[User] Please transcribe this text

[Assistant] That is a story about the enlightenment. That is a story about technologies of the West. That is a story about a particular set of actors who operate in the post-enlightenment


[User] Please transcribe this text

[Assistant] What if you went and looked, if you could, at the same technologies in other places?


[User] Please transcribe this text

[Assistant] Would they create the same moments of anxiety?


[User] Please transcribe this text

[Assistant] There's a couple of places you can go, right? You can go to some of the principal scientific and engineering documents in Islam. You can go look at the Book of Mechanical Wonders and the Book of Ingenious Mechanical Wonders written in 820 and 1206 AD respectively. These are books that encode a series of mechanical objects.


[User] Please transcribe this text

[Assistant] So think of them as pre-clock parts, but a whole series of objects that are designed to do certain kinds of work.


[User] Please transcribe this text

[Assistant] Uh, these are pictures of water clocks. So for time telling, they have different things that happens rather than bells striking its birds that sing when the movement of the water happens across a series of objects.


[User] Please transcribe this text

[Assistant] Sitting in these books are also a whole series of mechanical and mechanized objects to do things. But none of them are about Vaucassin's notion of simulacrum. They're not about the simulitude. They're not about making objects.


[User] Please transcribe this text

[Assistant] To do the things, effectively making replicas of things. They're not about making mechanical ducks that do duck things. There is a mechanized pellet, a mechanized peacock in here, but it doesn't do anything peacocks do because when you turn it upside down water pours out of its mouth and that does not happen with peacocks.


[User] Please transcribe this text

[Assistant] But it is something, an object where water pours out of its mouth that's hugely important for Salah so that you can have your appropriate ablution so you can wash your hands and feet before you pray. And this object means that the water comes out of a beautiful thing, water never touches the ground, the water comes out of a bird's mouth, it is a mechanical object designed


[User] Please transcribe this text

[Assistant] To be part of a particular set of religious practices and it is not considered to be something that is faking life or about to replace us.


[User] Please transcribe this text

[Assistant] And there's a very different scientific tradition that builds on top of this. But what if we wanted to take exactly the same technologies, the same ones that focus on users, where would that get you? Well, fascinatingly enough, clocks got around.


[User] Please transcribe this text

[Assistant] As part of a whole, right, we'd like to, you know, give you things you're gonna find exciting. So a bunch of clocks that kind of went from Switzerland to Peking. Uhm, where they were regarded with somewhat of wonder because they made bonging noises if you wound them up. So mostly they were wound so that they chimed, they were not wound so that they kept time.


[User] Please transcribe this text

[Assistant] So the clocks of China ended up in Tokyo, where they were promptly taken apart and reverse engineered.


[User] Please transcribe this text

[Assistant] And there is one particular inventor, innovator, scientist in the Edo period who becomes really interested in this mechanization stuff and what you can do with it.


[User] Please transcribe this text

[Assistant] Uh, these objects have lots of names. The piece of the set of them I'm most familiar with are Karakuri. So these things here, which are sometimes called automatons, they're sometimes called little robots. This one is a teacup Karakuri. It is effectively a teacup-bearing automaton. It's little. It's about this big. You put tea in its teacup, and it runs across the coffee tea table. When you take the teacup up, it runs backwards. It's excellent. It's beautiful. It is an object of exquisite grace. It is a beautiful thing.


[User] Please transcribe this text

[Assistant] And it sits inside of a really important set of rituals amongst the Chinese aristocracy of the tea ceremony, which is a ritual about grace and relationships and a certain set of forms of aesthetic pleasure. So here is an object, same machinery.


[User] Please transcribe this text

[Assistant] Full of all the instructions for how to make it. Uhm, and the, you know, leading roboticist in Japan got a hold of this book in the 1960s and it took him 20 years to work out how to remake these things.


[User] Please transcribe this text

[Assistant] And you see a very different landscape for technology in some ways, same technology trajectory. So after the Edo period, you go through a rapid industrialization period, looms, the mechanizing of silk production happens in Japan, the same technology turns up, it is accompanied with a completely different discourse, there is no Luddite moment.


[User] Please transcribe this text

[Assistant] By the time you get to the 20th century you see an incredible proliferation of technologies and in some ways that are genuinely fascinating.


[User] Please transcribe this text

[Assistant] I found myself in Tokyo 18 months ago. I'm with some of my colleagues and I see this sign out of the corner of my eye and I get out of a moving vehicle.


[User] Please transcribe this text

[Assistant] The joys of being an anthropologist every now and again, one is compelled to kind of do things that are possibly not helpful. Because this is just, I'm like, what the hell is this? And like, what are those robots doing to each other? There's so many questions.


[User] Please transcribe this text

[Assistant] So I'm out of the car, I take a picture of this thing and my colleagues, from Intel, kind of go, what are you doing? Get back in the car, baaah. I'm like, what does it say? Like, it says it's a robot zone. Okay.


[User] Please transcribe this text

[Assistant] What does it say in the bit I can't read? They said, oh, it says there's an autonomous robot zone and there are robots two meters in from the curb. Like what are the robots doing? Like they're two meters in from the curb. Like whose robots? They're autonomous robots. What are the robots doing? Well, they're being autonomous.


[User] Please transcribe this text

[Assistant] Coming one of those moments where you know that culturally speaking, you are not communicating. So I tried one more time, I'm like, about the robots. They're like, yes, that's the sign. They're autonomous. I'm like, who do they belong to? They're like, they're autonomous.


[User] Please transcribe this text

[Assistant] The robots are our friends. And then proceeded to unpack for me a whole history of...


[User] Please transcribe this text

[Assistant] Japanese television, Japanese political discourse, Japanese iconography, everything from Astro Boy to a whole series of pieces of anime and many other things that we see the glimpses of.


[User] Please transcribe this text

[Assistant] But technology here was being understood as part of the Japanese psyche, as part of the Japanese landscape, not as a part from it and not as a replacement to it.


[User] Please transcribe this text

[Assistant] When you go and push on, you know, the kind of primary writers in the Japanese tradition who are the roboticists, you know, the principal one talks about the fact that


[User] Please transcribe this text

[Assistant] Robots stand a better chance of achieving Buddhahood than people do because robots are capable of infinite patience.


[User] Please transcribe this text

[Assistant] And a perpetual state of grace, basically, in the Catholic sense. So there's a very different idea here about what it means to think about technology. Same technology, different set of cultural discourses.


[User] Please transcribe this text

[Assistant] So, how do you start to think about that together, right? Well, one of them is to realize that not all technologies produce fear even locally.


[User] Please transcribe this text

[Assistant] So my favorite American technology story of kind of stories of splendor and wonder is actually about the early introduction of electricity in the United States.


[User] Please transcribe this text

[Assistant] So, as many of you I'm sure know, when electricity is introduced in the US, first it starts on the coast in the big cities. People literally go to see electricity. So there were bus tours arranged from the Midwest so that you would go overnight to go to New York.


[User] Please transcribe this text

[Assistant] To see the lights on Broadway. That's what that expression is actually about. It's about going to see the lights.


[User] Please transcribe this text

[Assistant] Not the shows, but the lights. Cause like Broadway was lit up and that was kind of nifty. Like the Great White Way was all about this notion of visiting electricity cause you didn't have it in your hometown in the Midwest.


[User] Please transcribe this text

[Assistant] Extraordinary, right? The flicker of light in the darkness, that moment where suddenly...


[User] Please transcribe this text

[Assistant] And as electricity slowly marches off the coasts and into the sort of smaller towns, there was a moment in upstate New York in Niagara where the man who ran the local electricity plant had a surplus of electricity. There's a lot of stories of surplus in this, you know. He'd wired all the factories, he had leftover electricity, he wanted to convince the local population they could electrify their homes.


[User] Please transcribe this text

[Assistant] They had gas, they had windows, they had candles. So you're kind of like, really? You want to drill a hole in my wall to give me this uncertain thing and all it does is give me light.


[User] Please transcribe this text

[Assistant] So there was a lot of resistance on the domestic side to having their homes electrified because frankly they already had light and they mostly liked the light they had and the whole electrical light thing wasn't particularly compelling.


[User] Please transcribe this text

[Assistant] So for people who were selling electricity, they had to find a new way to connect the dots, right? So here this guy, he has a surplus of electricity. He decides that he needs to introduce the entire of the people who have money in his town to the wonders that are electricity. So he has parties in his house, as you do.


[User] Please transcribe this text

[Assistant] He takes the ground floor of his very large house and he lays track on the floor.


[User] Please transcribe this text

[Assistant] He puts light bulbs in their hands, and he runs a vault through them.


[User] Please transcribe this text

[Assistant] Now, electricity in the early days was only about 5 or 6 watts, so not really particularly painful.


[User] Please transcribe this text

[Assistant] So these girls would skate around the downstairs of this floor with light bulbs in their hand and a current running through them to show that electricity was safe and beautiful.


[User] Please transcribe this text

[Assistant] And light up like a Christmas tree, which is really quite speccy, so long before Christmas lights, she was her own Christmas tree.


[User] Please transcribe this text

[Assistant] And for an even longer period after that, the kind of wealthy households in London had a doorbell girl.


[User] Please transcribe this text

[Assistant] I don't know what to tell you, it really did happen. So it was this moment right where technology is about, you know, even in this particular place, a technology we almost take for granted, now it was about creating the spectacle. It was about a moment of wonder, it was about beauty. It was about something delightful.


[User] Please transcribe this text

[Assistant] Press a doorbell, doesn't just make a doorbell sound, there's like a woman who lights up standing right there. I mean, that's kind of nifty in it.


[User] Please transcribe this text

[Assistant] Gets Nobel Prizes, good. Uh, many other people think Radium's kind of nifty. Uh, it gets immediately named in the commercial press as Eternal Sunshine.


[User] Please transcribe this text

[Assistant] Because indeed it does glow. Uhm, many people try to commercialise it. So it comes out of medicine. So Mario Curie discovers radium. Radium moves into hospitals very quickly as a way of actually treating localised cancer. It then moves out of the hospitals into a whole series of other things. Uh, radium infused drinks.


[User] Please transcribe this text

[Assistant] I know, sunshine everywhere. And a whole series of other things, right? It becomes a piece of a series of musicals. So the musical Biff Baff Boff, who knew, was actually a musical all about radium. It had a thing in it called the radium dance. This is a grab from its program where the costumes would dripped, dipped in radium.


[User] Please transcribe this text

[Assistant] And then put on the players and stuck them on stage. We know this doesn't end well, right? Uhm, and truthfully the kind of fascination with radium comes to an abrupt end in about 1925, when the Radium Dial Factory in New Jersey, who had been creating watch faces, first for the military and then for the consuming public,


[User] Please transcribe this text

[Assistant] Those of you who are old enough to have a watch dial, there are numbers on the dial.


[User] Please transcribe this text

[Assistant] Mhm. So they used to take this bowl of radium infused material, stick the paint brush in it.


[User] Please transcribe this text

[Assistant] Pull the paint brush out, put the paint brush in their mouths, pull it through their lips to get a fine point, and then paint the dials on the watch.


[User] Please transcribe this text

[Assistant] Uhm, and the lawsuit that was taken on their behalves is actually the first time we have labor laws in the US that start to set a standard about reasonable exposure to things. So this is why, you know, photocopiers can't emit a certain amount of radiation. This became the test smart case that actually set in place a whole series of


[User] Please transcribe this text

[Assistant] Things that we enjoy today, right? But it also became one of these stories about how does something go from being wonderful to terrifying in less than five years.


[User] Please transcribe this text

[Assistant] So we know there's this constant kind of cycling, right, of the things that are splendid also become the things that are very scary. So where does that leave you, right?


[User] Please transcribe this text

[Assistant] Well for me, I think it's about a couple of things. I actually think that there is a desire.


[User] Please transcribe this text

[Assistant] For magic. I think there's something inherently powerful about that moment.


[User] Please transcribe this text

[Assistant] The first time a television flickers and you see a story, the first time you went to the movies, the first time you see a photo, first time the radio came on.


[User] Please transcribe this text

[Assistant] First time the telegraph arrived, the first time you heard a voice on a telephone, the first time we had a touch screen, the first time we got money out of an ATM, those were all little tiny moments of wonder. Some of them lasted longer than others.


[User] Please transcribe this text

[Assistant] But there's something really powerful about those moments of it being wonderful.


[User] Please transcribe this text

[Assistant] And we spend all of our time thinking about how to design things practically, whereas designing for wonder, I actually think, and magic, hugely important.


[User] Please transcribe this text

[Assistant] He says that any sufficiently advanced technology should be indistinguishable from magic. Of course he doesn't have it quite right here, right? Like any sufficiently advanced technology should deliver magic.


[User] Please transcribe this text

[Assistant] It shouldn't be indistinguishable from it. What it ought to be aiming at is making magic possible. That moment of kind of...


[User] Please transcribe this text

[Assistant] It's not always about being practical and pragmatic and solving every problem. Sometimes there is something genuinely beautiful.


[User] Please transcribe this text

[Assistant] About our capacity to deliver magic and to deliver wonder.


[User] Please transcribe this text

[Assistant] And I know because I spend my time in this field, we often spend a lot of time thinking about how are we going to get things to be more real, more lifelike, more indistinguishable from ourselves or from the real. And I actually wonder if that's not the right idea.


[User] Please transcribe this text

[Assistant] Uh, you know, a famous Japanese roboticist says that the challenge with robotics is that there is this moment when they go from being very robot-like to being almost human but not quite human and that almostness makes us really uncomfortable. He calls that the uncanny valley.


[User] Please transcribe this text

[Assistant] To magic, to wonder to the importance of those things is actually to linger just a little bit in places like the uncanny valley, in places where it is possible to imagine the light.


[User] Please transcribe this text

[Assistant] In places where what we might be engineering for isn't always efficiency and practicality and high levels of usability but also for a small moment of


[User] Please transcribe this text

[Assistant] Efficient of delight, of a little squeal of glee. I actually think those aren't bad things to imagine should also be the targets of our work, right?


[User] Please transcribe this text

[Assistant] Because for me, I do believe where I started this talk, right, that part of the function we all have.


[User] Please transcribe this text

[Assistant] Those of us who are teachers and in the industry, it is our job to think about those things. Those of you one day, you'll get to design these things. It's not just about what we design, it's about the stories we tell about what we design. And it's the stories we tell about what those designs will be. And it's the worlds we shape.


[User] Please transcribe this text

[Assistant] Through our actions, through our words, through our visioning of the future, that are hugely important.


[User] Please transcribe this text

[Assistant] And for me there's something really powerful in starting to imagine that it's not just about the work we do, it's about the stories we tell.


[User] Please transcribe this text

[Assistant] And it would be, for me, kind of amazing to think that some of those stories we should tell are also going to be about a little bit of magic and a little bit of wonder. A little bit of moment of Vaucassin's Duck.


[User] Please transcribe this text

[Assistant] Waddling across the stages of Europe. And of that moment of being kind of mesmerized by things. I actually don't think that's bad. I think there's something about our fascination with everything being explainable that makes us forget that sometimes what we desperately want as humans is a little bit of mystery and a little bit of magic.


[User] Please transcribe this text

[Assistant] So my charge to all of you as you go through your careers here is that you have to help me think about how we deliver just a little bit of magic and a little bit of mystery. So thank you.


[User] Please transcribe this text

[Assistant] And less visible. I think there's something about the casing of things that makes stuff more complicated. So, you know, when Frankenstein gets a skin, when Frankenstein's monster gets a skin and all the innards are encased, I think there are pieces of what makes that more scary than if it had just been bones, right? As it gets closer to


[User] Please transcribe this text

[Assistant] There is certainly fear to it, but what's fascinating to me is that the fear precedes the things, the levels of complexity, right? When you talk about the early kind of appearance of clock towers and of watches, there's already a discourse of anxiety, not yet full blown fear, but there's a discourse of anxiety about what it means, I mean people really weren't happy about clock towers either.


[User] Please transcribe this text

[Assistant] There was a whole thing about well, but how do we know that's women? And then the arguments about time, you know, don't actually get resolved to the international time zone conference.


[User] Please transcribe this text

[Assistant] About who gets to declare what has a suffix and what doesn't. So I think there's something about, it's not just about levels of complexity, it's about


[User] Please transcribe this text

[Assistant] My kind of, my working theory here is that as you start to...


[User] Please transcribe this text

[Assistant] Impact our relationships to time, space and each other. Those are moments when fear appears. And when you have something that does all three, lots of fear appears.


[User] Please transcribe this text

[Assistant] Rehearsing a whole set of other things, right? It is referring back to things that we're not even conscious of, right? I mean, in its replacement of humans, it is playing on the Luddite trope. In its, you know, bodiness, it is replaying on, you know, Frankenstein. It's about, you know, all this. I mean, it's, it's ability to create fear or to be an object of that.


[User] Please transcribe this text

[Assistant] Or to be put up as an object of that, those all being different levels, right, is because it is playing on all those earlier tropes too, right, as those don't go away.


[User] Please transcribe this text

[Assistant] So you know, the Terminator may have its own anxiety that it creates, but partly it works because the implicit reference.


[User] Please transcribe this text

[Assistant] Uh, change the way we thought about stories, but it also changed how we talked to each other, what the medium of our interactions was. It created a whole set of kind of practices and it did have that moment to it. I think, you know, the telephone is certainly part of all of that. Electricity once it got past light bulbs.


[User] Please transcribe this text

[Assistant] Is very much a part of how did you create other possibilities for human interaction, right? And you know, truthfully, you know, some of the stuff around light is really interesting that way about, you know, the possibilities it created of being able to socialize after dark, of being able to think differently about food and food preparation. But, I mean, actually for me, the stuff about whimsy,


[User] Please transcribe this text

[Assistant] Which is partly, you know, the peacock is for me in that tradition. That one's a harder one to come by. We've sort of, I mean particularly I think...


[User] Please transcribe this text

[Assistant] The most interesting experiments I've seen with that either come out of Japan, Korea.


[User] Please transcribe this text

[Assistant] Uh, a little bit out of places like the Royal College of Art in London where there's an ability to think that not all technology needs to be about problem solving. Sometimes it's just about fun.


[User] Please transcribe this text

[Assistant] And you know, truthfully think about, you know, some of the most persistent technologies, services and applications. And you know, Angry Birds is more popular than,


[User] Please transcribe this text

[Assistant] The American Tax Office website. Now, one of those is practical, one of those is clearly not. You know, Candy Crush the same, right? I mean, those are about...


[User] Please transcribe this text

[Assistant] Pleasure activities, not just about practical activities. So there's something about, you know, we know how to design for pleasure in the content space.


[User] Please transcribe this text

[Assistant] We know how to design for wonder and whimsy in some of the industrial design space. I mean, there were certainly, you know, moments of visceral pleasure that people got from Jonathan Ive's designs.


[User] Please transcribe this text

[Assistant] Between the first generation of the iPod and the iPhone and the tablet, right, there were clearly moments of...


[User] Please transcribe this text

[Assistant] Aesthetic pleasure. Not quite the same as wonder or magic, but you know, there was a kind of a pleasurable-ness to that. So I think we know how to think about that, at least in some ways in terms of industrial design and a little bit in the application space. I think it's interesting to imagine what does that also look like in the technology space, in the interaction space, and rather than thinking that everything is about practicalness, when for me there's sort of a piece about


[User] Please transcribe this text

[Assistant] Right, there's something about the fact that we've come out of a period of time in computational devices where the devices insist on telling you everything.


[User] Please transcribe this text

[Assistant] We're just like, why? Stop telling me you're connecting to the network. Okay, I just want it to happen. So there's something about, you know, how do we imagine a different set of possibilities there that for me is sort of interesting. But I also suspect it's one where we need to cast a net much more broadly.


[User] Please transcribe this text

[Assistant] I mean, I think about what DoCoMo was doing in Tokyo 12 years ago, some of what was happening on SciWorld 10 years ago.


[User] Please transcribe this text

[Assistant] There were some really interesting things going on in those spaces that aren't necessarily what happened on AT&T's network or Facebook.


[User] Please transcribe this text

[Assistant] Yes, you who knew the answers to every slide before I finished them. Yes.


[User] Please transcribe this text

[Assistant] So, you know, every new technology brings with it the same set of rhetorical anxieties. Uhm, so.


[User] Please transcribe this text

[Assistant] And the language is almost identical. There's some lovely stuff that was done when Congress was considering legislation to


[User] Please transcribe this text

[Assistant] Mandate this particular infrastructure. It's going to make women and children incredibly vulnerable to predators.


[User] Please transcribe this text

[Assistant] And then it would be bad if every household had this technology because they'd be vulnerable. Now that technology is electricity. And the argument was if houses had electricity, the light would be on so predators could see women and children at night and then attack them. You're like, really?


[User] Please transcribe this text

[Assistant] Like, you know, but you've got this incredible kind of work put on it. I think what's fascinating is generationally irrespective, while you may have grown up with a whole set of technologies, the next disruptive technology will provoke a new


[User] Please transcribe this text

[Assistant] A not dissimilar set of anxieties because those anxieties are cultural logics, they are not generational logics. What the technology is that provokes the anxiety will be completely different.


[User] Please transcribe this text

[Assistant] I can guarantee you it will be much more about things like always on, always connected, always sensing camera surveillance technology, not about electricity.


[User] Please transcribe this text

[Assistant] But I can guarantee you there'll be a similar set of conversational and rhetorical responses to it.


[User] Please transcribe this text

[Assistant] The next round of technological anxieties. Uh, and some of it you're right is about who has the data, but some of it's about what does it mean to have surveilling objects.


[User] Please transcribe this text

[Assistant] The kind of, you know, notions of what does it mean to start thinking about our platforms as being always on and always sensing. So my kind of, my working hypothesis here is that we are going through a moment where the fear is moved to a particular place, but I don't think it's gone from the devices entirely. Right? I'll see.


[User] Please transcribe this text

[Assistant] Bad, back to a kind of a, like a steady state, right.


[User] Please transcribe this text

[Assistant] Hi everyone and welcome to this community event. I'm Sylvain, a research engineer at Hugging Face and I've led the team that developed the Hugging Face course. I'm very excited to release part two of the course today with content that should help you tackle any NLP task.


[User] Please transcribe this text

[Assistant] You can check it out at hookingphase.co slash course and it's going to look like this.


[User] Please transcribe this text

[Assistant] Thomas Wolf is our very own co-founder and chief science officer at Hugging Face. On top of creating many popular libraries like transformers and datasets, he is also the initiator and senior chair of the largest research collaboration that has ever existed in artificial intelligence called Big Science.


[User] Please transcribe this text

[Assistant] And today you will talk to us about transfer learning and the birth of the Transformers library. So Tom, ready when you are.


[User] Please transcribe this text

[Assistant] So we have, we have questions that could go here, uh, Tom. Is there a systematic way to estimate how many annotated samples, uh, to train, to fine tune an existing model, for instance, on a, on an R task?


[User] Please transcribe this text

[Assistant] Uh, one is, uh, you talked about a lot about transformers, but what about using graph neural networks in NLP tasks? Graph structures could capture data with complex structures and relationships, and GNN provides an opportunity to study on model complex data representation for NLP tasks.


[User] Please transcribe this text

[Assistant] That makes sense. Another question, you hinted a little bit about that at the end, but a more fun question is, how did the name hugging face and the youth come about?


[User] Please transcribe this text

[Assistant] And the last fun question that people were interested in is more personal. What do you use to build train models? Notebooks, VS Code, Vim, other?


[User] Please transcribe this text

[Assistant] And I think we're ready for our next picker, Lewis, if you want to interview him.


[User] Please transcribe this text

[Assistant] So, I'm going to give you a quick overview of our part two of the course, while we wait for our next speaker to be fully ready. Let me just share my screen.


[User] Please transcribe this text

[Assistant] The basics of how to use a transformer model, especially a pre-trained model and fine-tune it on a text classification task before sharing it with the community using our tools to put it on the model hub. And part two of the course main goal is to show you how to tackle any NLP tasks.


[User] Please transcribe this text

[Assistant] So for this, we started by a bit longer dive into the Datasets library and the Tokenizers library because they are very helpful. They have lots of useful tools to tackle some of the most challenging tasks like, for instance, question answering.


[User] Please transcribe this text

[Assistant] So the data set, Chapter 5 goes over the data set library and shows you how to load the data set, especially if it isn't from the app that you have your own data, which is going to be super useful if you want to tackle your own problem.


[User] Please transcribe this text

[Assistant] And then, uh, it's showing a little bit, uh, how to preprocess that data, uh, so to select a few random samples, how to preprocess it, uh, and how to split, uh, one dataset into a training and validation set.


[User] Please transcribe this text

[Assistant] Uh, it's showing exactly how datasets work behind the scenes, uh, to, to make sure that you don't, uh, go out of RAM if you have a huge dataset, uh, using Apache Arrow behind the scenes to, to make sure that, uh, your dataset is on disk and not really in your, in your RAM.


[User] Please transcribe this text

[Assistant] And then it explains, it will explain to you how to create your own data set and upload it to the hub. And there is a last section about how to use, how to do semantic search and using embeddings with F-A-I-S-S. I'm not sure if I'm supposed to spell it or to say it, Lewis.


[User] Please transcribe this text

[Assistant] So this section will explain to you how to train a new tokenizer. For instance, if you, you have the GPT model in English, but you want to train a GPT model in your own language, uh, so you will need to train a new tokenizer for that, but you want to use the same algorithm as for GPT.


[User] Please transcribe this text

[Assistant] So there is one single method to learn and this chapter tells you all about it, uh, that helps you train your new tokenizer, uh, like GPT or like BERT or like T5.


[User] Please transcribe this text

[Assistant] Uh, then you have a section about the fast tokenizers, uh, special powers. Uh, the first thing is to evaluate why they are faster and how much faster they are compared to slow tokenizers. And then, uh, it goes in depth into the features that we added.


[User] Please transcribe this text

[Assistant] In the tokenizers library that are very helpful, uh, to go back from a token to the span of text that generated it, which is super useful for token classification or question answering.


[User] Please transcribe this text

[Assistant] And so that's why, uh, it's then, uh, you, we, we do then dive into the, inside the token classification pipeline and inside the question answering pipeline to explain exactly what happens, uh, between the model predictions and the answer that those pipelines give you.


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[Assistant] And then we, we make a deep dive into what happens inside the tokenizer. So the first step on the normalization, uh, which is like me cleaning the text and for instance, removing the accents, uh, for, for that models or lower kissing everything. If you have a model that is used to that.


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[Assistant] And then there is pre-tokenization, which is splitting a text into words before the model of the tokenizer.


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[Assistant] So the first, uh, this section goes over normalization, pre-tokenization, and then you have three different algorithms that are supported by the tokenizer library, which are BPE, WordPiece, and Unigram, and they all get their own sections.


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[Assistant] To explain to you how they work to tokenize inputs, how they were trained.


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[Assistant] How to build a tokenizer block back block using the tokenizer library and then how to use it in the transformers library to tokenize all your text before training or fine-tuning a model with your new tokenizer.


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[Assistant] And chapter 7 is really the big chapter of this part 2. So as I said, the goal was to teach you how to tackle any NLP task, and this is the chapter that delivers that. So it begins with token classification, which is a bit like text classification, but you have to predict one label for each token inside your text.


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[Assistant] Then, uh, we go over mass language modeling, we fine-tuning, uh, a birth model of the MDB dataset, and we can see how, uh, a model


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[Assistant] That was, uh, used, uh, that was very generic, uh, so this is a great mask, uh, it would predict some generic terms and then after being fine-tuned in the MDB dataset, uh, it will predict movie or film to, to feel the sentence because it's been used to a dataset full of, uh, movie terms.


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[Assistant] The next text is translation, how to translate the text into another language. Then there is a whole section about summarization, which is


[User] Please transcribe this text

[Assistant] Taking a very long text and extracting a very small summary, so like very very short summary for that section. To train the model fast, we decided to train an extreme summarizer of movie reviews.


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[Assistant] Then there is a section about causal language modeling which is also going to show you how to train a numeral from scratch.


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[Assistant] And lastly, uh, a section about question answering and how to pre-process and post-process the data, uh, to create a model that is capable of finding, uh, answer to questions inside a context, like this one, uh, which we try to answer this question from the, from the,


[User] Please transcribe this text

[Assistant] And then in chapter 8, we did a little bit about how to tackle the most common NLP tasks. Sorry, how to ask for help when you get a problem.


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[Assistant] So it begins with what to do when you get an error, and in this chapter we teach you how to read the Python traceback, which can be very overwhelming at first if you don't know what to look for.


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[Assistant] Hopefully this will help you analyze your error and help you debug your training.


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[Assistant] Then we wrote a small section about how to ask for help on the forums, uh, which is to make sure that you include all the necessary information, uh, so that all the members of the community can help you very fast to solve your problem.


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[Assistant] We've then made a section that is more specific about debugging a machine learning training, whether if you're using the Trainer API in PyTorch or Keras with TensorFlow.


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[Assistant] And so it's going to explain to you what to look for, so the steps to look for, first debugging your data, then your model, then perhaps a backward pass as the optimizer step.


[User] Please transcribe this text

[Assistant] So that's it for a quick overview of that part two of the course. We hope you really like it. If you have questions about any of those parts, you can go on the forums or the course section.


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[Assistant] Uh, which we showed you before and in this course section, there is one topic for each of the chapter that we just created today. So don't hesitate to go there to ask any questions that you have about the course content. And I think we're ready for our next speaker. Lewis, what do you think?


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[Assistant] So our next speaker is going to be Margaret Mitchell. I'm going to introduce her in a moment, but you should go on this topic in the course category to ask any questions that you want. And if you find a question that you would really like to be asked,


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[Assistant] Let's go for instance in this topic. You can see that there is a little heart beneath each topic. So make sure that you like the question that you want to be really asked, because if we have too many questions and don't have time to ask all of them, we'll select the one that has the most likes.


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[Assistant] And so like I said, our next speaker is gonna be Margaret Mitchell. Hi Margaret.


[User] Please transcribe this text

[Assistant] She has published over 50 papers on natural language generation, assistive technology, computer vision, and higher AI ethics, and holds multiple patents in the areas of conservation generation and sentiment classification. And today she will talk to us about the values in machine learning development.


[User] Please transcribe this text

[Assistant] So let me put your screen in the stream and then we'll let you go and read your talk.


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[Assistant] We have a question that's very relevant, uh, on the data.


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[Assistant] And which is, how do we push for adaptation of more ethical datasets? And let's imagine that CC4 lay on, for example, in major conferences.


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[Assistant] So how do we process, how do we protect ourselves against machine learning systems designed primarily without ethics in mind? Because we are obviously, uh, algorithm being run out in the world right now. And, uh, yeah.


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[Assistant] Okay, lots of questions about de-biasing. I have another one here. While training the pre-trained model, is it possible to add a step including de-biasing algorithm? There are emerging algorithms today. Could this make some interesting bias disappear?


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[Assistant] Another question, let me scroll a little bit. Yeah, there might not be a magical solution, but what's the easiest way to determine whether a text generation model is not racist or sexist? And how do we solve this quickly without losing too much of training data?


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[Assistant] The bias in data is how could we balance the filtering of bias between freedom of speech and offensive bias consent?


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[Assistant] Definitely. And around like the, the compensating participant for creating the data, I have another question which is, is making it mandatory and forcing to create datasets where each participant is highly paid, going to put a lot of pressure on researchers from low resource institutions and researchers from low resources countries.


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[Assistant] And one last question which is a bit more personal is what does the regular day-to-day work look like for you?


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[Assistant] Yes, so we finished a bit in advance the previous talk, and, uh, Matt and Chen were in advance, so we could begin Veras in advance, but we're waiting for Veras.


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[Assistant] Uh, so that's a very good idea. Thanks, Lewis. So let me share my screen again.


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[Assistant] To show you how to go on the forums. So the idea is that we'll use the forum to coordinate a team project. And...


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[Assistant] But this is going to look like this, there is a small description of the project, there is a model suggestion to fine tune on that project, there is a suggestion of data set, how to scrub it from the web or how to use an existing one if it does.


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[Assistant] Uhm, it's kind of a demo of one model. So one that is very popular right now is AnimeCanv2. You may have seen some of it on social media. I won't be able to show you live what it does because I'm pretty sure there's a queue.


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[Assistant] The Amazon reviews demo space. So the goal of the event from Wednesday to Friday is to use the free compute from Amazon SageMaker to build, to fine tune a model on some data set and then finish with some kind of project like this.


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[Assistant] So, uh, this is a model that's gonna predict a review score from a customer review. So if I tap something like this is a great, uhm,


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[Assistant] The first topic that you will see in this category, since I have already read it, it's not appearing first for me anymore, but when you arrive on the forum, it's going to be first for you. This topic is going to explain all the rules of the community event, so you should


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[Assistant] Outputs, uh, proper results, and then every member of the team will get the certificate.


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[Assistant] So we've created a few ideas of projects in this category, and you can create a new project by creating a new topic yourself, just like the person project I was editing before.


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[Assistant] And then we recommend using fine-tuning for your projects, like not trying to pre-train a full model on a new language because that would require lots of data and lots of compute, which you may not have time to finish that before the deadline of next week. Fine-tuning an existing pre-train model is going to be way faster.


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[Assistant] And so Amazon is sponsoring the event Biofrying Free Compute from Wednesday to Friday using AWS SageMaker. You will learn how to use that tomorrow with the Hugging Face talks, but I'm more focused on how to do things than marginal talks around the landscape of Transformers.


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[Assistant] Uhm, so you will have to file a form, you just have to click here for the link to get access to a special AWS account. Don't worry if you don't hear from AWS before Wednesday because the account will only be created on that day.


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[Assistant] If you didn't register for the event in advance, you should see a button, I'm a member, but you should see a button somewhere here to ask to join that organization and you just have to click on it and it will accept you.


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[Assistant] And of course you're welcome to use your own GPUs and cloud providers as you prefer, but that's not going to be free. And then you can, as I said, you can live chat with your team members on our Discord.


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[Assistant] And then if you have any questions that's more general and could benefit the whole community, you should ask it on the main forums.


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[Assistant] So that it's more visible, but that's pretty much it for the rest of the event.


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[Assistant] Well, thank you. So Mark is a partner engineer at PyTorch, working on open source software production tools, including TorchServe and PyTorch Enterprise. And Mark was also an applied scientist and product manager at Graphcore before Uri.ai, Microsoft, and SSGPL.


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[Assistant] My passion is to make programming more fun and today Mark will explain to us how to train a model with PyTorch.


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[Assistant] If you want to share your screen, Matt, I can then post it. And then like before, you should go on our forums to ask your questions. And I've also put the link to the column notebook for Matt's talk there.


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[Assistant] Uh, we have a second live event tomorrow at the same time. It begins at 8 a.m. PST and it's gonna end around noon PST, uh, with the Hugging Face team more focused on giving you technical talks on how to fine-tune a Transformers model either with PyTorch or TensorFlow.


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[Assistant] Uhm, and then to see the comments I can look maybe on the right side of the screen.


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[Assistant] I would be really happy for people to ask questions as I go.


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[Assistant] Uhm, that said, I might get lost in what I'm doing and miss that someone is asking a question. Uh, so I'll try my best.


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[Assistant] Okay, so hello, uh, thank you for having me. Uhm, I recently joined Hugging Face.


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[Assistant] So by now the machine learning development pipeline is relatively familiar to people who work in AI. So first you collect data, then you train and test a model.


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[Assistant] In order for the model to output an eventual decision or answer and people see the output.


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[Assistant] But before we even begin the process, the data itself encodes a subset of human perspectives. So what people choose to talk about and how they talk about it based on their experience.


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[Assistant] So the idea that a data set can be unbiased, which is one of these sort of questions that comes up in the


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[Assistant] Data versus model debates, uh, it's actually both can be biased. Uhm, and saying that a data set can somehow be unbiased misunderstands what a data set is by definition.


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[Assistant] Uh, in the world ever, or in the universe ever, right? By definition, it is a slice of all of that. It's a subset. It's a snapshot.


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[Assistant] So that means that it is biased with respect to everything else in the universe ever, right? It's a very limited subset, specifically collected from the world, with all kinds of SKUs reflecting the way that people represent themselves, as well as SKUs in data collection and annotation.


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[Assistant] Uhm, and the way that people talk about the world.


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[Assistant] So we see biases, uhm, we see biases like reporting bias, which is selectively sharing only specific kinds of information.


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[Assistant] Uhm, think about how often people announce that they're breathing, for example, right? Uh, all these things are happening all the time, but we don't describe them. We don't talk about them. They sort of go without saying. And that means we don't see it in the data, right? So for to guess,


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[Assistant] How often people breathe, based on a web crawl, we would probably get it wrong.


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[Assistant] Uhm, which reflects just basic human behavior, right?


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[Assistant] So these biases, uh, then have a ripple effect throughout the rest of model development as the training data itself


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[Assistant] Uhm, other kinds of human biases are then layered on top of this and injected into model development.


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[Assistant] By the choice of model, the choice of the objective function, how the model is evaluated, uhm, these choices persist in the rest of the model lifecycle.


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[Assistant] So for example, there's the default effect, which is going with whatever is default, uh, for the model you're developing and assuming it is appropriate. So this happens in our model architecture choices.


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[Assistant] Uhm, although, you know, this talk about transformers, these talks about transformers are sort of showing what happens when you move outside of that.


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[Assistant] There's a similar effect of anchoring bias, uh, which is sticking with what one is familiar with. So think of what your loss function is, right? We don't tend to think a lot about that unless we're specifically writing a paper on that.


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[Assistant] Making the choice to have whatever is in there as a bias persist as a bias.


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[Assistant] Confirmation biases, uhm, both are biases where you tend to pay attention to outcomes that are familiar to you and leave out outcomes that aren't. So that means we need to do additional work in development to think through the ways that the model can affect different people.


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[Assistant] On the output in the outlets where the data is collected. So think about content moderation that you see on YouTube on Facebook.


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[Assistant] Uh, that, you know, the recommendation systems you see, um, in so many of the different apps we use.


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[Assistant] And then that becomes training data for an updated model.


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[Assistant] Basically encoding, uh, all of these biases essentially permanently, right? So this creates an echo chamber feedback loop where the model then learns from the data that has already encoded the biases in the first model decisions.


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[Assistant] And so this can amplify problematic decisions over time.


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[Assistant] Uhm, I like to refer to this as bias laundering.


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[Assistant] Uh, it is part and parcel of the model. Uh, so really important to actually model and factor in.


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[Assistant] Uhm, I like to call this bias laundering also because it allows me to use this gif. It is one of the only gifs I have made by myself and I'm very proud of it.


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[Assistant] I just read that when women make jokes in talks, it reflects poorly on them, so, uh,


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[Assistant] Uh, the default approach to development is to simply use the training data that is available in a specific dataset


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[Assistant] Right, so if critical aspects of the problem space aren't represented in the pipeline, then deploying the model means you are making the value judgment that these aspects should not be represented. So if the data, the training, the post processing creates models that


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[Assistant] And what this means is that we have to think critically about values encoded throughout development if our goal is to advance more human-aligned, more ethics-informed kinds of artificial intelligence.


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[Assistant] So this brings us to the role of ethics in AI and the role of values. So ethics doesn't provide an answer, but rather it provides frameworks for analyzing problems in light of different human values. So some example human values are shown on the right.


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[Assistant] Uh, and these are exactly the values that can be incorporated not only within the development pipeline,


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[Assistant] So having a basic understanding of the major ethical theories will help us in learning how to articulate and justify our decisions.


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[Assistant] So let's dive a bit more deeply into the ethical theories that can drive the production of AI systems. So broadly speaking, there are four main families of ethics.


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[Assistant] Metaethics and descriptive ethics are concerned with, uh, analyses of ethics in and of itself.


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[Assistant] Uh, normative is concerned with the norms, the standards, the criteria that define principles of ethical behavior.


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[Assistant] These are what provide the tools for ethical analysis and development. And applied ethics are


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[Assistant] Uh, essentially ethical theories realized in practice and within ethics, uh, informed development in a technology company, this would correspond to something like the organizational ethics or the organization's principles.


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[Assistant] Uhm, one of the key ideas here is that we all have different senses of morality.


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[Assistant] We have different cultures and religions that prioritize different kinds of values.


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[Assistant] Uh, so for example, respect and honor for family, uh, tends to be relatively underrepresented in Western philosophy.


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[Assistant] Uhm, but when we're working within an organization, these values need to be defined at the organization level in order to provide a common ground of values that employees can build off of and base, uh, different decisions on.


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[Assistant] So there's often this question of whose ethics, right? Like, it's so hard to decide, there has to be one, and it has to be one that we all agree on, but that's not right. The question of whose ethics is simply, it's the organization's ethics, and every organization will be different. You can think of this as a business differentiator, or maybe a grad school differentiator, whatever it is, uhm,


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[Assistant] And these ethics, these values can be defined by culling together the values from all different sources.


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[Assistant] Uhm, including all the people who have been hired, right?


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[Assistant] So, uh, there is not one good ethics. There's not one way to say this is ethical and this is not. It's all a function of the people there and what the values they prioritize are. Alright, I hope that makes sense.


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[Assistant] So as a vehicle for operationalizing ethics, uh, within a larger development pipeline or within an organization, normative ethics in particular,


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[Assistant] Uh, provide several different perspectives. Uh, so at a high level, uhm, these pers- these perspectives concern standards of criteria.


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[Assistant] Uhm, there are three main theories. Uhm, I'm probably doing a horrible job. I'm not an ethicist by training, but I've very much tried to learn ethics within AI.


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[Assistant] So these are things like virtue, consequentialism, uh, deontological ethics, uhm,


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[Assistant] And in the context of ethics-informed organizational processes,


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[Assistant] Uh, where we earlier highlighted how different problematic behavior can be propagated and amplified in a model. In the collection of data, this first bit,


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[Assistant] And the right to something like contestation, the right to say that you don't want to be in the data.


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[Assistant] Uhm, so generally copyright uh, is not a large part of what's taken into account during uh, data development. Consent is...


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[Assistant] For people whose data is being used for training models, uh, fair pay, often it's no pay, so, you know, that sucks.


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[Assistant] Uhm, and we generally fail at diversity in part because the culture around machine learning looks to get annotations as if the production of annotations is some black box where you throw in what you want annotated and you get it back.


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[Assistant] Okay, uhm, and then in model training, values of concern include


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[Assistant] How appropriate, uh, different decisions are in light of the foreseeable effects of the model.


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[Assistant] Right? So you have to think about how this will be used in context. Will these people know what a decision threshold is and how to change it based on what they want to do?


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[Assistant] Usually not, uhm, and so it's very critical to think through how these models will be applied in practice.


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[Assistant] Uhm, and in post-processing, values include the consideration of trade-offs, uhm, and aiming towards fairness of outcomes.


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[Assistant] So we can now further examine what these ethical values mean within this first data stage.


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[Assistant] Uhm, and I would say defining values in data collection and annotation.


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[Assistant] Is one of the most pressing issues in technology currently.


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[Assistant] Questions of who owns the data, whether it can be deleted.


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[Assistant] What sort of profit or benefit can be derived from it all come into play?


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[Assistant] Uhm, and I like to call this the data bottleneck because it's a touch point of information flow uhm, from human thoughts to AI. But it's the source of an incredible amount of issues.


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[Assistant] So in particular, there is not a way to have something like the most values, right? Or the most ethical thing. There are trade-offs and tensions in all decisions.


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[Assistant] And so it goes back to the organizations, uh, or the individuals, uh, maybe if you're working alone.


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[Assistant] What your defined values are uhm, and what your prioritization is among them.


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[Assistant] Uhm, so for example, we may want to release a dataset to help with benchmarking and reproducibility.


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[Assistant] But this comes with the trade-offs of lack of informed consent.


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[Assistant] perpetuating problematic ideas that, uhm,


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[Assistant] And the fact that the data can't realistically but be deleted yet. We don't yet have a state of the art where we can remove data.


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[Assistant] Uhm, this means details of individuals in the data can not only be shared, but continually amplified and proliferated without their consent, right? There's this sort of misunderstanding that, well, if it's out there, it's already public, so we can use it.


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[Assistant] It's different than being, uh, spread all over the world to hundreds and thousands of people. These two things are not the same.


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[Assistant] Uhm, and so, you know, in the effort, uh, in the effort to have benchmarking and right for disability, we're often making the choice to amplify and proliferate problematic content without consent.


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[Assistant] Uh, and tokenism and stereotyping. That's when data is collected with a specific goal of diversity.


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[Assistant] Uhm, it's not yet clear what the best methods are.


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[Assistant] But there's still a lot of work to be done and at least doing somewhat better. So I'll give you an example.


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[Assistant] Uhm, a couple years ago, Google was working on face verification phone unlock, uhm, uh, for the Pixel.


[User] Please transcribe this text

[Assistant] And hired a vendor because they needed more photos of Black people.


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[Assistant] And paid people there who were black five dollars for their face.


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[Assistant] So, uh, a few issues there. One is equating black people with homeless people.


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[Assistant] Uhm, so that's some sort of disgusting stereotype I don't think I was even aware of, but there you go. Uh, exploiting people, obviously, five dollars and without, uh, very informed consent.


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[Assistant] And also tokenism, where you're not looking at how to include this person in light of what their general life is like, their culture, everything else like this, uh, but actually just focusing on who you can quickly get because of this single characteristic. That's tokenism. Right.


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[Assistant] So one of the key solutions to operationalizing ethical data development means defining values and priorities upfront and constructing data specifically based on the values.


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[Assistant] So this massively expands what it means to collect and work with data, making data collection itself a detailed and nuanced process.


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[Assistant] With dedicated roles to handle different aspects of data development and corresponding artifacts that allow the values encoded in data selection to be further examined throughout the pipeline and then within final release decisions.


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[Assistant] So this can be seen as a life cycle for the data as a whole.


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[Assistant] Uhm, as it requires continual monitoring and analyses of the effects of the data and updates then based on these analyses.


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[Assistant] So obvious, maybe not obviously. So a lot of researchers, uhm, have increasingly been paying attention.


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[Assistant] And a subset of researchers have been publishing papers on this.


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[Assistant] So, uh, Abe Berhane has done incredible work on this. Uh, Jesse Dodge has led some amazing work on this for, um,


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[Assistant] C4, right? So the fact that these papers are starting to get accepted is a massive step forward in sort of this general understanding.


[User] Please transcribe this text

[Assistant] Uh, so there's been an increase already in understanding why these things are important, uh, in part because of, you know, the small subset of researchers really crying out for it over and over again.


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[Assistant] The key now is to make sure that reviewing rubric actually cares about this stuff.


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[Assistant] So when you're reading a paper that talks about model training and model evaluation, you have to know critical aspects of what that data set for training is, what that data set for evaluation is. And if it's too much to include in the paper, you can have it in the appendix, whatever it is. But while we continue to


[User] Please transcribe this text

[Assistant] Uh, which are, is pretty underspecified and tends to, uh, bias in, in certain ways. But once we can start breaking this down into what we really want, uh, both in terms of reviewing rubric and awards, then I think we can really get somewhere.


[User] Please transcribe this text

[Assistant] Uhm, I didn't see the question, so I guess I'm not, like, in the right chat or whatever.


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[Assistant] So the next bit here in this model sort of pipeline is the model training.


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[Assistant] Uhm, and that's where values come into play around the appropriateness of the loss function, uh, and the model.


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[Assistant] So I'm gonna talk about this via a case study. Uhm, I was able to work on


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[Assistant] Uh, coronavirus forecasting, uhm, which was, you know, amazing to be able to work on. Uhm, and we quickly found that there


[User] Please transcribe this text

[Assistant] If you're not technical, bear with me. I'm gonna try and explain if you are. This might be just a repeat. Uhm, but this is very important.


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[Assistant] So false positives in a coronavirus forecasting model means that hospitals might be over-equipped.


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[Assistant] Expecting a need for more beds and ventilators, but a false negative.


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[Assistant] Means that there would be less resources than needed, which could end in death, right?


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[Assistant] So there are trade-offs in different kinds of errors, but arguably a model that tends towards false positives is preferable to a model that tends towards false negatives.


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[Assistant] And one of the issues at play is the choice of loss function. So if you take a look at the different kinds of objectives that can be used, which are on the bottom left.


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[Assistant] These are all measured in terms of absolute values, right? And so that means positive, false positive, false negative, these basic ideas are treated as equivalent.


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[Assistant] And so for the value aligned goals of the model, this suggests that some additional work may be necessary for the selection of the most suitable model.


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[Assistant] Perhaps unsurprisingly, trying to bring this forward at Google mostly meant hostility and it was a terrible experience. Now I'm telling all of you, still important, even if people are mean. Okay.


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[Assistant] So moving on, we come to the post-processing and final evaluation. And so here too it's critical to think through different types of outputs and errors in order to select the most suitable metrics to measure model performance.


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[Assistant] And it's in this stage that concepts like fairness, diversity, and inclusion are really important.


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[Assistant] Uhm, some of these are more developed than others, uh, so let's talk about them a bit.


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[Assistant] Uhm, so for those of you who are not deeply familiar with fairness,


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[Assistant] A two by, is a two by two of references, the ground truth, the thing that's actually there, and model predictions, the thing that the model is guessing.


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[Assistant] Uhm, when something exists and it's predicted, that's a true positive. When something doesn't exist and it's not predicted, that's a true negative.


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[Assistant] The error types we really care about when we're talking about fairness, uhm, are false positives and false negatives. So these falses, right? So if it doesn't exist, but it is predicted, that's a false positive.


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[Assistant] But in one case, a false negative may be better than a false positive. And in another case, a false positive might be better than a false negative.


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[Assistant] Uhm, so it's very critical to define what kind of fairness you care about.


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[Assistant] As the equality of essentially false negatives across different groups equality of essentially false positives across different groups. When you have these equalities based on these kinds of errors, this is what we mean when we talk about fairness.


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[Assistant] Uhm, so a case where false positives would be better than false negatives is something like privacy in images.


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[Assistant] Uhm, so a false positive here, something gets blurred.


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[Assistant] Uhm, that shouldn't be blurred, that's just sort of usually a bummer.


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[Assistant] Uhm, a false positive email flag, the spam is removed from your inbox. So if it's from a friend or a loved one or it's a job opportunity, uh, that's a loss, right?


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[Assistant] That the usage and context really, really matters here. So if foreseeable users include those that are easily scammed, leading to a loss of wealth,


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[Assistant] Uhm, so finally, uh, people see the output, uh, in this process and, uh, it begins again as people interact with this output and then the biases, uh, are further encoded, uh, and more deeply ingrained in the model. Uhm.


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[Assistant] Yeaah, so I wanna have time for questions, uhm. I have questions.


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[Assistant] Yeah, yeah, what I'll do is I'll just kind of like wrap this up really quickly, uhm, and then we can go to questions.


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[Assistant] Yeah, so here's some basic steps forward. Uhm, and this is sort of what I would like you guys to remember if you take anything from this talk.


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[Assistant] Uhm, so, uh, we need to align considerations of our own values to this larger group that we're working in. These stated values. Uhm, thinking through, uh, if the technology should exist at all.


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[Assistant] Uhm, by using this sort of analysis of foreseeable harms and risks by the expected populations, by the people who will be affected by the model, and how it will be used in context.


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[Assistant] Once we start establishing these things as intentional processes throughout the pipeline, requiring artifacts and documentation, we can start to be able to foresee


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[Assistant] This goes to the question, uh, earlier, one of the critical ways to make this move forward is incentivizing ethics informed development. Uhm, so this is things like what gets, uh, put in review rubric, what kind of awards we get.


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[Assistant] Uhm, what sorts of things are rewarded when they're talked about?


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[Assistant] Uhm, and one method I've worked on to capture much of this is model cards. Uhm, a large part of why I joined Hugging Face is because they were trying to come up with ways to scale model cards and I thought I could help.


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[Assistant] Uhm, but it's essentially an artifact to report sort of all of the above.


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[Assistant] Uhm, and finally throughout, uh, the critical, critical, most serious thing that you can do, uh, is encourage and reward foresight.


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[Assistant] Thinking through the outcomes with different people in different contexts.


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[Assistant] Don't say we can't develop AI with foresight. Just accept that maybe some people have a strength in this and you don't, right? Uhm, I've been corrected that instead of foresight, uh, what's more important is rapid hindsight.


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[Assistant] No, some people are good at foresight. Let them, uh, stop you from having the need for rapid hindsight, right? Uhm, so this requires placing people in the appropriate roles to make this aspect of development really shine. Okay, thank you. I'm dead. I appreciate it.


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[Assistant] Uhm, I previously thought that part of the solution was to


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[Assistant] Make sure that the development that has already taken place is retrofitted as much as possible.


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[Assistant] Uhm, so taking a look at the kinds of evaluation datasets that were used, the kinds of training datasets that were used, doing a more further analysis into what errors it makes, and then essentially fine-tuning, updating the model based on that.


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[Assistant] Uhm, unfortunately, I think in part because of this issue where a lot of people in ML are just not that great at foresight, uh, and don't really see the point of it, uhm, we're gonna need regulation.


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[Assistant] I would say all of the above is important. I'm not sure how to extract that to numerical values to say that it's equality.


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[Assistant] Uhm, but, uh, one of the reasons why I do have a focus on transparency is because it's incentivization of good practices throughout, right? So if you have to report,


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[Assistant] Uhm, input data I do have a focus on in particular because it tends, because it's what the model knows. It's, it's the model's knowledge source. Uhm, so there's so much that can be said about evaluation and all these other things, but the issues very much start, uhm, with


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[Assistant] Uh, you know, data that has a lack of consent, data that our licenses and copyrights are not being paid attention to, data with PII. Uhm, so I'll assume, uh, that's what you mean by unethical data.


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[Assistant] I do think it makes a lot of sense to retrain and fine tune and actually some of the work that I've done has been specifically on bias mitigation techniques and identification of stereotypes and things like this.


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[Assistant] If you're writing a thesis, maybe this will become your topic of how to start from scratch. If you're in an organization and you're retrofitting, maybe the best way is to retrain or fine tune a bit. Uhm, so I think it depends a little bit on context.


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[Assistant] Yeah, I guess that's the sort of most relevant answer I have for that.


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[Assistant] So there's a lot of work to be done in data that has tend to be not incentivized. There's also a lot of work to be done in models and this and this has been more incentivized. And so I just want to make clear, I guess that one of the cool tricks is around sort of multitask learning.


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[Assistant] Uhm, so, uh, bias mitigation techniques that, that tend to work


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[Assistant] Uhm, you know, reasonably well have to do with predicting a few things at once and then discouraging one thing while encouraging another. So, for example, say that you're trying to predict whether or not, uhm, someone should get promoted. At the same time, you can predict gender. And if you find that the promotion decision


[User] Please transcribe this text

[Assistant] Uhm, is predictable via the gender, then you know that you have a bias system that's biased for gender.


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[Assistant] So one of the adversarial mitigation techniques, uhm, that, you know, that's pretty fun and there's a couple of papers on this. Uhm, I wrote one, uh, Alex Butel.


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[Assistant] Uh, let a really cool paper on this is you essentially, um, invert the gradient. So you predict both and for everything it's learning about gender, you say the opposite of that. You essentially multiply by negative one and back, back propagate, right? So that says like, you don't know what gender is, you don't, and you just tell the model until it's essentially random.


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[Assistant] Uhm, and that's really a cool way of sort of removing a gender bias. There's other things you can do with like pairing, uhm, but, but a lot of the key techniques are around


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[Assistant] Yeah, I mean, obviously, as quickly as possible is good if there's sexist and racist things in the world, so, you know, fair enough.


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[Assistant] So I think that it's, I don't know that it'll ever be possible to have a model that's not sexist or not racist because I don't know that it's ever possible to have a person who's not sexist or racist, right? I mean these are all sort of based on heuristics and how our brains work as we're brought up.


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[Assistant] And so uhm, part of, you know, going through like ally training and you know, these kinds of things means that you have to recognize it and proactively address the kind of patterns that you tend to do.


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[Assistant] So you would imagine that similarly for a model, you would put in place normative constraints, like you do with people. Uh, so that means saying that


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[Assistant] Uhm, you know, we are going to mask gender, so there's no distinction between gender when we're training our toxicity model. And so when we have an input gender, we have no way of handling, we don't know what it is, right?


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[Assistant] Uhm, and so, so we won't correlate the two. And toxicity, uhm, actually, like, particularly has a bias towards the term gay and LGBTQ terms.


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[Assistant] Uhm, so there are things you can do like masking those or adding more data. Anywho, uhm,


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[Assistant] The point here is that uhm, we can put in place specific normative constraints based on our social knowledge. So this is where social scientists come in, sociologists come in on how racism and sexism and things like this are expressed.


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[Assistant] So we can do that, and that's definitely like a quick fix.


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[Assistant] Uhm, but a full on fix might, might not be possible. Uhm, but you know, yeaah, do as, do as much as you can, definitely.


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[Assistant] Oh, that's such a good question. Yeah, I've been struggling with this because uhm, ideally we can ground AI development in like fundamental rights or human rights.


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[Assistant] Uhm, but if you are American-centric, then freedom of speech is like, you consider that like a fundamental right, right? But like in other places, uh, like privacy is nice, or like you can imagine freedom from discrimination as to be a cool thing.


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[Assistant] Uhm, so, I mean, I think part of the issue here is that when you say free speech, uhm, there's, there tends to be an assumption that there is some sort of uniform sampling


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[Assistant] Uhm, but what in fact happens is that uhm, the data sets tend to be heavily biased towards men and using uhm,


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[Assistant] And using sources where there's misogyny, so like Reddit. Uhm, and that is not counteracted by free speech about, uh, women being awesome or whatever.


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[Assistant] So uhm, so yeaah, I mean like one of the things about free speech that is kind of fun to do is actually data collection. So I was part of a project called Project Respect that went to like gay pride parades.


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[Assistant] And have people say like really proud things about their identity in there.


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[Assistant] Uh, you know, uh, their LGBTQ like associations and stuff like that. And that created like tons of data that we can use in training a toxicity model.


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[Assistant] Uhm, that's a lot of work and you have to have willing participants and hopefully compensate them whatever they-


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[Assistant] Is a little bit hampered by the fact that the free speech that we collect is biased and tends to be more misogynistic and racist than maybe a larger sample would have.


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[Assistant] Yeah, that's a really good question. Uhm, I think this is where things like, uhm, governments start to play a role. Uhm, and,


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[Assistant] It's, it's, I think it's also kind of important to note that this also is very destructive to private companies that are, uhm, that have, you know, a lot of money.


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[Assistant] Uhm, it's sort of like, I sort of feel like the larger,


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[Assistant] Uhm, so, you know, in some ways we might think of methods where


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[Assistant] Different compensation tiers, you know, make, make sense for different kinds of organizations. You could get this via regulation. So, you know, if you're a grad student, uhm, paying, you know,


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[Assistant] A minimum wage or whatever for your annotations. Uhm, that's, that's pretty reasonable. That's pretty normal.


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[Assistant] Uhm, if you're, you know, a multi-billion dollar corporation, uhm, perhaps paying a lot more is worthwhile.


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[Assistant] Uh, especially because, like, you're making profit off of this, so it should be a percentage of that profit in some way. Uhm, so I think there are ways not to over-disadvantage, uhm, low-resource, uh, groups.


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[Assistant] Uhm, or, or organizations, uhm, and I think that it could go the other way, like very much harming larger private.


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[Assistant] Oh, well, so I just joined Hugging Face full time on November 1st.


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[Assistant] Uhm, and before that I was doing like a lot of contracting and consulting and so I've just been sort of all over the place.


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[Assistant] I'm someone who wears many hats. Like, I like talking about philosophy and then I like talking about statistics and then I like coding and then I like writing.


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[Assistant] Uh, so I really don't think I have a traditional day.


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[Assistant] Uh, I will say that I hate waking up before 9am Pacific time. And so presenting at 9.30 was a feat. Uh, I hope I seemed coherent.


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[Assistant] Uhm, I think that's, that's basically the only, the only standard throughout my day. The only common thing is that I try and not get up until nine.


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[Assistant] Uhm, but other than that, yeaah, I jump around between coding and thinking through math stuff and writing papers and reading papers and sometimes answering email. Yeah.


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[Assistant] There's nothing of that sort here, Mr. Baggot. We brought our sons up to be god-fearing Christians, and our Molly is a good girl. If I thought any different, she'd be out on the street. Two shakes for dog's tail.


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[Assistant] Amidst the heathen savages and wild beasts, I might be persuaded by a very personable man.


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[Assistant] Somebody, uh, examined his skull once and pointed out that the muscle


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[Assistant] Above the temples was almost like he had a football helmet inside and under the skin. So you couldn't hurt Marvin.


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[Assistant] You take the Olympians out of the mix, then Marvin did as well as anybody in that time.


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[Assistant] He was. He was also really messing with Hagler's mind.


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[Assistant] He was really trying to get into his head and get him to fight his fight, trying to tell Hagler, you're nothing but a, a puncher, you know, you're a slugger, you don't know how to box. He said, slow down, you little bitch.


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[Assistant] And that's when the fight really got exciting and got very close because he was winning those rounds.


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[Assistant] Like, I'm actually good at games. It might take you, like, a little while to understand that every game I touch and play, I'm actually really good at. Not just a bot.


[User] Please transcribe this text

[Assistant] Today we're going to talk about two different brewers. Now this isn't really a head to head. They're doing things a little bit differently, but they have something in common. They're both designed first and foremost as travel brewers. So here we have the umph, which is a kind of weird name. I'm going to be honest.


[User] Please transcribe this text

[Assistant] That's a weird name. And then, uh, the Coflano Gobro. It's their latest product. Now, quick note, I reviewed the, uh, compressor from Coflano previously.


[User] Please transcribe this text

[Assistant] And I did say I would like to see the maths behind their claim of nine bars. And then they left a comment with the maths in it on the video. So if you're curious, go check that out. I'll leave a link in the description below. Now, both of these are crowdfunded products. This, uh, umf went on Indiegogo.


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[Assistant] Almost identically priced. I feel like the world of travel coffee brewers got really distorted by one successful thing.


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[Assistant] And that is the Aeropress. It's been an undeniable success story of coffee. And people like it for a whole bunch of reasons, one of which


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[Assistant] Is that it travels well. It's light. It's unbreakable. You can sort of fit a little hand grinder inside it often. It's very easy to travel with. However, I think a lot of people have looked at the coffee brewer market and seen the success of the AeroPress and said, oh, it's, it's a travel brewer. That's why it's successful. That's why people like it. Let's make travel brewers.


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[Assistant] And that isn't really the case. I would say most error presses are used most of the time at home.


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[Assistant] It's bought and it's loved because it's a good coffee brewer that has the bonus of traveling well. But if you build something from the beginning to be purely travel.


[User] Please transcribe this text

[Assistant] Well then you're likely to build in a bunch of handicaps and problems. And actually the number of occasions that people will want to use your product is way less than building a great brewer.


[User] Please transcribe this text

[Assistant] That travels well. I might travel and make coffee 10, maybe 20 days a year. But the rest of the time I'm probably brewing coffee at home, so


[User] Please transcribe this text

[Assistant] If I'm buying a travel-focused brewer, I'm almost never going to use it. What then if it isn't optimized for brewing? That's the question.


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[Assistant] So let's look at both of these broers, cause like I said, they're super, super different to each other. And we'll start, I think with the slightly weirder of the two.


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[Assistant] Which is the umph. The umph is not easy to explain. I guess the best thing I could say is it's kind of like an AeroPress with a travel cup built around the brewer itself. I should take it to pieces.


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[Assistant] So you've got your main plunger piece, which is also the drinking piece, I'll explain. And then you have your sort of main brewing chamber. So your coffee and water go in the top and at the bottom there is a little sort of mesh filter. There's no paper filters in here. You're just going to use a reusable mesh filter. Now you can take it apart further, which is kind of necessary to explain where the coffee goes when you press.


[User] Please transcribe this text

[Assistant] Through here and the coffee is pressed out from this filter at the bottom. It goes into this main chamber here and obviously there's a small gap at the sides around the brewing chamber and it runs up the sides and then it actually flows back into the top of the brewing chamber. Now the reason it doesn't just recirculate is at that point the piston has come down and it's actually gonna flow and sit


[User] Please transcribe this text

[Assistant] On top of the piston inside the brewing chamber, right? So it's kind of recirculated up around and back in, and it's separated from the grounds by the plunger piece.


[User] Please transcribe this text

[Assistant] I'm terrified that this makes absolutely no sense. So what we'll do is, is let's, let's.


[User] Please transcribe this text

[Assistant] I'm going to brew some coffee with it and try and make a bit more sense. I'm going to brew about a third of a liter and I'm going to dose a little heavier because it is an infusion brewer. I'm going to brew at 75 grams a liter. A third of that is 25 grams. So 25 to 333.


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[Assistant] I'm now going to add my third of a litre, start my little timer.


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[Assistant] Now what's slightly distressing to me is that some water inevitably does flow through the filters, and it has brewed a little bit, but it is pretty much out of the brewing process for the most part. It will have extracted something from the coffee, but it's not going to be taking part in most of the brew.


[User] Please transcribe this text

[Assistant] With an AeroPress, people conquer this by brewing either inverted or by just popping the top of the plunger on to create a vacuum to prevent the liquid flowing through the coffee prematurely. Here, we don't really have that same way of doing things. So we're going to get on, give it a little stir and give it a plunge.


[User] Please transcribe this text

[Assistant] So at this point, you can see the liquid has flowed through the bed, back out and around. Got a reasonably pleasing sort of, you know, nice pleasing hiss. And then you can lock it into place. And thusly, we have made coffee. Got a little lid, fits on top of the brewer. And now we're done.


[User] Please transcribe this text

[Assistant] I'm not sure I'd take this with me on a train, on a commute. It's just so bulky. I'm not sure it would fit into most cup holders unless you've got, you know, the big cup holders. But in, in this country, we don't have very big cup holders. So it, it probably fits, but it'd be a little squeezy. In terms of the construction.


[User] Please transcribe this text

[Assistant] Obviously you're taking with you a, a brewer, uh, full of hot liquid because it's watertight. There are a couple of little gaskets in here that can be a little bit fiddly if they get loose. When you're taking the whole thing to pieces to clean it properly, to give it a good deep clean. Uh, I got my gaskets loose. I had to reset them. That was just a little bit of.


[User] Please transcribe this text

[Assistant] Finicky kind of work. I get that they're necessary to make this watertight to make it safe, but


[User] Please transcribe this text

[Assistant] There are to me a frustration in the design and the fact that I can't really use paper filters with this.


[User] Please transcribe this text

[Assistant] More heat than a thermos would have done. So I don't think this is going to keep your coffee hot.


[User] Please transcribe this text

[Assistant] Like I said, the coffee tastes okay, but that's cause there's good coffee in there, good water, and I got the grind setting right. So now let's talk about the Coflano Go Brew.


[User] Please transcribe this text

[Assistant] And here they've tried to do something a little bit clever, which is have a nice portable bottle that happens to be like a little coffee transformer, a home brewing setup.


[User] Please transcribe this text

[Assistant] Now that's going to act as a way to suspend what is in fact not just the lid of a bottle, but...


[User] Please transcribe this text

[Assistant] A little filter brewing cone. So this pops on the top.


[User] Please transcribe this text

[Assistant] Now, quick note, there's a couple of fins here to hold it in place.


[User] Please transcribe this text

[Assistant] Here it is. We're going to brew in this thing. Uh, coffee is going to brew at the bottom into the bottle. We can throw away the coffee afterwards, put our lid on.


[User] Please transcribe this text

[Assistant] We're good to go. So I'm going to brew with this, but I'm going to brew a third of a liter again, but because this is just a percolation, just a pour over, I'm going to use 60 grams per liter. So I'm going to use 20 grams of coffee to my third of a liter of hot water or a third of a kilo. If we want to rehash that conversation from a previous video, but let's not get into it right now. Let me grind some coffee for this. Let me get this brewed. So 20 grams in.


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[Assistant] And what's notable to me is that at 20 grams,


[User] Please transcribe this text

[Assistant] That's a pretty full filter basket. There's not a lot of space left in here for water, for brewing. It's going to be pretty, pretty slow pours. Now I'm brewing with a domestic kettle because I'm imagining this is a travel scenario where I don't have a pouring kettle with me. I'm likely in a travel scenario not to have specialist pouring equipment. Let's begin. Little bloom.


[User] Please transcribe this text

[Assistant] Give it a stir to make sure it is sufficiently agitated. And then let's brew. So I don't really have much choice here.


[User] Please transcribe this text

[Assistant] We have a bottle of coffee. All right. So lid goes back on. So I think we're about max capacity. There's probably 280 mils of liquid inside.


[User] Please transcribe this text

[Assistant] That's about, that's about all we're going to get, I think. Now in previous efforts, I'd had a small issue.


[User] Please transcribe this text

[Assistant] Now previously I'd had a small issue in that there's a little gasket that seals the lid.


[User] Please transcribe this text

[Assistant] To the main body. And that had gotten loose, which meant that when I poured my coffee out into my cup, it leaked.


[User] Please transcribe this text

[Assistant] Which was not good. And I've gotta be honest, gaskets are necessary in this design, or a gasket, and they're necessary in the design of the oomph.


[User] Please transcribe this text

[Assistant] In both cases, they're fiddly flaws to me, right?


[User] Please transcribe this text

[Assistant] Now I would expect the coffee to taste absolutely fine here. Again, good coffee, good water, the right grind setting, the brewing environment is okay as far as I'm concerned, the little wave filter shape is okay and it took quite a sort of


[User] Please transcribe this text

[Assistant] It does. It's fine. It's totally fine. I haven't drunk out of the bottle itself yet, which I'll, uh, try now. It's fine. It's totally fine.


[User] Please transcribe this text

[Assistant] And for me, with both brewers, there's just that lingering question of why, when, where do these have real strength over something I might already?


[User] Please transcribe this text

[Assistant] If I had to pick one of the two, I would guess I'd probably pick the Keflano. It's paper filtered and weight wise it's just a little over half a kilo full of coffee compared to nearly a whole kilo for the umph, so a big difference there.


[User] Please transcribe this text

[Assistant] It doesn't make dramatically better coffee than the umf. If you prefer paper filters, then you'll probably prefer the coffee out of one of these, but that's primarily about filtration rather than brewing. They both brew okay.


[User] Please transcribe this text

[Assistant] Right? But I'm not sure where they really, hands down, win. Where do they beat


[User] Please transcribe this text

[Assistant] An Aeropress and a Thermos. Where do they beat a V60 and a reusable takeaway cup? I don't think I have a good answer for that. And for travel brewers, this is really the question to answer.