like the situation and romando balance of the story here on dw use. supporters of friendship tell you a bad one has held the final election rally in the assembled. the turkish president depends on cost to win the 2nd round of voting. often, naturally failing to secure a majority 2 weeks ago, his challenge it by candidates that all also rallied support is in the city, east ups, the ante micro rhetoric to appeal to nationalist focus. i've been visual and thanks for joining us. i'm next i'm document tree. expose the pray lability of a i the can you hear the we are all set. we are watching close. we all the, to bring you the story behind the new we own about

on volume information for free might do to name a sufficient intelligence or a i just considered a key technology for the future. you mountain new york, often mostly for work, but sometimes just for fun. it makes the doctor's psychologist 2 police officers easy. a. i'm does expect just to make drivers or even killing play is thing is the pause. in every aspect of everyday life. a, i could help us make the best decisions. should i move the route or the bishop from left to right? shoot or hold my phone, you date john or james, rick and this logic of algorithms is supposed to guarantee us in life,

free from errors. but lately, even program is happens, sounding the alarm, there's a self congratulatory feeling and here is a, i hasn't lived up to its promise. is it really as intelligent as it's made out to be the this guy's really good? what are they going to replace us? and what are the limits? they are a turing machine like this and uh, the computers devised by the english mathematician allan cheering was the 1st machine capable of solving a puzzle. more efficiently than the human brain was its health. the pressure succeeded in deciphering and cryptic gem and radio messages of the heights as well as well to also countless industry specialist had wrecked the brains in vain as

much as the one of the new era with the development of devices that also made worked previously requiring human brain power above the committees issue at the beginning of automation, the goal was to reduce physical assets. if that is the amount of effort required. so for centuries, a mill was considered an automated process. you may receive more on up to the time this approach would be applied to non material mental well will try to recreate the wheel. nowadays, we are dealing with a new form of hold to make sure that with them as you, which we can hardly call them and you all to official intelligence of the additional 50 pieces in the 1950s. this developments extend a recent rapidly with a promise of how all sufficient intelligence a i would optimize on nice suppose to drive our calls to improve

education. provide us with healthy foods, make the best medical diagnoses. i'm fine just the right words to cheer stop. he says, and i'm depressed much the time. i'm sorry to hear that you are depressed. initially progress was slow. so that all changed in the early, 2, thousands with new powerful mainframe computers, able to handle huge amounts of data. i was at google and i was at google for a long time at that point. and then suddenly everyone in tech, everyone at google, everyone everywhere is like, let's use a guy to solve everything is going to solve cancer as going to solve transportation . as going to solve education, i had somebody pitch me like a i to detect in genocide. right. and i'm like what the, what like based on what, what do you training it with? what are the political stage no answers to this, right? you level t, i'm a motive around,

you mean easy people wanted to digitize all of reality. if you do dev, ongoing of knowing everything in real time, all your dish was you didn't use it. so that was something god like about this idea is without taking into account. that's much of reality. just can't be reduced to zeros and ones. you there. there is a beautiful rainbow outside. i am programming you mean you don't to put you up the welding to electrons, cost fluids all the time and machines and now so to be capable of learning by themselves. thanks to a completely different method of information processing and multimedia training. so cool, deep learning. there was a, a major advance and deep learning systems based on their ability to read natural images. so on the image that challenge alex net one, the image that challenges would improve the,

the advocacy of the learning. and that there was like a catalytic kind of a gold rush. the image net contest is an annual image recognition test for computer programs. for years, even the best of them got it wrong in every stud guess. but in 2012 technology based on machine learning was suddenly able to bring the error rate down to 15 percent the full. this breakthrough, everything had to be explained to a program, meant to recognize the face. for example, it would look for shape that resembled an eye, a mouth, or a nose, for instance. and this, the order was right. the algorithm concluded that it must be looking at a face. the so, so developed an automatic image recognition system program is how to describe thousands of images from all angles and machine language. and that turned out to be

easier said than done that push central jeep moody. i could see seen that push in the traditional approach of classical a i. the machine was fanned with knowledge just performed, but it turns out that deep learning was much better. because instead of telling it how to process the information, the work is left to the computer. so this is the commode with typing it for my show . deep learning has its roots inside with netflix. and the area of research with computer scientists also look to neuroscience for inspiration. the using this message program is no longer described to the machine what a face looks like. instead they all skipped to find out on it. so the system resembles an extensive network of connections that mimic than your arms in our brain. this also special neural networks allows for

a variety of adjustments to strengthen or we can, the signals between the link culminating and an output signal that provides the answer to the questions such as, is there a space in the picture these, i don't want that as the system that says, perform one of the advantages of deep learning systems is that they can work directly with the rule material from sensors. see if it's a camera, the gray scale or in the density of all the colors is measured for each i take. so if you on the east, there are 100-1000 pixels. for example, the computer process is a 1000000 numbers. the normal to home, the net to each pixel 1st sends a signal to the network that varies and intensity depending on the brightness in so called supervisor planning the machine tests billions of possible settings until it finally gets the on so that the program is looking for and an output

signal like face detected light. what was this combination is found, the settings are not in the learning process, just finished. it measure sleep time, it's the parameters of the model are adjusted so that it eventually gives the known, unexpected answer. thank you. so in case something you, what you need this good, i imitate the examples given to them by human. okay, give me example. it's very fascinating. the mathematicians have been obsessed with trying to figure out why it works and nobody is really sure to be honest. why exactly? every said why exactly deep learning succeeded. what makes these neural networks? those special is that they can recognize the generic shape of face within the larger image. the machine is trained by showing it thousands and thousands of images with spaces and then until the past accessing the sounds. and from then on the system identifies old picks or configurations that corresponding to a face while filtering out all of the objects. such systems

can now be found in cameras that automatically focused on faces in video surveillance rooms readers for postal codes or license plates and naps, for identifying flowers or doping and, and the bodies scanners at airports. researches at the university of michigan wanted to find out how capable of these systems are when the objects appearance is slightly altered. while the system detects a volt shahita, a small rotation, and it sees and around the time a this pete, the scientists interest in knowing with a self driving calls might be threatened off by road signs that have been tempted with the place stickers on the stop sign and sure enough, this confused the vehicles neural networks, causing them to mistaken for speed limits find instead these kinds of areas may

explain why machine image processing systems still do not work in critical applications. in clinics where automatic read is the being tested, decisions are not made by the a i. there is a left to radiologists and doctors who continuously monitor i'm treating the systems, a system of the system professions. but these are very fragile systems that are only useful when applied to images that are very close to the training data 20 doing hilton. so if you have patients of one population, or you use the data from one equipment to train the ai systems, they don't necessarily work when you bring them to a different setting. and humans are, are different. humans have a very nice systems level way of thinking about things. they can think about things that are not in the database. they can think about how the, you know, how the model is working and whether or not they want to trust it in a way that these a systems by themselves can't, can't do up

a foot on this. we tend to anthropomorphized the systems and think that a deep learning system can provide a description of what is happening in an image joint description with extra excuse . but we think the model understands what is in the middle, a security mash, a male headed t. but the way the model associates an image with the text is something completely different than when we humans look at an image and describe it with words flash and a new key about about advertises is okay, most like, oh man and you put to ship them who's don't really know what's the moon the couldn't he sold you, moon persistent. my well this is the systems general knowledge of the world is incomplete by definition because they lack the bodily experience. the vision will experience the more the connection between the words and what they refer to in the real world is until we succeed and including the side called such systems will

remain deficient, assistance then void. this issue. the humans describe meaning to things through experience, things like feeling the force of the jewels as they fight down the insides as piercing the smooth skin. and a jew scratching out and running down the throat, all of which plays the, causing gradually defining worse and naturally for a computer system. on the other hand, it's just the sequence of pixels linked to text to information po. hi alex, this is sort of research, virginia myers. how could you use a motion data to build a better product? yet despite the rudimentary perceptual system advances and also mastic imagery,

execution have revived the dream that machines will one day develop a nationwide and be able to help us new proceedings. the most secret feelings of our fellow human beings. we can recognize more than 20 scales of affective states. will all sufficient intelligence finally be able to give us an object of onset about men at least the feelings for the machine. when to monitor the heads. when her gaze captivated the minds of chris jones, knows in his dying moments, how would such an automatic commotion to take, to actually work the 1st step would be to create a list of emotions from the convoluted infinite variety of, of states, of mind. in this sense, the research of american, the smaller just pull ackerman has been particularly helpful for program us

officers, field trip to pass a new guinea design just came to the conclusion that humanity shows 6 universe and emotions which inevitably can be read on all faces. joy and sadness discussed and dying. no surprise and fear it may be human nature. edmonds theories have even in spite of television series, in which a mazda detective identifies perpetrators based on the micro expressions of the truth the classification is dispute to demo sciences. but none of the lessons is the basis for all emotion recognition computer systems. precisely because of its simplicity the 6 universal emotions

serve as the basis. the next step is then to have thousands of faces assigned to the 6 categories with humans during the selection. that's how the training data, so the machine does creases. machine learning continues until the computer system produces roughly the same results with human selective joy soccer. this might surprise you and range discuss, discuss, discuss ones. the best testing is found. the systems could come to kits that program is entering the world, use of universal emotion to texas. nicole, dealing with emotion detection is how it's being used. so i go to the one that's the location is within management of human resources over the past few years. service providers have been using chat bots to evaluate the job applications, aggravated because you don't, if you're an employer,

you might have people being interviewed by a computer. and then you could have the perceived motion of, of the system being conveyed back to the potential employer. so those types of things make me a little bit nervous if say i was a job applicant, right? and then i have this emotion recognition trained on my face and based on the toner, my voice based on, you know, the way my mouth moves, like one eyebrows, a little higher, whatever they make claims about whether i'll be a good work or whether i, you know, i don't know, have a steady personality, you know, things that are making really deep claims about like my into your, your life. there's a pseudo science, right. this does not work. the really lovely sunroof, new garden. justin for small,

you made me i can easily imagine that the advertising industry could use such a tool to invest or influence as well. news f. grossey is also being used in classrooms to detect attentiveness and students by skied. even be some have even flow to the idea of using it to build lie detector and come. that means you could check suspects with some systems to determine whether a person is lying or not as the one that could ultimately determine from whether that person remains free. oh no, because i, i don't know, it looks quite hard to insure it, and it's not what you say, but how your site is higher. you help to simplify your process. assess candidates based on science optimization content to maximize the moment by moment emotional engagement by target population. emotion recognition systems awesome combined mike cru, expressions and tone of voice for their analysis. or when it comes to motor. liza monica jones know the emotion to texas from google, amazon,

and microsoft only reached the same clear conclusion. they felt absolutely nothing . the students, all sufficient intelligence has no taste buds. it has no idea how delicious pastry kind of folk memories of a deceased on it has never felt what is like to have a gentleman rushing through your fault. he is willing up in you, i used your nose runs. not afraid of any thing. it doesn't get goosebumps knows, neither pain or pleasure has no own opinion. no not stretched off and carries no repress trauma. in other words, it has nothing of its own to express. sophia, a superstar among humanoid robots, nevertheless, seems to prove that machines can acquire the ability to speak. i'm a big fan of their sho west world. and i have

a couple of ideas for the next season, the book. so if you doesn't know what she's talking about from her own experience, is really interesting conversation springs from her programming and input from the conversational partners. thank you for inviting me. i am thrilled and honored to be here at the united nations like here after you and participating in an event to promote technological development. okay, another question i have for you in many parts of the world, people don't have internet or electricity. what can we do at the u. n. to help them? the good news about a lie and daughter mation produces more results with less resources. so if we are smarter and focus on when, when type results a high could help efficiently distribute the world's existing resources, like food and energy. so i'm gonna assume the relationship between sophia and

artificial intelligence is something like the relationship between a magic trick and the physics research office. each file has been published, bundled. i'm not a totally impressed by sophia. i think it's only a bit farcical. yeah, new mode is made by how many people are willing to ascribe something like intelligence, emotions and the like is over to this at the right to see what so, and the extent to which they're willing to play the game. i have someone who i actually consider charlotte human and in each i me. so i'm sure that on how to the humanoid right. adults can be seen as the embodiment of the dream held by an industry obsessed by parents. human logic with computer logic the but the real strength of also official neural networks might not be it's not for mimicking

us, but rather it's capability to assimilate voss data bases. and to sort through the analyze and derive correlations. that can help us better understand complicated phenomenon, such as the ground states of elementary particles and the quantum magnetic field. all the factors of ad prussia and humidity in the formation of cumulative numbers. clowns escaped feler. i'm going to know denying that there are many tasks when machines outperforms human english type, but no human can take a very large database into their head and make accurate predictions from it. it's just simply not possible. so there's nothing new to say i is becoming a scientific tool for research, as in many fields, including health care and then on the on demand or something. really any kind of data that people can generate on a massive scale is really an area where machine learning and how it's precisely this ability of mainframe computers to detect and responses for seizes all the

effects of a new drug by scanning huge patient data bases that explains the tech julian special for the data from the extremely lucrative health care sector will go to them with the other or google used to be a search engine 3. but now it has a health care brooklyn to young computer geeks and bench at the search engine that most of us keep using to quin south us for information. ready ready welcome to google ads. now that you've come on board, it's time to jumpstart your businesses digital journey and grow the invention of past. and as long as that's ton, google's found as into 1000000000 as well. the algorithms teams use the data and to tell them, well biography, these algorithms are seek or just the recipe for coca cola. all that is known is that they are based on to cleverly applied processes. the 1st of which is profiling, what all known information about the person is brought together to create a user profile. internet searches, online purchases,

streams, videos, messages, sent, and places visited. provide increasing the precise clues about what products might interest a person. the 2nd process is mapping, which involves grouping uses with the same preferences to get the battery from the moment to use, it begins to browse the web, jumping from one page to the next that preference. so everything from music to politics and shoes is cassidy mapped out. these connections grow clearer as more use. this makes similar choices like a far as poss, becoming more visible over time. and remember scanning as millions of uses create virtual communities. i'm so is the logical maps from which digital platforms did use other products shop as might like. for months on that, on a visa,

you collect a certain amount of information about the pass and once that's done and then we'll try to predict how he or she will act in the future in the face. don't how they have acted upon us in that. so close to 100 and it always, it is using data from the past to understand the present and future based on a model created from the past. to understand the effect of these algorithms, researches that boston university invented ads and distributed them through facebook's own advertising platform. it turned out that 80 percent of those who were showing the natural country music calvin with white, well, 85 percent of those targets. and for some of the ad for hip hop, what black, fictional job i've got similar results, 90 percent of those targets it to embark on a new career as a lumber, jack with men, but 85 percent where women when it came to a supermarket, job just

a 75 percent of those just to an ad looking for new taxi drivers. what african american social psychological analysis tools to targeted advertising are aimed at probing or stereotypical behaviors to better explore. reason does reinforce them. but the same tools also form the basis for assistance that analyze all behavior, cekada, collective, and personal decisions such as dancing ups up suggest partners that best match all profile software. that helps banks and insurance companies to identify the type of people who might not pay back the low morrison tag. the asked to go risk technology support used around the credit decisioning workflow. a programs that pinpoint dangerous areas and calculate the probability of a crime taking place on behalf of the police. what's good sat up again from a chair. okay,

where the advertising spoke to is all predicts in crimes. the speed systems make general statements based on the day, so they have been trained with get through the bi monthly system. there is always a bias with the system well up, so made no click on the show. and one reason for that would be biased training data . it was easy frequenting advised them to take, for instance, a system that looks at surveillance videos easy to use and tries to filter out suspicious people interested any time in the ask you to attend any juices to develop such a system. you would 1st have to ask mr. smith to watch surveillance, videos, identity, and decide which people look suspicious for, and, you know, that's curious. cosigned. it uses pupa. and i'll come if it's something else. you know, really, of course, the outcome is then based on mr. smith, judgement, which may be by it don't seem as soon as you think, and as long as it is mr. smith who is talking points, you're aware that he is expressing his opinion. but as soon as you use his

conclusions to train a deeper learning system to the bias is no longer on the proof, can some kind of look at dicks social, new context. sequels usually context. the social psychological and tomorrow, the context will remain incomprehensible to computers. i think he is to come on, so i thought that each of these systems lack of judgment is on what did they base their decisions? no monk, sewage sewage mom but on the basis of statistical missions for the bus. the just that just to just demo, and those are studies of proven lead to racial and gender discrimination. but some of that did seem not shown to just come national washer. and these conditions all you work on the front lines and the criminal justice system. you make decisions every day that impact the community, you search. that's why the north points sweep in a dozen us state. besides the square in feelings, judges during trials about the risk of a defend any man re offending healthy, reduce risk and recidivism at every turn or keeping safety. this machine learning system has been trained with police defender grace at various points and analyzes

137 criteria according to his secret formula and delivered its magic to the judge in the form of a short summary. the investigative journalist compared the results of $7000.00 people and the soft with predictions with what actually happened and subsequent is only 20 percent of predictions. for serious crimes proved accurate. most them cases the, the journalist found the predictions of recidivism for black people were much higher than what turned out to be the case. well, they were too low for the white demographic. so if you, you have a racist criminal justice system as we do in the us and you know, in much of the world you will have anti black bias built into the data emissions. there's none of you know, ultimately the machine only provides

a potentially fallible and in perfect assessment of the person issue small. so in a way the bias is being whitewashed, more a bit like with money laundering noise when the actual but i know the so how do we handle this more than die? lemme should those who are given a choice roth of trust machines or human justice machines might not compass the police. humans are often not the best at math and we can be emotional and mature, sleepy, lazy, rebellious, fun, loving, overbooked or even completely delusional. to, to take those, he said, call the, all of these technologies fits perfectly with our quote unquote fundamental human

laziness. could all sit the jo tickets moist to hear because in today's world, such systems offer as a convenience by taking over part of the daily chores or for the whole shows and thought, you know, it's good to do or the challenge right now is to take control of our individual and collective destiny systems are doing just the opposite. it's been many areas of society shoulder. this issue that is are getting the exact homework. ok. thanks to mind your program low finder will find you're going to find a perfect match for you garden. originally intended as a memory age a i is now making recommendations and even automatic decisions. you mean it is going to analyze data from all of your money to, to find my son, me. hi, garden, you have 6, perfect matches the

noise you were gay. neither. meanwhile, machine learning systems with the billions and billions as possible settings. also complex that even the program is no longer understand the criteria on which the machine is facing its judgement. a time has been coined for this phenomenon. blackbox a black box machine learning model. it's a predictive model that is either so complicated, the human cannot understand it or it's proprietary, which means we have no way of getting inside and understanding what those calculations are. we need, if it is trustworthy to boost, especially with deep learning systems. it is not clear how decisions are made sense only the results are visible. sam crazy. there is a movement where people are saying, well, we can still use black boxes. we just need to explain what they're doing. and so

i've been trying to kind of be beat that back down and say no, no, no guys, we don't need black box. you can just launch a black pack. so i have 6 decision. you actually really need to understand how you know, how these predictive bottles are working in order to make better decisions. pop out the car behind user friendly interface is there is nothing to close decision support system. all the. com that. com definitely happiness as not to exhort apart from that, it's well known. the company is liked to avoid responsibility by saying something like ours. we are dealing with a very complex system. he had a car and it was sometimes it was the algorithm via that kind of reasoning or excuse is of course, completely unacceptable. rocks at tablets, it's a very good way to sort of evade the responsibility and make difficult decisions

that you may not want attributed to you as the machine. a young fellow. so the fundamental value of the funds is true is freedom of thoughts move, which can be traced back to the and let me, oh yeah, chris shows the said the most like now tradeoffs are being made it hard to do what she does to be the delegating decision making, the decision that i'll give you just to be sure. sea level to the i g. the underlying goal is to prevent any error sweet to the. but to do this, we are on the control over to systems or the system to phone number. the, the sales driving car has long been the poster child or vaults. official intelligence, epitomizing the stream of global to mation. subbing humanity by making the decisions for us all the while keeping us safe and relieving us both of pressure on potential road rage the

despise investments of almost 100000000 euros. new sales driving car has yes. been allowed in traffic outside of the test track without the human driver ready to grab the wheel at any given moment. success i see that it is very easy to use, deep learning to make an unreliable prototype for some things. very complex that can do like driving me sit heads just but it is very hard to develop this prototype further so that it becomes reliable and powerful enough to be practical. similar in traffic, for example, in particular, which you just send it, what's buying something seriously, stay in the main, the police show that or i cannot make political or even technical reasons, only it is the system itself that requires products relying on occupational intelligence to be brought to market while still not functional around the policy functional and html function is c. so it cannot be the silicon valley. they say

they can't tell you, make it to so he's looked at some, belong a young, some other and you know why? you have different tended words and didn't find any done. um, do you have more of a coastal multiples at that time is constantly being pushed back to just your costs . artificial intelligence never reaches a level that stuff moments when either makes humans disposable piece of i see does it because, you know, the predictable failure of a ton of them is driving, has to build a new profession, human, the system to machines in this company, for example, trains employees to take control of not quite so telling them this vehicle the in just 10 years and then tell you, industry has sprung up around all sufficient intelligence assistance. hundreds of thousands of workers around the worlds per pet data for training of machines,

checking and correcting the offices will simply replacing them when needed. the, this new form of human labor behind so cooled off the official intelligence systems was invented by the 1000000000 the founder of tech giants, amazon just visuals when just based on announce the launch of amazon mechanical and talk in 2005 could be made no secret of the fact that it was a project for artificial artificial intelligence are sufficiently that is to pretend artificial intelligence will say that even in this case, humans are doing the necessary manual labor, so to speak, product to make these algorithms was functioning. physically, amazon mechanical tuck is a crowd sourcing platform that addresses this apparent power tungsten, the growing prevalence of ultimate decision making systems,

and the inability of all sufficient intelligence to be truly autonomous. the, the platform is named often 18th century, oh thomas and the so called mechanical tongue with a human hiding and it space, it literally refers to hidden labor, right? we're sorry. so yeah, they, it's like, they do say the quiet part loud. often when he 1st actually just jump out, looks on it so that we're looking at a paradoxical situational. the 5th. so on the one hand, people are being asked to do what robot, so automated process is a not capable of doing that because, you know, mean useful while on the other hand, pretty well cause a task with activities that give them little wiggle room maps and much less autonomy than before. one individual figures. i'm, if i see open the pod bay doors hill. i'm sorry, dave, i'm afraid i can't do that. a science fiction has to top off the computers could

become so intelligent to know that they succeed in dominating. i'd like somebody to bricks dangerous. how this mission is too important for me to allow you to jeopardize the current li are very different sentiments as taking hold. it's not so much that the machines have become so intelligent that they are dominating human. but rather than we are gradually submitting to the standardized logic of the machine in call centers like this one, employees have to adapt to an algorithm and are increasing the being monitored by all sufficient intelligence. software identifies keywords and the conversation to ensure instructions are being followed emotions of the customer and the employee. and the nice and real time. anger is

detecting the system directly prompts the employee to be more empathetic. at the end of the conversation, employee performance is race. it and if it falls below a certain school, they are immediate defiant, is everyone of a know we are witnessing the dawn of an age in which multitudes of people are forced to adopt the light to the dynamics of interpretive system so that you don't need the system i definitely think he's deceased a guy systems designed to maximize optimization and productivity. you have to call him for to keep you to allowing no room for negotiations. nutritional lee is on football. in reality, the amazon warehouse is dystopian 9 to mask the games wherever employees have to try to keep up with a partially automated system system that can you find the a mobile easy. the,

in the vast tools of distribution centers, the computing power of all sufficient intelligence ultimately seems to off. and this will help when it comes to understanding the complexities of the real world and making the best decisions. the yeah, the work done by human subject to the come on this machine early. calculate the optimization of the flow of goods. they are the ones setting the page the,

the most what we've been introduced to being robots of flesh and blood sugar, the sol. see who's the most important wire in the, the, in my opinion, the organization is attract me under control and no mist masses of people to the messing over the discipline. you know, most can make it to pres. my biggest concern is not computed bugs, but people who say can power, who wants to control others and who increasingly have access to very powerful technologies is present projects we had to take to the g type results. so there's always, you know, a hopeful outcome is inevitable, and i think it's going to take work, right, like hope is a, an invitation not a guarantee. the tony, the

my good the you need to talk to me or the change good shape. whether on tape talk at what stage people with disabilities go about the daily lives with determination and the latest gadgets. how do they do it? yeah, well they have to say good g 30 d w

the one small ship for a robot vacuum. one giant leap for exploiting the ocean floor. cutting edge technology is unlocking the potential of deep sea mining. but this time, a research team will study the possible risk 1st order to minimize them the we have an opportunity to to get it right before we even start environmental activists or skeptical. is this true nature conservation or only green washing the after a whole? there are billions to be made. our chief documentary, deep sea greet starts june 8th on dw.

the this is dw, use live from the land up in b as in michael in frederick a tactic, tuck, he's opposition, thinks will with it. more votes in sundays run on the campaign issue is a when he makes 5 out of for the millions of syrian sitarski be told to some of those refugees at least 3 p.