so basically that's the kind of idea that that we want to try to push our computers to do to try to study harder to try to learn things that are more generalizable that are not just good enough to pass the test but to actually understand the world okay so that's kind of the the preamble and the the way we have been working on this in in in my lab is we have been doing it in three different paths and I'm just gonna kind of quickly show some of their some of the results of it so the first is self supervision the idea of not having a


expert tell you the correct answer but let the computer figure out the correct answer second is what we call meta supervision it's I'm not sure how standard this term is I think we might just have came up with it and the idea here is that you don't supervise the data the correct answer you supervise how the answer supposed to behave okay and finally if there is time I also want to mention a little bit about you know what if there is no correct answer what if you're just learning by just playing around you know if you don't have a goal

Alexei Efros: Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder


there is no there is nothing to cheat there is no need to cheat because you're just playing around there is no goal and so the idea here is to see if we just get removed the goal remove the you know the whatever whatever we're trying to optimize and see if we can just just play and be curious can that get get us some representation that's more generalizable okay so I'm gonna show some examples of all of these in the next oops alright okay so first is self supervision here is a evocative drawing by Asher of what we mean here and this


is something that actually kind of had been classic in in in in deep machine learning under the heading of representation learning so we basically want to somehow have a compact representation of an input image and we want to compute this representation maybe without any labels and the kind of classic way to do this is what's called an out encoder which says let's have a representation that is small okay so there is a bottleneck here but that if we unpack it and compress it we can reconstruct the original input okay and


then you train this kind of a out encoder set up for many many many images but you don't have any don't need any labels here right it's just just pairs of just just a single image and this is this a very influential idea unfortunately it doesn't actually work in practice the representation that you learn here if you're doing it for any kind of real data like a big image for example not a tiny you know third you do way third you image but a big image that doesn't actually work okay and the reason it doesn't work is that this is


you can think of it really as data compression right you're compressing your data but and data compression is related to machine learning but it's not quite the same because data compression doesn't care about how do you perform on new images it only cares about how you compress the training images that you got and so what we propose to do is to think about this in terms of not data compression but data prediction to make the computer try to work harder and say instead of just compressing the data let's see if we can train it to


predict the data so one way to do this very simple way is you only give it half of the input signal and you train it to predict the other half so now we not just the compression it's not just that you keep taking the pixels and compressing them you need to think a little bit more you need to think about context and what should go well with with the input that you got ok and one very simple way of doing this is to split the data in terms of color and and and luminance so this was our paper a few years back where we said okay let's take let's take


a color image separate it into a luminance channel and a chrominance channel and then train a network to predict from the grayscale to the the color and then you know you can get a nice beautiful image but hopefully also you'd learn the representation that is actually meaningful and somehow captures something about the natural world okay so of course you need to show some pictures first so it actually does learn a reasonably good representation of color but the cool thing that suggest that maybe is also learning something


else is some of the failures so here is a couple of instructive failures can somebody see what the failure is it's let me what that year the year is a little bit off but it's a but somebody said the tongue do you see the tongue there is no tongue and yet it's it's point it's coloring it pink why could this why would this be well we were confused too but then we looked at the training data and in the training data these poodles they all have their tongues out so if this was just a stupid compression it would not this this air would not


happen but here it seems like the network is actually recognizing that this is a dog it's recognizing the breed of the dog it's remembering the the similar dogs that has seen before and then it's making and mistake but it's a reasonable mistake that maybe if all the dogs have had their tongues out in the training set maybe that's also true in a test ok and indeed so if we could you know we did a various tests but I will just show you one way to to see what's what's been learned in this representation is to to


do what we call deep net electrophysiologist so we kind of probe the different represent features of that compressed representation and see what they wear with a fire think of Amalek neurons firing so where do they fire and so what we found is that there is a we found a neuron that fires only on faces there another neuron that only finds and fires and dog faces another one that fires on flowers ok so it's basically it was able to kind of disentangle from massive pixels of the input it was able to to find the kind of specialized neurons for


different different parts of the visual world even though the labels we had were just the color it was it's a very very weak label and a way label that that that didn't have any semantics in it and yet we are basically getting something out that is semantic okay and so this is this is kind of a hopeful direction it's their presentation is not as good as all the kind of semantic train representations yet but I still feel that it is it is optimistic direction because hopefully it might be more general in in the long run but this is


still to be to be to be found out originally actually this concept of self surprise learning one of the early papers ended by psychologist Virginia dasa was on thinking about it intra of the different modalities of of the sensory signal so instead of saying okay Kohler versus grayscale although that that true is kind of biologically plausible you have the rods and the cones and yeah rods in the cones and you can say that the rods in the codes kind of code train each other but it's much more reasonable to think about it in


terms of different modalities for example sight and sound okay so the idea here is that you know you learn about cows by seeing the cow hearing the Moo associating those two together and using that as a kind of learning signal okay and so just recently we decided we're going to try to try to try to use self surprise learning in this in this domain and this is one motivation for why this kind of this kind of thing is is is is a need at any one moment we are being bombarded by sensory information our brains do a remarkable job of making


sense of it all [Music] it seems easy enough to separate the sounds we hear from the sites we see but there is one illusion the reveals this isn't always the case have a look at this what do you hear ba ba ba bi yes ba ba ba ba but look what happens when we change the picture and yet the sound hasn't changed in every clip you are only ever hearing bar with a bee ah it's an illusion known as the McGurk effect take another look ba concentrate first on the right of the screen ah now to the left of the screen ba the


illusion occurs don't because what you are seeing clashes with what you are hearing in the illusion what we see overrides what we hear so the mouth movements we see as we look at a face can actually influence what we believe we're hearing if we close our eyes we actually hear the sound as it is if we open our eyes we actually see how the mouth movements can influence what we're hearing so did the people here I wonder how it works for non-english speaker so so yeah so I think that some some of it is specific to English speaker that the


first sound is you you make your mouth like this right and so even though the sound is exactly the same the lips move very differently and so your brain how many people got the the effect how many people heard the five okay very good very good so it seems to be working very good right so this motivates the idea that if you want a representation for example a video representation you to combine the visual and audio and you probably want to combine it pretty early this is this is a very kind of powerful effect even if you know about the effect


if you're very well aware you you steer this thing all the time you you cannot turn it off it is very very powerful low level effect and so said it this suggests that the coupling of audio and video probably happens pretty early on and so the idea that we had was to recreate an video representation that takes in audio and visual features at the same time okay so kind of classic video representations you basically have some some way to go from a series of frames to representation and then also the same thing for for audio and what we propose


is to to combine it together and then have a joint audio and video representation you know for for a number of layers at the top so that there's this information can kind of a cook together and get us a join out your video representation okay but how do we train this representation and again we want to train it and without supervision it will self supervise training so one thing that was kind of the obvious first idea that we had was why don't we train say a binary classifier where the positives are videos with the correct


sound and negatives are videos with a wrong sound of sound from some other video for example okay and then we train this classifier and hopefully it will learn to to figure out that the correct sound correct video correct sound there is a correspondence okay this unfortunately doesn't work well at all and the reason it doesn't work well at all is because again it's a problem of cheating because if you have if you take a random video and a random audio the video could be me giving a talk and the audio could be you know


some sort of a you know sound from the restaurant for example right you just look at the picture you look at it but people sitting and listening and you know that this this is not a restaurant so just just to look at overall picture just a single frame will be enough to tell you you know this is a presentation that is a restaurant that is a rock concert so you don't even need to listen to anything you can just have a kind of a 1:1 label which says okay this is a restaurant or a rock concert so we need again to try to make the computer work


harder and so here is our also simple idea - and the idea - is that we're going to have as positives again videos with the correct sound but as negatives we're going to have that same video that same audio but we're going to displace it in time a little bit okay now this becomes a much harder problem to solve because it's the router is correct the video is correct the only thing that's not correct now is that there is a little bit of a time lag so this representation really needs to very carefully pay attention to to the


connected to the registration between between those two okay so here is the idea so the correct the correct samples are line samples are just you know how would you and video together okay and then and then incorrect once all was the same except couple of seconds is placed and we have to careful because because if you displace for maybe like a second or less than a second humans are actually not even that sensitive to it so it needs to be a little bit of displacement okay so now we trained this representation for a


long long time you know weeks of GPU time and then we have a representation that has the whole thing working right here hope the whole thing working and then we can think we can look at what we can do with that representation so one thing we can do is we can now visualize the source of the sound because what we can do is we can say okay given this task of you know is it is it aligned or not aligned we can actually just use the kind of classic class activation visualization maps to see what pixels is it using to tell if things are aligned


or not because those are the pixels that are the sound producing pixels okay so here is an example of some of the places where it thought those were the important places that it's that it decided where the sound producing places and here is some some some visualizations of this over time [Music] okay so this is again completely automatic no labels of any kind okay another thing we can do is we can just plug this in into your stand the kind of action recognition data said a lot of those data sets have audio in them so we


thought okay can we use this audio channel to improve our our method and we are actually definitely getting improvement over audio and we are improving other self supervised method we're still not as good as something that is kind of trained with lots and lots of relevant semantic data but again the hope is that as we get to harder and harder data sets that semantic labels will be harder and harder to get and so the self supervised methods hopefully will get better okay and finally kind of a cool fun thing we


could do is to see if we can do off on screen separation of the audio sources so an example would be if you have a speaker speaking in on to the camera and then there's somebody else speaking that is not being seen our feature is only going to focus on the on the speaker that is being seen and so we can subtract away the speaker who is not being seen okay so let's let's see what I so unfortunately we thought oh this is such a cool idea nobody would ever think about it at the same time like four more groups who are basically doing the same


thing luckily we heard about each other in time so we actually cross sighted in so it's all fine our method most of these folks actually that was the goal of their papers so they could have they basically work on that particular problem for us it's just basically one application of our feature that just kind of falls out of our method so we'd say that we're kind of a it's it's more of an application of our stuff but hopefully we are also able to to do other other things as well and the idea here is basically we take our


representation and then we seed it into kind of a standard encoder/decoder representation that starts with a spectrogram and basically learns the separated into into things that part of the spectrogram that have evidence in the image or in the video and the part that does not have evidence in the video and then you can then you can play either one or the other right once you separate you can play either one and so here is an example then asking about it because they're not interesting facts to you that's not true I have a plenty of


questions integration you a sample about your by talking about your flight now I know there's a bunch of people talking one is old screen one is off screen let's see what we can do so this is just the on screen been asking about it because they're not interesting facts to you and then people all about no I'm not I don't want to okay and this is the off screen that's not true I have a plenty of questions integration you have to disability Oscar by talking about your flight so there's there is a little bit of noise there but mostly does the right


thing thank you so much thank okay and this is midnight is no second have day let's talk about digital omens there are some fears so much were able to show to the rest of the world the unshakable japan-us alliance okay we have both speakers here okay you're saying that they're not and we can we can hide one of the speakers and then gets away okay could even do something all right laughter okay all right all right okay most of it is gone but not all okay so so that is that is kind of a various ways of trying to get data to


supervise itself and hopefully learn the representation that that is that is you know useful for other tasks the second topic I want to mention briefly is what we call meta supervision and the idea here is instead of telling what the correct answer should be we tell how that correct answer should behave so what do I mean by that so the kind of the direct supervision is a the direct supervision is is you have input X and you train a function f of X and you want that function f of X to produce Y's okay that's direct supervision you know from


X's to eyes what are other ways we can we can set up this problem one way is we train a function of f of X that produces something in the domain Y in the set capital y so we don't tell it what particular y we want we just want it to be in the set of Y's okay and one example of this is generative adversarial networks I'll give a brief overview based on a paper we had a couple last year the colorization example that I showed it had a lot of kind of a ways of hacking it to make it look good and some what you want is you


really want this white wall to be white on the back but because we kind of told it to all you need to be more colorful the wall becomes not white okay so it's kind of overshooting it and the annoying thing is that there is no way for the algorithm to look at this that's just not looking realistic you know do something better make it better have a try to optimize for things looking realistic so we don't know how to do that well actually we do so you know for any kind of a problem we do have like colic or is polarization or


super-resolution or whatever it would be nice if we had this function that would tell us you know make something realistic enough it is this loss function this commune universal it says make make images look real we do have that function right now that's that's called a graduate student okay that's where the graduate student basically keeps hacking all algorithm until you know enough of the pictures look good and then then we send it to to publish but it would be better if the computer were doing it themselves okay so one way


that we we can do this is we can basically have somehow the computer send the resulting images to Amazon Mechanical Turk ask a whole bunch of people if this is good looking or not good looking and use that signal to update the algorithm very very expensive okay but what what we can do is we can use this idea that that recently came out that kind of does something similar okay because remember we have a lot of real images so what we could do is we can have another network that can act as as an amazon turk are deciding if


something looks good or not okay and that network is basically going to tell us if the image that we generated if it looks real that is does it can you distinguish that image from a set of real images and if their answer is no that means that we are doing well okay and this is the idea behind this giant material models in Goodfellow and colleagues that that has kind of really energized this whole field of image synthesis let's think about it you know what is it actually doing quickly so we have our function that


translates say from greyscale to color okay and now what we want is we want to add another network on top of that and that network we want to decide how to decide if the image here if it looks real or it looks fake okay so what we want is we want G the network G to fool the network D so we want D to think that this is a real image whereas in fact it was generated by by by G okay and of course G who doesn't want to get fooled so do you really trust ooh to figure out if it can if it can if it can tell and the idea is to basically have these two


networks battle it out in a kind of a duel like an arms race and the idea with as just any arms races when you have competition when you have a duel both get better and better and better that's that's that's the beautiful story Oh of gas right so here is a little bit of math so let's say that in this particular context if we're trying to see what G wants to do d wants to have a high probability for generated images if the image was generated by G we wanted to have a high probability of saying that it's it's a fake image okay whereas


if the image is actually a real image then we want to have it say no this is a low probability that it was faked okay so we want to have a G such that it maximizes this quantity okay at the same time of course what we want G to do is to do the opposite it wants to minimize that same quantity right so d G is going to get back signal from D that says okay I figured you out and then G is gonna say okay let me see what I can do better now to improve my generator generated image so that G will have a hard time so maybe I'll


something that looks a little bit better okay and try to fool it but of course I don't want to fool just this particular G I want to fool a the best possible D so this is where we get this whole minimax formulation where you want to have the best D and then minimize the the G to - to do the best of that okay so one way to think about it is that now this D you can think of it as a loss function it's kind of like l1 or l2 it basically tells what you need to do how do you optimize G such that it gets closer and closer to the goal the only


difference is that instead of being nel - now this G is learnt the G learns what does that mean to get closer to the goal for that particular problem and what it means is that it basically means to be indistinguishable from the real samples from this from this data domain okay so we're almost done but not quite because here is an example imagine that my G went completely crazy and started producing cats for any input image it got it produced the cat okay now is this a real immature a fake image it's a real image it's a cat it's a it's my Student


Union's cat named Aquarius very nice yet so it's a real image but it's not really what we met so we need to give it a little bit more constraints so basically what we want to do is we want to give D not just the generate image but also the input X so it can look at the pair of both of them together to say is it is the G of X the result of starting with X so that is is this a real pair or a fake pair and now we're all good now this is the conditional gann case and now we're we're able to to get it to work and


this is the the final thing that is being optimized now I'm of course hiding a whole bunch of things under the rug here this is not a pretty optimization as you might imagine it is very complex complicated to optimize this thing and and and there is a lot and a lot of work on trying to make it simpler so far it's still it's it's it's it's more art than a science how do you optimize this thing but my graduate students are really amazing at doing this so so we were able to get this working and so now we can we


can you know plug in greyscale and call her pair and then we can colorize images we can do the exactly the same code we plug in Google Streetview and satellites and then we can you know we can basically hallucinate satellites from from maps or we can do it the other way around exactly the same code but because the G is getting optimized for every different pair it basically learns what is important for every domain we can generate from labels we can generate facades we can go from day to night we can go to from thermal imaging to to


normal RGB imaging we can take edges image edges and produce images that could have come from those things okay this kind of looks cool but actually it's not that complicated H maps actually contain a lot of the information the cool thing is that we can then train on this and test on just you know human sketches and even there it's actually doing something reasonable which is which is quite kind of neat and then we we put this online the code online and and a lot of kind of artists decided to do cool things with this and


so this is this is kind of a neat thing where you you don't even need to do results of your papers anymore you just kind of post the code online and then just download results from from that other peoples have done and and somebody even did a little edges to cats thing you can try to yourself you you you you draw something you hit the the pigs button and then it will get you and get your cat okay there you go the the the best the best yeah the best use of company computer technology in my at least for me I don't


know this is yeah this is the the pinnacle of my research career I think I get lots of cats on the Internet so so this is an example of Gann so as a again we we talked about direct supervision again basically cell we supervise of not on the particular label but on a set why there are other types of metal supervision we can think about so one is one of my favorite ones its cycle consistency okay the idea is that we don't know the answer why we don't have label for that but what we know is that if we if we have our f of X which


produces some Y and then we apply a G of that of that we should get back to X okay and this is a constraint that people have used a lot especially in tracking in computer vision you track forwards you get somewhere you don't know where you are then you track backwards in time along the video and the idea is that you you should end up where you started with and if you don't then something is wrong okay but we can use this as a constraint to again for optimization so for example let's say that we want to do this kind of a pics


to pix image to image translation but we don't have labeled pairs so let's say we want to translate from horses into zebras right there is no possible label data for this so how do we do this well we can take an inspiration from actually Mark Twain and ID of back-translation in in in in in in language and the idea of back translation is that if you want something translated in a foreign language you don't know you hire one translator to translate to that language and you hire another one to decide back into language you do know and then you


double check that it kind of still make sense right and Mark Twain wrote this book jumping frog in English then in French then clawed back into a civilized language once more by patient and renew merited toil so here he was showing that in this particular case the translation was not a good one so he translated back and and sure that was it was it was not not looking good okay and so what we're gonna do here is we're going to basically do the same idea we're going to now have a translator G that goes from domain acts of the main why okay


and then translator F that goes back okay and and and and that's all now because we one do we don't want it to cheat and just stay where it is we want it to also have this adversarial loss this this disg and loss it says that when you get to domain why you better be indistinguishable from a real thing and why and when you get to domain X you better be indistinguishable from something real in X okay so what we're doing here is we're since starting with an image X we translate into a zebra domain again we don't have a label for


that but we know that it should look like a real zebra and then we translate it back and then if we didn't if we don't get exactly where we started with well that's our loss that's what we want to minimize that is exactly this the thing that we are going to want to minimize okay and if you kind of a step back and squint at this thing what does it look like it's an hour old friend out encoder right you have the input you reconstruct that input the only difference is that it's an out in Golder that instead of a


bottleneck it just has a different domain in the middle so it basically just has go through something else it's forced to go from some other representation and then come back and then you also do it the other way around okay and so then we can turn horses into zebras and vice versa we can even do it in in in videos just one frame at a time the failures are kind of fun I showed this picture in Moscow last year and I thought that's it I've they'll not let me out but they did we could also do kind of nice things on


going from images to paintings okay by just kind of matching from domain of photographs the domain of in this case Suzanne paintings and I'm particularly happy about those clouds and you've probably seen a lot of the stylization papers and results they usually look at a single single image that you want to stylize for a particular image here we can take a whole domain we can take all of Suzanne's painting all the thousands of them and learn the representation that kind of models the whole Cezanne okay and and the clouds look pretty nice


we can also go the other way around we can go from a painting into something that kind of hopefully hard to distinguish from a real image now if this was a perfect talk this would also be Suzanne and Suzanne didn't work Monet is simpler so so I will show you Monet but we're still working on Suzanne hopefully we can get Suzanne - okay we can also apply this to translating between video games in the real world so this is Grand Theft Auto and this is making it look like kitty so now you can see it's like old German looking and you


can go the other way around which is even cooler they kind of walking around with a with with like make your reality like a video game so here is an example of reality as a video game I can let me show you just because this is just such a cool such a cool result this is this is again people have been just playing around with with these things all right I know it's not alright never mind it's forgot this is like artists just taking our code and running with it and okay I don't have the okay nevermind I will I will I'll give give you the


pointer but yeah finally I want to show a very cute example of you know I in in my you know in my group we have been playing around with a lot of making fake imagery from from early on and so now a lot of people are worried about you know all this fake news and you know put in screwing things up so we thought we could try to play on the other side of the fence as well and see if we can detect if image is not being realistic okay and the top one is actually from my old paper with James Hayes where we learn to kind of fill in holes and


create these image composites and here we can finally you know detect this and and and recover the that this image was what's fake and we are also going to do it with this the same idea of of self supervision and metal supervision this is with a couple of wonderful former Berkeley undergrads and and under Owens so given this image it might not might look reasonable to you but in fact of course it is fake and how do we how do we detect this well if we had enough fake examples we can just again do this you know supervise


direct supervision they're out but we just don't have enough of these positive examples and so what we're going to do is we're trying to think about this as a normally detection so we're going to find to see if we can learn if an image is consistent with itself okay and the idea here is the following we can look at a couple of patches of this image and we see is there some sort of some kind of fingerprint in these in this patches that tells us that they might have come from different imaging systems that they


are not from the same camera or and not from the same image okay now if we had access to the actual images that this was taken from created with then we could actually look at the the metadata that comes with the image and then we can realize that it's actually you know the cameras are different the focal lengths and different cetera et cetera but of course in real life we don't have access to any of that and so what we're going to do is we're going to train an algorithm to see if for a pair of images if those images if we can learn if a


pair of patches comes from the same image or not now that by itself you could also do that that doesn't work as well because then again you don't have enough enough data for it for that so instead what we're gonna do is we're gonna train if a pair of patches have the same is it metadata tag there's a many different metadata exif metadata tags like camera brand focal length JPEG compression etc etc and for each one we can predict not the value of that tag but is it the same or is it different okay and so the idea is that we have a


whole bunch of we train with a whole bunch of real images we don't need any fake images for this so for every pair of real images we take a couple of random patches and then we look at the exif tags that are similar in this images and we train those things to say okay yes those are similar and the different ones we say no those are those are different so basically we train something like 80 different classifiers that says for every different for every single active tag is this going to be the same or is it going


to be different okay and so now we have a kind of a way to establish if a pair of images if pair of patches are consistent and more along one of the dimensions the dimension being you know are they do they come from the same camera do they have the same resolution do they have the same jpg etc etcetera okay so here is the different task tags and how well the how well we can predict if they if they come from the same image or not so you can say that the lens make is one of top-performing months so it is basically like the the what who produced


the lens then you have a custom renderer is basically some Apple iPhone thing it basically says iPhone then then then you have a bunch of various things that really code for a different processing that's done by different cameras so they're all kind of things that different cameras do differently whereas things like image date and time or GPS coordinate are basically a chance level as you would expect okay and then what we do is we combine them all together oh and we also have some other consistencies that are like try to do it


like blurring and and our Erie JPEG in etc etc and then a test time here is an image here is a manipulated image and what we do is we just have a pair go and find a whole bunch of different pairs of images and for every single active tag we could predict a map of if those two images are consistent or not consistent with each other these two patches okay and so we have a kind of consistency map for every single tag like camera or or focal length etc and then we combine them together into an overall consistency heat map and then


once we have a heat map we can we can run normalized cuts and actually cut it into inter and thing or not and so here we can predict that all look at this this is this is a this is the inconsistent part and here is here is what it found and consistency is here here actually we didn't even notice it but it detected that the shadow on the on the floor was also painted in not just the guy on the top not sure nice nicely it works it you know for normal images it doesn't fire usually which is good and you know we're beating most of


the pretty pretty much all all the other methods that are have been supervised we are beating them with this kind of self supervised method and for some images we don't have the ground truth so we don't know so I don't know who knows maybe this is how conspiracy theories are are born so I think I'm over time there so I I will I will I will skip the last part but I'm happy to talk about curiosity 101 so I think oops there you go thank you very much [Applause] any questions actually all know on the last part detecting detecting fake images so


manipulated images so I would I wonder what would happen if you actually plug it into this game cycle where you're trying to actually make make wadding image look like it was taken with different objects with different camera and then having this discriminatory trying to discriminate it it which of the sides would win eventually if any so yeah so in I think in in you know longer so I think what they were saying is what happens if you have the the the critic the guy who is trying to find fakes connect with the generator that tries to


make them better right and then have them battle with each other with this because there's an ongoing thing where people are trying to find fake images and then on the other hand improving that's right that's right that's right no I think this is this is actually that that's that's what we are talking you're thinking about doing doing doing next this kind of to have the the the fake detector not be a kind of a static one but to to to learn by having a generator generate better and better fakes and so hopefully then the


detector becomes better and better and better technology to improve the detectors of face is exactly the same technology that you will use to improve the producing of the images right that is that is true but at least for now with these grand formulations in the end so that it this is kind of a weird thing that began really the Khmers converges when the detector cannot know the difference between the real and the fake right so that would be when the the thing actually converges in reality though the detector always wins so we we cannot


fool the detector so those gaps never really converge so for now and it kind of may be reasonable because it's always easier to criticize them to create right always easy to be critic than it would then than a than a painter right so for now that might be okay but but in generally I think it's a it's it's it's an arms race it's an arms race and and and yeah there is no there is no gonna be a perfect solution there is always going to be something that the generator can do that that defeats the the defenses that's why I think it needs to


be active that's why the the the this the the the fake detector needs to keep thinking of some other ways that the generator could potentially be fooling it and and be prepared for that if I may just smoke before so many of these fake images are created by copying you know patches of the same image somewhere else in the image is that something that this detector will be able to detect no no we actually we looked at this and most of those most of the fakes are not copied from from the same image at least the ones that we have looked at they're


usually you know you go you find something on the internet you put you know picture of the Pope or Putin or whatever or Trump yes yes so in this thing we're basically where we're focusing on what's called image splicing where you have two sources and then you create an image out of those two sources yeah so if you if you move things within the same image it might still possibly detect things we have seen a couple of examples where that happens of kind of the copy/move thing because it sometimes it screws up the the JPEG compression for example but


in general it's not it's not trained for that it's not it's that's not what it's looking for it's looking for really different imaging pipelines so it's not it's not supposed to be working for that example for that thing yeah mm-hm allow me I don't hear it that close doing videos I will I will I will repeat yes so the question is if we have tried it on on videos with people imitating other people different voices we haven't but I think this is this is yeah we can or any of the the papers online the code is online so anyone can actually run it


and see that would be an interesting thing to to run and see we have played with them very very ventriloquist's so when you have a puppet and you talk as a puppet and then you talk as as yourself and it kind of works it kind of works for that so hopefully it should be it should be doing something like this yes it's not it's not meant to be kind of a detective detecting detecting fakes it's it's really mad it's meant to be fooled by the same things humans are fooled by so if the impersonator is a good one hopefully our method will work too


yeah yeah question about this domain to domain mappings and you have to like one directional and cycling example Google Maps in the wandering back to them the paintings were in the cytosine in the cytosine you care about the consistency that you come back to the center and you say the painting were actually working only in one direction the pay they the the paintings work in both directions paintings two two two photos they just don't look so good so III didn't well I didn't show the results that didn't didn't work very


well so remember in them in the Google Maps you go to the Google page and you can you can copy you can you can you can you can copy the the Google map and then you switch to the satellite and you can copy the satellite of that same thing so you have aligned inputs and outputs right so this is a much simpler problem so there you don't need any consistency because you have the X in the Y's given to you and so then you can just do it directly with the paintings and them the photographs there is no alignment you


have the suzanne paintings and you have photographs and you don't have a one-to-one correspondence and so for that the cycle is is is imported so that's the only thing we have basically we there is no other constraint so then it's very portable and we come back to the polarization for example okay then the colored image is not strange today oh it is it is because there is a there is a hold on the the gun yeah so so let me see let me see zouri so in this in this setup you are given the input and the output okay and you are your


basically where is the picture your training on the grayscale and the color okay so you're given the input and the output and the Gann is just making sure that the output that the generator produces is similar in the perceptual space to what we humans expect okay because if you don't have the Gann then then you get weird results like this one right this one right this is what happens if you just kind of do regression with the sake something like l2 loss yes yeah I I did not mention that I I'm sorry yes it does have the


kind of this standard l2 loss plus the gamblers yes you're right I'm sorry I yeah I dropped out that that's like yes so for this you have the Delta loss plus the Gann loss in the second one with the cycle we don't have in the l2 loss because there is no there is no data for that yes yes I'm sorry yes this good good point yes mm-hmm no it'sit's true I think I think it's it's it's not it's not clear what heat map what do you want to call her because again the the the producer of the sub for example when you're when you're


playing the the organ the producer of the sound is the big the big pipe that's the thing produces the sound but of course what what it's being it's it's coloring is the guy player pressing the keys and so I think it's it's not a very clear what is it that we want you know do we want the actual physical thing that produces the sound or do we want the the actuator and so here I think we are not we're not we just wanted to see what what would it be sighs so we are happy with anything that connects with with sound production in some way or the


other but yeah it you're completely right it's not I think if I think it's really it's it's going to be looking at correlations not causations and I think actually I would be happy if the dance party thing if it shows the people dancing I think that would be actually pretty cool but yeah you're completely right it's not a it's just kind of a type of visualization that that shows okay this these are the pixels that connect [Music] so the question is I think this is a very very very good question that that there was a little bit of a slide of


hand I said that you know the the tasks were too easy and the computers were easily cheating on the kind of the classification tasks and then I ended up making tasks harder but also changing the tasks to something like you know pretty pictures or connecting audio and visual etc etc what about going back to you know detecting car detecting dogs and and and classification so I think I personally am NOT a big fan of the classification task to begin with I think it's a it's a it's a task that is designed to to be cheated on because


it's a task that basically assumes that you have a close do you have a close the world your world is a thousand classes and you're basically deciding one of the thousand things right so your your your your your chance performance is actually not that bad your chance performance on on something like image net is is one in one and two hundred right it's it's actually the chance is pretty high in the real world we're in the open world where the potential number of that you need to recognize is almost infinite okay


and so I think that actually a lot of the problems with with with the with with the come with these networks cheating is because we're testing them on tasks that are very constrained we're cheating is actually actually the right thing to do so we're testing them on something that is that is not that that that that that is a kind of a very it's a it's a it's a it's a specialist task right what I think these methods will excel at is the generalist tasks something where you train on something and then you you you apply to something


completely different and hopefully it will work better and we have already seen this like for example the colorization feature that we have trained it does better than imagenet if the task is for example then to predict depth from a single image right so if the task is very different from the task it was trained on the cell supervised features work better if the task is similar to what it was trained on then the semantic tasks work better so I think that the the big goal is that we want to produce a generalist computer


something that is able to deal with novel situations in a reasonable matter not something that we already have if your goal is to just learn a specialist that will tell you no different types of of you know of Viennese pastries from each other you know you have a thousand different pastries and you want to tell name all of them then I think the current direct supervision methods are exactly the thing you need to do give in this kind of closed world but if you want to have a general algorithm that can do the pastries it can do the


The human voice: It's the instrument we all play. It's the most powerful sound in the world, probably. It's the only one that can start a war or say "I love you." And yet many people have the experience that when they speak, people don't listen to them. And why is that? How can we speak powerfully to make change in the world? What I'd like to suggest, there are a number of habits that we need to move away from. I've assembled for your pleasure here seven deadly sins of speaking. I'm not pretending this is an exhaustive list,

How to speak so that people want to listen | Julian Treasure


but these seven, I think, are pretty large habits that we can all fall into. First, gossip. Speaking ill of somebody who's not present. Not a nice habit, and we know perfectly well the person gossiping, five minutes later, will be gossiping about us. Second, judging. We know people who are like this in conversation, and it's very hard to listen to somebody if you know that you're being judged and found wanting at the same time. Third, negativity. You can fall into this. My mother, in the last years of her life, became very negative,


and it's hard to listen. I remember one day, I said to her, "It's October 1 today," and she said, "I know, isn't it dreadful?" (Laughter) It's hard to listen when somebody's that negative. (Laughter) And another form of negativity, complaining. Well, this is the national art of the U.K. It's our national sport. We complain about the weather, sport, about politics, about everything, but actually, complaining is viral misery. It's not spreading sunshine and lightness in the world. Excuses. We've all met this guy.


Maybe we've all been this guy. Some people have a blamethrower. They just pass it on to everybody else and don't take responsibility for their actions, and again, hard to listen to somebody who is being like that. Penultimate, the sixth of the seven, embroidery, exaggeration. It demeans our language, actually, sometimes. For example, if I see something that really is awesome, what do I call it? (Laughter) And then, of course, this exaggeration becomes lying, and we don't want to listen to people we know are lying to us.


And finally, dogmatism. The confusion of facts with opinions. When those two things get conflated, you're listening into the wind. You know, somebody is bombarding you with their opinions as if they were true. It's difficult to listen to that. So here they are, seven deadly sins of speaking. These are things I think we need to avoid. But is there a positive way to think about this? Yes, there is. I'd like to suggest that there are four really powerful cornerstones, foundations, that we can stand on if we want our speech


to be powerful and to make change in the world. Fortunately, these things spell a word. The word is "hail," and it has a great definition as well. I'm not talking about the stuff that falls from the sky and hits you on the head. I'm talking about this definition, to greet or acclaim enthusiastically, which is how I think our words will be received if we stand on these four things. So what do they stand for? See if you can guess. The H, honesty, of course, being true in what you say, being straight and clear.


The A is authenticity, just being yourself. A friend of mine described it as standing in your own truth, which I think is a lovely way to put it. The I is integrity, being your word, actually doing what you say, and being somebody people can trust. And the L is love. I don't mean romantic love, but I do mean wishing people well, for two reasons. First of all, I think absolute honesty may not be what we want. I mean, my goodness, you look ugly this morning. Perhaps that's not necessary. Tempered with love, of course, honesty is a great thing.


But also, if you're really wishing somebody well, it's very hard to judge them at the same time. I'm not even sure you can do those two things simultaneously. So hail. Also, now that's what you say, and it's like the old song, it is what you say, it's also the way that you say it. You have an amazing toolbox. This instrument is incredible, and yet this is a toolbox that very few people have ever opened. I'd like to have a little rummage in there with you now and just pull a few tools out that you might like to take away and play with,


which will increase the power of your speaking. Register, for example. Now, falsetto register may not be very useful most of the time, but there's a register in between. I'm not going to get very technical about this for any of you who are voice coaches. You can locate your voice, however. So if I talk up here in my nose, you can hear the difference. If I go down here in my throat, which is where most of us speak from most of the time. But if you want weight, you need to go down here to the chest. You hear the difference?


We vote for politicians with lower voices, it's true, because we associate depth with power and with authority. That's register. Then we have timbre. It's the way your voice feels. Again, the research shows that we prefer voices which are rich, smooth, warm, like hot chocolate. Well if that's not you, that's not the end of the world, because you can train. Go and get a voice coach. And there are amazing things you can do with breathing, with posture, and with exercises to improve the timbre of your voice. Then prosody. I love prosody.


This is the sing-song, the meta-language that we use in order to impart meaning. It's root one for meaning in conversation. People who speak all on one note are really quite hard to listen to if they don't have any prosody at all. That's where the word "monotonic" comes from, or monotonous, monotone. Also, we have repetitive prosody now coming in, where every sentence ends as if it were a question when it's actually not a question, it's a statement? (Laughter) And if you repeat that one, it's actually restricting your ability to communicate through prosody,


which I think is a shame, so let's try and break that habit. Pace. I can get very excited by saying something really quickly, or I can slow right down to emphasize, and at the end of that, of course, is our old friend silence. There's nothing wrong with a bit of silence in a talk, is there? We don't have to fill it with ums and ahs. It can be very powerful. Of course, pitch often goes along with pace to indicate arousal, but you can do it just with pitch. Where did you leave my keys? (Higher pitch) Where did you leave my keys?


So, slightly different meaning in those two deliveries. And finally, volume. (Loud) I can get really excited by using volume. Sorry about that, if I startled anybody. Or, I can have you really pay attention by getting very quiet. Some people broadcast the whole time. Try not to do that. That's called sodcasting, (Laughter) Imposing your sound on people around you carelessly and inconsiderately. Not nice. Of course, where this all comes into play most of all is when you've got something really important to do.


It might be standing on a stage like this and giving a talk to people. It might be proposing marriage, asking for a raise, a wedding speech. Whatever it is, if it's really important, you owe it to yourself to look at this toolbox and the engine that it's going to work on, and no engine works well without being warmed up. Warm up your voice. Actually, let me show you how to do that. Would you all like to stand up for a moment? I'm going to show you the six vocal warm-up exercises that I do before every talk I ever do.


Any time you're going to talk to anybody important, do these. First, arms up, deep breath in, and sigh out, ahhhhh, like that. One more time. Ahhhh, very good. Now we're going to warm up our lips, and we're going to go Ba, Ba, Ba, Ba, Ba, Ba, Ba, Ba. Very good. And now, brrrrrrrrrr, just like when you were a kid. Brrrr. Now your lips should be coming alive. We're going to do the tongue next with exaggerated la, la, la, la, la, la, la, la, la. Beautiful. You're getting really good at this. And then, roll an R. Rrrrrrr.


That's like champagne for the tongue. Finally, and if I can only do one, the pros call this the siren. It's really good. It starts with "we" and goes to "aw." The "we" is high, the "aw" is low. So you go, weeeaawww, weeeaawww. Fantastic. Give yourselves a round of applause. Take a seat, thank you. (Applause) Next time you speak, do those in advance. Now let me just put this in context to close. This is a serious point here. This is where we are now, right? We speak not very well to people who simply aren't listening


in an environment that's all about noise and bad acoustics. I have talked about that on this stage in different phases. What would the world be like if we were speaking powerfully to people who were listening consciously in environments which were actually fit for purpose? Or to make that a bit larger, what would the world be like if we were creating sound consciously and consuming sound consciously and designing all our environments consciously for sound? That would be a world that does sound beautiful, and one where understanding would be the norm,


machine learning it's a buzzword but I would also claim it's a lot more than just a buzzword how many have experience with machine learning it's a small portion area it's great to see when I started learning machine learning I found it difficult to understand how the different algorithm worked and what the main difference between them was I find it really difficult understand where I should start to learn machine learning but in this lightning speech you hopefully will learn fundamental difference between different categories

Machine learning algorithms, choosing the correct algorithm for your problem - Joakim Lehn


of algorithms this will helpful both beginners with also those who there who have a little experience with machine learning all right so real quick about myself my name is Joakim Lyon I'm a consultant here in Oslo for the Nordic consulting firm knowit's and I've been fascinated with machine learning for the last couple of years doing some project both personally and professionally and I must say there are many different algorithms out there just take a look at this this is a small portion of really great algorithms so


where they begin obviously some algorithms are more suited for certain problems that others and the result will vary greatly of how well your algorithm is suited for a problem but luckily you can divide these algorithms into four different categories of machine learning so the four different categories is supervised learning and supervised learning semi-supervised learning and reinforcement learning and when you face a machine learning problem it is important to understand which category it fits into so today we're going to


explore these four different categories before we get through really good stuff I need to explain two key words there in machine learning you have something called features this is basically a property of your training data and a label is the output you get from your model after training it so you could say features input levels output it's partially true because you can also have labels on your input data I'm gonna explain that with an example let's say you want the machine learning algorithm to estimate the height of a person based


on age and gender then age and gender are features and the height you want to find is the label and if you have a training set with a lot of people with their height corresponding to age and gender then you have a labeled training set so the first category is called supervised learning and in supervised learning you have training data that consists of a set of training examples you have a labeled training set and the basic idea is to find the most optimal model parameters to predict unknown labels on other objects let's look at a few examples


let's say I want to estimate the value of a used car based on age mark the mileage you name it then a machine learning algorithm can do this pretty well if you give it a training set with a lot of Sol cars with the corresponding value another example could be a mail it spammers it not spam a machine learning algorithm can do this if it has a large training sets a great algorithm within the supervised domain is called decision trees the reason I picked this one is because it's more intuitive than most others so in


decision trees you have nodes and in every node you choose the best split between a lot of features and you make this procedure recursively until we finish with the stopping criteria again I'm gonna illustrate this with an example let's say you want to find out we should accept a new job offer the first thought might be well how much is the salary is it above some threshold if it's not you're definitely not gonna take the job but if it is then do you have to commute for a long while do they are free coffee we'd have a food table I


don't know you might ask yourself questions like this but at some point you can end up and of stopping leaf and I'm gonna either accept or decline the job offer so that was supervised learning next category is called unsupervised learning and then unsupervised learning you only have input data and no corresponding output variables you have no labels in your training sets and the goal phone supports learning is to model the underlying structure or distribution in the data in order to learn more about the data so the algorithms are left on


their own to discover and present the interesting structures in the data let's look at a couple examples let's see you want to group customers by their purchasing behavior people buy item 810 to buy item B then obviously you should recommend these items to people interested in one of them another example could be in Netflix Netflix video recommendation system it recommends TV series movies and whatnot and they do this by using a series of unsupervised learning algorithms a great algorithm here is called k-means not for


Netflix maybe but for other purposes and here we try to divide all the data into K clusters and you select random K points in new clusters and in the Centers and the cluster of other objects are defined by the closest cluster Center you tune this algorithm by selecting the K number of clusters you can use this algorithm for many things let's say you have not hotel chain anyone to open a new hotel in a city where do you place your hotel hopefully you start off by looking at potential sites gather a lot of data


like we see close to downtown the restaurants nearby is it easy to get to the hotel and so on and maybe hopefully from all this data and algorithm can find clusters in this data to show interesting spots for your tab so most unsupervised learning third category of problems falls between unsupervised and supervised problems and it's called semi-supervised learning here we have partially labeled data and many real problems fall into this category because it can be really expensive or at least time-consuming to


try to label all the data let's save a million pictures and you're gonna label them takes too much time unlabeled data however is cheap and usually easy to collect and store so here a mixture of techniques from the unsupervised unsupervised domain can be used an example here could be as I already mentioned if you have a photo archive you might label some of the images like there's a cat in this picture people skiing topless person on the beach I don't know and from this label pictures you have a lot of unlabeled pictures and you can


try to label those with an algorithm so the last category of problems fall into reinforcement learning and it's not like any of the supervised learning algorithms because you don't have any label data and you don't have any unlabeled data usually you don't have any training data the idea is to create a software agent and it's got some states and it's gonna perform some action in environments the environment is gonna either punish it or reward it somehow and they can end up in new state and you do this recursively and you can imagine this by


saying you're a robot and you wake up and in a strange place you can perform activities and you're gonna get rewards from the environment so after you get more rewards you get more clever and your actions get more complex and you're training to behave the most effective way on each step so this is kind of a human way to learn and human way to think and we made some incredible progress within the reinforcement domain last years as first speaker mentioned alphago was a great great example here they managed to be the best player in


the Google ingo and the reason why I bring this up is because it made some moves that humanity has never seen before and they now teach some of the moves it did during the game at go schools in China they have go schools in China which is surprising and I find it really interesting that humans can now learn from machine and not just the other way around another really cool example I think is from pretty recent time from open AI they managed to create an AI that could beat some of the best players in the world in dota 2 and dota


2 is a real-time game so the world was quite shocked to see that this happen already we fought this was the years and years until it could happen because it's vastly more complex than traditional board games like chess and this is my personal dream project I'm really hoping I can beat myself but by creating an AI that can beat me in Mario Kart and not to brag but I'm quite good at Mario Kart so I'm not sure if I'm program skills is cute enough we'll see so hopefully the last 10 minutes you learned what's here


in supervised learning all there is labeled and the algorithm learns to predict the output from the input data when unsupervised learning all there is unlabeled and the algorithm learns the inherent structure from the input data semi-supervised learning have some data labeled some unlabeled mostly unlabeled and a mixture of supervised and unsupervised techniques can be used reinforcement learning is an area of machine learning concerned with how a soft region out to take action in environment so as to maximize some


thank you it's a pleasure to be here I will give a talk for probably only about 30 minutes and we're really going to talk primarily about the basics of stroke and then I want to leave plenty of time for people to ask questions about topics of their interest stroke is a very broad topic lots of different research going on so hard to summarize it all in a short talk but be happy to address anything that's that's on your mind so you heard a little bit about me I actually came to Stanford first in 1984 right after graduating Medical

Stroke: The Basics


School came up here for my internal medicine neurology training and then a stroke fellowship so I've spent my entire career thirty years here at Stanford have never looked for a job elsewhere because I love being here it's a great place to interact with other physicians and have a team approach to taking care of patients as well as doing research and as was mentioned we formed the Stanford Stroke Center in 1992 I began on the faculty after finishing my training in 89 at the same time that Gary Steinberg who's a neurosurgeon was

Stroke: The Basics


just finishing up and starting as a new faculty member and neurosurgery with an interest in stroke and Michael marks was a young guy who was interested in putting catheters up into the brain to try to treat stroke so he was a radiologist I was a neurologist and Gary was a neurosurgeon and the typical approach to stroke back then was that those three groups did their own thing that they didn't work together that they worked independently with what they could offer and the idea that we had is that it would be a novel approach to

Stroke: The Basics


work together as a comprehensive type of team where we could try to use the approach of the the team rather than the individual to treat patients and Stanford Hospital was very welcoming to that idea to give it a try gave us some funding to try to start the program together and all three of us have stayed around for all this time because we really enjoy doing stroke research taking care of patients and trying to Train individuals who want to do make a difference in stroke so we've have medical students residents fellows

Stroke: The Basics


who are training to be strokes about and now they've moved on to different places to start stroke programs and now it's in vogue to do comprehensive Stroke Center to have multi collaborative approaches and they're doing accreditation now for comprehensive stroke centers so we were very happy to be the first in the country to be chosen as a accredited comprehensive Stroke Center so that's the background on the center and now what I want to do is just give a bit of an overview about stroke how to prevent it how to treat it

Stroke: The Basics


and then we'll end with a case example of somebody who recognized a stroke and alerted us to it right away so that we were able to to treat her husband and you can kind of look at the the different impression of a stroke in somebody who's having one versus somebody who's watching one and hopefully by at the end you guys will be ready so that if you see somebody who's having a stroke you're gonna know what to do alright so stroke is a big problem it used to be the third leading cause of death it is now the fourth leading cause of death

Stroke: The Basics


but death is really the least of the problems with stroke because most people with strokes don't die but most people with strokes are disabled so what people typically fear most about stroke is not that they're gonna die from it but it's going to be disabling and that they're going to wind up in a nursing home or not be able to do the things that they like to do before because of the injury to the brain stroke continues to be very common it's become a little bit less common because of the treatments that we've had for risk factors particularly

Stroke: The Basics


high blood pressure as we'll talk about so that the chance of a given individual to have a stroke is actually less now than it was a few years ago because of risk factor control but the population of course is getting older baby boomer boomers like myself are starting to head up into the stroke prone age groups so that means that even though we have a little bit better control of the risk factors the number of strokes that we anticipate over the next two decades is very high and that we're going to go well above the 780,000 strokes that

Stroke: The Basics


occur right now in the US every year so this is a huge problem and I suspect that all of you have had your lives touched by stroke in one way or the other because you're here but even if you go to a general audience you'll find that most people usually about one out of every three individuals has had their life touched by stroke either because it's a family member for them themselves or somebody very close to them so incredibly common and this is one of the things that that drew me to stroke that it was not treatable at all

Stroke: The Basics


when I started and that this was such a common problem that it was something that could really get excited about trying to make a difference so let's look at a little bit of the prognosis after stroke as I mentioned stroke is not a major problem in terms of given individual who's having a stroke is relatively unlikely to die from it even though it's the fourth leading cause of death it's only about a 15 percent chance that you'll die from the stroke the type of strokes that are most likely to cause mortality are the bleeding type

Stroke: The Basics


of strokes the hemorrhages and we'll talk about that the strokes that also can cause mortality or where you have a very large ischemic stroke and we'll will discuss that as well but usually what we're looking at is rehabilitation recovery and that the the brain can rewire itself which again is something new that we didn't realize when I was in training is that the brain has the potential for rewiring and that with patients and with therapy that most patients will make a lot of improvement particularly in the first several months

Stroke: The Basics


after a stroke but the improvement can go on for many years after the stroke stroke is one of the most costly medical diseases not just because it's expensive when somebody's in the hospital and they may be in the hospital for many days or even weeks with a stroke but it's because stroke can hit at any age and even though it's more common in older patients many people of a stroke are still working so when you're looking at not only the cost of the stroke itself but you're looking at the cost of the lost productivity permit from the person

Stroke: The Basics


and people who have major stroke with major disabilities and you're looking at nursing home care so it really adds up to being one of the most expensive medical conditions that we have okay so time for some audience participation who can give me a definition of what a stroke is right here okay that's a good definition loss of oxygen to the brain is what was said anybody want to add to that or take a little different approach a blood clot in the brain that certainly can cause a stroke any other definitions yeah that's one of the things that

Stroke: The Basics


stroke is going to do cause paralysis so hemorrhage absolutely bleeding in the brain is in addition to a blood clot going up into the brain and blocking off the blood flow a blood vessel rupturing and causing hemorrhage is a stroke so you can start to get the feeling from the responses that stroke is not totally straightforward and it is complicated so the definition that I like is that stroke is the brain injury that occurs when there is an abrupt disruption of the blood flow to the brain so that abrupt disruption of the blood flow to

Stroke: The Basics


the brain can cause for two major reasons and you guys hit on both of them one is that the blood flow is blocked by a clot that a clot comes from somewhere or it's formed in the Blayne and it prevents the blood from flowing into the brain the other cause of stroke is a blood vessel that ruptures so then you either get bleeding into the brain or around the surface of the brain and you can imagine those are very different problems right a blood vessel that's blocked up with a clot we're going to approach that very differently than we

Stroke: The Basics


would approach a blood vessel that has ruptured and now has blood spilling into her around the brain so two major problems which one do you think is more common absolutely absolutely this is much more common so we're going to focus on this but we'll talk briefly about some of the ruptured arteries so ruptured arteries there's two major flavors here one is that it's a blood vessel within the substance of the brain that ruptures and this is a CT scan which is an x-ray picture this white blob is blood bleeding into

Stroke: The Basics


the brain patient didn't make it and you can see the blood here in the center of the brain so this is a blood vessel that's piercing into the brain which ruptures and why do you think that might happen yeah so an accident usually will cause some injury to the surface of the brain but a blood vessel deep in the brain usually ruptures for a different reason than an accident so an aneurysm is a good reason for a blood vessel to rupture we're going to get to that in the minute the aneurysms usually form at the blood vessels at the base of the

Stroke: The Basics


brain but a blood vessel that's piercing deep into the brain typically will rupture because of many years of high blood pressure that many years of high blood pressure pushing against the surface of that blood vessel wall weakens the blood vessel wall and one day it gives way and it starts bleeding so if we could control everybody's blood pressure we would see very few of these hemorrhages because the vast majority of these deep in the brain hemorrhages are caused by high blood pressure there are some other unusual reasons like having

Stroke: The Basics


an abnormal cluster of blood vessels in the brain that we call an AV malformation certain drugs like cocaine or amphetamine can cause blood vessels on the brain to rupture but hypertension is the one that that we could really make a huge difference if we controlled it so the aneurysm which you guys mentioned causes what's known as a subarachnoid hemorrhage so the aneurysms sit actually on the surface of the brain they sit in an area called the Circle of Willis that we're going to talk about in just a few minutes and when they bleed

Stroke: The Basics


the blood goes all around the surface of the brain so you can imagine this is something that the brain doesn't like to happen to be covered in blood because blood is very irritating to the surface of the brain so having blood in the brain like in this example or having blood on the surface will cause bad headaches alright so typically somebody who's having a bleeding type of stroke will have a severe headache often the worst headache of their life now that's going to be very different from the more common types of stroke

Stroke: The Basics


usually have no headache or very mild headache which are the blood clot blocking off blood flow but these more serious brain hemorrhage are typically going to present with neurologic symptoms and a very very bad headache because of that blood okay let's now shift gears and we're not going to talk so much about the bleeding types because that's only about 15% of strokes about 85% of strokes or when you have a blood vessel either in the neck or in the brain that's blocked off with a blood clot so we need to know a little bit

Stroke: The Basics


about the anatomy to understand stroke there's two sets of blood vessels that bring blood to the brain the anterior circulation the front circulation or the carotid arteries and these are very easily accessible you can feel your own carotid if you put your finger just below the angle of the jaw and don't press too hard there but if you press gently you can feel the carotid artery right here which is pulsing so you've got one on either side going up the front of the neck in the back of the neck you have the vertebral

Stroke: The Basics


column and you have the vertebral arteries and if you look at the picture you can see that these blood vessels actually go through little holes in the spine and then the two sets meet together to go up to the brain stem so the carotid arteries are going up to the hemispheres of the brain the vertebral arteries are going in the back towards the brainstem now these two sets of blood vessels come together with what's called the Circle of Willis so you see this circle here it links the two together and it's very nice if you're

Stroke: The Basics


born with a good Circle of Willis because what that means is that if you have one blood vessel that's blocked up that you can get help from the other blood vessels that's known as collateral circulation means one blood vessel can help out another one that's blocked up and some people can block off the whole carotid artery and not have a problem because the other vessels are helping out some people can actually block off both of their carotid arteries the biggest blood supply to the brain slowly blocks off and the other

Stroke: The Basics


vessels in the back can take over so the Circle of Willis is important then how the blood vessels come together on the surf to the brain to help each other out is also important these are things that we don't have control over this is how you were born you're either born with a nice Circle of Willis or not and you're born with a nice collateral circulation it doesn't necessarily matter how old you are but how you were born so you can thank your parents for if you had good collaterals or not there's things that

Stroke: The Basics


you can do to make the collaterals work less well like smoking cigarettes will block up the collaterals having high blood pressure high cholesterol will block them up but you're kind of born with a set of collaterals so one of the things that you can imagine is since these blood vessels go to different regions of the brain the symptoms of the stroke are going to be quite different depending on what blood vessel is blocked so we'll talk a little bit about that first we'll show a little bit more detailed picture of what this Sokolov

Stroke: The Basics


Willis looks like you can see there's a whole bunch of blood vessels that come together to make this circle we can take pictures of that to try to understand when somebody's having stroke what other blood vessels are going to be available to help out okay so let's get into the symptoms the symptoms depend on what part of the brain is involved symptoms of a stroke are much more complicated than symptoms of a heart attack if we're talking about the stroke that is due to blood clots we refer to that as a scheme 'extra which means

Stroke: The Basics


there's no bleeding in the brain and if there's no bleeding in the brain it usually doesn't hurt okay so that's one of the disadvantages for stroke compared to heart attack if it doesn't hurt people are much less motivated to go to the emergency room right stroke also frequently occurs in the middle of the night so you're asleep and you don't notice it because it doesn't hurt yeah I had a heart attack in the middle of the night it would wake you up from sleep the brain is very complicated so the right side of the brain is going to be

Stroke: The Basics


doing things that are very different from the left side of the brain so the hemispheres of the brain are primarily fed by the carotid arteries we call that the anterior circulation the brainstem in the cerebellum which is a coordination area are primarily fed by the vertebral we call that the posterior circulation so depending on what blood vessel is blocked up the symptoms can be incredibly variable so what would you expect might happen if you blocked off the left carotid artery what kind of symptoms yeah so we heard language and that's

Stroke: The Basics


right most of the language function for both left-handers and right-handers is in the left hemisphere so and then heard numbness so what side of the body would be numb if you blocked off the blood flow to the left side of the brain right and what in addition to numbness and language trouble what else would be a prominent symptom somebody mentioned it earlier what's that vision yeah vision can be lost because the carotid artery is going to supply the blood supply to the eye on that side so if your left carotid is blocked you may

Stroke: The Basics


have some trouble with your left eye it is also supplying so part of the visual areas that look at the right side of the world so you could have be missing vision from the right side of the world or the left eye if your left carotid was blocked well one of the most prominent symptoms is going to be weakness all right people mention that before so it's the right side of the body that's going to be weak if you have trouble in the left hemisphere and the weakness from stroke normally comes on abruptly it's not the type of weakness that builds up

Stroke: The Basics


slowly over days weeks or months it's a type of weakness where you're absolutely fine one minute and then you can't move the arm the next moment so it's abrupt onset because when that blood clot blocks off the blood flow suddenly the function stops doesn't mean the tissue dies right away but suddenly the function stops so the symptoms come on very quickly so somebody with left carotid stroke may not be able to speak or they may not be able to understand words so they may be able to make no words nonsense words or have trouble

Stroke: The Basics


getting the words out they may also have trouble understanding what you're saying so when you're talking to them it sounds like you're talking a foreign language so that if you ask them to do something like close their eyes or lift up their arm they won't because they can't understand what you're saying so different language areas in different parts of the left hemisphere can affect the patient differently so it could be very mild where there having trouble finding the right word or saying a few nonsense words all the way

Stroke: The Basics


to what we call a Broca's aphasia where you can't get any words out you become completely mute okay now on the right side of the brain there's usually no language function some left handers I'll have some language function on the right side but what they will have would be the control of the left body and they have something called neglect particularly if it's kind of the mid portion what we call the parietal lobe which means they don't realize they're having a stroke okay so people who are having a stroke on the left hemisphere

Stroke: The Basics


usually can't tell you about it very well because they can't talk so they can't call 9:1 and say I'm having a stroke people in the having a stroke on the right side they don't think they're having a stroke so even though they can't talk and call nine-one-one they see no reason to because they don't realize that their left side is weak so they often will be confused but neglect means that they don't realize what's going on that they can't tell that that their left arm is not working or the left leg is not working they just seem

Stroke: The Basics


to be you know thinking that it's not even their own arms sometimes you mean you can lift up their weak left arm and say whose arm is this and they say well that's my aunt Tilly's arm or they or that's your arm they don't even recognize that part so again you can see why we have trouble getting people in because stroke has a lot of complicated different symptoms and that it's hard for the patient who's having a stroke to call 911 so somebody else has to do it right stroke in the back part of the brain can be very confusing also because sometimes

Stroke: The Basics


it doesn't have as dramatic a presentation with the weakness and the language trouble it may just be double vision the sir seeing double or it may be some vertigo things are spinning around and people are off-balance they're trying to walk and they wind up leaning to one side or the coordination goes off when they're trying to use their arm or leg so different symptoms and sometimes it can be very dramatic where you have the right and left hemisphere are all funneling down through the brainstem so if you have a

Stroke: The Basics


bad stroke in this posterior area you could lose control of all four extremities which we call a quadruped heiresses you can't move your arms you can't move your legs and if it's really really bad it may be the only thing you can move is to move your eyes up and down which is called a locked-in syndrome so again it could be anything from very subtle a little bit of double vision a little bit of unsteadiness to all the way to having no movement in the arms or legs so it's not so easy to figure out that somebody is having a

Stroke: The Basics


stroke from other conditions if they're having some of the subtle symptoms and sometimes you know even the neurologists are puzzled in the emergency room so oftentimes we're going to have to do some imaging to sort it out but you can see what the main symptoms are the main symptoms are going to be trouble with the language weakness on one side numbness on one side headache if it's a bleeding kind of stroke so if you have those symptoms coming on abruptly that should be a key to call 9-1-1 okay so what's going to cause a blood clot to

Stroke: The Basics


block off a blood vessel and cause a stroke well most of the causes are summarized here the number one cause is atherosclerosis also known as hardening of the arteries so it's the same process that causes a heart attack it builds up cholesterol plaque and the blood vessels of the heart and that's going to cause a lack of blood flow and a lack of oxygen to the heart if it does it on the way to the brain it's going to cause a stroke so this is the aortic arch that comes off the heart this can develop atherosclerosis here's the carotid

Stroke: The Basics


artery here's the blood vessels in the brain atherosclerosis can happen anywhere along this tree that's the number one cause of stroke atherosclerosis next most common is going to be an embolism from the heart an embolism means a clot that started in one spot and it travels somewhere else so the most common heart condition that could cause a clot to form in the heart is called atrial fibrillation so atrial fibrillation is when the top chambers of the heart instead of beating nice and regular like this start to do

Stroke: The Basics


this they fibrillate so the blood swirls in the top chambers of the heart and swirling around it can form a clot the clot can then break loose and favorite place where to go is up into the brain so patients with atrial fibrillation are at risk of stroke and we have treatments to prevent them from having a stroke if you have a sick heart valve that can be a spot where a clot can form a sticky heart valve can form a clot and if you have a big heart attack then the main pumping chambers the ventricles aren't working very well the

Stroke: The Basics


blood can also swirl around there and form a clot so when somebody comes in with a stroke we're thinking about let's take a look at the blood vessels and look for atherosclerosis let's take a look at the heart and see if we've got something going on with the heart that could predispose to a clot formation and if that's not the cause then you can start thinking about the blood right what if the blood gets too sticky for some reason sticky blood could form a blood clot and the blood clot then could cause a stroke so a variety of causes

Stroke: The Basics


but most of them relate to the blood vessels the heart or the blood itself okay so number one cause of stroke is atherosclerosis we're gonna show a movie to show you how a thorough sclerosis progresses over time and unfortunately everybody gets some there is no way to go through a long life and not develop some atherosclerosis in fact even when you look at soldiers who who died in a military accident if you look at their blood vessels you'll start to see the beginnings of atherosclerosis so we all have to deal with it a little bit one

Stroke: The Basics


way or the other so here is a speeding up over what one might see over many many years okay so this is the blood vessel and this is where the blood would travel and this is the atherosclerosis plaque you don't need an infection to start that having infections may promote some atherosclerosis but infection it's not the biggest cause of atherosclerosis we'll talk about the risk factors in a minute but what do you think is in that plaque this is not there is some inflammatory component but that's not the main component cholesterol exactly

Stroke: The Basics


so what we're seeing here is a buildup of cholesterol on the wall if you look carefully you would see some inflammatory reaction so the cholesterol itself is really not such a huge problem in this stage it's well contained this is called the endothelium the inside lining of the blood vessel wall and as long as this endothelium is smooth and shiny and not sticky it doesn't matter so much that there's cholesterol building up here okay because it can't really do much as long as it's encased in this smooth and Ophelia lining the blood here does

Stroke: The Basics


absolutely fine until you block off about 70% of the area here right so here is less than 50% blocked there is no reduction in blood flow here blood flow is getting in just fine if this gets to the point where it's starting to block 70 80 90 percent it's going to start to reduce blood flow so it's like a plumbing problem not enough blood flow but that's not usually how atherosclerosis gets you usually it's what we're going to see here is that before it blocks off all the vessel the plaque is going to rupture okay so you

Stroke: The Basics


see this spot here this is the lining the lining is now ruptured this is called plaque rupture this cholesterol material is extremely sticky so when this cholesterol material comes in contact with flowing blood what's gonna happen here we go alright it comes in contact with the blood boom we form a clot so that clot can do what it's done here and completely blocked off the blood flow now we have a completely blocked vessel or if it doesn't completely block it off pieces of that clot may blake loose and head up into

Stroke: The Basics


the brain so this is what we're fighting against this progression of atherosclerosis so causes a stroke atherosclerosis and the large vessels meaning the ones we talked about in the neck about 25% atherosclerosis and the small vessels vessels in the brain about 20% of stroke 20% are clots that form in the heart from atrial fibrillation or other heart problems 15% we talked about bleeding bleeding in the brain or bleeding in the subarachnoid space and look at this even now when we do a very detailed evaluation we take

Stroke: The Basics


pictures of everything 30% we don't the cause cryptogenic stroke is what we call that or idiopathic some people say idiopathic means the doctors an idiot they couldn't find it right and this is frustrating for the doctor it's very frustrating for the patient because the patient wants to know why did I have a stroke it's actually a good thing overall because the prognosis for having another stroke is quite favorable if you've been very thorough and you've found no cause so even though patients are not happy overall it's a good sign

Stroke: The Basics


if you find that the vessels are all blocked up you can imagine the prognosis is not as good but sometimes you can deal with that get the vessels cleaned up and usually the heart conditions can be dealt with pretty well so that if somebody's stroke came from the heart typically we can find ways to prevent it from happening again okay so lots of stroke is caused by atherosclerosis atherosclerosis affects the vessels in the heart in a similar way to the vessels in the neck and the brain so no surprise how to prevent them right

Stroke: The Basics


you've all heard this before cigarette smoking promotes atherosclerosis it makes the blood vessels stickier it makes the blood vessels narrow so this is one of the you know biggest you know most treatable risk factors because treating it is going to can reverse the situation with a lot of the damage to the blood vessels alcohol abuse is a problem for stroke for a number of reasons it can cause heart rhythm problems it can cause increased risk of bleeding in the brain and it can cause an increased risk of blood clots forming in the brain so

Stroke: The Basics


heavy alcohol use is a bad thing drinking a glass of wine a day is slightly protective right so it's not a recommendation to start drinking if you don't drink but drinking a glass of wine it's not a risk factor for stroke actually it may reduce stroke risks just to touch compared to people who don't drink at all but drinking too much is a big risk factor physical inactivity lack of exercise is a risk factor and then the whole host of the usual medical conditions for stroke high blood pressure is the most important it's the

Stroke: The Basics


most powerful prevalent risk factor for a stroke so again both for the atherosclerosis type of strokes as well as the bleeding the brain type of strokes this is what we could do to prevent the most and that would be control everybody's blood pressure atrial fibrillation we talked about cholesterol as obviously what's building up the blood vessels and patients who have diabetes have more atherosclerosis faster than people who don't have diabetes so getting good control of the diabetes is important if you've had a stroke

Stroke: The Basics


you're increased risk for having another one so again we need to try to sort out why did that stroke occur and what can we do to prevent it so a transient ischemic attack is atia who knows what a T ia is how would you define that I like that definition so you've got a blood clot that's gone into the brain and before any injury to the brain has happened your body dissolve that blood clot you have little enzymes in your blood that try to dissolve blood clots and if it dissolves it before it does any damage it's a TI a so the symptoms will be the

Stroke: The Basics


same symptoms of stroke but they'll be brief you know 10 15 20 minutes would be typical duration of symptoms and we know it's at EIA by taking a picture and seeing exactly what you said there's no injury if the symptoms last for 20 minutes and we take a picture and we see that there's a little bit of injury then it's a stroke just like a heart attack versus angina a heart attack is when you damage some heart muscle and Gina's when you've got chest pain because not enough blood flow to the heart but you didn't damage any heart muscle

Stroke: The Basics


and what you can see is that if you've had at EIA the risk of having a stroke in the next two days is 5% okay so what does that tell you about a TI a better not ignore it right it's not the type of thing you schedule an appointment and a week and tell your doctor that you had a 20 minute episode where you couldn't lift your arm or you had a 15 minute episode where you couldn't talk in the right side of the body was numb because the highest-risk time is in the first couple of days so we want to see these patients get into the emergency room we

Stroke: The Basics


want to have them evaluated very rapidly if they have the symptoms of a stroke but they disappeared so what are we going to do well if you control the blood pressure you're going to reduce your stroke risk by up to 40% that's huge absolutely huge to reduce stroke risk by 40% so if you're somebody at high risk of stroke 10% per year you could reduce it down to 6% per year just by getting the blood pressure under control smoking is amazing that people who've been smoking even they've smoked for many years if they stop they can cut

Stroke: The Basics


their stroke risk in half within a year of stopping smoking so incredibly motivational - you know it's hard to stop smoking but a huge payoff if you stop and the risk from stroke smoking goes almost away after five years if your cholesterol is high it will your stroke risk can be cut down by about 20 percent by using these statin medications okay like torva statin or lipitor is a popular one or simvastatin pravastatin these are medicines that reduce cholesterol and probably have other beneficial effects that we'll talk about

Stroke: The Basics


in a minute and look at blood pressure so here's a graph that shows you the relationship between cardiovascular events and blood pressure over a 15-year time period so this includes both heart attacks and strokes and you can look at the rate of heart attacks and strokes and somebody whose blood pressure is in the 140 over 90 type of range 130 over 85 and 120 over 80 and you can see that the lower the better so that lower blood pressures lower risks of cardiovascular events particularly strokes so what we like to

Stroke: The Basics


see is blood pressure is getting down towards 120 over 80 if people are spending a lot of time up in the 130s or one 40s that's too high okay so eventually you get to a point where you can't go too low you go too low with your blood pressure you pass out but most people even elderly individuals can tolerate blood pressures down in the low 120s and for the top number the systolic and the diastolic down close to 80 and that's what we try to shoot for and particularly somebody who's had a TI a or a previous stroke

Stroke: The Basics