right, the we will say the way started february 14th on the artificial intelligence as part of our lives being on social media platforms or in the am a checkbox we use among other things a i make sure we're protected from harmful violence content. but there are real people behind this technology. we do not too far from the world litzy check homes. these data workers train a i systems by sifting through massive amounts of harmful data. it's tells you explain to me how does the flesh of a human face like while they carry out the awesome ruling task of keeping a systems clean. international corporations,

on the other side of the globe reach the profits we'd be in use made before the unit is from kenya and germany. we set out to investigate this back story of a i on covering a web of global connections that power a heating industry. the without us there will be no end for that because we have the most important part of the, of this ai. what's life like for those training e i systems that the entire world now relies on our quest to find out begins in kenya is bustling capital of nairobi. here we meet junction, you are a single mother of one in her twenty's. she left university to support her family, bouncing from one job to another until 8 years ago when she began taking on data

annotation of work that you found on platforms online. for years this work mostly meant labeling elements and images you know, to, to may be vehicles, cheese, people like everything that is on the road that is visible, including road signs, but not in other words, in the woods of san francisco. the joel in town to all sorts of content these odd jobs came in around the clock. she was paid by the task. awesome. just sense. ready over time to work became more and more sinister as time goes by, the content that you've exposed himself to. now it starts to happen. what does she mean by that? we'll get to that. but 1st, let's house for a 2nd to understand the role of data and a tater is like her in training, cutting edge a systems. most of today is may i analyze this huge amounts of data. the

goal is to identify patterns. for example, how to identify a cat or a dog. this is how the system learns that cats tend to have pointed ears and of triangular faces, while dogs tend to have brought her faces and fluffy years. but what is the data, isn't straightforward. basic is where human data imitators come in. their job is to make sure data makes sense to computers by teaching them. this is a cat. this is a dog. and these are neither a man, however, this often means dealing with questions find when we're done here, then cat or dog lad,

while pregnant, she sometimes worked 18 hours straight until 3 in the morning, pushing herself to the brink. that's the variety of them going to the happiest. and that is when may be a v, a line to that, the content that have been exposed to has affected me to a point that i do not even know the world's bank estimates that hundreds of millions of people around the world are now engaged in online gig work often with uncertain income streams and lack of contracts. in the west, many check giants have started outsourcing this work to countries in the global south, such as kenya, data workers, they are reported to sometimes earned less than $2.00 an hour. compared to more than $20.00 in the us, we meet another data worker somewhere called face. she asked us not to reveal her face to face trend large language models without being told which specific

a chat bought her efforts were supporting. with time also, her work became more troublesome between give you a question. describe kind of boise, you have to put yourself in the shoes on that, that worked in church. and you said in that topic that you have been given faith chose as to john allowed her to help her elderly parents pay their medical bills. but there was only so much she could take. it tells you explain to me how does the flesh of a human face like, can you boy, can you phrase a human meet? i don't know. face realize this work was more than she could handle when she concluded her job was to train the chat. bob, to provide answers to user questions about these topics. i was like, i'm out if they are know more about projects that i can do, that not least i'm being then the amount but was she really training the check

bought to instruct people on cannibalism? what's behind all of this? we ask an expert to explain large language models. ready the thing is they don't understand commands in a way. we know this is all young shipboard chart, a leading expert in a i and large language models. we show him our interviews and ask him what he makes of faith account for us humans. that's absolutely. i have an 11 year old daughter, she knows nothing about kind of but as i assume to her, i couldn't explain, i guess in 5 to 10 minutes what it is about and from there on see what know. yeah. when it is okay to talk about it and what is out and what is what is not okay to talk about, but for the machine. so you have to give examples. unlike his daughter, when a i system has no moral compass, that's why human data annotate her is like face are hired to add their tax. they

help to a i understand when it's okay to talk about topics like cannibalism, that it's okay to provide historical information for example, but not instructions on how to do it. it's all about context. there are contexts in which you want to talk about, kenny, but isn't when you talk about more situations, but you don't want to talk about kenny, but it isn't in the topic cluster of cooking recipes. and now this is the, the, the critic a point with where these people we're talking about today come into play. so you would feed these examples to the set but as negative example. and that's one a aren't you? i thought we used gave us no instructions. and referred us to a with a p d, a article when we asked it to describe how to boil human flesh. our research has shown that data workers like face, awesome, have little to no knowledge of the i systems. they're helping to train. training them involve a complex network of companies face since she found her freelance work through

a silicon valley based platform called remo tasks, a subsidiary of scale, a i it's a company so successful is that it c e. o. alexander wang briefly became the world's youngest self made 1000000000 there, and whose customers include us, tech giants and companies from around the world scale a i declined to be interviewed in an email, a spokesperson wrote that training gen a i models to prevent harmful and abuse of content is critical to the safe development of a i. adding that we have numerous safeguards in place, including the ability to opt out at any time. the company we are working for is making millions or billions that mix these by phone feelings each. we've been used to make before being done as like face gentlemen, spend years doing freelance work one day in march of 2024 out of the blue,

her employer remo tasks seized operations in kenya. ready new skills, you do not have any security, especially in what you're working on like it's not even safe because how does a company where you're working, you've been working for for more than 5 years, just exited out in the low diesel. anything in an email to d, w. a remo tasks spokes person explained that people were not notified due to operational errors. so when i say that platforms and components are predatory, that's exactly what i mean. this brings us to says ologist and computer scientist, milan pulse meat shelley in berlin. they go to a place as long as it is profitable for them as long as it is convenient for them, and then they leave whenever they feel. this is not great anymore. so what happens with renaissance is just one case, among many, many others that we have of serve. in the summer of 2023 here at the bytes and bomb

research institute. researchers launched an initiative called data workers inquiry we work with data work or things. so far and for continents, the situation on the ground depends on where these data workers live. but researchers say there does seem to be a common denominator. workers were often marginalized, in my experience, so i was doing this research in kenya. and one of the work also told me that they were told when they, you, in the organization that we completely any playable. and this was something that was the cost of need repeat it so that you, you know, in your place, you come here, you do this with the so already, you know, $1.00 of the one you, i incredible and about to give it to you to grow it is great, that's people who otherwise don't have a lot of chances. get this type of, of, of jobs. the problem is not without the problem is with the quality of their jobs, their perspectives to actually grow. all the remo tasks has less kenya. several

other platforms continue to do business there. it's one junction you, it has decided to join the countries growing data workers movement. on the one hand, it's about better pay the union and we'll get full scale working conditions, say a bit and push for legislation and policies for people working in this field on the other. it's about better psychological support. you work on very difficult projects, things like dead bodies and such but is not to me and i see it affects the mental states. but then that thing is provided for taking care of that. like being for psychologist as an example, similar efforts by data workers to you, and you know, i've suffered a setback last year when a local contractor working for us tech joined meta fired 185 facebook content moderators. those dismissed are now assuming the company. meanwhile,

jonah and her comrades in arms are continuing their fight for union. and data workers and other countries do the same thing. it's not that dumping place. yes, we might have economical issues and crisis, but we are no stupid. why is it so hard for us to be recognized? why is it so hard to be given for performing the proper medical coverage? why is it for hard for us to be may be properly compensated and given with what conditions? if it is being done in the offensive, and it should also be done as the a, our revolution continues to ramp up. it's clear their flight is just beginning the i don't know it's been, but i would say 4 is my 1st love. proceed. clean, successfully cook to combat her home sickness and opened her own restaurant

colon every journey from india to cpas area. madame chesney and the taste of freedom. next, on the w. how do i optimize my brain? research as doctors and trainers are constantly making new discoveries. our most important oregon is even more powerful than we thought the and in sports, knowing about neural systems is a key to success tomorrow. today, in 30 minutes, d, w, the

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