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[Assistant] Five minutes to go! You'd better be right this time! Question navigator, fella! Can old principal apply?


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[Assistant] Very strong forces of RAF bomber command delivered another concentrated attack upon the inland port of Bremen. In addition to high explosive bombs, 200,000 incendiaries were sent down.


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[Assistant] RAF pilots report that the files were still visible when they were...


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[Assistant] Is he liked by most people? was my first question.


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[Assistant] Oh yes, his family have always been respected here. They've owned the land round here for years, she replied. But do you like him? What is his character like? I've always liked him, and I think he's a fair master to his servants. He's a little peculiar perhaps. He's travelled a lot, you know. I expect he's clever, but I can't tell really. What do you mean peculiar? I asked, interested.


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[Assistant] It's not easy to describe. You're never sure whether he's serious or joking. You don't really understand him. At least I don't. But that doesn't matter. He's a very good master.


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[Assistant] I could get no further information from Mrs Fairfax about Mr Rochester.


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[Assistant] But instead, she offered to show me around the whole house. We went through many large impressive rooms, finally reaching the top floor.


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[Assistant] And thought for a moment they looked like prison doors hiding evil secrets. No sooner had I turned away to go downstairs than I heard a strange ghostly laugh.


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[Assistant] They weren't really fighting for the heavyweight championship of the world in Manila. They were fighting for the heavyweight championship of each other.


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[Assistant] Joe Frazier and Muhammad Ali were the equal of each other. Yet today,


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[Assistant] Ali has sold an 80% interest in the use of his name and likeness for commercial purposes for $50 million, and Joe is living in a room above his gym in Philadelphia.


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[Assistant] That's an interesting look at how America treats its sporting icons. Some are accorded special status and others are largely forgotten.


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[Assistant] First fight, Ali had branded Joe an Uncle Tom, and he knew that hurt. Second time, he called him ignorant.


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[Assistant] And that had some very immediate repercussions because they got into a fight in the studio when Ali set it.


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[Assistant] Incongruity between the fact that he was a man who in many ways seemed devout in his religious beliefs and preached the sanctity of women and respect for women and yet didn't show an awful lot of respect for his wife.


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[Assistant] In the early rounds, Ali was dominant. He was moving, jabbing, Joe couldn't get to him. And the first few rounds, you said, okay, Ali's gonna win this fight.


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[Assistant] Both men were completely spent. The difference is that Joe's eye was closing. Joe by that time was almost blind in one eye before the fight. Now his good eye was closing.


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[Assistant] I don't think Joe is mellowing towards Ali, and I don't think Joe will ever mellow.


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[Assistant] He'll look at Ali with great satisfaction and say with a not particularly nice smile, I did that to him.


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[Assistant] Every now and then he'll say something along the lines of forgiveness, usually before some economic opportunity, but then very quickly afterward he reverts to form.


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[Assistant] Thornfield Hall was a large gentleman's house in the country, near a town called Millcote.


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[Assistant] There, after my 16 hour journey, I was welcomed by Mrs Fairfax. She was a little old lady, dressed in black.


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[Assistant] But I was surprised to discover on my first full day at Thornfield.


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[Assistant] That Mrs Fairfax was not in fact the owner, as I had assumed, but the housekeeper, and that my new master was a Mr Rochester, who was often away from home. My pupil was a girl called Adele, seven or eight years old, who was born in France and could hardly speak English. Luckily, I had learnt French very well at Lowood.


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[Assistant] And had no difficulty in communicating with young Adele. A pretty, cheerful child.


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[Assistant] It appeared that Mr. Rochester, who had known Adele and her mother very well, had brought Adele back to England to live with him after her mother had died. I taught her for several hours every day in the library.


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[Assistant] Although it was not easy to make her concentrate on anything for long, as she was clearly not used to the discipline of lessons.


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[Assistant] Le gouvernement, à cette époque, ne contrôle pas l'émotion.


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[Assistant] There's a particular railroad, the South Side Railroad, that connects Petersburg, Virginia, all the way


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[Assistant] To Appomattox and beyond to Lynchburg, Virginia. At Appomattox, General Lee had multiple trains full of food waiting for his army just three miles southwest of the courthouse village where we are now.


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[Assistant] However, General George Custer, in a division of United States Cavalry, captured all the trains at Appomattox Station here. The next day, General Lee surrendered in this village, Appomattox Courthouse. That ended the Civil War in Virginia.


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[Assistant] And would practically and effectively end the entire American Civil War over the next two months of 1865.


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[Assistant] 99% of America isn't right about most of the things. I set the standard for what will be done with my team. It's just that simple. I always felt that that was a problem to some whites.


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[Assistant] W.E.B. Du Bois said once how whole lives we have spent answering false accusations and I'm not gonna spend two minutes doing it. I thought any time a black was making statements and not asking questions, setting the rules and not asking for permission, some of them had difficulty in dealing with that.


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[Assistant] One of my assistants had asked me to go to Boston with him to see a young man play.


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[Assistant] I'm sitting in here. I can't keep my eyes off of this other kid.


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[Assistant] Are there any other black people at Georgetown? You know, I said Washington is 70% black. I said if your kid can't have social life in a place that's 70% black, I said he got a problem that I can't solve.


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[Assistant] I knew exactly the significance of me winning that game. You know, a young boy said to me in the press conference, did it mean anything to me to be the first African American? And I told him, if that means that I'm the first African American,


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[Assistant] We're not gonna be humble. We weren't Jackie Robinson. So what happens? People right away, they start to say, they're playing dirty. We were playing defense.


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[Assistant] You know, they were going to class. If they didn't go to class, I'd be ready to kill them. But we get out on the floor, we took no prisoners.


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[Assistant] We expect it to make a run. If we keep going, they'll stay with us, they'll stay with us. We'll break them down and then we'll get away from them.


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[Assistant] If the ball is not being rebounded, they're putting it in a basket, you're not going to force the pace. They shot the ball extremely well, they think.


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[Assistant] ...was extremely important. And then we get this turnover at a very crucial time.


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[Assistant] I love em, I love em, you know, but they wanted to win as much as I did.


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[Assistant] You will talk if they get stopped.


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[Assistant] Sorry, sir. Once you work for the Gestapo, you work for the Gestapo.


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[Assistant] Say, five days, 10,000 marks. Suppose it lasts a month. So what? Another 50,000 mark. Why, Chucka, you're a big beer magnet. Just another drop in the barrel.


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[Assistant] Well, well, my young friends, I don't mind admitting...


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[Assistant] Looks like both of us had a solution to that. Huh?


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[Assistant] It'll need something more concrete to force them to surrender their beloved assassin. Something to make them jump.


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[Assistant] That's good. Priests are always good. They do us more harm than anyone else. Oh, stinging every sermon.


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[Assistant] We checked your story, Miss Novotny. In all of Prague, no architect called Vanek exists.


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[Assistant] Answer properly! You know we have means to make you talk. Maybe you have heard about the


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[Assistant] What else can we do? Are we supposed to interfere with lovers? Or are we?


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[Assistant] All right, you'll have your police card. Where you want them and when.


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[Assistant] Perhaps you're also clever enough to want to avoid a visit to our vault downstairs.


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[Assistant] The situation here would certainly tend to exclude the presence of any third person.


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[Assistant] Sorry, but you see, we are looking for the uncle of a certain Mr. Bartosz, who asked me to deliver his last farewell.


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[Assistant] I'm surprised. You see, doctor, personally...


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[Assistant] The rug. You better be careful when you stumble near me again, doctor. My boy is here. Got a little nervous just then.


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[Assistant] The same gentleman who visited your fiancé that other night. Under the name of Carl Vanier.


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[Assistant] Well, I guess all this becomes a rather personal matter now between you three. Up trading. I think I'll leave you alone.


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[Assistant] Well, now we'll go look into this fine alibi of Dr. Svoboda. It's really worth looking into, you know. Then we'll take care of you and your faithful sweetie. You'll excuse me, Mr. Horak, but I'll have to hurry.


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[Assistant] May I use your telephone? Dr. Sloboda?


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[Assistant] Not bad, doctor. That was close. But stand right where you are, or you get a slug in the gut.


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[Assistant] Joe Frazier scored a technical knockout over Buster Mathis in the 11th round.


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[Assistant] An Uncle Tom is considered someone who is subservient to white people or the wishes of white people. Uh, that is probably the greatest insult that one black man can call another.


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[Assistant] It's easy for a white man to say that. If another black man calls me an Uncle Tom or a gorilla or something of the derogatory, that will not be easy for me to dismiss.


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[Assistant] On his hips, this rage, this raised hematobis, huge hematobis, so all these hips started locking up on him.


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[Assistant] There are five or six times where Ali throws combinations where you expect Joe to fall.


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[Assistant] And he staggers, but his will keeps him up.


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[Assistant] But as they went through the tournament, it was a remarkable defensive performance. They were holding people into the forties.


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[Assistant] I've seen Mr. Brownlow, the kind man who was so good to me. Where?


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[Assistant] Going into a house, said Oliver, crying with joy. I've got the address here.


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[Assistant] Quick, said Rose, call a coach. I'll take you there immediately.


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[Assistant] The idea came to Rose that perhaps Mr. Brownlow would advise her.


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[Assistant] She had been afraid to tell Nancy's story to Dr. Losburn, since the good doctor was very excitable, and often acted with more enthusiasm than wisdom.


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[Assistant] Roy, you deserve a great deal of credit for that preparation and also for everything you've done to turn a group of tremendous individual players into a team that will go down in the history books.


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[Assistant] It's possible! It's possible!


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[Assistant] By the- by the literal hairs of my crack, dude. By- I couldn't put that any fucking closer. That's it. I got- how did I- I just had a feeling today.


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[Assistant] No way! How about making fire?


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[Assistant] English Channel was opened again and England was able to receive her necessary war supplies. The blockade was broken.


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[Assistant] What's that? The rate of liberty at 1430, is that right? Till tomorrow.


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[Assistant] Giving up your time for a buddy, eh? Real Navy spirit. Thank you, sir. I'll report to Lieutenant Wells, then.


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[Assistant] It might be dangerous, Nash. Maybe a booby trap. Let's send Smith up first. Answer Smith.


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[Assistant] Steel ring, 8 inches in diameter.


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[Assistant] That's enough, Smith. The booby trap could be right behind the diaphragm. Don't touch it.


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[Assistant] There are fewer camels here. Look over there.


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[Assistant] Makh ching ngih nir ee, Makh tando dyaa, Manna notigi, Malle nam titae.


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[Assistant] We are off this week for Memorial Day, but we wanted to briefly check in to run a new segment, uh, by you. Because on our show we often ask the question, how is this still a thing?


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[Assistant] Uh, which we inquire in an effort to understand why we're still subjected to things like Daylight Savings Time and the Sports Illustrated Swimsuit Edition. But tonight, in the interest of innovation, we'd like to do the exact opposite of that and pose the question, how is this not a thing?


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[Assistant] How is there not a search engine built just for parents that lets them search terms like the guy who's on that show we like and oh she's married to what's-his-face?


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[Assistant] I saved you. Well, uh, Humba helped. A little. Thanks for your help. Hey, where you going?


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[Assistant] Ah, you're an outcast. That's great, so are we. What'd you do, kid?


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[Assistant] It's, you gotta put your past behind you. Look kid, bad things happen and you can't do anything about it, right? Right. Wrong! When the world turns its back on you, you turn your back on the world.


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[Assistant] When I saw Oliver running away so fast round all the corners bumping into walls.


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[Assistant] And all the time I had the hecka chief in my pocket.


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[Assistant] And it's, you know, it's facing death with not only, you know, bravery, but with a smile, with a laugh. He's laughing in death's face right now.


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[Assistant] That's probably what they're expecting, right? They can't believe that 21 men would stand against 10,000. They can't, it doesn't even, they can't fathom it.


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[Assistant] Grumbe grumbe grumbe grumbe grumbe grumbe grumbe.


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[Assistant] I need a ki- I need a win like that so you guys can say, you guys can't say FAKE, that was a stream sniper, paid actor. I need one.


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[Assistant] That I get no kills and I win. That way you guys can't say I didn't win.


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[Assistant] That's terrifying. Manka S.


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[Assistant] My name's Ninja and I think I'm the coolest


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[Assistant] Dr. Lupo? Yeah? Yeah? Cotton-headed ninnie muggins.


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[Assistant] You serve your food. People don't like it. You close it and you move on. This is education. This is students are getting left in the middle of the year without a school to go to. So I just think that there needs to be some filter as to who's opening up these charter schools.


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[Assistant] You see this girl, began the black marble column. She is young. She looks like an ordinary child. Nothing about her tells you she is evil. But she is all wickedness. Children don't talk to her. Stay away from her. Teachers watch her.


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[Assistant] Said the two Brocklehurst daughters, each wiping a tear or two from their eyes.


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[Assistant] I learned this fact, continued the great man, from Mrs. Reed.


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[Assistant] The Brocklehurst family stood up and moved slowly out of the schoolroom. At the door, my judge turned and said,


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[Assistant] So I'm really excited to be here today. I was an undergraduate and a master's student here and I see some faces that were part of that educational journey in the room.


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[Assistant] And a lot of the kinds of questions that I look at in my research might seem like they're, they're really in conversation.


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[Assistant] Come talk to you about this project work today and try to draw a loop between


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[Assistant] So today I'm going to be focusing on how to, different ways of centering the human in human computation. Uhm, I'll be talking pretty much about Mechanical Turk as the central example because that's the work that I've been doing and that's the example that I know best but I'm going to argue that Mechanical Turk


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[Assistant] Is a way of organizing workers and programmers and digital data production.


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[Assistant] That also tells us some things about high tech production cultures more generally that we can watch out for as very elite and privileged participants in those cultures.


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[Assistant] Uhm, and I'll talk about Terkopticon as one intervention in, in, uh, into that kind of culture. Uhm, and actually I'm getting into my overview now. So,


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[Assistant] And after talking about Tricopticon and some lessons that I learned working on Tricopticon, point out to other ecologies of worker mobilization that are happening both in this department and in the worlds of workers.


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[Assistant] So to start, for any of you who don't know what Amazon Mechanical Turk is, I'll just do a very quick overview. So the basics of Amazon Mechanical Turk is that it's


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[Assistant] Images that you have of things people are uploading. Say you're gonna make the cute cat site. And you need to know, are these images actually images of cute cats? Or are they not cute cats? Or are they chairs? Or are they some kind of malicious data? Uh, you need a way to sort through that mass of data that's come to characterize Web 2.0 systems.


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[Assistant] But these are the kinds of cultural data processing tasks that artificial intelligence tends to be bad at. And for me,


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[Assistant] But the short version of the argument is that computers are not embodied in the world culturally.


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[Assistant] And socially the way people are, and so how could you expect a computer to keep up with what a cute cat is? Unless you train its artificial intelligence algorithm and keep retraining it to keep up with that.


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[Assistant] So for this kind of data processing problem, uhm,


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[Assistant] For example, Amazon built Mechanical Turk as a way of saying, well if AI


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[Assistant] And that's done by mechanical Turk workers. And then sometimes these workers actually create training sets in semi, you know, uh,


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[Assistant] How is this different from any kind of like data processing?


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[Assistant] I mean, we've had a couple of decades of sending large amounts of office documents to places with lower labor costs.


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[Assistant] With, uh, and a lot of English skills like India, uh, where people will go and digitize what people have written in hand on forms and upload into a system.


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[Assistant] Well, one big difference that I'm going to focus on today is that Amazon Mechanical Turk actually


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[Assistant] Organizes all these workers over here so that they're accessible through APIs, through algorithms, and through acts of coding.


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[Assistant] So instead of going out and finding a business processing outsourcing center, signing a contract, having that person, that center having to have a manager,


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[Assistant] This in some sense, uh, Amazon Mechanical Turk in some sense allows programmers to become managers through familiar acts of coding.


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[Assistant] So when Mechanical Turk, Mechanical Turk came out I think in 2006 and in 2000 and I think


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[Assistant] The debate, it seemed to me like this was a way of creating a lot of jobs with no minimum wage, and I was a little bit puzzled that the tech culture that I'd come from that was so utopian in aspirations for creating a better world, I worked at Google, uhm, you know, had also produced the system that produced so many jobs, but they seemed to be so low paying. And at the time, there was sort of,


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[Assistant] Sort of a debate of, you know, does AMT empower workers? Or does AMT exploit workers? So on the empowerment side, you had people saying, well, you know, people are doing this work for fun. Clay Shirkey's Cognitive Surplus was, I think, about to come out.


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[Assistant] They're just killing time and getting paid instead of paying to play World of Warcraft.


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[Assistant] Or, one to two dollars an hour is a good income for people in developing countries where


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[Assistant] The assumption was that a lot of these workers would be doing this work from, because why would an American do work for a dollar or two an hour?


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[Assistant] As it turns out, there are mostly Americans working in these systems, but we'll get to that.


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[Assistant] Also, in some ways, Mechanical Turk allows workers to work from home, to work from their pajamas, they don't have to pay for gas to get to work, and they can, to some extent, choose their employers. So there's a sense of freedom that was seen as part of the platform.


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[Assistant] Uhm, one dollar an hour is nothing in comparison to the potential profits that are being made. Uhm, Amazon Mechanical Turk is taking jobs that maybe would have offered benefits or some stability and making them even more precarious. We have to choose a new employer every couple of days. Uhm, and this was,


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[Assistant] Coming into it as graduate students, my colleagues Six Silberman and I are the people who, we've been really on this journey together for the last six years working on this.


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[Assistant] It seemed exploitative to us, but we were not workers working at this system. So it still seemed like an open question as to what the experience is.


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[Assistant] Uh, workers were having in the system were and whether they experienced them as exploitative. So,


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[Assistant] I wasn't in a social science class, I was in an art class, and so I put out a non-erigorous, non-social scientific, open-ended survey asking workers, if you could have a Turkers Bill of Rights, what would you want to be in that Bill of Rights? So I got 67 responses, uhm,


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[Assistant] Thirty-five of the responses complained were workers complaining about unfair arbitrary rejections. So one of the things that Amazon did was to attract employers to the system, because it was a really new technology, was give employers a lot of discretion about whether the employers had to pay or not. So if employers didn't like


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[Assistant] The kind of data work they were receiving, they could say this is low quality or this is spam, and no questions asked, they could refuse payment. But it seemed like a number of workers felt that they were, uhm,


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[Assistant] Getting caught in a trap of overzealous employers rejecting their work. And they didn't, there's no way in the system for arbitration to happen. Um, employers don't have to respond to workers when they complain.


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[Assistant] Uhm, some workers said that they weren't getting paid quickly enough, that employers would take 30 days to pay them for a task that only took 3 minutes. And for ones that were working to make ends meet, uhm, or to make money last minute to sort of make final rent, this payment speed was a real problem. Uhm,


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[Assistant] So that Amazon employers seem generally unresponsive to workers' concerns because there are way more workers, uh, it seems, than employers. So if this worker went away, then another worker would come to fill the spot.


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[Assistant] And a much smaller number, but some complain that there's no minimum wage. But the upshot from this experiment was actually that


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[Assistant] A lot of the things that we assumed would be a problem, like the lack of minimum wage wasn't the dominant thing people were complaining about. And there were things that neither the empowerment or the exploitation side had thought of, had not thought of.


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[Assistant] That workers recognize as really salient, immediate problems.


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[Assistant] So as one worker put it, the way Amazon had organized the interface, there was this pervasive sense of unfairness about


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[Assistant] Uh, what, about uhm, who made the decisions in the worker-employer relationship?


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[Assistant] So this worker describes his feelings this way.


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[Assistant] A feeling that within this workplace, they generally don't have power, and it's kind of by the policy and interface designs. Even if the actual monetary outcomes of that imbalance might be small, it still bothers them as a matter of fairness.


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[Assistant] We started to see that simply enabling employers to respond to workers


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[Assistant] Wouldn't necessarily make sense because the part of the point of Mechanical Turk is that an employer programmer can hire 60,000 people to do a bunch of data work.


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[Assistant] In a couple of days, an academic can hire a bunch of experimental subjects and do that, uhm,


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[Assistant] So mediation and arbitration is fundamentally unscalable. The promise of Mechanical Turk is large-scale data labor.


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[Assistant] As one requester put it, you can't spend time exchanging mail. The time you spent looking at the email costs more than what you paid them. This has to function on autopilot or as an algorithmic system and integrated with your business processes.


[User] Please transcribe this text

[Assistant] So this led us to a broader insight that really what is powerful about Amazon Mechanical Turk, and I hinted at this in the beginning, is that Mechanical Turk makes workers.


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[Assistant] People into a scalable computational infrastructure.


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[Assistant] And what happens when you, and this has been part of the dream from the beginning, so.


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[Assistant] When Jeff Bezos launched Mechanical Turk at MIT, he gave a talk where he had a little pseudocode snippet where


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[Assistant] This is a pseudo code showing how a mechanical turk work can be brought into an algorithmic loop to do image processing.


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[Assistant] So this is the call where, you know, call Mechanical Turk, is there a human in the picture, photo, 02.


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[Assistant] So a programmer can code this up, walk away and get 60,000 images processed. You guys are familiar with this.


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[Assistant] This is actually a picture from the MIT home page in 2010. It was actually about Michael's work.


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[Assistant] Which is one of the reasons I'm really thrilled to be working with Michael, because he's very, uhm,


[User] Please transcribe this text

[Assistant] Engaged and interested in having a critical dialogue and a productive, uh, interventionist dialogue about this. But, so MIT advertised Michael's amazing work with crowds.


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[Assistant] Uh, by putting a whole bunch of people on a microchip. So this thing that gets manufactured in large numbers by Dell, that we buy, that we put in our houses, and has been promised to empower us with, uhm,


[User] Please transcribe this text

[Assistant] The powers of computing and interactive technologies. Now you can put workers inside and they can power, they can power that same, uh, computational platform. So one of the things that was interesting to me was that simply putting people in a chip


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[Assistant] And putting it on a website doesn't make that narrative make sense to a broad number of people. But we kept seeing this narrative show up in different places on the MIT homepage and Jeff Bezos' launch dialogue. When Mechaniculture came out, people said, oh, it's like the human API. It's humans as a service. So, continually there's this reimagining of


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[Assistant] All the power of a coder at a machine building amazing things, but now you can get people.


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[Assistant] People in social theory, in policy, uhm, in western culture, where people tends to take on the char- we imagine those people having the characteristic of males. So like, these are people that are always on in the processor, and they're not-


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[Assistant] Having to run out to give milk to the child that they just born, they're at home taking care of, for example. So, yeaah, I mean the fact that they're not gendered actually is interesting, because it sort of assumes that everyone can be the same in the processor, because they're just producing data. As far as you care, they are the same, as long as they produce data.


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[Assistant] Uhm, so, and this is just another example of a lab at Berkeley that is algorithms, machines, and people. And they're also doing a lot of data processing using this kind of human labor.


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[Assistant] So this example actually brings me to some of the political economic forces that also make micro labor really, really attractive is that


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[Assistant] As web 2.0 companies like Facebook have become huge and people are putting more and more of their social and cultural lives online,


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[Assistant] There's a lot of data to moderate. There's a lot of is this porn or is this a policy violation or is this webpage something we want to, uh, you know, some web user to go to after clicking on an ad that is really hard for AI to do. And servers are expensive to run.


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[Assistant] So you need really accessible large amounts of data laborers to actually process that kind of cultural data.


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[Assistant] So one of the points that I've been building up to is that Amazon Mechanical Turk isn't just a way of getting lots of data processed. It isn't just a way of


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[Assistant] Making people compensate for where AI can't quite reach yet, but it will soon. But actually Mechanical Turk is about making innovators. It's a platform that organizes workers in an easily accessible way so that people working on a lean startup


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[Assistant] So grad students or faculty doing research experiments who need a lot of data fast.


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[Assistant] People who want to make ma- amazing interactive technologies.


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[Assistant] Can work on soft, can keep working on the software stack and use those workers as media.


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[Assistant] So if you think of media, media is a platform that takes your inscriptions, right?


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[Assistant] You have paint, you have canvases, you have pencils and paper. Pencils and paper


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[Assistant] Are not known for fighting back, for being recalcitrant, for not taking your orders. They're meant as strata that promote human expression.


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[Assistant] So Mechanical Trip takes that idea of an expressive artist and says, hey, you can do that and do it on a factory of 60,000 people producing data.


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[Assistant] This also lets employers who don't know how to be managers, who don't know how to write outsourcing contracts actually do this through acts of coding that they've already learned.


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[Assistant] And one of the things we found with the employers we talked to is that it lets people who see themselves as part of the creative classes, designers, journalists, redistribute the tasks that they consider to be repetitive or rote or low value.


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[Assistant] Put those out into the Mechanical Trick platform so that someone else can do it and that these more elite creative class workers can focus on


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[Assistant] More valuable tasks. At the end of the day if, you know, you're a journalist and you get all your interviews transcribed by a mechanical Turk worker, your work as a journalist is not, uh,


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[Assistant] Devalued, you actually publish more articles that way and you get more credit for in some sense of things that really matter.


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[Assistant] Because the mechanical transcription work that you got done, it does matter to your project, but getting it done faster and cheaper only helps you in the way we measure status in these creative class professions. So this kind of, the other thing that interface layer


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[Assistant] In high-tech cultures, I mean, we have a long tradition of computer professionals for social responsibility, for places like Google really caring about information becoming accessible because they believe information helps people.


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[Assistant] Amazon Mechanical Turk make, puts the workers so far away that you, it's really hard to figure out what conditions workers are working under even if you do care. So people can think things like, well people are mostly doing it for fun and you can't coerce someone through a screen.


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[Assistant] I'm arguing that this mediated doubt is actually something that's very powerful in making mechanical trick take off among really well-meaning programmers and engineers.


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[Assistant] So uhm, putting those two things together, uhm, the argument here is that Mechanical Turk makes labor invisible in a way that sustains the myths of pure production and high tech. So for me this came, uhm, I gave, I,


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[Assistant] I was really struck by a story a designer told me that I met in India. He was talking about making a system that processes webcam images of traffic in different intersections in an African city.


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[Assistant] And he said, well we have a bunch of low paid workers in the design studio that are processing these images and making them into data so that we can have this mobile app that, uh, lets commuters figure out which streets to avoid. But it's kind of awkward having such different kinds of workers in the same studio because so much of


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[Assistant] How we talk about creating spaces of innovation is about creating spaces of flow, of comfort, of play, of quick and easy interdisciplinary sharing.


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[Assistant] Lower status data processing workers in that space was a problem for him and he said, oh, maybe Mechanical Turk can help me solve that problem. So,


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[Assistant] I'm emphasizing this because I think this is one way that what Mechanical Turk represents is actually a feature of


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[Assistant] Janitors don't get paid very much because they get hired through many levels of subcontractors, but that


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[Assistant] Kind of wage structure is hidden from workers who are well meaning but work in these large tech companies.


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[Assistant] Uhm, workers on Mechanical Turk had a really different range of problems. Some of them wanted unions, some of them said no unions, no way, I want to be a freelancer. And so we tried to identify places where workers did agree. They had some kind of common issue. Yea, Gilbert.


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[Assistant] So in figuring out where to proceed, we didn't want to side with the empowerment or the exploitation. We wanted to figure out a project that would let us meet workers where they're telling us they are.


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[Assistant] At the same time as somewhat improving their conditions and provoking a debate about ethics in human computation that we felt like wasn't happening in human computer interaction, AI, and the media.


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[Assistant] So that brings me to the main, uh, project that Michael is talking about, which is called Terkopticon.


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[Assistant] Uhm, which we built to interrupt invisibility and support mutual aid among workers in Mechanical Turk. And that's the work that I've been doing as Six Silverman, as well as...


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[Assistant] There's three mechanical Turk workers who are moderators, uh, Taint Turk, Hanwan, and Midwitter, and also workers who write all the reviews that make Turkopticon at all meaningful. So, I'll tell you a little bit about what that work has been.


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[Assistant] So, Terkopticon is an attempt to take, uh, this, uh, configuration that makes workers invisible and reconfigure those optics. So,


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[Assistant] What it does is it lets workers in Mechanical Turk review employers. Uhm, so we have a website, there's a database that contains the reviews that are matched to requester IDs, and uhm, there's also Firefox and Chrome tools that workers can install. So as they're browsing Mechanical Turk tasks,


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[Assistant] We insert a button next to each Mechanical Turk Requestor, pulling up any reviews we have for that ID, and


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[Assistant] We insert them in the page where the worker will be making the decision about who to work for. So,


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[Assistant] What we ask for in the reviews, uh, are four categories that we got from talking, from reading those mechanical turq-


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[Assistant] Bills of rights responses. It seemed like a lot of workers complained about communicativity of the employer, the generosity of the employer, wages.


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[Assistant] The fairness of the employer, you know, does the employer respond when there's been a mistake? Uhm, and promptness. How quickly does the employer pay? So, within that, we have workers rate.


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[Assistant] So we ask workers to quantify, not because we think that these particular qualities are readily quantifiable, it's actually really, how do you quantify generosity if you are


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[Assistant] But, because mechanical chart workers are working in such a fast production culture, they're choosing among many employers, they have a lot of judgements to make, they're choosing employers every day, maybe even every couple of hours, so uhm,


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[Assistant] Expecting them to pour through open-ended reviews just seems sort of unrealistic. So the numbers say, well, if this employer has really good numbers, then go ahead and work for them. But if it's, if it's anything more ambiguous, then we really wanted there to be a more interpretive archive of what makes this worker good or bad.


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[Assistant] And so, that tool, and that website, this is the home page for it, so it's more than just functional. It's more than just a way of evening the information playing field. Uhm, it was actually meant to...


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[Assistant] So agonistic democracy is the idea that rather than working towards consensus, democracies function better when you encourage people to


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[Assistant] So this homepage is meant to provoke a response, provoke work, you know, we have a classified out of here, help wanted, 120 an hour, risk of repetitive stress injury, no care for on the job injuries, no guaranteed minimum wage, mechanical trick industries.


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[Assistant] And the purpose of that, it wasn't that we thought that people were going to believe us. We knew that people have lots of different opinions. We wanted to provoke the debate.


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[Assistant] So, we were using design not only just to make something usable, but also to make something that provokes a set of discourses around it.


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[Assistant] So, within that, uhm, we've been fairly successful in...


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[Assistant] Serving, uh, serving some needs of workers in the Mechanical Turk workforce, uh, we have, our usage has grown.


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[Assistant] We have 20, I guess something like 20,000 unique visitors.


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[Assistant] Uhm, but I don't want to present, this is an unalloyed success, like this is not enough to actually claim impact.


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[Assistant] So on the one hand, Tricopticon enabled workers to aid one another.


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[Assistant] On the other hand, there are problems, so in some sense,


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[Assistant] Tricopticon, sorry, I meant to say Tricopticon enables workers to aid one another, but


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[Assistant] Trikopticon also in some sense legitimizes Amazon Mechanical Turk. Like what if some competitor that had better labor practices could come along and replace Mechanical Turk and Trikopticon is actually making it harder for that competitor to break through.


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[Assistant] I also learned from Mary Gray at Microsoft Research who's doing work on crowdsourcing that a lot of workers who are in India find


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[Assistant] Tercopticon and other American run worker forums to be inhospitable places because a lot of workers diagnosing the low wages in Mechanical Turk will say, oh, it's because workers from these other countries will work for anything.


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[Assistant] And that's our problem. So while that might relieve stress or be one theory, uh,


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[Assistant] Indian workers stay away and she, Mary was saying they share information in other formats on the web. So.


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[Assistant] I'm making an effort to present this as ambivalent because I think a lot of times in design we talk about coming up with solutions and improving and here it's very clear that there's an intervention and it certainly changes the conditions of reality.


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[Assistant] But it all depends on what perspective you take, whether it's for the better or for the worse. But at least it's different than when it was six years ago.


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[Assistant] So I wanted to conclude by pointing out to other kinds of horizons and ecologies of workers engaging in mutual aid and trying to improve conditions in- within crowd work. And I'll explain a little bit about why.


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[Assistant] Uhm, so, one of the really important places where workers talk with one another, and it's been like this for six years at least, uhm, are forums like Tricker Nation.


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[Assistant] CloudMe, baby. And these are forums that are operated by workers, where workers can share


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[Assistant] Workers will take really stable kinds of computational infrastructure, like PHP-based forums, put them on a server, and then use this to their own ends. And the communities that get built here, the discussions that get built here, affect the ability people have then in Tricopticon, workers have in Tricopticon to


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[Assistant] Talk with one another, resolve disputes, or sometimes fights spill over from here into Turcopticon. So Turco- taking the design of Turcopticon alone doesn't explain the social dynamics of Turcopticon because Turcopticon exists as one place that people from these forums pass through.


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[Assistant] More receptive requesters or employers will come to these forums and ask workers, why isn't anyone doing my task? Or, why are you guys giving me such horrible reviews and how can I fix it?


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[Assistant] So these forums make possible some of that negotiation at a larger scale than email interactions for requesters that actually want to be fair, but where email was an inadequate way of promoting that kind of problem, supporting that problem resolution.


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[Assistant] Anecdotally, I've actually heard a number of workers talk about holding out for a minimum pay rate of $6 an hour, which as I understand it is coming out of just conversations in these forums and they have a really hard time enforcing.


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[Assistant] That pay rate because you can't have a, it's hard to have a boycott when a lot of workers desperately need the money. But.


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[Assistant] It's an emergent tactic and it'll be interesting to see what happens with it.


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[Assistant] Some workers actually use the IRB, the Institutional Ethics Review Boards that universities have to have.


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[Assistant] To hold employers who are academic researchers accountable. So if a researcher runs an experiment and doesn't pay, there's one worker in New Zealand and one of our moderators who seems to take a thrill out of calling the IRB and using that institutional mechanism to


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[Assistant] To call those employers to account. So that was one of the favorite things I'd ever heard about IRB. And I mentioned that Nilou Farfand.


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[Assistant] And they tend to not be valued. So one of the experiences that we had with Tricopticon


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[Assistant] Was the journalist who wanted to say, oh, Mechanical Turk is a digital sweatshop, would then come to Tricopticon and say, oh, look.


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[Assistant] Techies caused this problem. Techies are solving this problem. Thank goodness we have smart people working on this. And workers who are reading these news articles will say, these articles cast us as dopes. That we're just, you know, being engineered through these different platforms and we have saviors coming in to tell us what we need to help ourselves.


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[Assistant] So to me that was a real lesson that when we do these design interventions,


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[Assistant] To improve people's lives. No matter how much on the ground work is necessary to actually make that design intervention sustain and have life.


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[Assistant] We tend to focus on the software authors or the authors of the design interventions.


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[Assistant] To credit them as the heroes or the entrepreneurs that instigated this change. Not only is that bad social analysis in this case, it's also really bad for politics because it creates a, it created a lot of uhm, it basically insulted


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[Assistant] It basically insulted the very workers that it sought to describe and the possibility of moving forward in a more collaborative way.


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[Assistant] So uhm, that's why I'm taking, uh, making an effort here to focus on some of these things that don't look like writing.


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[Assistant] New algorithms are making new mechanisms, uhm, or making something that will be recognizable as inventive and intellectual property, but has been sustained and innovative.


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[Assistant] So to conclude, so one of the, the third project, and this is where I'll conclude, is work that, uh, Niloufar is leading, and Michael and I are supporting, which is, uh, actually I'll just skip ahead, uhm, called CrowdFlash. And so in brief, CrowdFlash builds on


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[Assistant] The insight that a worker had during some interviews I did at Irvine, where he said, this guy, this worker had done labor organizing, uhm, this guy Nick, he lives in Ithaca.


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[Assistant] And he's, uh, the interviewer was an undergrad I was working with, Andreas asked him, you know, have you thought about trying to organize people online? And he said, well, I've thought about it, but the community is so, like, loose that I don't even know where I'd really begin with that. I could go to all the forums and they would say, that's a great idea, we should do that. And then, you know, where's it gonna go from there?


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[Assistant] And we did see examples of workers saying, hey, let's do a petition or let's do some public action in forums where some people said, yes, this is a great idea. And then it just sort of petered out.


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[Assistant] So, eh, Niloufar's work is geared towards...


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[Assistant] Uh, giving workers a way of finding common issues, developing action plans, and then giving them the kind of social media...


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[Assistant] Social computing support to actually act on that and motivate people to kind of participate all along the way and so hopefully she'll get to give a talk about this at some point.


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[Assistant] So uhm, in conclusion, what I've argued here is that making humans into computing infrastructure, it expands the agency that we have as programmers with the skills that we get, while sustaining the myth of peer production, but it does it by sort of externalizing


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[Assistant] The labor that's actually necessary to making digital technologies possible.


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[Assistant] Uhm, Tricopticon was one attempt to interrupt that invisibility and is a platform for mutual aid, but it's definitely not enough.


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[Assistant] So we want to take that kind of mutual aid in that community that we've been part of producing along with other worker run forums and continue participating in this ecology of giving workers


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[Assistant] The ability to hold employers and hold Amazon more accountable to pressure for changes that would make their work conditions better.


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[Assistant] So with that, maybe I'll get questions or address Gilbert's question. Thank you very much.


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[Assistant] So should I try to start with your question? Uhm, I'm really not trying to, I'm not a question-avoider. Uhm, so your question was, was about whether a, uhm,


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[Assistant] So there's lots of ways that people, uhm, either have gaps in empathy or understanding, or that uhm,


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[Assistant] The structures of needing to produce profitable businesses or to produce more academic papers.


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[Assistant] Also make it possible for people to kind of self interestingly ignore dimensions of the kinds of social relations they need.


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[Assistant] Very publicly committed to uhm, producing progress through the work that they're doing. Like, a McKinsey consultant


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[Assistant] Or, you know, private equity or outsourcing companies. But here we have an industry where people actually are invested in that kind of progress through the work they do, and yet still.


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[Assistant] This interface layer, making humans sit behind an API sort of helps mitigate some of the tensions of the vast inequality that actually is being produced here.


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[Assistant] So I think that's the only incremental bit that's different.


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[Assistant] That's a really good question. Uhm, one of the things that I've been startled by, the more I think about this, uhm, and the more MOOCs become something that, you know,


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[Assistant] I mean I think at the end of the day one of the lessons for me is that you can't act-


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[Assistant] You can design some things into the system, and that's why I'm really glad to be working with you, Michael, and Needlefire6 has been thinking about how would you design a system that makes, uhm,


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[Assistant] That changes the economic incentives that requesters have for the ways that they behave.


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[Assistant] Within that, you're also within, say with Mechanical Turk and the social structure where, you know, companies need to...


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[Assistant] Make profits or promise profits in order to get investments. And so it still remains profitable.


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[Assistant] To outsource work that's less valued in a market economy, uhm, to people who can do it and, uh,


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[Assistant] That, you know, so, and to own those, the means of that production. So like economic, at some level economic structure is also a question here.


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[Assistant] At least kind of my ethnographic sense is that the only reason workers will do LinkedIn tasks is if their approval rating goes down because they've had a huge rejection and they have to get it back up so they can get access to the good jobs again.


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[Assistant] So the wages, the wages really are an issue, but that was more, uhm, in 2006 when I was writing, uhm, when I was doing the Bill of Rights.


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[Assistant] The fairness was sort of the issue that cut across and the wages were a little bit more like depending on where you're living, uhm, were, were more or less of an issue. But I think to your question of how would you design it differently, uhm,


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[Assistant] That's still having these problems. Because if you want to have an API that is served by humans, and you want it to be cheap, I mean how-


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[Assistant] People are doing this, people are doing this because they need the money in many cases.


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[Assistant] We don't have updated data, but in 2010 it was 20% of people needed it to sometimes or always make ends meet. And that was before the economic crisis. And now most of the workers who are doing this are in the United States.


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[Assistant] Poss- I would say that they're majority- So, okay, several years ago, I was at Crowdconf, and I heard from a product manager at a mechanical trick, that your Copticon had


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[Assistant] Made it so that there's the best practice among requesters so that when they're new they should just approve a lot.


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[Assistant] Screw up in a more timid way. So you could say that screwing them over, or you could say that that's just making them extremely cautious, and what still remains a fairly low cost. Uhm, there are some, uh, Taint Turk has a blog, uh, I think I skipped that slide, where he, uhm, it's a Tips for Requesters on Mechanical Turk.


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[Assistant] So he actually publishes best practices for requesters. There are efforts to educate requesters within this as well.


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[Assistant] I think the fact is if you're in an incentive structure, whether you're a graduate student or a faculty member or


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[Assistant] Is, uh, it would be an open question whether a good will makes a significant dent.


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[Assistant] But all the incentives are pointing the other way. And I, so I'm coming at this as a designer and an ethnographer, so I'm cluing in more to uhm,


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[Assistant] What can I do not as a big corporation? I can't design the whole platform because I don't have a way to maintain it. Even maintaining Tricopticon is really tough. And uhm, there's a, there's really hard to actually sample all the workers to get a sense of what the wage is.


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[Assistant] You know, our, how the relationships with different workers are in a rigorous way. Mary Gray has found a way by collaborating with requesters. Uhm, but it's something where you need like very high access to...


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[Assistant] Limitations that prevent me from taking the kind of...


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[Assistant] Overview of the system. And Citric Octocon is very much a project that comes in kind of from the side and tries to change how a real world system works without being in it.


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[Assistant] Uhm, we did meet Sharon Chiarella, who's a director of Amazon, at a crowd, uh, at a crowd meetup in San Francisco in 2009.


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[Assistant] Luke Biewald, CEO of Proudflower, actually, would put on those meetups, and he invited us to come speak there, and he was actually early supporter of Tricopticon. Uh, he...


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[Assistant] When we first started Tricopticon, we wanted there to be reviews out of the box when a worker installs it. So it would be helpful so that they would then start contributing. Uh, and Luke helped, Luke and Crowdflower helped gather the initial batch of reviews, the one system, the system. So I want to credit him with that first. Uhm, but Amazon, uh, doesn't,


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[Assistant] They, I believe that they own Turkopticon.com and it points to a webpage that has random ads on it. And uhm, so they're,


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[Assistant] Who knows? Who knows how they feel? And that's...


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[Assistant] From what I've heard from people who used to work on that team at Amazon,


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[Assistant] This is a kind of small experimental project that doesn't have a lot of resources.


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[Assistant] Uhm, and that explains, that is what they use to explain some of the limitations in Amazon's innovations on the worker experience side.


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[Assistant] But if you also look at how Amazon runs its warehouses and its general employment policies, it's somewhat well known in high tech for, uhm,


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[Assistant] Attempting to drive great, great efficiencies out of its workforce at many different levels. So.


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[Assistant] Well actually it's not at all clear from the way I've given the talk, right? But to me one of the key questions is okay, what kinds of people are we becoming and what kinds of labor relations are becoming conventional and what are their properties? Like what do they afford and what kinds of pressures or vulnerabilities or risks do they produce? Uhm, and so in terms of what we can change


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[Assistant] Research that I would do differently looking at it through this lens, like, instead of thinking about human computation as a kind of new management science that is primarily allied with uhm,


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[Assistant] Cooperative, cooperative organization, like Gilbert actually brought this up in an email conversation we had a year ago. You know, what would it look like to actually organize a micro labor sort of...


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[Assistant] Co-operative and what kind of software would be needed to allow for governance and for the distribution of payments? Like if I...


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[Assistant] Or, okay, if I'm a requester and I want to profit share on something that I make on Mechanical Turk,


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[Assistant] I have no idea how to do that. Do I, I think I would have to have all the requester IDs and then I would go and take the money that I make off of this item and then would have to distribute bonuses to those workers if they even have the same accounts that they had before.


[User] Please transcribe this text

[Assistant] You know, these things are hard and these are not the things that we're building to make it easy to organize things in a just way, kind of out of the computational box. So like, that's an example of a kind of platform that I feel like shifting a focus to a just kind of distribution of labor would bring our attention to.


[User] Please transcribe this text

[Assistant] The way we think about where change in value comes from. This is sort of what I was referencing in the way that journalists kept coming to Tricopticon and saying, you guys are really saving the workers with your technology. Someone had the headline, unions 2.0.


[User] Please transcribe this text

[Assistant] But our Union 2.0 wouldn't work if there wasn't all these moderators, if the workers weren't actually writing reviews for the thing, if there weren't these other forums where workers were giving each other the other kinds of support they needed.


[User] Please transcribe this text

[Assistant] To teach each other how to be workers and just to tell each other about Dracopticon, that kind of thing.


[User] Please transcribe this text

[Assistant] So, and I think that's built into the way we value intellectual property and patents, because patents say if you planned it and you kind of implement it, then now you have some kind of property ownership because merely implementing it is not a big deal.


[User] Please transcribe this text

[Assistant] I think that's built into some of the ways we talk about entrepreneurship and really focusing on the personal qualities of the entrepreneur and their chitspah to implement, but we don't


[User] Please transcribe this text

[Assistant] Or the way value gets distributed within those enterprises. Uhm, so that's, that's where I'd point for like larger scale.


[User] Please transcribe this text

[Assistant] Is a task that can be done by whoever might land on your task.


[User] Please transcribe this text

[Assistant] So it's a broad, it's a skill that's in broad distribution.


[User] Please transcribe this text

[Assistant] You don't know if the worker is going to be good and so you need ways to quality check and you kind of treat workers like a security threat in addition to the source of value.


[User] Please transcribe this text

[Assistant] Because of the sort of the level of skill Amazon Mechanical Turk tries to make widely distributable, uhm, my guess from talking to people who studied Odusk is that


[User] Please transcribe this text

[Assistant] The social dynamics look really different. But yeaah, it's a good question. I'm sorry I don't have the answer. But yeaah.


[User] Please transcribe this text

[Assistant] The mechanical trick story that I've told is most, so I think of it as a kind of sensitizing lens, right? I don't want to speak for all crowdsourcing because


[User] Please transcribe this text

[Assistant] I think the mechanical trick story I'm telling is a sensitizing lens for looking at, uhm, organizations in tech that rely on really different valued kinds of labor, uhm, where


[User] Please transcribe this text

[Assistant] Some, you know, people at the upper levels have an incentive to want to get rid of that. So like lift doesn't feel like that kind of...


[User] Please transcribe this text

[Assistant] Labor wear. Oh man, you know, driving is such a low value activity. If I hire a Lyft person, I can...


[User] Please transcribe this text

[Assistant] You know, write all my books during that time, you know? Like lift, the lift story seems like it's more about...


[User] Please transcribe this text

[Assistant] Uhm, adding a lot more supply to the transportation market, uhm, and trying to treat that like a gift.


[User] Please transcribe this text

[Assistant] And managing the affective relations between the driver and the passenger so that everyone feels like it's kind of sharing, but it's actually, there's a donation that's expected.


[User] Please transcribe this text

[Assistant] So I think that opens up a whole bunch of different kinds of issues. The mechanical trick on the affective emotional side is interesting in that


[User] Please transcribe this text

[Assistant] People use mechanical trick workers to do emotional, what is it, like, uhm, sentiment analysis? Like, let's analyze all the tweets and find out if people are angry.


[User] Please transcribe this text

[Assistant] For our brand. But you don't actually care about the worker emotions, you care about the emotions in the tweets. But you need workers who have an emotional processing kind of, uh, a kind of, uhm,


[User] Please transcribe this text

[Assistant] Emotional literacy for the tech so they can produce that as data. So these systems are...


[User] Please transcribe this text

[Assistant] These interfaces are sometimes about sort of cutting down the interaction to just the parts that...


[User] Please transcribe this text

[Assistant] To just the parts that matter, and I guess then the question becomes by cutting it down to just the parts that matter,


[User] Please transcribe this text

[Assistant] Are you cutting out all the, all the unjust externalities? Or are you just creating a space where it's actually really flexible?


[User] Please transcribe this text

[Assistant] Uhm, so that's where the political economy kind of comes into view, but uhm.


[User] Please transcribe this text

[Assistant] Yeah. It's a good question. It's not like I have an answer for it, so I was just trying to think it through with you. Yeah, it's really nice.


[User] Please transcribe this text

[Assistant] Why do we have a limitation of what we imagine wanting to make possible through crowdsourcing? And can we imagine more interesting outcomes that we could have?


[User] Please transcribe this text

[Assistant] I think one of the reasons why that's hard is because sometimes...


[User] Please transcribe this text

[Assistant] In my experience uhm, you know, when I was coming out of computer science, I had very little exposure to


[User] Please transcribe this text

[Assistant] The complexities of being a tech writer or of being a photographer. I mean there's a lot of...


[User] Please transcribe this text

[Assistant] Or even being a cook or a child caregiver, you know? Like you learn that code is this virtuosic thing, and uhm,


[User] Please transcribe this text

[Assistant] So part of the paucity of imagination might be a paucity of sort of the disciplinary trainings that a lot of people who are using those systems might come into.


[User] Please transcribe this text

[Assistant] Like, the mechanical trick is designed for the kinds of data processing tasks that programmers at Amazon had.


[User] Please transcribe this text

[Assistant] Uhm, and now lots of people can use it. Uhm, but so the people that are doing, designing these platforms are not at all the people


[User] Please transcribe this text

[Assistant] Are people who are already kind of come through a very limited pipeline and gateway? So, I'm not sure. That might be part of the reason why it's hard. I don't know off the top of my head right now, like, we could...


[User] Please transcribe this text

[Assistant] But I think one of the things that's interesting about your work, for example, with MOOCs, MOOCs are very controversial when people get scared that


[User] Please transcribe this text

[Assistant] It means we're going to teach 150,000 students to get a stamp from a prestigious institution.


[User] Please transcribe this text

[Assistant] Will be devalued because you just want to get a Stanford certificate.


[User] Please transcribe this text

[Assistant] But a lot of your work is about making actual peer evaluation something that's viable.


[User] Please transcribe this text

[Assistant] Giving the crowd a sense of richness in productivity as opposed to an extraction of uhm,


[User] Please transcribe this text

[Assistant] Looking at worker motivations, how do you make more tasks fun? So I think that people are concerned with that. It's not actually clear to me that


[User] Please transcribe this text

[Assistant] The majority of workers right now are doing it for fun.


[User] Please transcribe this text

[Assistant] I'll say anecdotally slash ethnographically, because all I know is what I know from the people I work with, the moderators, the forum posts I read on Turcar Nation.


[User] Please transcribe this text

[Assistant] I don't know anybody who just does it for fun. They might do it because it's more fun than driving out to the next town to get a part-time job.


[User] Please transcribe this text

[Assistant] So, uh, Sixth Silver Moon, my collaborator, was doing some number crunching, and I can't retrace the logic for it now, but his sense was that maybe, like, very small proportion of the work,


[User] Please transcribe this text

[Assistant] gets done by the people who might be doing it for fun or doing it once in a while and the bulk of the work gets done by people who are doing it kind of


[User] Please transcribe this text

[Assistant] I think the project Niloufar has been heading up will let, will hopefully support workers in taking on some of those tactics.


[User] Please transcribe this text

[Assistant] So when you say in the food movement they're focused on brands, do you mean focusing on brands of employers as a tactic to leverage, to pressure those employers to have better practices? Ah, yeaah.


[User] Please transcribe this text

[Assistant] Gets into a little bit of a dangerous territory, right? Like, this academic is encouraging a boycott of this exact company or whatever. So uhm, I think that there might be an institutional issue about where this research comes from. And no, my focus has not been to sort of


[User] Please transcribe this text

[Assistant] Lead boycotts of particular companies, but uhm,


[User] Please transcribe this text

[Assistant] You can use the question of why hasn't that come up from a, from a grassroots level and ask well what kinds of infrastructures.


[User] Please transcribe this text

[Assistant] Are missing or is that the wrong answer to the problem?


[User] Please transcribe this text

[Assistant] Among other kinds of actors in this ecology, like people outside, workers, other interested allies.


[User] Please transcribe this text

[Assistant] Kind of be, accompany migrants crossing the borders from Mexico to California. And the app would map out where there's water that's been placed by activists who want to support these, these migrants. And it also would, you know, have bits of poetry you could read to kind of give you hope along the way.


[User] Please transcribe this text

[Assistant] Made his project into a huge issue, came after him, uhm, there were investigations.


[User] Please transcribe this text

[Assistant] Trying to assess how much research money went into that because it seemed like too political, uhm, too political an application and so,


[User] Please transcribe this text

[Assistant] You know, sitting in a pasture at a critical distance, but we are, we are responsible parties. We're accountable, uhm, and we have to make choices about which accountabilities are worth it, which, uhm,


[User] Please transcribe this text

[Assistant] Kind of which risks are worth it and which risks are not, you know? So Ricardo Dominguez's story has been really instructive for me in that


[User] Please transcribe this text

[Assistant] There are people who are in a more powerful position to...


[User] Please transcribe this text

[Assistant] If y'all think that it's worthwhile, I'll take the responsibility for designing the platform and for imagining the possibility that the platform might suggest such a thing, but uhm,


[User] Please transcribe this text

[Assistant] After that particular person who may have really powerful lawyers to go up against my university's really powerful lawyers. I think that's key actually when we talk about CHI research and the features we design and the features that we don't, we need to be talking about this institutional


[User] Please transcribe this text

[Assistant] But it's not only. So there's actually an Alt-Kai paper that's about that.


[User] Please transcribe this text

[Assistant] Then this kind of theory or this kind of mathematics should also translate to understanding why biological systems learn well.


[User] Please transcribe this text

[Assistant] Uh, we like networks that can do lots of diverse, uh, that work well on lots of diverse data and to model all this variability in the data, we need to have a lot of parameters.


[User] Please transcribe this text

[Assistant] Uh, by all intents and purposes, non-convex optimization problems, which is what training in neural network amounts to.


[User] Please transcribe this text

[Assistant] So it looks a little bit like the Grand Canyon. Uh, no matter where you are standing, there is the river below you. Doesn't matter which part of the river you fall in. It's the same river.


[User] Please transcribe this text

[Assistant] Uh, the second question, uh, uh, is, uh, uh, in high school, everyone told you that, uh, if, uh, you have 10 data points, you shouldn't be fitting a polynomial of degree 15 to the center data points.


[User] Please transcribe this text

[Assistant] Because it can over fit and it can be very wiggly in parts of the domain where you do not have enough data. In deep learning we love doing this. We don't even seem to get hurt by doing this.


[User] Please transcribe this text

[Assistant] So something must be easy about the problem that you are trying to solve. Otherwise, this wouldn't work.


[User] Please transcribe this text

[Assistant] So this is the kind of questions that I hope to shed light on in this talk. Uh, but maybe before we go to the answers, uh, it behooves us to ask why do we care?


[User] Please transcribe this text

[Assistant] Uh, if you ask the practitioners, they are not exactly waiting for answers to these questions. They are doing fine, way better than fine, without knowing anything about this. Uh, I'll give you two answers, two of my answers.


[User] Please transcribe this text

[Assistant] Uh, and the first one is simply pride, okay? Uh, we have been taught some stuff in textbooks. Uh, these neural networks clearly seem to go against those things, uh, at least at the face of it.


[User] Please transcribe this text

[Assistant] Uh, so are the textbooks wrong? Should we just burn them? Or uh, maybe we did not understand the textbooks as well as we think. Okay.


[User] Please transcribe this text

[Assistant] The second thing is perhaps a bit more important. Uh, I believe that the success of deep learning is telling us new things about the nature of data.


[User] Please transcribe this text

[Assistant] Uh, it's a little bit like building a new microscope. Over the last 10 years, 30 years, depending on how you count, we were trying very hard to build a good microscope.


[User] Please transcribe this text

[Assistant] Uh, now we have a great one. Uh, but the reason we were doing all this was to look at what is underneath the microscope. And so I would like to understand what natural data looks like.


[User] Please transcribe this text

[Assistant] Uh, and uhm, the fact that the networks are working well is just the first step of that process. Okay?


[User] Please transcribe this text

[Assistant] So over the years, uh, I'm not the only person asking these kinds of questions. Many other people have asked these questions before. Uh, and in our attempt to explain, uh, the efficacy of these networks, uh, we have attributed a lot of mystery and peculiarities to them.


[User] Please transcribe this text

[Assistant] Uh, and I'll give you two examples from my research. There's many others, uh, uh, in, in other papers. Uh, here's one.


[User] Please transcribe this text

[Assistant] So, a few years ago, we noticed that after training, typical neural networks end up in regions that are rather wide in the energy landscape.


[User] Please transcribe this text

[Assistant] Uh, which is this wide region. It doesn't make sense that you're finding this tiny part, which is very rare, with local descent algorithms.


[User] Please transcribe this text

[Assistant] Okay, and, and, and you do. So every time you train a network, you'll get a hessian that looks like this. Uh, we didn't know why it re- uh, happens like this, why, why it is so.


[User] Please transcribe this text

[Assistant] Gaussian kernel of bandwidth gamma that smooths the loss and this smoothing basically destroys all the regions that are not wired. It keeps the regions that are wider than gamma and then it makes them easier to find.


[User] Please transcribe this text

[Assistant] Uh, these algorithms also work very well. So they are about two to four times faster than SCD. Uh, still some of the fastest methods I know to train neural networks. So you can exploit this peculiarity. But the point is that you are ending up in regions like this even if they are somewhat rare.


[User] Please transcribe this text

[Assistant] And now when a physicist looks at this, they will call it a non-equilibrium stochastic process. This stochastic process doesn't have detailed balance because taking a trajectory that goes in one direction is not the same probability as the trajectory that comes exactly in the opposite direction with the same two points. Okay. And such things are somewhat exotic.


[User] Please transcribe this text

[Assistant] The most likely locations that you can find SGD at the end of training are not even the critical points of the training loss.


[User] Please transcribe this text

[Assistant] Okay, so before this, we were wondering whether or not you find global minima in nonconvex urgent landscape. This result says that you don't even find local minima. Or you can happily converse to regions around local minima. Okay, so with some, it seems a little bit spooky.


[User] Please transcribe this text

[Assistant] Uh, you will also be able to show that, uh, the networks, uh, the weights of the network can evolve in subspaces arbitrarily. They don't have to converse. They don't have to stop so long as the gradient of some function phi is zero. It's not very important at this point what this function is, uh, but this is peculiarity number two.


[User] Please transcribe this text

[Assistant] You have a very pathological noise in the way we are computing gradients. Uh, and that gives us a pretty exotic phenomena even by the standards of physical systems. It's an artificial system. Okay?


[User] Please transcribe this text

[Assistant] And there are many other examples. So, uh, what I want to convince you of is, uh,


[User] Please transcribe this text

[Assistant] Over the years, there are many such versions. So for instance, uh, uh, some people might say that if you want to understand neural networks, we should really understand what overparameterization is, uh, because they seem to be, they're distinct from the other machine learning models we know in the fact that they have so many parameters.


[User] Please transcribe this text

[Assistant] I don't know, if you think a little bit like a first year undergraduate student, I have many parameters. I do not have enough information to constrain all these parameters. So why the heck should they matter? The ones that I do not constrain.


[User] Please transcribe this text

[Assistant] Uh, overparameterization cannot be the only game in town to explaining why these networks work, because I'm not even constraining all the other parameters.


[User] Please transcribe this text

[Assistant] Uh, if you ask some other people, they'll say, oh, inductive bias is the reason why these networks are special. Well, inductive bias was the reason why convolutional networks were so successful in 2014, 2013. Uh, transformers are also exactly as successful, if not better.


[User] Please transcribe this text

[Assistant] So it cannot be that inductive bias is again the only theory that explains why the networks work. And you can kind of go around the room and then, uh, lay blame to gaps in arguments of very many different attempts to understand this. Uh, what I want to convince you in this talk is that we are missing the elephant in the room.


[User] Please transcribe this text

[Assistant] The data that we train these networks on is kind of special.


[User] Please transcribe this text

[Assistant] And that is really the secret of all these peculiarities or that is really the reason for all these mysterious properties. And if you were to understand the data a bit better, we can explain them. Okay?


[User] Please transcribe this text

[Assistant] Uh, so first I'll tell you a result that we presented at ICML last year. Uh, it's a paper called Does the data induce capacity control in deep learning?


[User] Please transcribe this text

[Assistant] And what you notice immediately is that the ratio of the largest eigenvalue which I have normalized to 1 here and the smallest one is 10 raised to 8.


[User] Please transcribe this text

[Assistant] This is a gigantically low dimensional data set. If you think of a point cloud of the images, this is an ellipse that subsumes this point cloud and the longest axis of the ellipse is 10 to the 8 times longer than the smallest axis.


[User] Please transcribe this text

[Assistant] In computer vision, we've always believed that inputs, typical images, lie on low dimensional manifolds.


[User] Please transcribe this text

[Assistant] Uh, it is not exactly low dimensional because there is also this small eigenvalues, but it is effectively low dimensional. About 100, 150 dimensions are taking most of the, uh, bulk of the variance of this one cloud.


[User] Please transcribe this text

[Assistant] Uh, this is a picture from Plato's theory of forms. So you can imagine that along the long axis of the ellipse, uh, on one side lie all the pure cats, on the other side lie all the pure dogs, and then the other axis, the longitudinal, uh, directions consist of aberrations.


[User] Please transcribe this text

[Assistant] Uh, blue dog, there is not very many of them in the world, would also lie along the small icon.


[User] Please transcribe this text

[Assistant] Uh, this is just a name and here is why we named it so. Uh, we showed that if the input data looks like this, if the input data decays very sharply at the beginning and then is linear on a log scale, then essentially any quantity that you calculate inside the network,


[User] Please transcribe this text

[Assistant] Be it the Hessian, which is the curvature of the loss, we just saw it. Uh, the Fisher information matrix, which is the curvature of the output, we also talked about it.


[User] Please transcribe this text

[Assistant] The reason why we call this sloppy is as follows.


[User] Please transcribe this text

[Assistant] Think of an eigenvector here with a very small eigenvalue. It is corresponding to a direction in the weight space where I am allowed to perturb the weights by seven or eight orders of magnitude before I see any appreciable change in the output of the function.


[User] Please transcribe this text

[Assistant] Okay, so this is gigantical, really stark, big redundancy in how the network uses all its weights. This is a wide residual network with all bells and whistles, about 3 million weights.


[User] Please transcribe this text

[Assistant] Uh, network is using only about 150 or 200 or so parameters to actually make the predictions. All the others are decorations of the accuracy. Your 95% accuracy is coming from the head of the spectrum, and then if you want the remaining five short, you have to dig all the way deep into the tail. The tail is also very long, and it has a very bad aspect ratio. Okay?


[User] Please transcribe this text

[Assistant] The reason this is important is because you can imagine that if noise were to come and change this part of the spectrum, you would see zero TT radiation.


[User] Please transcribe this text

[Assistant] If data looks like this, if the task looks like this, then nothing can overfit.


[User] Please transcribe this text

[Assistant] Okay, so in a sense, this decay is the right level at which we are allowed to solve these problems in a computationally reasonable way and yet solve interesting enough problems that we care about. Okay.


[User] Please transcribe this text

[Assistant] Just a quick picture of the mathematics. We would really like to show that sloppy inputs, so the blue line being shaped in this peculiar way, lead to sloppy Fisher information or sloppy Hessian.


[User] Please transcribe this text

[Assistant] We can't quite show this entire sentence as is, so that is why I put a little tilde here. We can show it in parts.


[User] Please transcribe this text

[Assistant] So we can show that the trace of the Fisher and the trace of the Hessian is upper bounded by the trace of the input correlation matrix. The input correlation matrix is exactly whose eigenvalues I was plotting, the blue line. And then there is a bunch of terms that depend on the weights.


[User] Please transcribe this text

[Assistant] Now this is a rather simple inequality to show. The way to interpret this is as follows. So imagine that I have a network, uh, where I force the weights to lie within some L2 ball and so I can uniformly bound this entire term.


[User] Please transcribe this text

[Assistant] The left hand side grows more and more terms, so at least that tells you that the eigenvalues of the Fisher of the Hessian have to decay. Doesn't tell us that they have to decay exponentially, but at least they got to decay as 1 over k or so.


[User] Please transcribe this text

[Assistant] Again, not a very difficult thing to show a short calculation, but this tells you that if the activations are sloppy,


[User] Please transcribe this text

[Assistant] So, uh, it's very difficult to go directly from the inputs all the way to the fissure because you cannot get very general bounds on the activations as you go up the layer. Uh, for infinitely wide networks or for kernel machines, all of these things can be done pretty easily. You can show that sloppy inputs gives us sloppy fissures. Okay?


[User] Please transcribe this text

[Assistant] So there is some insight into mathematics. What I want to tell you next about is that all the stuff that we've talked about, sloppy inputs and sloppy fissures, doesn't seem to be very specific to neural networks. Many, many models in nature are sloppy.


[User] Please transcribe this text

[Assistant] Uh, this is, uh, you can think of it as a catalyst for the growth of cells, uh, PC-12 cells in particular. And, uh, people have noticed that, uh, for instance, there is a protein called EGF, epidermal growth factor. If EGF is large, then ERK shows spikes.


[User] Please transcribe this text

[Assistant] If ERK shows spikes, then the cells increase in numbers. They all remain the same, their function is the same, but they increase in numbers. If the neural growth factor is large, if this protein has a large concentration, then the cells, uh, uh, differentiate in their functionality.


[User] Please transcribe this text

[Assistant] The output of this system is the kinase, is the catalyst. The input to this system or the parameters of this system are the concentration of proteins that take play a role in this or the kinetic rate constants of all these reactions, whatever you pick.


[User] Please transcribe this text

[Assistant] We are allowed to calculate the sensitivity of the output with respect to perturbations of the parameters and these are again the eigenvalues of the Fischer information matrix or Jacobian if you want to think about that.


[User] Please transcribe this text

[Assistant] When a systems biologist sees these kinds of eigenvalues, they're happy because they'll say, of course we know that the output is insensitive to many, many changes in the parameters because that way your body can make sure that functions remain intact even if the environment changes a tiny bit.


[User] Please transcribe this text

[Assistant] So this makes sense, uh, in, in a, in a, in a post-hoc way. Uh, what is, uh, there is a pretty cool question that I believe, uh, uh, we are trying to solve right now is, uh, why did this circuit have to become so?


[User] Please transcribe this text

[Assistant] Uh, and the basic theory or the basic line of attack is, uh, uh, when a system wants to become resilient to changes of the environment, then it has to become sloppy to preserve its function. Otherwise it would be too sensitive to changes in the environment.


[User] Please transcribe this text

[Assistant] So, systems in nature are sloppy. Many, many other systems are sloppy. It doesn't have to be a natural system. An Ising model is a pretty fake model that physicists use to understand disorder in materials. You can think of it as a bunch of spins. You and your neighbor like to take the same value in the sense that both of you point up.


[User] Please transcribe this text

[Assistant] If the coupling constant between these spins is positive, if the coupling constant is negative, then the spins are pointing in opposite directions.


[User] Please transcribe this text

[Assistant] Uh, now in, in, in statistical physics, people have a pretty sophisticated understanding of why the Fisher-Infra-Fisher Matrix, in this case, it would be the sensitivity of the probability distribution of the spins to the coupling constants.


[User] Please transcribe this text

[Assistant] Uh, some microscopic quantities that emerge because they don't depend in sensitive ways on all the disorder in the system. And that is why, uh, for, for inanimate matter or for these kinds of systems, we know pretty nice way for why the system has to be sloppy.


[User] Please transcribe this text

[Assistant] It is, it is more of a matter of asking a different question. If you wanted to understand the magnetization, which is the average value that the spins take, uh, then it is a sloppy model. If you wanted to understand the actual value of the spins, then it is not a sloppy model.


[User] Please transcribe this text

[Assistant] So the most important thing to appreciate from all this commentary is that sloppiness is a property of the trained model.


[User] Please transcribe this text

[Assistant] But it is also a property of the question that you're answering with a trained model. If you want to know the locations of molecules in this room, then you would have a very complicated model and it would not show this simplicity. But if you want to have a cat, then it doesn't depend on all the pixels in the image, and then you're talking about sloppy models. Okay?


[User] Please transcribe this text

[Assistant] Uh, let me show you one example. So, uh, one application. So I'll talk about what I call PAC-based bounds. PAC stands for Probably Approximately Correct.


[User] Please transcribe this text

[Assistant] Uh, learning theory people will write inequalities of this kind. So they'll say take a probability distribution q on the hypothesis space or on the weight space if you will. Uh, little e of q is the probability that you make a mistake on a test datum, uh, for a hypothesis drawn, uh, from random or from the distribution q.


[User] Please transcribe this text

[Assistant] Uh, something smaller than one. Uh, e hat of q is the probability that you make a mistake on the training data.


[User] Please transcribe this text

[Assistant] Okay, and then they'll say the probability of making a mistake on the test is less than the probability of making a mistake on the training set plus some complexity term and it's not very important what p is at this point, uh, but the way to interpret these kinds of inequalities is if q puts its probability mass on many, many different hypotheses,


[User] Please transcribe this text

[Assistant] Uh, then you need requisitely large amounts of samples to distinguish between all these hypotheses. If you put this much on many of them, then I cannot possibly pick one hypothesis with few samples, okay?


[User] Please transcribe this text

[Assistant] And this is how people will think of them. And in this form it is about 30 years old, maybe more.


[User] Please transcribe this text

[Assistant] Uh, what has recently been noticed is that when we plug in numbers into these inequalities, things don't quite work. Uh, so a typical neural network will have basically zero training error because they're interpolating the data and so this term is zero.


[User] Please transcribe this text

[Assistant] The test error, let's say it is 5%, so this would be 0.05, but the complexity term is much larger than 1. It will usually be about 100 even for MNIST. And so, you don't really need a theory, or certainly a theorem, uh, to say that the probability of making a mistake is less than 100.


[User] Please transcribe this text

[Assistant] It's obvious, right? And so this is why people have been very anxious about this and they call such bounds vacuous. They're vacuous not because we did anything wrong while deriving this equation, this inequality, but we don't know. It's because neural networks have a large number of parameters. That is why this term is very large.


[User] Please transcribe this text

[Assistant] What we showed in this paper is that we can give an analytical non-vacuous bound on the generalized error of general neural networks simply by the, by an assumption that says that the inputs are sloppy.


[User] Please transcribe this text

[Assistant] This kind of a test error is stochastic test error of let's say 2%, 3% or so. And in the paper you can also develop some numerical techniques to get basically pretty tight inequalities on the generation error.