.  How did I decide on when to multiply, when to square? This is based on the binary representation of 29. Basically, you know, each time I had to add, you know, if I think of the exponents, either add one to the exponent or multiply by two. So it's essentially the binary decomposition of 29. Okay, so this is it for Schor's algorithm. Told you it's n squared gates, right, and the key, the slowest step is, is having to compute this exponentiation. So before moving on, here's an idea, here's a very naive idea. Schor's algorithm takes this a to be a random number. I didn't say that, but I should say it takes it to be a random number. Let's try to be clever. Maybe let's use a small number. Let's take a to be small number. Maybe four. I gave you an example before. Let's use number four. Number four is small. So intuitively, maybe it's easier to work with four, because, you know, it's easier to multiply by four, right? If I want to multiply by four, it's very fast. Turns out, this doesn't help at all. Because if you think about what's going on here, you're doing those n steps and those numbers very quickly are going to become big n bit numbers. There's really not, not much you can do, just even just multiplying them by four requires n operations because those are n bit numbers. And of course, something like squaring the number takes n operations and gates because this is this n bit number that you're trying to square. If you start with a small number, sure, it buys you a little bit in the beginning because this is four, which is a very small number. And no, this is 16, because you square it. But very quickly, those numbers become basically like a kind of generic n bit numbers, you know, more to do your composite. So you actually don't gain anything, almost anything by using a small number. Because very quickly, those numbers become huge, they become n bit numbers, basically. So we don't really benefit much, but you'll see later, it will be really one of the main ideas is to use a small number, but we need another idea to make it work. Okay. So that doesn't help us much. Let's see how we actually introduce another idea and maybe it will help. That's the idea. So what's going on here? That's a two dimensional picture now. Okay, so previously we had like one dimensional, everything was in one dimension. Now I want to do two dimensions. So I choose two numbers, I choose four and nine. No, just four, previously I had four and all the exponents of four. Now I took two kind of two basis of the exponent, and I have a different function. It's a two dimensional function. It takes two numbers, z one, z two, and computes four to the power z one times nine to the power z two. Why? Okay, it's not clear. Or maybe let me all this spoil and tell you this will turn out to be easier or faster to compute for some reason you'll see in a minute, but bear with me. Let's just see why it even makes sense. So what I'm showing you here is a similar kind of plot. Basically each kind of pixel here is the value of four to the power z one times nine to the power z two, right? For some value here, the z one is I think on the y-axis and z two is on the x-axis. Okay. And this function as you can probably appreciate this function also has a period, right? So kind of here, this point here is basically corresponds to one because it's four to the power zero times nine to the power zero. This is one. And I think so you can see my mouse cursor, right? Hope it's visible. Okay. And this is like this point here would be out here is four and 16 and here's nine and 81 and so on. And this function also has a period and you can kind of see it here. It actually has many periods. It has this period here, here and here. So it's a kind of two-dimensional, it's a two-dimensional periodicity. It's not one dimension like before. So there are multiple periods, but that's okay. See the period. And let me show you again the same animation. Let's see what it looks like here. So look carefully and I hope you see the animation smoothly enough. I'm kind of shifting it and you kind of see now how nicely this aligns with itself, right? So this point here is a period and I think it's like 47, something, we'll see in the next slide. So there's a period here and let's see what this tells us and how we use it to factorize numbers. Okay. So again, all I did now, it just did the same thing that chore does, but in two dimensions. Eventually we'll have to go to higher dimensions. It's not going to be two-dimensional, but for this nice picture, I prefer to do it in two dimensions so we can actually see what's going on. Okay. So this is exactly like before. Once you figure out the period, once you know the period is 19,47, you're done. And let me show you why. It's basically the same reasoning as before. Let's just see it again to make sure everything is fine. So once you know the period is 19, 47, so what does it mean? It means that 4 to the power 19 times 9 to the power 47 is the same as the origin. It's the same as 4 to the 0 times 9 to the 0. In other words, it's 1. So we found that this number here is 1 modulo 81. But that immediately gives me a square root of 1. It immediately gives me the 2 to the power 19 times 3 to the power 47 is a non-trivial square root of 1. It gives me this number 6888. This number, if you square it, obviously you get this number here, the 419 times 9 to the 47, which is 1. So this number here, 6888, if you square it, it should be 1 modulo 8051. And once you have a square root of 1, you're done, just like before, because 8051 divides 6888 minus 1 times 6888 plus 1, because if I expand this, I get 6888 squared minus 1, which is divisible by 8051. And then I can recover a factor using GCD. I take a GCD of 6887 and 8051, I get 97. And we're done, right? 8051 is 83 times 97, and we're done. All checks out? Okay, so basically the exact same idea before. So all I told you so far is that, sure, you can take Schultz algorithm, you can do it in two dimensions, and you can see a factor as numbers. But it's not clear if you actually gain anything. If anything, it looks more complicated, right? What's the advantage of going to two dimensions? So let's try to understand the number of gates and try to understand if we actually gain anything, and what we have to compute is this. So in general, it's not in two dimensions, it's in D dimensions, and D would be something like square root of N, you'll see later why, D would be square root of number of bits. Okay, so if I have a thousand-bit number to factor, right? So this would be around 30 something. D is the dimension, it would be like square root of N, and what I want to compute is this. I want to take a couple of integers, Z1, Z2 up to ZD, and I want to compute this product of A1 to the Z1, A2 to the Z2, and those AIs are going to be some small squares like 4, 9, 25, okay? The main idea is this. The way we're hoping to gain is that because you're in dimension D, you don't have to go so far to see the period. You don't have to raise those numbers, AI, to such a high period, and you can kind of see it here. If I go back to this animation we had, the period was not so far from the origin, right? You only had to go a little bit here or here to see the period. I don't have to go to like, you know, 6, you know, some 8,000 far into this function. It's relatively close to the, in terms of some, you know, kind of some distance, it's relatively close to the origin. And that's to be expected because, you know, I have two dimensions, so I kind of explore much more in the same volume, like there are many more points to explore here in the same volume. So it's to be expected, I'll find the period closer to the origin, in terms of distance from the origin. Okay? So basically, and you can see this by, basically by some kind of pigeonhole, right? If you think of how many different exponents or how many different such inputs I explore, if I go all the way up to 2 to the n over D, well, each of them can go up to 2 to the n over D and I have D of them, right? So in total I explore 2 to the n different possible inputs. So by pigeonhole, there must be a collision. At some point I will start seeing a period, right? There must be a collision with powers up to 2 to the n over D. Okay? So previously it was 2 to the n, now it's 2 to the n over D, which sounds like it might help us because it means we don't have to exponentiate so much. We don't have to raise things to some very high power like 2 to the n. We only have to raise to power 2 to the n over D. Okay? Maybe it's an advantage. Actually, once you write things down, each exponentiation requires less, that's true, because you only have to do n over D multiplications, right? We're doing this repeated squaring trick. So I know a square, a square again. So to get those powers 2 to the n over D, I only have to multiply n over D times, which is cool, right? It's great, but I have to do it D times. So it seems like we really didn't gain anything. Okay?  What I told you now is that's like, you know, extend to D dimensions. Sounds like great idea. We don't have to raise things to such high power. You know, all sounds great, but actually when you do the, you know, actually work out the calculation, I don't, you know, it's not obvious if we gain anything because I still have to raise to this power. So I have to do n over D multiplications, but I have to do it D times. So it's something like n in total and I didn't gain anything. Previously, it was the same thing. We had to do n multiplications in Schroes algorithm. Each one involves like n bit numbers. So it involves something like n gates. In total, we still have n squared gates. Too bad. Okay. So what's the idea? We need just one more ingredient and that's- Sorry. Can I ask a question about the previous slide? So yes, in this process, so 8051 divides the product of two numbers. If one of them ended up being prime, then it wouldn't have worked. Like you find the GCD, it would be one. So I think, yeah. So I mean, it has to be, 8051 has to divide this number just by arithmetic. We know we divide it. I think with the concern you're alluding to, I was trying to hide it. So it's a great grade that you brought it up. It's an occasion for me to mention it. The concern here is that it could happen that this number here, 2 to the 19 times 3 to the 47, it could happen that this is a trivial square root of 1. Okay. This is something that could happen. It could happen that this number is really more to do 8051. It's really just one. Okay. In which case, what you'll have here is not interesting. In other words, maybe this first number will divide 8051 and the other one would be whatever prime or whatever you want. There's nothing to do with 8051. So this is really the concern. The concern is that this is the only thing I did not really justify. And actually, if you go back to this picture, you might see maybe you were wondering why I took this period and not the one here. With this animation, why I decided to take this one here at this position, 47, 19 and not the one here. And that's exactly because the one here happens to correspond to a trivial square root of 1. If you actually calculate whatever this one is, it's like 15. This will happen to be just one, a moderate 8051. So there's a real issue here. And maybe I'll get back to it at the end. And this is this number theoretic heuristic that I mentioned at the beginning. So there's a heuristic here saying that those numbers can behave randomly enough. Those exponents that I expect to find a period that's both short, both close, like this close to the origin, like here, and not trivial. So there are trivial periods that don't help me factor as a number. And the heuristic says that it behaves randomly enough that this will not happen typically. Okay. So I can talk more about this. It's a great third point. Thanks. Thank you so much. Thanks. Thank you. Great. So this is great that this came up. And I might maybe get back to it at the end. And yeah. Okay.  And the only missing idea is that there's actually a way to make this calculation faster. And I should say this is really kind of classical. In this point, it's really just about classical computation. There's nothing quantum here, of course, just how to compute this product more efficiently. And it turns out you can do it, but you need something to assume. You need to assume that the ARs are small. So this is the point where it's actually very useful to assume that the ARs are small. For Shaw's algorithm, we didn't gain much, but here it's really crucial. So let's choose the ARs to be small. Let's take them to be 4, 9, 25, 49, just maybe squares of the smallest primes. Those are very small numbers. They will not have n-pids there. They have some long n-pids. They're very, very small numbers. And it turns out once the ARs are so small, we can compute this product here. We can compute it using only n over d multiplications. Not n like we had before. It's only n over d. So really gain something. I should actually be more precise. It's n over d big number multiplications. You also have to multiply small numbers like 4 times 9 or 9 times 25, but that's very easy. That doesn't require many gates. To multiply 4 times 9, even I can do the piece of paper, right? The issue is multiply big numbers like n big numbers. That takes time. And that you only need to do, luckily, n over d times. So this is really how to do this. It's kind of boring, but it's really crucial. Let me show you how this is done. You can give it as an exercise in the computer science algorithms course. But let me show you how it's done. You have to think of it. It's not obvious, but it's also not very sophisticated anyway. So let me show you how it's done. But let me just say once you do that, you only have n over d multiplications. So the number of gates just went down from n squared to n squared over d, which is great. We have the advantage. And I told you I'd choose d to b square root. So it kind of all works out fine. I have n to the 3 halves gates as I promised in the new algorithm. You might have some questions now. And if so, let me know. Questions? Okay. So let me just show you how it's done. It's really just a basic arithmetic. So first, let's just talk about something simpler. If I just want to multiply numbers, say some d small numbers to multiply. So I want to multiply A1 times A2 times A3 up to A8. How do I do that? This is not a good way to do it. This is not a good way to multiply. Because if you do it this way, like A1 times A2 times A3 times A4 times A5, this is not good because very quickly numbers become bigger. They become like d bits because you have d numbers. So they become d bit numbers. And then each time you have to multiply, you have to spend another d gates just to multiply by the next number. This will end up like being like d squared. So you really want to do like a binary tree. You want to first multiply the small numbers together. So A1 A2 and then A3 A4, and then multiply the result together. Turns out maybe you would expect it's actually much more efficient doing this way because most of the multiplications are done with very small numbers because the A's are very small. And I just have to do at the end, at the root of the tree, at the top of the tree, I have to do the big multiplication, but very few of them. So you know, if you just do the math, it's something like d gates or d log t or something like that. So this is how to multiply numbers. And now how do I go to exponentiation? It's a similar idea. You want to combine with repeated squaring. Basically, you know, even if you don't follow the details, the crucial thing is that you always want to multiply small numbers. Whenever possible, you want to multiply small numbers and minimize as much as possible, having to multiply big numbers. So here is how it's done. Imagine I want to compute this thing here, like A1 to the 13, A2 to the 9, A3 cube, A4 to the 6. I start with just number one, you know, so all the numbers are to the power zero. And I first multiply by A1, A2. And again, computing A1, A2 is very simple. Those are small numbers. This doesn't take, it only takes, like, you know, d time to compute. It's very easy to compute those products. So I multiply by A1, A2. I get A1, A2. I square, I get A1 squared, A2 squared. Now I multiply by A1, A4. I get A1 cube, A2 squared, A4. I square this, I get A1 to the 6, A2 to the 4, A3, no, no, no, A3 yet. A4 to the 2 or A4 squared. Multiply by A3, A4, I get this. I square, I get this. And one more multiplication, I get what I wanted. So it's really the same trick, same repeated squaring trick. The idea is that kind of then you want to do the least significant bits, you want to introduce them using those multiplications of the AIs, which you compute as I showed you before using this binary stream. So, you know, if you didn't follow all the details, I'm sure you can complete it offline. The trick again is you want to use small AIs, you want to minimize the multiplications of big numbers. You only multiply the small numbers and then just introduce that into this cumulative register at each time squared. So, you know, trust me, this works, this gives you a faster way to multiply, but there is one important missing detail, which maybe you already noticed because I said something very strange. I said, you only need n squared over d gates with all this trick of optimizing multiplications. But then why not take d2bn? Like, why was I, I mean, clearly the way to optimize this is to take d2bn and not square them, right? If this is really that simplistic, I should really take d as big as possible. So, there's an important detail I did not tell you about, and this is this one here. I told you that it shows algorithm, there is a way to do the quantum fluid transform, and you get the period at the end. Okay, I'm very happy. The same thing is here, but it's a bit trickier because you're dealing with a d-dimensional object and the periodicity itself is d-dimensional. And to extract the periodicity from the results of the quantum computer, you need to work a bit, and you have to use an algorithm created by those three people here. You have to use the lattice reduction algorithm. So, this is Lenshram, Lenshram-Lovas, and you have to use an algorithm that can solve d-dimensional lattice problems. Okay. Again, Shor didn't have to worry about this. Actually, Shor, if you look at the algorithm, there's also some kind of lattice algorithm there. It's basically continued fractions, if you've seen the algorithm. This is basically a lattice problem in one dimension, some kind of continued fractions it has to do. Same thing here, but we're d-dimensions. So, that's why we need to run the quantum circuit multiple times, specifically d times, because that details the basis of this lattice. And once we have this basis, we can use lattice reduction to extract the period from that basis. So, I will not show the details of this. This requires some, you know, some calculation with lattice reduction. But trust me that this works. The issue with this, of course, is that lattice reduction generally is difficult. Luckily, here we can actually do it efficiently using the LLM algorithm. Okay, so it's an efficient polynomial time algorithm. However, the algorithm gives you an approximation factor of 2 to the d. It's not exact. We're not solving lattice problems exactly here. And this 2 to the d hurts us because it means that we'll have to explore more exponents, because we don't actually find the shortest possible period. We don't solve lattice problem exactly. We solve it with the approximation of 2 to the d as a result of which the exponents have to go more than what I told you before. So, I told you before that by pigeonhole, there is a period only up to 2 to the n over d. But actually, because I only approximate the lattice problem, I have to go a bit further up to 2 to the n over d plus d. Again, this d coming from the 2 to the d approximation factor of LLM. And the optimal choice, once you look at this, you can see the optimal choice is d being square root of n, and that gives you 2 to the square root n exponents, which means that you have to use n to the three halves of gates. Because once the exponents are 2 to the square root n, I have to do square root n kind of multiplications of n bit numbers. So, in total, I have to spend n to the three halves gates in this new algorithm. Okay, so it was a bit fast. I hope you can see that there's some trade-off with the performance of LLM. And that's how the n to the three halves really comes about. Okay, this is kind of trade-off. So, I'm slowly running out of time. Let me summarize. Here's the algorithm. We start with small numbers, a1 up to ad. Okay, those are the squares of the first primes, for instance. It could be something else, but we want them to be small numbers. Okay, I should say the fact that we work with small integers, that's why we need to heuristic. If I chose them to be random numbers, you don't need heuristic. You can say there's enough randomness that you can say that whatever period you find is likely to be non-trivial. And the fact that we work with small numbers requires some kind of heuristic, because I don't know really how the small numbers behave, but they negatively model a composite. As far as I know, even using something like GRH will not really help us here. So, we choose those numbers, be small, whatever way you want. You apply the following quantum circuit d times. The circuit simply computes this multi-exponent, computes it in superposition over all those possible exponents together in superposition. You apply QFT, you measure, you get an approximate dual lattice vector. That's really what you get from the QFT. You get some lattice vector in the dual. You use LLL to recover the period, and that once you have the period, as I showed you before, you can factorize it. That's the end of the algorithm. So, I assume you have many questions. I want to leave some time for questions just to summarize with some open questions. Many open questions reduce the memory, the space, the number of qubits we need. Currently, it's about 15N. It would be great to have even fewer. Do an actual calculation of actual number of gains, number of physical qubits. This requires understanding the architecture. There's some work now coming also from Kera and Gartner that's trying to analyze it a bit better. It's interesting to see what comes out of this. One thing I did not mention, maybe one you can try, is to improve the lattice algorithm, not use LLL. That would be interesting to understand this trade-off. Maybe I use fancier plastic lattice reduction algorithms, not LLL, and maybe allow ourselves to use smaller quantum computers. This trade-off. Interesting trade-off going on here. There's something more technical. Maybe I will mention this. One interesting thing that comes up is that you're running the quantum circuit multiple times, squared and times. The way I presented this, it seems like it has to always work, or squared and have to work. But you might think that some of them will just fail because quantum computers, they're not very reliable. So, some of those squared and might fail, turns out there's a way to deal with those failures. There's a way to make the post-processing more robust so it can deal once in a while with a failed outcome, with a failed output. And of course, there's number-theoretic assumption, which I could say more about. Okay, so I'll stop there and I'll take questions. Thank you.


Hey, look, Cindy's on the title screen. There was actually a patch that for some reason she wouldn't appear here, so Luckily they got that fix. Anyways, hello people of the internet. My name is Johnny and welcome back to Candy's Remastered. We're jumping straight into night five and again, like always, I'm super pumped to be back playing this game. I've been having the best time playing and I think the rat is active this night But I'm not entirely sure. Let me just get to blanks blanks camera Two, okay Hello, right Isn't the rat like in one of these and bullies only room. I think it's this one Yeah Geez, thanks. Yeah this You just take a sip of his treat Just a storage room. Hey, we've got so many easter eggs. Darko mark the cup. Oh Hello Maybe I should start playing the game and use stuff. All right. What's the next message? All right, so something in reverse obviously I was focusing way too much on the phone call and not actually the game. Oh my god. I Forgot that old candy's a thing and then I can't actually see his eyes through the Through the doorway. Sorry about that guys. Let me get back to where I was Maybe like a certain rat. Yeah, this phone calls definitely something in reverse Candies there Yeah, definitely something in a row. It's hello penguin Okay, I definitely know that old candy is coming up soon. Where's he? Oh, yeah. Oh my god Only I was too focused on candy to keep an eye on blank. Let me get back to where I was again But I missed old candy again. Oh Okay blanks out. Hey, he's coming up, but he's not there yet. Hello, of course All right, I guess just hang out here for a tiny bit. Okay, so that means penguin should be gone as well Yep, is the rat out yet? I don't know, but I really don't want to risk just looking around the cameras If I have to I do have enough power to just shut all the doors Candy's coming around the left side Cindy's coming around the right Hello Hello, okay Blanks doing his thing Don't see the rat. Okay blanks out. Sure. He's coming out Shoot don't do that. Okay. Now he's at the door. I'm just gonna keep that close unless he just left what she did Interesting I know my con commentary is not the best at the moment, but hang in there guys.  Hello Chester Cindy. Hello Good with him a bit. Are you kidding?  Yes, hold up Um, I'm not entirely sure how much of this night you guys are gonna see Simply because I have not been talking So I think I should just leave these doors closed. I think I'm just gonna leave these doors closed Yeah, okay, I'm not entirely sure how much the night five you guys are gonna see because I was not talking at all I had to go full concentration mode for this night. It was just man. It was crazy Anyways night five cutscene Load that recording actually not feed. It's a recording this time Sup guy. Oh, hey, what's up there? Hey a mistake a mistake a Mine mistake your mistake not my mistake a problem a huge problem Your face looks so smooth your problem my problem or your problem deep lower stuff. That's happening. All right night, oh hey, look at that Mary Smith $100 and 50 cents November 20th 1987 Nice CTC entertainment. You're one of us now Awesome. Oh, hello Blank. How are you doing? I'm gonna give night six ago. I'm pretty sure the rats active this night. So Awesome, I did have a lot of power by the end of night five though But just like night five. I'm not sure I like I'm not gonna be talking so Yeah, sorry about that. You guys can hear the phone call. Where's the rat? That's Chester I will not at least have a look at the rat because I want to see what he looks like in the remaster. Oh, no Don't do this to me boss. Don't sit down and talk or don't fire me man Hello, did I hear some footsteps coming from I did hear some footsteps Interesting all right rat. Hello. No, okay, you've got to be gone It's gonna double check. Yeah, okay. Yeah, I'm not sure how much I'm gonna be talking. It's so oh shoot. Nope Yeah, enjoy some silence Sorry guys. I just really got to concentrate Oh, whoa flashing lights. Hello That was the purpose of the flashing lights. Okay. Oh my god Why rat? Where did you even come from? I didn't even see you. I didn't see him once There's the rat Look at that dude. He actually looks really cool. I really like that Oh Ready, ready. Oh my oh my god. He's he's fast He's like really fast Hold up. I'm not actually playing the game. I just want to see He's so fast My guy damn it All right one last try one serious try. Hey guys, it's me editing Johnny blocks I know it's a bit weird to uh be watching me and then hear me talking to you without my mouth Moving in the video. It may seem weird, but let me explain I barely talked throughout night six, but I wanted to keep the majority of it in So you guys could see my strategy for the rat old candy their footsteps the camera's blank All of the mechanics in the game. I wanted you guys to see my strategy Um to to using them to to my benefit, you know I definitely do think the footsteps that you can hear all of the animatronics have Is the key to winning almost every single night It's maybe even the key to beating night seven custom night. It's really important every single character Including old candy and the rat have footsteps when they approach your door And if you listen closely for those footsteps, you can tell when someone is at the door So, you know when to close it without ever having to go into the cameras You will see me occasionally check the door cameras just to reinforce my idea of whether or not someone really is there or not But that's just a precaution. You know, that's really just to make sure That someone really is there and if someone is not there, it's just double checking You know, it's like when taking the test you want to go over all the All your answers before you pass then just to double check to make sure you're right And because you're not moving around cameras all that much You really only have to go into the cameras every once in a while to again double check who's at the door And but besides that you can just stay on blank basically the entirety of the night Make sure he's not at the window and if he is simply just close the door Everything else is kind of self-explanatory if you see someone's going eyes at the door where you just close it Really the only thing I wanted to show off for this night was my strategy for the footsteps So that's why I left the majority of this night in anyways. I think I've done enough explaining. Please enjoy night six That was intense Everything came at once. No, I heard the footsteps Why is that closed? Oh my god. How long has that been closed? And I just haven't realized Footsteps are key There there are footsteps Uh, maybe not. Oh no, I might have heard chester's footsteps.  Yeah Night six that was good a real challenge definitely an improvement from the first game The the first game was um way too easy This was this is definitely an improvement definitely much more of a challenge Hey guys Stop. Uh-oh Don't do that Stop please Whoa, whoa Those are some smooth animations My face, ah Ack my face. All right, so custom night Yeah wait What is you guys can't see my cursor, but on there's a If something near the peach is that candy is candy looming behind me while i'm picking up my paycheck Only $20 over Oh $20 for overtime. All right, let's check out extra's menu. This video may be a bit long, but You know honestly I'm cool with that. Oh my god. We can zoom in as well. Look at this dude Let me zoom in on his face.  This is the music. I was talking about Chester you look terrifying the penguin. Oh, he's so cute. He's so cute blank. Uh Uh Hello jane bob. Is that chica? Old candy Bruh You guys know i'm be using these for thumbnails.    Yeah Blank this is interesting. He's definitely a lot lighter Uh old candy Not much change again. They're more lighter And then the rat Definitely smoother all right some jump scares Nice Nice I don't think i've ever seen chesters That is cool Nice. Oh we can put that Okay. All right, then custom knight I'm nervous about this, but I again this video may be a bit long I want to give this one attempt I want to give custom knight one attempt Just one Oh look we got the little candy plush Oh, I can't poop the nose makes sense. Oh wait. Did I uh you're dead Nice. Thanks game. Thanks for the uh Oh god. Uh, oh god Oh, oh god. Oh god. I need to stop talking And concentrate. Oh god. Oh god There's someone at that door. You're kidding They don't leave your door man. These guys just straight up don't leave your door Now they leave This is This is insane.  That was insane We legitimately might have to do a live stream on that but anyways This has been candy's remastered episode three. I think we're just crazy the fact that we're already on custom knight Going into episode four. That's insane the amount of progress we made Uh two stars already two episodes two stars. Oh, no, this is the third episode third episode two stars I'm really happy with the focus that we've made in this game and We're definitely gonna have to do a live stream on custom knight. Not only do I think it'll be fun You know, we're visiting an old game doing custom knight It's a hard challenge the hard game and we'll just have fun You know hanging out on the live stream that'll hopefully be coming soon I'm not sure exactly when but we're definitely gonna have to do a live stream on custom knight soon But I don't want to drag out this video any longer So thank everyone so much for watching candy's episode three and I'll see all of you on the flip side Goodbye


Live from Stanford University in Palo Alto, California, it's theCUBE, covering Women in Data Science Conference 2018. Brought to you by Stanford. Welcome back to theCUBE. We are live at Stanford University for the third annual Women in Data Science Conference, hashtag WIDDS 2018. Participate in the conversation and you're going to see people at WIDDS events in over 177 regions in over 53 countries. This event is aiming to reach about 100,000 people in the next couple of days, which in its third year is remarkable. It's aimed at inspiring and educating data scientists worldwide and of course supporting females in the field. It's also got keynotes, technical vision tracks, and a career panel, and we're excited to welcome back to theCUBE a CUBE alumni, Zia Ma, the Vice President of Software and Services Group and the Director of Big Data Technologies at Intel Zia. Welcome back to theCUBE. Thanks for having me, Lisa. You have been, this is your first time coming to a WIDDS event in person and your first year here, you are on the career panel. Yes. That's pretty cool. Tell us about, you just came from that career panel. Tell us about that. What were some of the things that excited you? What are some of the things that surprised you and what you've heard in that panel? So I think one thing that was really exciting is to see the passion from the audience. So many women excited with data science and with the future of what data science can bring. That's the most exciting part and also it's very exciting to get connected with so many women professionals. And in terms of surprise, I think it's a good surprise to see so much advancement in women development in data science comparing where we are and where we were two years ago. It's great to see so many women speakers and leaders talking about their work in the data science space, applying data science to solve real business problem, to solve transportation problem, to solve education, healthcare problems. I think that's the happy surprise, the fast advancement with women development in this field. What were some of the things that you shared, maybe recommendations or advice? You've been in industry for a long time. You've been at Intel for quite a long time. What were some of the things that you felt important to share with the audience? Those in person here at Stanford, which is about 400 or plus and those watching the live stream. Yeah, Lisa, I provide a career coaching actually for many women professionals at Intel and also from the industry. And a lot of them expressed interest of getting into data science field and they asked me what is the skill set that I need to develop in order to get this into this field. I think first you need to ask yourself what kind of job you want to get into in this field. There are marketing jobs, there are sales jobs and even for technical jobs, there are data engineering type of jobs, data visualization, statistician, data science or AI engineer, machine learning, deep learning engineer. So you have to ask yourself what kind of job you want to move to and then assess your skill set gap and work to close that gap. Another advice I give to many women professionals is that data science appeared to have a high bar today and it may be too significant a job to move from where you are to data science field. You may want to move to adjacent field first and to have a sense of what it is it like to work in the data science field and also have more insights with what's going on and then to better prepare you for eventually moving into this field. Great advice. I think one of the things that jumped out at me is talk about skill sets and we often hear a lot of the technical skills that are essential for a data scientist but there's also softer skills. Maybe it's more left brain, right brain, creativity, empathy, communication. Tell me in your ascension to now the VP level at Intel, what are some of the other skills besides the technical skills that you find as data science as a field grows and infiltrates everything? What are some of those softer skills that you think are really advantageous? Great question. I think openness and a collaboration are very important soft skills because as a data scientist you need to work with data engineering team because a data scientist, you extract business insights from the data but then you cannot work alone. You have to work with the data engineering team who prepares the data infrastructure, stores and manage the data very efficiently for you to consume. You also have to work with a domain expert. Let's say if you're applying data science solution to solve a real business problem, let's say in a medical field, you need to work with a domain expert from the medical field so that you can tailor your solution towards addressing some medical problems. So you need to work with that domain expert who knows the business operations and processes in medical field really, really well. So I think that collaboration is key and of course you also want to collaborate maybe with academia and open source community where a lot of real innovations are happening and you want to leverage the latest technology, building blocks so that you can accelerate your data science application or solution advancement. So collaboration and openness are the key. Openness is a great one. I'm glad that you brought that up. We had another guest on talking about that earlier in terms of being open one to not expecting, you know, the scientific method. You go into it with a hypothesis and you think you know what you're going to find or you want to know I want to find this and you might not. And being open to going, okay, that's okay. I'm going to course correct because failure in this sense is not a bad F word but also being open to other opinions, other perspectives that seems to be kind of a theme that we're hearing more about today. It's be willing to be open-minded. You know, that's an excellent point, Lisa. You know, I can share one example, one coming from engineering background. When I first moved into this field, we always had the assumption that when we talk with our customers they must be looking for something that's high performance. So our initial discussion with our customers centered around Intel product roadmap that will give you the highest performance for deep learning training or for analytics solution. But as we went deeper with the discussion, we realized that's not what customer are looking for in many cases. The fact is that many of them have collected massive amount of data over the years. They have built analytics applications and to end on top of that. And so as the data representation get more complex, they want to extract more complex insights. That's the time they want to apply deep learning but to the existing application infrastructure. So they're looking for something, let's say deep learning capability that can be easily integrated into the existing analytics solution stack, into its existing infrastructure and reuse its existing infrastructure for lower cost of ownership. That's what they're looking for. And high performance is just nice to have. So once we are open-minded to that learning, that totally changed the conversation. Actually in the last couple of years, we have applied that learning and we have collaborated with top cloud service providers like Amazon, Microsoft, Google and Alibaba and Baidu and a few others to deploy Intel-based deep learning capabilities libraries, frameworks into cloud so that more businesses and individuals can have access. But again, it's that openness. You really need to understand what is the problem you're solving before simply just selling a technology. Absolutely. And that was one of the best examples of openness. It's obviously in this case, listening to customers. We think we know the problem that we need to solve and they're telling you actually it's not that. It's a nice to have and you go whoa, that changes everything. And it also changes, sounds like the downstream collaboration that Intel knew we need to have in order to drive our business forward and help our customers in every industry do the same thing. Exactly, exactly. So a couple of things that I'd love to get your perspective on is the culture at Intel. You've been there a long time. What is that culture like in terms of maybe fueling or being a nice opportunity for bringing in this diversity that we so need in every industry? Yeah, one thing I want to share actually just now during the panel discussion, I shared this. I said Intel will be the first high-tech company achieving full representation of a woman and underrepresented minority by the end of this year. Wow, by the end of 2018. Yes, we put in our timeline by two years. Yes, we're well on track for this year to achieve that. And I personally, I like this quote from Brian Kazanich, our CEO, that if we want tech to define the future, we must be representative of that future. So actually in the last actually few years now, Intel has put great effort into hiring and retention for diversity. We also have put great effort for inclusion. We want to make sure our employees, every one of them come to work, bring the full selves and for the value add. We also invest in diverse entrepreneur through Intel capital initiatives and most importantly, we also partner with academia, universities to build the pipeline for tech sectors. So we put a lot of effort and we committed about $300 million for closing the gap at the company but also for the high tech sector. So it definitely we're very committed to the diversity and inclusion. But that doesn't mean that we only focus on this and of course we make sure that our people are bringing the right skillsets and we bring the most qualified people to do the job. On the pipeline front, one of the things I was reading recently is some of the challenges that recruit that organizations that are going to say college campuses to recruit, some of the missteps they might be taking in terms of if they're trying to bring more females into their organization in STEM roles, don't staff a booth with men, right? Or have the only females that are at a recruitment event be doing, handing out swag or taking names. Obviously there's important roles to be had everywhere but that was one of the things that seems to be, well what is simple thing to change, just flip the model so that the pipeline to your point is fueling really what corporations like Intel want to achieve so that that future is really as inclusive and diverse as it should be. Second thing that you mentioned before we went live and from an Intel perspective is you guys were challenged on the talent acquisition front and so a few years ago you started the Women in Big Data Forum to solve that problem. Tell us about that and what have you achieved so far? So great question. So this is three or four years ago and Intel, because I managed the Big Data Engineering Organization within Intel and we are working to hire some diversity talents. So we opened some regs and we look at our candidate pool. There are very few women, actually barely any women in the candidate pool. Again, yes, we always want to hire the most qualified people but it also does not feel right that when you don't even have any diversity candidates in that pool, even though we exhausted all possible options even try to bring the relevant diversity candidates into the pool, but it's very challenging. So then we reached out to a few industry partners to see is Intel the only company that had this problem or you have the same problem, it turned out everyone had the same problem. So yes, people value diversity, they all see the value but it's very challenging to have a successful recruiting process for diversity. That's the time the few of us gather together. We said maybe there is something that we can do to support a stronger woman pipeline for future hiring. It may take a couple of years, it may take one year but unless we start doing something today, you know, we're going to talk about the same problem two years from now. Exactly. So then, you know, with sponsorship from our executive team, you know, Doug Fisher, the Intel software and so this is group GM and also Michael Green and a few others, we, you know, bring the team together. We started, you know, look at networking opportunities, training opportunities. We work with our industry partners to offer many free training classes. We also, you know, start reaching out to universities to build the pipeline and especially to motivate the female students to get passionate about big data, about analytics. So as of now, we have more than 2,000 members globally for the forum and also we have many chapters. We have chapters along West Coast in the Bay Area, also East Coast. We also have chapters in Europe and Asia. So we're definitely seeing more and more women getting excited with, you know, big data and analytics and also we have great collaboration with women in data science at Stanford, so. Yeah, and it sounds like the momentum, it doesn't sound like the momentum, you can feel it, right? You can feel it online with, I can see a Twitter stream in front of me here on this monitor. People are getting involved in droves all across the globe and I said to Margo, I asked her earlier, Margo Gerritzen, one of the founders of Woods, I said, first of all, you must be pleasantly, pretty shocked at how quickly this has ascended. And she said, yes. I said, what do you go from here? And she said, it's really now going to be about getting involved with Woods more frequently throughout the year. Also kind of going up funnel, if you will, to high school students and starting to encourage them, excite them and start that motivation track, if you will, even earlier. And I think that is, in terms to your point about we can't do anything if the pipeline isn't there to support it. One of the things that Woods is aiming to do, and it sounds like what you're doing as well, similar in women in big data form at Intel, is let's start creating a pipeline of women that are educated in the technical side and the software skill side that are interested and find their passion so that we can help motivate them that you can do this. The sky is the limit where data science is concerned. Absolutely, absolutely. And it's great to see actually everybody recognize the value of building the pipeline and it reaching out beyond the university students. Because we have to get more and more girls getting into the science and tech sector. And we have to start from young. And I totally agree. I think we really need to build our pipeline and a pipeline for our pipeline. Yes, exactly. And also that sort of sustaining momentum as women go in university and study STEM subjects, get into the field.  But that retention, that motivation, and I think organizations like this, just with this, you can feel the passion when you walk into this alumni center at Stanford is really key. We thank you so much for carving out some time to share your insights and your career path and your recommendations on theCUBE and wish you continued success at Intel and with women in big data forum. I'm sure we'll see you back at WIDS next year. All right, thank you. Thanks, Lisa. Absolutely, my pleasure. We want to thank you. You have been watching theCUBE live from the Women in Data Science Conference 2018. Hashtag WIDS 2018, join the conversation, get involved. I'm Lisa Martin from Stanford. Stick around. I'll be right back with John Furrier to do a wrap of the day.