were you actually doing this? i don't know hey guys i just parked outside of kevin's house to remember kevin with the bird marley he's gonna drive me down to the shelter today the one where you guys all spotted the green parrot i know it's not Picasso because sheen was able to get in touch with them and they said that bird doesn't have a band but they don't really give out much info and they didn't put a picture so it's worth me checking it out fingers crossed at least i'll give them a picture of Picasso we did go to the shelters but now it's good to update stuff you know and let them know that we're still looking and i cannot drive downtown like just driving here like stressed me out today i swear my hair is falling out guys but i mean that's fine because i have enough of it to go around i am so stressed okay we're almost at the shelter kevin's driving because i can't i just can't drive guys and we already like passed like seven highways which Picasso probably did not come here i love you guys though i got an email from one of you saying you might have found Picasso or a text and um it was a mustache parakeet in london that's why i love you guys because gush tried so hard how it meant that Picasso got there to england in a day but you see that's why i love you guys so much see i just ran into kevin in a bird store and look at us now don't be fooled i'm still extremely depressed but you gotta smile sometimes right and at least they'll get a flyer here it's kind of far out but you just never know where people brought birds well guys i tried not to be too weird and like be youtuber-y and there or anything but i did get footage of the birds in case you guys are missing in amazon or ring neck in los angeles i'm glad i didn't see a bunch of dogs and stuff in the shelter i i just can't break my heart i want to save all the animals in the world so one day when i build my sanctuary with all you guys behind me kevin's gonna be doing the iron work or what is it the welding or whatever he dreams to do he could do whatever he dreams so that's a story i'm so sorry it wasn't Picasso but just seeing a flighted ring neck in the shelter gives me so much hope doesn't it because it's like he could be found he could be found hey guys kevin's like literally brought me to all the shelters out of my area today they say there's no birds in this one but we could go look i mean if we come this far i'm gonna go look i don't like seeing animals in shelters though because one day i'm gonna have a big place where i can save animals it'll probably be birds though i want to save all the other animals but we got to start with birds i guess in this building there could be any animal oh there's turtles look guys do you want to come home why don't i get a turtle do i need one no because i have a pool do i need a turtle i always wanted one though oh my god baby in here oh little kitty sorry picasso i got a cat oh my god you are a cute looking cat why are there so many cats in here Kevin look this cat and the baby i never thought i would want a cat so much in my life i know and i'm allergic to cats this one's got like a regal face where's the birds though where would they even put a bird in here do you guys have any birds right now guys there are so many cats in here they had an 80% adoption right i suddenly want a cat you have cute ears this cat has some ear hair cute let's get this cat would your mom be okay if we came home with a cat so should we do it this one's really depressed my friend jenny should come rescue this cat very stressed out oh my god you're i guess you know that i'm kissing rabbits over here you're missing out jenna wants a rabbit guys i have to tell you the craziest thing kevin has never wanted a cat so much and i saw this cat that i thought was so cute and i didn't see and then what did you find out uh his her name is starbucks his name is starbucks guys i'm off of starbucks now for three weeks and we've been talking about it the whole car ride and then this this cat how do i what happens if i bring home a cat can i just do that i'm on him his name starbucks how do you get a cat though from the shelter somebody has to come adopt starbucks where are we what shelter are we even in these things can get anywhere though oh yeah we're dying like i don't think any of us could have a cat but and kevin's allergic to the cats and i kind of am too but like i love them let's go let's go find out the process it is serious i don't know what shall we do with the cat okay let's go find out how to get a cat maybe if we need a fix we could just settle for this turtle but how did we talk about starbucks the whole car ride and then we met a cat named starbucks what do we do are we actually doing this i don't know well let's find out i'd like to know you showed your friend the picture of the cat does she like it what about her mom i don't know we'll find out she's trying to convince her because we're we're number 81 i don't know i see a sad face oh no this is buster we need to find the cat i'm you're inquiring about the cat right now seems like it was a popular cat in there today i heard at least it goes to a home that's all we care about what happened no it's a no go okay which sucks it's really cute oh my god let me send i know who to send a picture i'm gonna send a picture to hunter okay hold on you're blaming me for starting this you like literally looked at him like this is gonna start and i was like so what if we get a cat a hole called hunter and viper i'm waiting for their response our love doesn't like other cats i'm like listen arlo doesn't know what he wants if he saw starbucks it might be a different store how would arlo not want a hot-looking cat like starbucks she's literally the cutest thing ever i call love this one i don't know i'm waiting for them to go okay i don't know how much longer we can wait though so we haven't eaten in like hours all day probably since yesterday we're gonna go to lotus vegan she's trying to make me go vegan not okay yeah but we're making sure you don't eat bread anymore so you won't have migraines we're starting our diet still a theory not theory it's a theory it's a fact you can't prove it i can prove it i just gave you a whole scientific explanation you want to help yourself or no i'm not doubting you but oh my god guys i'm using my phone tiffany said yes the text popping i don't even care anymore okay so the light we're getting a cat we're getting a cat i came to the shelter to look for Picasso and i'm leaving with a cat i'm rescuing it because i want somebody to find Picasso i'm not keeping it i'm looking for a home and i found the one so i'm gonna get the package and report to duty i guess i don't know what's happening here you want a turtle guess we're not together


Hello friends, I hope you're well and welcome to today's episode on the channel My name is Lee also known as Osiris and in today's episode We're going to be going over a bunch of Sample teams ready for you guys to utilize and use going into series six which will be starting tomorrow So the main reason why I've done this is because I'm actually going to be away for a week starting today I'm not going to be able to get any content up on the channel and it's a little bit of a shame Especially when we're going into a new series It's kind of the time where I want to pump as much new content out as possible So last week have been working really hard on putting teams together for you. We've already got six rental teams Ready to go and I will put those up on the screen before the end of the episode So you'll be able to grab those as well and to try out going into the new series which starts Tomorrow like I say the first of September on the rank battle ladder We're gonna have that ban list, which is the first time we're ever gonna have Experienced a ban list kind of coming into effect in the VGC format in the competitive play So it's gonna be very interesting It's brand new to us all and hopefully by doing this with these teams It kind of at least gives you guys something to pick up to start off with and Jump into the format and start learning it because that's the biggest key to getting your head around any new format and Developing and getting better at it. So I'm really hoping that you find all of this content useful this past week and Hopefully you have some success with these teams as well So we did it's gonna be a very quick one today as I say I'm just gonna go through the teams that we've got available to us Some of them have been played on the channel already and like this this Lapras G max Lapras Guja team and I'll link these videos where they have been featured already on the channel So you can go back in the top right hand corner click on them and go and check those videos out If you'd like to obviously they're in playlists on the channel if you want to just check them out after the video as well So we've got the the Lapras confi team here. We've got The G max Hatterine This is a team that we built on stream last Friday as a collective and the episode will be going up tomorrow with that So if you'd like to see the in-depth team building that we did on Friday's stream Then you can check that out on tomorrow's episode of the channel and then we've got the Doran team That's already been featured as well. We've got a gigalith team here with the dusklops Rotem It's a bit generic with the with the arcanine Covernite and confi here So you've got two ways to kind of set your trick room of proc the weakness policy there on the gigalith So that's a nice a nice build and then another G max Lapras variant this time with Talonflame This is one I really like because we're kind of going off a weakness policy crocodile I feel like drake results going to be quite big and prevalent in the format So having something like crocodile to protect Lapras and it works super well with the Talonflame where you can Utilize it with the tailwind is super nice And then you've got the confi as well It supports both the the Lapras with the trick room and the floral healing as well as the crocodile with the The priority giga drain or a draining case if you want there I mean you can always go I like switch on confi But it's not really something I like to play Promote but it's up to you guys and then a moonga's concolder on the end So you've got concolder there is a nice trick room Sweeper as well is the kind of Lapras as well that can operate in trick room depending on what you're going up against the Lapras in this team is a little bit speedier So you'd have to be going up against very fast teams Maybe if they set the the tailwind up you can get a trick room and then utilize Lapras there But Lapras in this team obviously life up meant to be kind of predominantly going down a tailwind route. So And we've got this nice rain team It's kind of a mix between rain and perish trap So this one's there and all the pace for these teams guys are going to be down in the description below There's going to be a list of every single pace. They'll all be labeled up properly So you'll be able to just go over to those links grab the teams and then take them forward and use them yourself Okay, so that this this was a nice team as well So there's this it kind of reminiscent of station Park Back in 2013 at Worlds where you use a very similar build with very similar Pokemon So this one's down there as well for you more trick room oriented with the jealous in there You got this derailed on team. We featured that on the channel just yesterday and then they come up Well, this isn't I don't know why we've labeled come on I think it had come up and to start with but primarily a little Olympian team here So that will be labeled up correctly. We've seen the weakness policy right here Chara's our team. So that's there. Then we've got also a talent flame Primerina, Mungus, Doraladon, Dracozalt So you got a Dracozalt call there as well another option with this team I think over the scrafty if you throw in Something like Grimstall here, especially behind the screens with their Dracozalt and Doraladon It makes it very difficult to break those two down if you got screen support So that's another option for this one. That'll be the Dracozalt team A PZ team we've got to have a PZ team. It's a big threat going into this format This is a really nice one I feel you've got the support there with the Clefairy and then you've got trick room on the PZ So if you can get your trick room up and get Concordar on the field It can be very strong I think Conk's gonna be an extremely good Pokemon in this format Going forward then the rest of team made up with the Rotomheat Gastonon and then we got Corvik Knight on there as well to make the rest of that team up Then we've got a nice sand cold with Lichenrock Obviously again with the Gigalith, the weakness policy there Arcanine, Amungus and Primerina making the the last few Pokemon there up You could also throw Pherothon in there So this is what I mean like there's lots of things that you can change on these teams But it's a good basis for you to kind of run off G-Max Hatterine team And we've got a Dragalgy team there with Lullan Ninetales Urshifu The Colossal team already featured on the channel But one of my favorites as well with the Sneasel there Hydraigen another team Then you've got a kind of a trick room tailwind switch there between Talonflame, Ryperia Sylveon It's just a solid call that you can kind of take advantage of and hopefully that's good But that rounds off all the teams that we've got Pace, Twul Like I say, it's a good collection to get you started and that's the main thing This is what it's meant to be A good way to give you a mix of Pokemon That are probably going to be quite prevalent in the format and some builds to start playing with Because there's nothing worse than starting a new format and sitting in front of your builder and just thinking Right, what am I going to build around? And you waste maybe a day, half a day thinking about what you can play around At least this way you've got a bunch of Pokemon teams put together already for you to just dive in test them all out See what you like and then go from there And that is hopefully going to be a big help to a lot of you And if that is the case, then I've done my job with this video Like I say, I want to keep it quite short today. I've just covered a few of the teams And hopefully they're beneficial for you to you all like I say they're down in the description below and What that's about it really so if you do try them out though Do let me know if you have any success with them If you've got any questions about anything in the team builds as well Let me know I am obviously going to be away for a week So I'm not going to be around as much for comments But I will still check them out and try and drop in at least once every day to pick those up So if I do take a little bit longer to get back to any questions you have don't worry. I'm not ignoring I will get back to you as soon as I can but um Yeah, so friends I hope this preparation or this little week stint that we've had preparation for series six has been useful We found it beneficial. Um, if there's anything you would like to see featured when I get back Do let me know down in the comment section below, but we will be hitting series six super hard when I get back I'll be streaming on the monday night that we're back, which is the 7th of september So if you want to come across to that, uh, we'll be actually on the rank battle ladder as well Which would be super fun. Uh, it'd be lovely to see you guys over there. So I'm going to wrap it up Thanks so much for tuning in have a great rest of your day Enjoy the rest of your week and I'll look forward to catching up with you all very soon So until then friends. Take care of yourselves and


Merci beaucoup, tout le monde, pour être ici aujourd'hui. Merci, tout le monde, pour votre invitation. Je vais vous parler aujourd'hui de l'enseignement de transfert. C'est quelque chose que nous avons tous l'expérience dans notre vie habituelle. Si vous étiez à apprendre une nouvelle langue, vous trouverez que c'est plus facile de apprendre une autre langue après ça. C'est aussi été montré que si vous étiez à apprendre de la musique à jeunes enfants comme il y a longtemps, comme 9 mois, vous serez capable de apprendre une langue plus vite après ça. C'est cette idée que nous avons tous l'expérience, que quand nous avons appris quelque chose dans un domaine, nous pouvons apprendre mieux et plus vite d'autres choses dans d'autres domaines. Mais avant de parler d'enseignement de transfert, je voudrais parler un petit peu de hockey pour ceux de vous qui ne savent pas ce que c'est. Et ensuite, je vais vous expliquer comment l'enseignement de transfert peut être appliqué à la médecine et comment peut-être être une solution pour des problèmes très grands. Donc, qui nous sommes à hockey et à quoi nous faisons. So Hockey is a new startup company that was founded last year and we have offices in New York and here in Paris. And I'm the chief scientist and I lead here in Paris the scientific team. Thomas Closal is my partner in this startup. He's an oncologist, so he's a medical doctor. And he used to work in the field of predictive medicine. He developed techniques to predict response to chemotherapy using epigenomic data. And I used to be from academia since I was working in ENS for the last two, three years. And I've been working a lot on applications of machine learning in the field of medicine. Right now, we are a team of about ten people and we have exceptional machine learning engineers and top data scientists that performed among the best in all the international competitions such as Kaggle. And what we do is that we develop algorithms and softwares in collaborations with biotech companies, pharmaceutical companies, hospitals and academic labs. So I will show you the four major topics that we are covering in the company. So what we do is that we develop these machine learning techniques to address fundamental problems in the drug discovery and development process. And in this process there are several steps that are very time consuming and that cost a lot. If you want to develop a drug today, it will cost you around one billion dollars to perform the drug design, drug development, clinical trial, all the process from the idea to the market. And it fails a lot. It fails more than one out of two, more than 50% of the case. And to achieve, to transform this process, this workflow end to end using artificial intelligence, we focus our development on drug design. How can we use machine learning algorithms to predict molecular activity at the chemical level? We work on clinical trials. Now that you have a molecule, you want to test it in a population, how can you use the data you have accumulated until the phase three, for instance, to select the right subgroup of people that will respond to the drug. And this is also, there is a clear need for predictive models. We work on real world evidence, meaning that we work, for instance, with hospitals, with data coming from general practitioners. For instance, we work with Institut Curie to work on clinical data in a free text form, meaning that this data is written by the doctors and it's a huge amount of data, millions of documents. And we also work on images. This is a little bit transversal because it can be used for clinical trials, for real world evidence. And this is maybe one of the field right now, medical images, where deep learning can have the more impact in the next few months or few years and a lot of people trying to do that. So now I'm going to talk about transfer learning, give you some ideas about what is transfer learning, how can it be applied in medicine and then after that we'll take some time to discuss about more general ideas about collaborative artificial intelligence and the data sharing problem. So just before starting, in medicine data is often has very specific properties. The first one is that it's something that should be kept private. Medical data is something that is not to be shared to everyone, one has to be very careful. Second point, it's often coming in small data. It's really not common to go and see a doctor or a lab and he says have data for one million patients. It never happens. Often you have data sets of a few hundred images. So these two specific features are really important to bear in mind and I will show you why these two key features put in perspective this idea of transfer learning as really something that can be very useful. So first, what is transfer learning? So this is a domain of artificial intelligence and machine learning which is focused on disability that an algorithm can improve its learning capacities. Either it can learn faster or it can learn better to achieve better accuracy on a given data set through previous exposure to a different data set. So you have the task that you want to perform on your data and then you have this other data that can be helpful to solve your task. And you can use transfer learning so that your algorithm can learn other stuff on this other data sets. It can be different, it can be different types of problems, it can be a different task, it can be anything but there are some things that can be extracted. One point that is very important also is that deep learning architectures are very well suited for the transfer learning approach and the reason is that these deep learning architectures they operate by different layers in a hierarchical way and often what we realize is that the first layers can learn some things that are very general on a data set, very basic such as discovering contours or basic structures, basic textures of a cell of any medical images and that can be transferred then to another problem. And I think this precise point is one of the key hidden reasons behind the success of deep learning. As I mentioned in the earlier introduction it's also a cognitive phenomenon in humans that learning things in one domain can help you learn things faster and better in other domains. And personally we will discuss about mathematics later. Personally I think that mathematics is really a good example of that. That people who are educated in mathematics can learn faster many other things after that. But I'm speaking in the home of mathematics so I'm safe here to say that but maybe not everyone will agree. Anyway, so it's very interesting that we are entering this era of artificial intelligence where we can have this connection between a cognitive phenomenon that seems very powerful and very abstract decided that if I learn mathematics I can be good at law or philosophy. This very abstract concept is now really present today in everyday life of people doing machine learning. So this is an idea, I think it's really exciting. So I just want to give you two examples of how can it work in general for deep learning and then we go into two, three specific examples in medicine. So very generally what people do every day in the deep learning community is something like that. It's called warm restart. So what they do is that they want to solve a problem. They want to be able to classify cancer versus non-cancer from medical images for instance.  If using 100 images of each class you want to train a deep learning algorithm from scratch using completely random initial condition of your neural network weights it will most likely be a failure because you have too many parameters to train and you have not enough samples. So what you can do is say well I can't do anything please come back to me with more data. This is something you can tell to the doctor that gives you the data. But what you can do as well is something that may seem completely crazy.  And the task here is to classify all these pictures. So you train your neural network on this database. It gives you what we call a pre-trained neural network at the end of training.  So it has learned things. It has learned how to see things but not especially medical images. So why are we doing that? Does it have any chance to work? Then once you have this pre-trained neural network then you can use it as an initial condition to train your deep learning algorithm on the data that you are interested in. And basically you do the same as if you were starting from an initial condition but you just change the initial condition. The training process is all governed by the same back propagation algorithm that you like. But you just change the initial condition. And if you do that with just a few hundred images you will see that the improvement in performance is really astonishing. And I will show you some examples after. We can also do other things. There are many, many other possibilities. I want just to show you another one that is quite interesting. It's the idea of shared representation. It's the idea that you can train your network to classify plane and cars and lions. Then it gives you this pre-trained network. Then you start from an initial condition that is random on your medical images. But what you do is that you penalize your loss function.  So it's a process of regularization that has the same kind of effect that the warm restarts but sometimes it can work better. So let's go into some concrete examples now. Okay, so there is always in teaching this debate should I start with examples or with the theory. So I decided to start with the general theory and then I show the examples. We'll see if it works. So here is the cover of nature February this year. So it's like two months old. So already very old. And the cover of nature, it shows a paper that has really shaken the world of dermatologists. So we have a lot of dermatologists that come and see Okin right now to see how we can help them. And basically these people in this article they applied deep learning techniques to classify skin cancer using pictures. And what they show in the article is that they obtain the same level of performance in terms of classification accuracy than a group of experts, dermatologists. So in 2017, this is where we are right now. These kind of systems, if they are trained on a database that is large enough, they are able to perform as good as doctors and there are other papers that we can discuss. I can give you the reference that shows that in other problems they can even perform better. And here, this picture is just an excerpt from their article in nature. And what they show right in the middle here is called deep convolutional neural network inception V3. What is inception V3? Inception V3 is the name that Google gave to the neural network they trained on ImageNet. The same pre-trained neural network I was talking about. So you have to know that these pre-trained neural networks on ImageNet there exists a zoo of models. There exist many models. There is inception V3. Maybe there is a new one that has come out. You have Microsoft that has another network. You have Facebook. You have also academic labs, etc. Each one has its pre-trained neural network on ImageNet. Then what these guys did in nature they took that as a starting point and then they do the warm restart. So they keep the architecture and they just do the gradient descent on the weights. And the real tour de force in this article is the size of the database. I told you that it can work with a few hundred images. That's true. But if you want to achieve on this very hard problem the same kind of performance as expert dermatologist then you need around 100,000 images. And this is not easy to gather. So this is really very impressive. So this is a very concrete example and right now all the dermatologist community is really trying to understand how things are going to organize around this kind of technologies. I want just to give you two more examples of things that we do in the company. So this is a work that we've been doing with a biotech company that is developing new drugs on a disease called pulmonary fibrosis. It's a very bad disease that can kill people in a few months. And there is no real treatment at the moment. And their goal was to develop a system that can grade fibrosis. So that you have low grade it means that the fibrosis is not spread and it's not very important. And high grade means that it's very bad. Ok, it's very severe. And when they develop new molecules they want to test if I give this molecule will the grade be lower after a few weeks? So they do clinical trials, pre-clinical trials and they want to assess the grading. And usually they do that with a team of expert pathologists that look at the image so these images are taken from biopsies of the lung and they are scanned at very high resolution.  So it's very big images and we were given only around 100 images with 5 different classes. Ok, so you have like 20 examples per classes and images are more than 1 gigapixel. And basically we are not still at the level of the agreement of pathologists but what we realize is that if we do the neural network training from scratch then it performs around 65% accuracy in the 5 class program but if we use a neural network that was pre-trained on this carplane, lion, etc. task then it performs way better. So this is a very precise concrete example of how transfer learning can give a very important improvement of the results due to this problem of having only a few data points. And this is a very common problem in medical problems. The last example I wanted to discuss here is a project that we've been doing in the last few months. It's a challenge organized by the Kaggle platform. So Kaggle is a platform for data science competitions that gathers all the best machine learning and data science engineers and teams over the world. So it has more than 100,000 users and the platform has been acquired by Google recently. And on this platform there was a challenge with a price of 1 million dollars. And this challenge is the one that we try to play and it's called early detection of lung cancer from CT scans. So what is the game? You are given 1,500 CT scans and for each patient that you have just a label that tells you if this patient has or will develop a cancer within 12 months timeframe starting from the exam. So what is a CT scan? A CT scan is a 3D representation of what's inside your body. You have like 200 slices and each slice is around 500 x 500 pixels and it looks like that and you can reconstruct in three dimensions if you want what's inside the lung. This is very challenging because we have only around 1,000 data points and the signal is really, really small compared to the volume of data we have because the signal is hidden in the idea that this guy has an audio so it's a mass in the lung that is very important to diagnose this type of lung cancer and this nodule is just a few centimeters so it's just a few dozens of pixels patch, 3D. So you have like a very, very small tiny needle in the haystack and you have a lot of information about the lung that is completely useless and that is completely noise for your machine learning algorithm. So it's a very, we call it a very strong weak labeling problem and in this problem everything that we tried just using the data given in the competition completely failed and I mean the guys in the team are very strong and they try a lot of things and I think it's almost impossible to solve this challenge with only this data. So we had to find another way we had to find a way to bring knowledge from outside because it was really not possible to design something that would work. So the final ranking is tense so we didn't win the 1 million dollars but we still win a small price but most importantly we learned about the strategy to solve this kind of problems. So I put aside the fact that it's a 3D problem and 3D convolutional neural networks are not as easy to manipulate than two dimensionals but I would say it's a side problem. The real idea was to bring external knowledge through another data set. So we used this idea of transfer learning very strongly in this case and it was the only way for us to get something that worked. So there is this other data set called LUNA that has been designed for another task with other patients in another challenge the year before. And the other task was to work on segmentation of nodules on CT scans. It was not about early detection so it was something with different labels different patients it was a different data set. So what we did is that we trained neural network to distinguish the nodules so we split, we extract patches in three dimensions and each patch we have this annotation that tells you here you have a nodule we can give you this diameter or we can tell you here there is no nodule and this is the kind of label that we have from the LUNA data set. So this is this is an information that is localized in space. Compared to the annotation on the KGEL data which is completely not localized. They just tell us if this patient has a concern or not where is the nodule etc. So there is pretty much nothing you can really learn. So you train first this three dimensional convolutional neural network on this LUNA data set so it has five convolutional layer with pooling so that at the end with 64 channels so that at the end you flatten everything and you have just 512 and 12 vectors ok and from this vector of dimension 512 it means that you can have a representation of a given patch of 64 pixels of size ok and now what you do is that you have trained this network you have the weights, it's here you take your data from the KGEL data set you extract patches so you do it with overlap but you just scan everything on the patient all the patches then you put it in the CNN you extract your vector of representation and then you do some max pooling so that at the end you erase all the spatial information so everything becomes invariant in space and from that you can do classification with these new features we could also have done some global fine tuning of the whole process but still it was very complex to make it work and at the end we do gradient reboosting to do the classification ok and this strategy we realized that among the top 10 almost everyone has done something similar ok so it was really maybe not the only way but I really challenge everyone here in the room to achieve a very a significant result without having this transfer learning approach so here we see there are some very important problems very difficult with a 1 million dollar price and the only way to find a solution that starts to work is based on transfer learning so this is not une anecdotal technique this is really at the heart of many many applications of deep learning so now that I gave you this compelling story about the use of transfer learning in the field of medicine I would like to maybe take the time heavier to dream a little and to try to move 1 layer of abstraction above and think about what this kind of technologies could make possible in the future right now if you think about classical statistics for instance each time you have a problem of hypothesis testing of estimation of parameters you start from a white page each time you have a new data set turn your problem and you start again with transfer learning the idea is that you can build up systems that learn from a given data set then they are able to perform a task and then you can reuse them to do another task and to learn other things and you can climb like that and you can build systems to become better and better when they are exposed to different tasks it's like humans it's like this sentence like standing on the shoulder of giants this idea that artificial intelligence systems are entering an era where they are not isolated anymore these algorithms can talk to each other through transfer learning ok so why is it so important so clearly what we have shown here is that transfer learning is one of the key for me one of the key reasons behind the success of deep learning and it brings the power of machine learning to small data sets that will not be amenable but it opens the way to collaborative artificial intelligence and I would like to show you how here at Okin we imagine the future so our idea is to build a collaborative artificial intelligence platform where each contributor would be a lab, hospital doctor, whoever has some data that is labelled suppose you are a doctor you have 500 images of breast cancer that respond to chemotherapy 500 images of breast cancer that do not respond well to chemotherapy you have this data this data is very important it needs to stay private most of the time and maybe it's not enough data to build a powerful algorithm but you know that other people around the world they have this kind of data so the idea is that as a contributor you can create new algorithms or you can participate in the creation of new algorithms and these algorithms can improve each other through transfer learning going from one centre to the other and then the community of users the people, the doctors if they want to use this algorithm to make predictions and say well I need to predict if this patient will respond well to chemotherapy or not can use the algorithms that are built on the platform and so the idea is that you don't need really anymore to have just a team of someone who has the data and someone who designed the algorithm and they work together but we are thinking for the future and we think that this can become a platform system and we can move towards something that is more collaborative and with the idea that people can keep their data so we have a prototype of that concept that is working that we are currently installing in several hospitals where each user who has medical image can create algorithm just without any coding skills can apply the algorithm to new new image that come in the clinic and share the algorithms that have been trained to other people on the platform but I think really here the final point that I want to make in this conference is maybe the most important way we are doing all these nice predictive models but clearly what is really lacking today and for the future in the medical community and in artificial intelligence for medicine is the is the motion, is the dynamic toward an actual data sharing solution right now the medical data is really spread across many different centers many different hospitals many different pharmaceutical companies etc all this data is really not at the moment shared in a common place and this is a huge problem because if we want to be able to train powerful artificial intelligence algorithms that can help doctors that can help patients that can help predicting the effect of a treatment to give the right drug to the right patient if we want to do that the first the most major obstacle is this data sharing problem ok and we hear conferences very nice people saying well we are committed to data sharing we are going to create this platform where everyone can put this data together etc etc if you say that people think you are someone good so it's good to say that but in reality it's not happening it's not happening right now and I think for many many reasons privacy intellectual property different things like that it's not going to happen soon at a large scale ok so if we want to do something powerful that exploits all this data that is already here we need to find another solution and I think that these ideas around transfer learning can be very very helpful and in fact it's very simple the idea behind because transfer learning is just you train your network on a given data set you use it as an initial condition for the next training so it's just about doing a gradient descent and following the path and just you stop at one point and you start again so the standard approach would be to gather all the data ok, so you go you knock on hospital A door you say can you give me your data I will put it in a secure place you go to hospital B, to hospital C or you can do that you can imagine doing that with general practitioners so it's not a few hundreds hospitals it's a few hundreds of thousands of doctors or you can imagine that this problem can arise with internet of things connected objects that measure our health ok, and we don't want to share our data with some cloud provider or whatever, but we would like to benefit from the artificial intelligence that this system could offer ok, so the scale of this problem is not only a few hundred hospitals it can be millions of users ok, so what can we do overcome this and say, either this is not feasible or we don't want to do that we think it's too risky to put all this data in the same hands so the idea is really simple is a kind of natural extension of the idea of transfer learning is the idea that you train your neural network on the hospital A so you have your 100 or 1000 images with a given task here and hospital B has the same kind of images with the same kind of labors so now that you have trained your network you can make it travel to hospital B so the data stays put in hospital A but only the neural network waits, travels ok, so it's brain traveling ok, you make the algorithm travel and then you put the algorithm in hospital B and you continue the training ok, so this is I would say so you can do that for all your users in your network and you do one round and you can do another round etc until your validation accuracy is good enough what you can do as well is doing something that will exploit all the computing power that can be put in each hospital by doing a parallel algorithm ok, so you can do like a parallel stochastic gradient descent so that suppose you are the coordinator of the process you first you spend you send the same initial network to all the hospital at the same time at the same time, ok each one does a training on his data set and then what they give you back is the gradient, the change of the weight then what you do is an average of this change ok, so in the gradient descent you average the contribution of each of the contributor who has data and then you send back these new weights to everyone to synchronize everyone and you do another batch like that ok, so these kinds of algorithms it's called parallel stochastic gradient descent and there has been recent research the last two years about how to improve that, how to make it work better, faster especially in the context where the network that we are making travel they can be 100 megabytes maybe 1 gigabyte soon ok, so these are objects that you should make travel not too often ok, so if you want to make it travel not too often, then you should try to optimize everything so that it works well it can be also you can use that in a peer-to-peer fashion, if you don't want any third party involved you can make the network travel between users and I think that this idea is very powerful for the future of artificial intelligence in medicine on a wide scale not only hospitals but as I said also for data coming from doctors or for consumers and it's going well beyond medicine as well because it's a paradigm capable to train artificial intelligence system without jeopardizing the privacy of data and I think as citizens here in the room we are all very concerned about that right now at the moment and hopefully there are technological solutions that can be developed that can have a huge potential for medicine but also for for the privacy of every citizen so I guess it's time for me to conclude I think my message was clear today so with transfer learning technologies all these artificial intelligence algorithms can cross-fertilize this idea of algorithm sharing has a potential to circumvent the data sharing problem which is to me the major problem right now for the emergence of a lot of new artificial intelligence applications in medicine and we hope that developing these technologies can have a big impact because we work every day with doctors and what we think first is how can we make it happen today thank you very much questions ? yeah so my question is when you use data to pre-train and then train for something else do you know how much the original knowledge is preserved after you did it yeah so you can destroy some knowledge you've learned but you can keep in the memory as well so you can keep both but it can destroy knowledge it doesn't work all the time of course and it still remains unknown from a mathematical standpoint how different the two data sets should be so that it works well because if the two data sets are too close then it's just a matter of adding some new points and it's not very useful ok ? so you need some kind of difference but not too much ok ? and it's like I would say it's like if you learn languages that are quite different if you learn Turkish and Finnish maybe it will be easier for you to learn Russian and Egyptian or whatever ok ? but if you learn only Spanish, Italian and French in good position I think to learn new languages yeah in the city thing why didn't you fine tune ? because I mean the transfer learning thing makes sense if you fine tune since it's different data yeah so we just can take random features no no no we use the features that were extracted from a network that was trained on the other we didn't fine tune plus the gradient boosting worked well and you may see the gradient boosting as just doing a fine tuning of the last layer if you want ok ? if it's a linear style the last layer is not fine tuning the last layer is learning the classifier is learning the classifier you cannot you cannot really call it transfer learning if you are only fine tuning you are just taking pre-existing features ok ? if you want I mean we call it as you want I mean to me it's still transfer learning my question is about privacy so you have this distributed scheme whereby as you say you don't need to don't need to actually share the data that you use for the training so this is preserving privacy so my question is if you only have access to the training network after and before some training what can you infer on the data that has been used for ? ok that's really an excellent question and this is a question we are currently working on in the team my intuition is that if you only have the weights of the network not the activations occur when you present the input but just the weights we say it's very very difficult to recover any data points maybe you don't want to recover the full data but you might be able to infer like how many patients were affected with this and that disease in a given data set that might be already too much information to yeah of course so that's an excellent question and we will be really happy to collaborate with anyone here to work on the theoretical proof of that question about what can we infer knowing the weights about the data that was used for training maybe there exists some very nice theorem already that I don't know ok I don't know everything but I think this question has not been very studied before but it's a very good question so the diameter of the nodule is used as the target for the training here so we have we split it into several categories like small, medium, large and we have another category which is not a nodule because in the database of Luna you have some by question is the outcome of your deep neural net on the competition was it was a vector where you had a number of diameters ok and that's what you used to predict no we used the layer right before and what means you said that you were tenth right on the competition but the best what was his performance are we detecting 80% ok so it was a log loss metric the data set was relatively balanced anyway it was like 0.4 of log loss which means in our cross validation it was like 0.83 AUC ok so you are 83% 83% AUC and this is useful yeah ok so you so this is meant to be used to do a general screening of the population and they want to reduce the number of false positive and false negative and this is a task that is really not easy for the doctors I mean I have a little bit of online and usually the problem is that for some of them they don't do this so we had examples in the database where these people were classified as having cancer or we'll develop cancer in the next 12 months there is a subtility without nodules but you have other markers like emphysemas we didn't use that you were based on nodules whatever we were only on nodules and we ran out of time but it was our to-do list and why you didn't try the whole network at the beginning we tried to do a lot of things on just the data set and you can try you will see you have two few samples and the information is lost in the middle of a lot of other things that are completely not related to the disease you have more samples than on the but the annotation in the Kaggle data set is global it just tells you if the patient has the concern or not and on the other data set you have a local information ok you can take the last question ok two questions the first one is related to the security one so in this process do you share your algorithm so in which process in the for example if you have isolated data centers and if you want them to provide you the output do you share your algorithm so we share the algorithm between the different users that are taking part in solving the problem but then the question is whether we share the resulting algorithms to the global community in the world it's something that can be done sometimes and other times is not done depending on the different parties at stake and different problems so of course it would be better to share the resulting algorithm to everyone but then if there are a lot of money that is invested on that and that people want to keep for them then you enter you have this problem that is all the time I think if you share the algorithm then as if if I'm a data center as if I have the control of the how to say several of the dynamic of the algorithm because I can provide some specific data such that I want some specific outcome yeah this is some kind of security problem that if I'm a malicious player I can provide some specific data such that I can have some control of the algorithm outcome ok so you mean a malicious player that will disguise as a hospital and take control of the that's ok so ok this could happen maybe in ok that's very that's a very clever question non-collective game yeah we are much on the ground right now and we talk with people on the phone and we say ok did you receive the network commercial system and this could happen yeah ok that's in the same direction of the other question like that is how this kind of system is really secure yeah ok that's very very interesting second question is normally we know that individual optimization or learning may not a sequence of individual learning may not lead to social optimal so how can you make sure that during this dynamic process you can how to say increasingly improve at the mathematical level you need at least some some proof or some yeah so I mean if you take the amnist data set with a hand written recognition problem ok if you split the data set into 10 different parts and you put one part in each hospital ok first I will call you crazy why do you send me but then you can train and you put back the weights and you send it back and you train etc and you will see it will converge almost as if you had all the data in the same place so it's right now it's more I would say it's a bunch of experimental results but also the process really mathematically is really like doing the gradient descent and there are also some you have some potential the fact that when you're optimizing some distributed problem then you are convergent to the potential of the problem yeah so there are connections and there is a lot of theory existing already on parallel stochastic gradient descent but ok I think that if we go into the specifics maybe there are some different things that should be proven to be sure mathematically but still in our first experiments things are quite working in the right direction yeah I think we should stop here and thank you


Great outer beach of Cape Cod facing the Atlantic Ocean Early morning a lone figure leaving footprints on the sands of time and you will see that indeed the waves move Lots of sand as the waves come in and out and in and out They keep moving sand Ceaselessly relentlessly notice the foot is on top of the sand when the wave comes in the foot is getting Really cold because that water is just straight out of the Arctic Ocean Practically and now they sand over the foot and when the next wave comes in and the one after you will see that indeed the waves are moving Sand in out in out as they do so they sort it little pieces are taken out to deep water and lost Big pieces are not moved at all and the in-between ones are swirling around the foot and another wave comes in And that was really cold and the hairy-legged soul here is soon going to be buried under the sand And you can see how much sand is moved in just a couple of ways


You know, I've watched Stephen King movies before, but I don't know if they really live up to all the hype. I guess I just don't get it. You've heard it before. I just don't understand why people X. I'm as guilty as anyone of a bit of rhetorical hyperbole, especially after a couple of glasses of wine, boldly proclaiming that I just can't understand why people like sports, or that I don't know the first thing about Justin Bieber, or daytime television, and have no clue why anybody else does. Of course, it's not meant to be taken literally. I don't really question my sanity every time someone gets chocolate ice cream instead of vanilla. It's usually meant as a figure of speech, or a joke, but sometimes it feels a little more sincere than that. Some people who identify as Democrats swear that they can't fathom what would bring a sane person to vote for a Republican, and I don't really get the sense that they're joking. They really can't. Many atheists really think they have no idea how anyone with a lick of sense could come to believe any religion, and several religious people feel the same way about people who believe in different gods. It seems that sometimes this supposedly insurmountable barrier of ignorance is used to reinforce one's social or cultural allegiances, to wave the flag for one's team and distance oneself from the other team. The inability to grasp ideologies or worldviews that we want to paint as being perverse or irrational becomes a point of pride in authenticity. You see, I'm so super reasonable that I can't even pretend to process these crazy ideas. For example, one might assert that terrorists, who are most certainly despicable and not on our team, are also crazy people, whose thoughts and actions simply cannot be understood by rational folk. US Senator John Warner asserted this position in response to suicide attacks by Al Qaeda, claiming those who would commit suicide and their attacks on the free world are not rational. But is that true? Are terrorists literally insane? The idea that they have some sort of pervasive path of psychology has been tested in numerous ways, and using all the normal diagnostic criteria for things like paranoia or depression or psychosis, there's no evidence that they're any more or less likely to have psychological problems than the general population. Furthermore, it's possible that the reasoning which leads them to such abhorrent behavior isn't really all that alien to us. In this 2009 paper, Patterns of Thinking in Militant Extremism, researchers from numerous institutions around the world search for similarities in the reasoning of violent extremists, people who had engaged in homicidal activities in the name of some ideology. They interviewed and examined the written works of dangerous fanatics from all sorts of different cultures and backgrounds, from Timothy McVeigh to the Muslim Brotherhood to the Tamil Tigers to the Irish Republican Army. The only thing these individuals shared was a dedication to some cause and a willingness to kill innocent people to achieve it. From their sample of 13 violent fanatics and terrorist organizations, the researchers identified 16 themes which occurred in multiple places. Themes like glorification of dying for a noble cause, the desire to purge evil from the world, and a feeling of imminent disaster which must be averted at all costs. It's quite a less, and reading them all at once is not hard to see how believing these things might lead someone to do some pretty drastic stuff. The investigators then generated a questionnaire with a series of statements that were meant to evoke those themes. Like government is illegitimate unless based strictly on God's authority as found in the holy book, or extreme measures are needed now to restore virtue and righteousness to this world. They asked groups of students to rate how much they agreed or disagreed with the statements. You'd expect that most reasonable people would be strongly opposed to all of them, but the responses ranged from a tepid maybe to a lukewarm probably not.  Not dear God, that's awful. Just kind of a general meh. Not only are these murders fanatics not insane in any clinical sense, but it seems that the general population isn't particularly allergic to the narratives which seem to motivate them to do horrific things. As much as we might like to believe that we're nothing like these legitimately awful people, the research suggests that most of us probably agree with them on some general principles, at least to some degree. That's a little uncomfortable, but it also seems suggestive. If we're really interested in combating abhorrent behaviors and ideologies like terrorism, throwing our hands up in the air and lamenting just how incomprehensibly crazy our opponents are isn't just inaccurate. It ignores a dangerous possibility that it doesn't take a whole lot of nudging to make a reasonable person do horrendous things. With such an unsettling and unexpected result, the authors of the paper went so far as to construct an anti-extremist ideology, a set of beliefs and attitudes antithetical to the ones they identified in their analysis, which they believe might undermine the processes which lead people to violent zealotry. It's a pretty good list, too. Moderation, adherence to moral and ethical rules even in service of sacred things, perhaps appropriate to this video, never dehumanizing or demonizing others. This collection of values may be a very useful tool for eroding the attitudes that lead to terrorism, and to obtain it, it was necessary to assume that there was some sort of comprehensible underlying structure to that mentality, a pattern that could be understood and beaten. Now, it is fun and comforting to imagine that the only reason we disagree with others is because they're simply too bizarre and divergent in their thinking, that the only way to really get where they're coming from would be to abandon reason entirely and also be insane. It removes any responsibility to try to see things from their side or even think of them as real people. But it seems that even those individuals who do what we would consider unthinkable things are usually operating under totally comprehensible and even familiar premises, and confronting the unsettling truth that we're not really all that different from those we despise can actually help us further our own ends and undermine theirs. And to be honest, it's kind of gross to where ignorance or lack of understanding is a badge of honor. There's probably more than enough celebration of not knowing stuff in the world without my hyperbolic ranting about vanilla ice cream, even on pie. What about you? Do you think there are people who we simply shouldn't seek to understand, or do you think that knowledge is power, and knowledge of someone is power over them? Please leave a comment below and let me know what you think. Thank you very much for watching. Don't forget to subscribe while I share, and don't stop thunking.


Modern data processing systems like these use thousands of magnetic cores. These cores are small, rugged, and they consume less power than most of the other devices that are used in these systems. You'll also find magnetic cores in automatic equipment employed for communication, fire control, cryptography, and high-speed computation. What are magnetic cores? They are tiny rings of nickel alloy or other magnetic materials. They have replaced vacuum tubes for many important functions in data processing systems. Cores come in a variety of sizes and in two basic types. The ferrite type of core is molded from compounds that magnetize easily and is used to provide data processing systems with their memories. The metallic ribbon type consists of a non-magnetic spool wound with a thin ribbon of highly magnetic alloy. This type is used in operating circuits of ADP systems. Both types of magnetic cores are remarkably adaptable to the language of data processing. Many ADP systems use the binary system of numbering, which employs just two digits, zero and one. These two digits are called binary digits or bits for short. A bit is either a zero or a one, nothing else. Why do we use binary? Because a bit can be represented by any bistable device and a magnetic core is a bistable device. Bistable because it can be magnetized in two directions. When the magnetic lines of force point in one direction around the core, they are a positive magnetic flux and the core is said to hold a binary one. If the direction of magnetism is reversed, the flux becomes negative and the core holds a binary zero. However, remember that this arbitrary definition of positive magnetic flux representing binary one and negative magnetic flux representing binary zero may be reversed in some systems. So far we've been establishing the function of magnetic cores. They store binary digits or bits, zero or one. Now we're going to see how they do it and also how they transfer bits from one core to another. To explain how cores store binary digits, we must know how they are constructed. Around the core is wrapped a conductor. Current flowing through the winding produces a magnetic field around the conductor. Because the core is made of highly permeable material, it forms a path for the magnetic lines of force. Because it is ring shaped, the path it forms is a closed loop. The conductor here is wound so that current flowing in this direction produces a positive flux, setting the core in the binary one state. Current flowing in the opposite direction produces a negative flux, which sets the core in the binary zero state. To show the polarity of the winding in a core diagram, we mark one side with a black dot. By definition, current that flows into the winding from the dot side sets the core to zero. Current flowing into the non-dot side sets the core to binary one. Remember, non-dot current for one and dot current for zero. Any winding around the core obeys the laws of electromagnetism. The magnetizing force it exerts is proportional to the amount of current flowing through it measured in amperes times the number of turns in the coil. Or, as it is usually expressed, the ampere turns. Since the number of turns in this winding is constant, we can simplify the rule here. The magnetizing force exerted by the winding on the core is proportional to the winding's current. Suppose we increase the amount of current. Will the total magnetism of the core also increase? It will, but not proportionately, as we'll see on this graph. We're going to take a minute and trace the magnetization curve of a core. Because in this way we can point up some of the characteristics that make cores so useful. We measure the magnetism of the core in terms of its flux density, which is the number of magnetic lines of force per square inch. The horizontal axis will represent the magnetizing force of the winding. Remember that the magnetizing force is the cause and the flux density is the effect. Initially, a relatively small amount of magnetizing force produces a relatively high flux density. The curve on the graph will begin like this. But as we increase the magnetizing force, the curve flattens out because we are adding proportionately less to the flux density. Further increases in magnetizing force will have no appreciable effect on flux density. The symbol for magnetizing force is the letter H. The symbol for flux density is the letter B. The relationship between magnetizing force and flux density for any given core material is represented by its BH curve. This is the point of saturation. The core is holding just about all the magnetism it can hold. We've been applying a positive magnetizing force here which produced a positive flux density and brought the core to a state of positive saturation. If we now reduce or stop the magnetizing force for all practical purposes, there is no change. The core material retains most of its magnetism. Here's how we plot this fact on the graph. The BH curve is brought back to the zero line on the horizontal scale. Watch the difference in flux density on the vertical scale near the plus B. It's negligible. This then is the amount of remaining magnetism. This amount is known as the residual magnetism of the core material. The magnetism that remains after the force that brought it about is removed. When non-dot current sets the core to the binary one state, the residual property keeps it that way indefinitely. Suppose there is a second pulse of non-dot current. It will have no practical effect on the core because the core is already saturated with positive magnetism. But if we send current through the other way into the dot side of the winding, then of course the core state will change. To plot the effect, we'll extend our graph. With dot current, we're applying a negative magnetizing force represented by minus H. Negative flux density is symbolized by minus B. We need a certain amount of negative magnetizing force to overcome the existing positive residual magnetism in the core. The amount needed is called the coercive force. At this point, the material contains no magnetism. The flux density is zero. Adding more negative magnetizing force produces a negative flux density. And again, the core is brought to a state of saturation, negative saturation. If we reduce or stop the magnetizing force now, there will be little effect on the flux density of the core. The BH curve returns to zero on the horizontal magnetizing force scale, but it remains at almost the saturation level on the vertical flux density scale. In other words, there is residual magnetism in the negative direction too. Therefore, when dot current is fed into the core, magnetism builds up and the core is set to binary zero. Residual property keeps it there. To switch the core, we apply a positive magnetizing force. Enough first to cancel out the negative residual magnetism. That is to act as a coercive force in the opposite direction. And then, by adding more positive magnetizing force, we can bring the core material back to a state of positive saturation. We have now plotted the positive and negative magnetization of a core. The original S-shaped curve is produced only when we are first magnetizing the core from a completely un-magnetized state. From then on, we are concerned only with the rectangular loop, which tells us the limits of saturation that can be reached with dot and non-dot current. Also, the amount of magnetism that will remain in the core after the current stops. The word hysteresis comes from a Greek verb, meaning to lag behind. A hysteresis loop, therefore, shows how much magnetism remains behind after the force that produced it is gone. The shape of the loop indicates the ability of a particular core material to retain magnetism. Shallow rounded loop, low residual magnetism. The core of this type obviously requires less force during a switch from one state to the other. Rectangular loop, high residual magnetism. A greater amount of force needed. If a core with a rectangular hysteresis loop is in the binary zero state, we're going to have to apply a substantial pulse of non-dot current in order to set it to binary one. A lesser pulse just won't make the switch. Now, how do we get information out of a core? By means of a second winding. Let's follow the transfer of information from input to output. Assume the core is in a state of positive residual magnetism. In the binary one state. To change the core to binary zero, we send current into the dot side of the input winding. Dot current switches the core to binary zero, reversing the direction of the magnetic lines of force. If we diagrammed it, we'd see the positive residual field collapse entirely and a negative field build up to saturation. Anytime a magnetic field builds up or collapses across a conductor like this winding, the movement of the lines of force induces a voltage in the winding and current flows. Observe the direction of current flow out the top of the winding. The way these two conductors are wound, a pulse of current sent into the bottom of the input winding results in a pulse of current going out of the top of the output winding. We used a dot to keep track of the polarity of the input winding and a similar dot will establish the polarity of the output winding. This gives us another rule about the dots in a magnetic core diagram. When dot current moves into the input winding, it causes the current to move out the dot side of the output winding provided the input current produces a change in flux. For example, our core is now set at binary zero, so a second pulse of dot current will have no practical effect. The core is already at the level of negative residual magnetism. There is no change in flux and therefore no output current. However, if we send current in the opposite way into the non-dot side of the input winding, the direction of flux in the core is reversed, switching it to binary one and produces a current that flows out of the non-dot side of the output winding. To repeat, a non-dot current input results in a non-dot current output. Remember these two rules. Dot current in results in binary zero and dot current out. Non-dot current in results in binary one and non-dot current out. Now, suppose we want to send non-dot current into the core, setting it to the binary one state, but we don't want to send current out, at least not yet. By connecting in a second core on the right, we can see more clearly what the problem is. Now, the first core's output winding is directly connected to the input of the second core. This input has been wound, so its polarity is the same as that of the input of the first core. The dot side is below. In a typical core circuit, current flows in only one direction, into the input of the first core. We'll take the non-dot side for our example. We'll assume that initially, the left core is set to zero and the right core to one. All we want to do is store a binary one in the left core. Watch what happens with the circuit as we have it. Current flows into the non-dot side of the first core's input. The first core is switched to one as we wanted, but this produces current to flow out of the non-dot side of the first core's output along the circuit and into the dot side of the input winding on the second core, switching that core to zero, which we did not want. If we are going to control the transfer of information from one core to another, we need something in the circuit that will block the flow of the current and do it selectively. The device commonly used is a crystal diode, which has a high resistance to current flow in one direction, the back resistance, and practically no resistance at all in the forward direction. Any unidirectional device or arrangement will serve the purpose and will symbolize this with a general symbol for one direction flow. The arrow points in the direction in which it allows the current to flow. Current coming the other way meets the back resistance of this device and is blocked. With such a device in position in the circuit, the second core is effectively isolated from the first. We'll create the same conditions we had before. The first core is initially set to binary zero and the second to binary one. We send non-dot current into the input winding of the first core, switching it to one. But due to the back resistance of the electronic device, no current can flow against the arrowhead. No current, no flux change, no change in the second core from binary one to binary zero. Thus we have changed the binary state of the first core and retained the binary state of the second core by the use of this unidirectional device. How can we switch either core to zero? We've said that current flows into the input of the first core on the non-dot side only and this would be typical in a core circuit. The device will allow current to flow through it in its forward direction only, as we've set it up here. That means that current can flow into the non-dot side of the second core's input but not into the dot side. What we need for dot current is a shift winding on each core. Wound so the dot side is at the left in our diagram. The shift winding shifts or transfers bits from one core to the next, as these two will demonstrate, but we'll complete our diagram first by giving the second core an output winding that is identical with the first core's output. And for this example, we'll assume that both cores are in the zero state. All right, we'll follow the events in their proper sequence. A non-dot input sets the first core to one. Nothing happens to the second core. Current flow in this direction sets a core to zero, but that's where this core is already. We can hold the binary one in the first core as long as we wish. When we're ready to transfer it out, we send a pulse of current into the dot side of the shift winding. And three things happen. The first core switches to zero. Current flows out of the dot side of the first core's output through the one-way device and into the non-dot side of the second core's input. And the second core switches to one. We have now transferred a binary one from the first core to the second and controlled the time of the transfer to the exact moment we wanted. The binary one may be stored here, or again, a shift pulse may be applied to make the transfer to the next core along the line, thus clearing this core back to binary zero. However, there is one final problem to consider here. A shift pulse will induce a voltage not only in the output winding, but also in the input winding. Current will flow out of the dot side of the input back toward the first core. The one-way device won't stop it because it's going around the circuit in the direction it allows. This backward flow of information would, if unchecked, switch the first core, interfering with its operation. It is checked, however, by the design of the output winding. The output winding of the first core has many turns in contrast to the input winding of the second core, which has relatively few turns. The ratio may be as high as 12 to 1. Consequently, the small current produced in the second core's input meets a high impedance when it flows back to the first core's output. There just isn't enough current to provide the force necessary to switch the first core. We have been diagramming the single diode transfer loop, the simplest and most basic of the circuits that transfer bits from one core to other cores. The diagram we have been building up is a schematic, but there's an easier way to represent these loops. Logical diagrams eliminate the complex details found in schematics by concentrating on what the magnetic core circuit does. It's logic for reason for being there. Only a few symbols are needed for logical diagrams, and they're easily understood. A circle represents a core. An arrow pointing into the circle is an input. An arrow pointing away, an output. A binary digit inside the core indicates its state, 0 or 1. If the bit is placed in front of an input arrow, it shows the state to which that input will set the core, in this case 1. A bit placed behind an output arrow means that when the core is switched to this state, an output will be induced. Here the core must be switched to 1 for an output to occur. If we remember that initially, the core is either at 0 or 1, we'll see that these arrows and bits tell us four things about this circuit. If the core is initially 0, the input switches it to 1, and an output occurs. If the core is initially 1, there will be no switch. Therefore, no output, since there will be no change in the flux of the core. Other symbols used for logical diagrams are just as simple. For example, if we wish to show an input that will set the core to 0, using direct current, the arrowhead is closed. In our discussion, we've been dealing with pulse current, shown by open-head arrows. Double arrowheads mean an override input, an input that will hold the core to the indicated state, despite the simultaneous presence of other inputs. The letter T is used to designate time. This override pulse will hold the core to 1 at a moment in time identified as T1. Other letters serve to label individual inputs and outputs where several may be present. We can relate an input to an output with the capital and small form of the same letter. Thus here, where the core is initially 0, an input Q comes in at time T1 and switches it to 1, an output occurs. This output is identified by small Q. Now let's take another look at the logical diagram of the single diode transfer loop. It gives us the whole story very quickly. An input pulse sets the first core to binary 1, unless it's there already. The core stays at 1 until a shift pulse comes in at time T1 and switches the core to 0. This switch induces an output pulse in the first core's output winding, which causes the second core's input winding to set that core to 1. In effect, a binary 1 has been transferred from the first core to the second, where it's stored until at time T2, another shift pulse clears the second core back to 0. We can draw logical diagrams like this of any magnetic core circuit, and we depend on them for a clear understanding of the work done by the circuit. In this picture, we've been dealing with the basic properties of magnetic cores, memory units, and a variety of operating circuits. Later, you'll be seeing how they work. They have demonstrated their efficiency and reliability in dozens of applications. They conserve space and power. They operate with precision at high speed. In fact, the development of the modern computer has been due in large part to the techniques devised for using these remarkable cores.


Ok guys so I am back with another Destiny video and today I bring you these top 5 3 cat reactions to epic plays. Now this is a sniping based episode because recently I've had so many amazing clips submitted that have been sniping based so yes I thought I'd separate them from the normal 3 cat reactions into their own separate video. Now if you do enjoy leaving a like is much appreciated on my behalf. Remember subscribe for daily destiny and turn on the notifications so you never miss a video and as always guys I will see you on that next one. Oh my god should I have a Colette?  Colette for all? Yes. One more! Double Colette for all. We have two Colette for alls on the back.  Did you see the thing? Oh my god.


Hwyl. OK, mae'n gwneud. Rydych chi'n cymryd. Hei, hon. Mae gennych amser ar y front. Nid yn ei gwaith. Mae'n gwybod. Mae'n gennych. Mae'n gweithio. Rwy'n gweithio i chi ddim yn gweithio. Mae'n gweithio'n 5.2 oed. Mae'n gweithio'n 5.3 oed i'n gweithio'n 5.2. mae'n oeddo i'r cyfnod i gyn supportersau pan i gael y ddweud y cyfnod. Fe wnaeth eich ffordd. Yn ymwyaf yma ymweliadau, bydd fydd yma'r ymddangos. Ymwyaf yw'r ffondasig yw casmar. Mae'r cyfnod yw'r cyfnod yw'r cyfnod yw'r cyfnod yw'r cyfnod yw'r cyfnod yw'r cyfnod. Mae'n fwyaf yw'r cyfnod yw'r cyfnod yw'r cyfnod, ond yw'r cyfnod. We don't really have Wordpress as we expect it today. There are three current supported versions of PHP 7.1, 7.2 and 7.3. And if you go on the php.net website, you'll be able to see what the road map of these supported versions are. I want to highlight one particular thing on here, which is the way they talk about active support and security support. So, once a version of PHP is released, it has active support for about two years, which means that bug fixes will happen and there are small increments. And then it turns into security support for one year, where only security critical things happen. And that's quite important because even today, security releases for PHP 7.1, 7.2 and 7.3 were released, I think, this morning or last night. Now, you've probably noticed that 7.1 is in orange and I've highlighted it even more here. And that's because the active support, if everyone remembers what today's date is, ended last December. And the security support has started, which means we've got eight months before that is also end of life, which makes me feel like this cat. So, if we're going to talk about WordPress and PHP, we should actually look at what versions of PHP people are using to run their WordPress sites. And there is a pie chart for that because there always is. On the WordPress.org website, there's a lovely page called Stats. And you can find out lots of information like WordPress Stats and PHP Stats. And this is the stats from not today, but yesterday. And I doubt they've really changed that much overnight, unless you've all gone and upgraded PHP, which is fantastic. And I can just get off stage. What you can see from this is over half or just under half of versions of PHP people are using are 5.6 and 7.0. Now, the end of life for those two editions of PHP was December last year. So, these are not getting any of the security updates that were released this morning, which makes me feel like this. The whole greyed out area is all the unsupported, non-secure versions of PHP. And it makes me just cry a little bit inside. And it's not just one zombie. It's really a whole army of them. It's funny. And I have done this talk multiple times in different iterations. And the pie for supported versions is getting bigger, but we all have a hand in this. We need to check if we have a zombie PHP lurking in one of our websites. How many of you here can be confident that you have a current version of PHP running your websites? That's not all of you. So, some of you have homework to do, but it's really easy. There is always a plug-in for everything in WordPress, and this includes PHP. You can download the Site Health Check plug-in, and that will tell you what version of PHP you are running. So, you don't have to try and work out where it is in C-panel. You can just look on the inside of WordPress, and it will tell you in the dashboard, which is fantastic. The good news, WordPress works on all the latest versions of PHP. It's one of the first projects to run on the latest versions of PHP, so we are doing good in that sense. But it's always the custom code, the plug-ins, the themes that we actually need to check. There's a tool for it, like all good things in the tech world. There's a tool for everything, and there's a tool called Static Analyzers in the tech world, and basically, they're spell checkers. They check for your spelling mistakes in your code, and they'll check against different versions of PHP, but they do not check that this code makes sense. So, the sentence might be gibberish, and it will pass. So, if you're not a developer, but you want to see whether your website can be running on a later version of PHP, there are solutions for you. Like all good things in WordPress, like I just said, there is a plug-in for it. The lovely people at WPA Engine made a plug-in of PHP Compatibility Checker, and so you can download this. It gives you a screen like this. Unfortunately, it doesn't do 7.3 at the moment, but I did talk to them last night. And it does go up to PHP 7.2 at the moment. And you can also scan between active plugins and themes, or scan all plugins and themes. I recommend all plugins and themes, and I recommend 7.2, because 7.0 is already dead. You end up with a search result page like this, and it means that you will know which plugins and which themes are compatible and which are not, and which you're going to have issues with.  There are tools for this. Now, there's 12-minute talk. I'm not going to explain how you do this, but there's a good thing called Google, and other search engines are available, so you can just search these tools. It is pretty simple for somebody who doesn't write that much code anymore. I even managed to do it yesterday, so you can all do it too. I would like to highlight PHP Compatibility WP, because that actually removes some of the false positives that you'll see in the regular PHP Compatibility one that the whole PHP community uses. Where in your development workflow you might do this, you might do this locally, manually, on your terminal. You might do this as a pre-commit hook, and you might do this as a pre-deployment hook as well, depending on what kind of system you're using in your company or on your own workflow. I tend to do it locally just because I'd rather see my mistakes myself before the rest of my colleagues see it, but it's up to you. If you run it locally on the terminal, this is one of the first things you'll see, which if you're not a developer or you don't like the terminal, it can be a bit daunting, but it's not too bad. It's just a graphical representation of what's going on. It tells you the number of files it's checking. This was a very basic version of WordPress. If it's got any dots, that's a good sign. If there's letters, that's a bit more problematic, and what you want to aim for is no letters, basically. Especially no ease, no errors found. Warnings. You can get away without it, but I recommend listening to them. Ss, I haven't actually worked out why that happens, so someone can tell me later. That's great. After it does all the scans, you end up with a report of all the errors and the warnings, and it ends up like this. This is really useful, too. You end up with information about which file the error or the warning was in. It tells you exactly what line in that file they found the error. It doesn't mean the code was there. It might be somewhere else. It also tells you what type if it's a warning or an error. Most importantly, which is what I think is important, is this piece of information at the bottom of each one in the brackets, which is the rule that the error broke on. You can actually take that rule, put it into search engine of your choice and find out what's going on. Lots and lots of people have documented how to fix these things because a lot of people have already migrated up to later versions of PHP. I also mentioned WordPress coding standards, which will tell you about deprecated functions in WordPress as well as in PHP, so please also use that. If you have a site which has errors, then you need to migrate. It's like moving home. Your best friend will be the PHP.NET manual. In there, the documentation is very verbose, and they have examples of how to move stuff. They'll tell you the backwards compatibilities, what's deprecated, new functions that you can use, and also the changes, which are important. If you're going to start migrating your code base, depending on where you are, the larger the numbers of PHP you're having to migrate from and to, the more work it's going to be generally. If you're having to migrate from anything which is five points something to seven, that's where the most of the work is, and the lower the number, the more work you need to do. Start out your current PHP version. Use the static analyzer to work out where the problems are going to be. Check PHP.NET for what you need to do, and then do it, and keep repeating this until you get to where you want to be. On the appendices to the side here, it actually has every single migration from 4.0 all the way to 7.3, so you can do the whole entire migration. They are some caveats because it's technology, and it wouldn't be technology without caveats. Some functions, outputs in PHP have changed their outcome, which means if you're expecting a particular thing, it might be something else, which is fun. A static analyzer or spellchecker is never going to find that for you, so you might end up with quirks when your client is working on it or you test things, and those are runtime errors, and they can't be detected by spellchecker. There are lots of upsides, though. Better performance. If you're on a small site, it's not going to see much significance, but if you're on a big site, you're going to see at least the benchmarks around double the amount in terms of performance. If you want to save polar bears by upgrading to anything higher than PHP 7, you're going to be using less computing resources, which means more polar bears happy. It means that bugs that we have been hashing together and putting plasters on and outfix, especially things like PHP datetime, it also means we get security support, so that's always a win, and there's lots and lots and lots of other new features and functions, but 12 minutes and one minute to go. If you care about keeping your WordPress site up to date, then you should also care about keeping your stack up to date. The foundations of your house are super important, and this is no different. Developers and hosting companies are your friends. Yes, we charge you because we need to eat money, eat and pay our mortgages and live, but if you're a plug-in author, there's way more information. There's lots of work in the community that we've been doing, so we can actually set the standard for everyone, and there's a whole bunch of resources on the internet, but my favourite is the Lorna Jane talk at Workup London in 2015, which was a long time ago, and also a whole bunch of stuff there. My slides will be online. Kitas.