[User] Please transcribe this text

[Assistant] Because it is one of the essential and distinctive characteristics of the elect.


[User] Please transcribe this text

[Assistant] These monstrous doctrines are essentially the same.


[User] Please transcribe this text

[Assistant] As the later teaching of popular educators and theologians.


[User] Please transcribe this text

[Assistant] That there is no unchangeable divine law as the standard of right, but that the standard of morality is indicated by society itself and has constantly been subject to change.


[User] Please transcribe this text

[Assistant] All these ideas are inspired by the same Master Spirit.


[User] Please transcribe this text

[Assistant] By Him who even among the sinless inhabitants of heaven,


[User] Please transcribe this text

[Assistant] had led many to a virtual rejection of the law of God.


[User] Please transcribe this text

[Assistant] Wesley steadfastly opposed the errors of the antinomian teachers.


[User] Please transcribe this text

[Assistant] This is good and acceptable in the sight of God our Savior, who will have all men to be saved and to come unto the knowledge of the truth.


[User] Please transcribe this text

[Assistant] For there is one God and one mediator between God and men, the man Christ Jesus, who gave himself a ransom for all.


[User] Please transcribe this text

[Assistant] Titus 2.11, 1 Timothy 2 verses 3 through 6.


[User] Please transcribe this text

[Assistant] The Spirit of God is freely bestowed to enable every man to lay hold upon the means of salvation.


[User] Please transcribe this text

[Assistant] Thus, Christ, the true light, lighteth every man that cometh into the world. John 1 verse 9.


[User] Please transcribe this text

[Assistant] Men fail of salvation through their own willful refusal of the gift of life.


[User] Please transcribe this text

[Assistant] In answer to the claim that at the death of Christ, the precepts of the decalogue had been abolished with the ceremonial law, Wesley said,


[User] Please transcribe this text

[Assistant] The moral law, contained in the Ten Commandments and enforced by the prophets, he did not take away.


[User] Please transcribe this text

[Assistant] It was not the design of his coming to revoke any part of this.


[User] Please transcribe this text

[Assistant] This is a law which never can be broken, which stands fast as the faithful witness in heaven.


[User] Please transcribe this text

[Assistant] This was from the beginning of the world being written not on tables of stone, but on the hearts of all the children of men, when they came out of the hands of the Creator.


[User] Please transcribe this text

[Assistant] And however the letters once wrote by the finger of God are now in a great measure defaced by sin, yet can they not wholly be blotted out while we have any consciousness of good and evil?


[User] Please transcribe this text

[Assistant] Every part of this law must remain in force upon all mankind.


[User] Please transcribe this text

[Assistant] Without question, his meaning in this place is consistently with all that goes before and follows after.


[User] Please transcribe this text

[Assistant] I am come to establish it in its fullness, in spite of all the glosses of men.


[User] Please transcribe this text

[Assistant] I am come to place in a full and clear view whatever was dark or obscure therein.


[User] Please transcribe this text

[Assistant] The entire extent of every commandment contained therein and the height and depth


[User] Please transcribe this text

[Assistant] The inconceivable purity and spirituality of it in all its branches. Wesley sermon 25.


[User] Please transcribe this text

[Assistant] Wesley declared the perfect harmony of the law and the gospel.


[User] Please transcribe this text

[Assistant] There is therefore the closest connection that can be conceived between the law and the gospel. On the one hand,


[User] Please transcribe this text

[Assistant] The law continually makes way for and points us to the gospel.


[User] Please transcribe this text

[Assistant] On the other, the gospel continually leads us to a more exact fulfilling of the law.


[User] Please transcribe this text

[Assistant] The law, for instance, requires us to love God, to love our neighbor, to be meek, humble or holy.


[User] Please transcribe this text

[Assistant] We feel that we are not sufficient for these things, yeaah that with man this is impossible.


[User] Please transcribe this text

[Assistant] But we see a promise of God to give us that love and to make us humble meek and holy. We lay hold of this gospel.


[User] Please transcribe this text

[Assistant] Of these glad tidings it is done unto us according to our faith.


[User] Please transcribe this text

[Assistant] And the righteousness of the law is fulfilled in us through faith, which is in Christ Jesus.


[User] Please transcribe this text

[Assistant] In the highest rank of the enemies of the gospel of Christ, said Wesley.


[User] Please transcribe this text

[Assistant] Are they who openly and explicitly judge the law itself and speak evil of the law, who teach men to break


[User] Please transcribe this text

[Assistant] To dissolve, to loose, to untie the obligation of not only one, whether of the least or of the greatest, but all the commandments at a stroke.


[User] Please transcribe this text

[Assistant] The most surprising of all the circumstances that attend this strong delusion is that they who are given up to it


[User] Please transcribe this text

[Assistant] Really believe that they honor Christ by overthrowing his law.


[User] Please transcribe this text

[Assistant] And that they are magnifying his office while they are destroying his doctrine.


[User] Please transcribe this text

[Assistant] Yeaah, they honor him just as Judas did when he said, Hail Master, and kissed him.


[User] Please transcribe this text

[Assistant] And he may as justly say to every one of them, Betrayest thou the son of man with a kiss?


[User] Please transcribe this text

[Assistant] It is no other than betraying him with a kiss to talk of his blood and take away his crown.


[User] Please transcribe this text

[Assistant] To set light by any part of his law under pretense of advancing the gospel.


[User] Please transcribe this text

[Assistant] Nor indeed can anyone escape this charge who preaches faith in any such a manner as either directly or indirectly tends to set aside any branch of obedience.


[User] Please transcribe this text

[Assistant] To those that urged that the preaching of the gospel answers all the ends of the law, Wesley replied,


[User] Please transcribe this text

[Assistant] It does not answer the first end of the law namely the convincing men of sin.


[User] Please transcribe this text

[Assistant] The awakening those who are still asleep on the brink of hell.


[User] Please transcribe this text

[Assistant] The apostle Paul declares that by the law is the knowledge of sin.


[User] Please transcribe this text

[Assistant] And not until man is convicted of sin will he truly feel his need of the atoning blood of Christ.


[User] Please transcribe this text

[Assistant] They that be whole, as our Lord Himself observes, need not a physician, but they that are sick.


[User] Please transcribe this text

[Assistant] It is absurd, therefore, to offer a physician to them that are whole or that at least imagine themselves to be so. You are first to convince them that they are sick. Otherwise, they will not thank you for your labor.


[User] Please transcribe this text

[Assistant] It is equally absurd to offer Christ to them whose heart is whole, having never yet been broken. Thus while preaching the gospel of the grace of God, Wesley, like his master,


[User] Please transcribe this text

[Assistant] ...sought to magnify the law and make it honorable.


[User] Please transcribe this text

[Assistant] Faithfully did he accomplish the work given him of God.


[User] Please transcribe this text

[Assistant] And glorious were the results which he was permitted to behold.


[User] Please transcribe this text

[Assistant] At the close of his long life of more than four score years.


[User] Please transcribe this text

[Assistant] Above half a century spent in itinerant ministry.


[User] Please transcribe this text

[Assistant] His avowed adherents numbered more than half a million souls.


[User] Please transcribe this text

[Assistant] But the multitude that through his labors had been lifted from the ruin and degradation of sin to a higher and purer life, and the number


[User] Please transcribe this text

[Assistant] Till the whole family of the redeemed shall be gathered into the kingdom of God.


[User] Please transcribe this text

[Assistant] His life presents a lesson of priceless worth to every Christian.


[User] Please transcribe this text

[Assistant] Would that the faith and humility, the untiring zeal, self-sacrifice and devotion of this servant of Christ might be reflected in the churches of today.


[User] Please transcribe this text

[Assistant] Yeah, strong, very strong. Strong, right? Thank you. Good, yeah.


[User] Please transcribe this text

[Assistant] The King jailed our Lord Bishop. Some might mark that in his favour, others less.


[User] Please transcribe this text

[Assistant] Okay, hi everyone. Today I'm very happy to make a talk a little bit different than what I do usually.


[User] Please transcribe this text

[Assistant] I thought I won't do an educational course like I do usually, but I'll actually talk about the origin of hugging face and the origin of transformers, the origin of our library and kind of walk you through.


[User] Please transcribe this text

[Assistant] Uh, very personal, uh, honestly, selection of, uh, what happened in the past few years are taking place and in the field of transfer learning.


[User] Please transcribe this text

[Assistant] So let's go back, uh, in time, uh, to the very beginning of, uh, why we're here today, at least why I'm here today, which is, uh, four years ago, five years ago now.


[User] Please transcribe this text

[Assistant] Uh, October 4, uh, Julien, who was, uh, an old classmate of, of me, we were making, we were playing music together in a kind of, uh, progressive rock band, uhm, kind of talk to me about the coming revolution of deep learning and, and say, okay, you, you've been doing a little bit of science at, uh, before, before doing your, your low degree.


[User] Please transcribe this text

[Assistant] And you want to come back to doing science. And I was like, yeah, I did. Yeah. And so we followed together.


[User] Please transcribe this text

[Assistant] And this time for CS22 for the deep learning for NLP class, which was a lot about, a lot about LSTM.


[User] Please transcribe this text

[Assistant] Uh, skip thought vectors, uh, for those who were already following a little bit. And actually this is, I think, interesting because today we kind of back to this, right? We're doing a, a course. Uh, at that time also we followed that over the internet. There was still no pandemic. It was easier, but still the course was at Stanford. We were both in Europe.


[User] Please transcribe this text

[Assistant] And so maybe today, uh, some of you will be following this class with friends, maybe friends in the music group band, or I don't know. Uh, and maybe in a few years, we'll have a very successful startup that started today, started this week by following this course together. And that's really all that I hope for you. I think for us, it's been an amazing, an amazing,


[User] Please transcribe this text

[Assistant] Uh, journey from this, uh, from this day to today. And, uhm, the early days of Hugging Face were actually quite different than the Hugging Face we know today. Uh, we were building an AI chatbot.


[User] Please transcribe this text

[Assistant] That was a little bit like a talking Tamagotchi.


[User] Please transcribe this text

[Assistant] Uh, you could, uh, you could like discuss a bit. It was, uh, the beginning of generative technologies, right? Or with LSTM.


[User] Please transcribe this text

[Assistant] Uhm, and you could also send it selfie. There was beginning of image classification to try to understand a little bit what you were sending and to try to reply with something fun. And that's how Hugging Face started. We were just three, four at this time.


[User] Please transcribe this text

[Assistant] Uhm, but quite quickly, we actually started to open source a little bit of code. So this is, uhm, this is a screenshot from the Huggingface GitHub pages.


[User] Please transcribe this text

[Assistant] Four years ago, uh, you can see, uh, probably libraries and frameworks you don't know because they've changed a lot, but it was the first one. We were really proud of our coreference, uh, PyTorch adaptation of a, of an emoji detector, of a like sentimental detector.


[User] Please transcribe this text

[Assistant] But why has this all completely changed? Because there was a couple of revolutions in the field of NLP, a few years after we started, so more precisely around the beginning of 2018, I would say.


[User] Please transcribe this text

[Assistant] So I think if you want to date back, this could maybe go back to this first unsupervised sentiment neuron.


[User] Please transcribe this text

[Assistant] Paper from Alec Radford already at OpenAI where they trained LSTM on the largest data set that people had at the time, which was data set of Amazon reviews.


[User] Please transcribe this text

[Assistant] Well, actually the internet was already there, but still not a common practice, right, to use that as a training data set. So they trained this LSTM and what they discovered is that the model, the LSTM learned to actually categorize sentiments.


[User] Please transcribe this text

[Assistant] Without being explicitly taught to do that. So this was trained in a generative fashion. This LSTM was trained to generate the next token, just like we do today, right?


[User] Please transcribe this text

[Assistant] Uhm, and they also identified, well, something that we don't really use anymore today, I would say, but some specific neurons that were very finely tuned to detect sentiment. And so this was the first, uhm,


[User] Please transcribe this text

[Assistant] This was a first hint that unsupervised speed training with a generative objective could lead to good performances on downstream tasks like sentiment classification.


[User] Please transcribe this text

[Assistant] But this was still, uh, using, uh, LSTM and the, the data set was still quite, I would say, domain specific. Amazon reviews are still not, I would say has widely, um,


[User] Please transcribe this text

[Assistant] Uh, you could actually have good performances on a lot of, uh, NLP tasks and not only sentiment analysis, but also like question answering, textual entailments and this kind of thing.


[User] Please transcribe this text

[Assistant] The idea here was still kind of to extract, um, token representation, uh, more than maybe what we do today with like full pre-trained model.


[User] Please transcribe this text

[Assistant] And then really concurrent to this work was the very nice ULM fit from Sebastian Rüder and Jeremy Howard, uh, where they also pre-trained this LSTM and they also showed that you could adapt it to many, many tasks. So these two papers really at the same time, uh, at the beginning of 2018, people were like, oh,


[User] Please transcribe this text

[Assistant] Maybe all this approach of transfer learning that we've been using in computer vision on ImageNet for a long time could be useful also in text.


[User] Please transcribe this text

[Assistant] And so this kind of led Sebastian to wrote this, this interesting piece in around the summer of 2018, which was maybe the ImageNet moment of NLP has arrived, right? And he was very, I think, insightful in saying, oh, pre-trained language model can achieve state of the art results. And that's something that's going to change everything.


[User] Please transcribe this text

[Assistant] But this was just one part of the revolution, which is transfer learning, pre-trained model.


[User] Please transcribe this text

[Assistant] And the second part, uh, was using transformers in place from LSTM.


[User] Please transcribe this text

[Assistant] And here again, uh, I think Alec Radford from OpenAI was really, uh, was really pioneering this by using the GPT, the first GPT in 2018. So this was around the summer.


[User] Please transcribe this text

[Assistant] Uh, with this generative pre-trained transformers.


[User] Please transcribe this text

[Assistant] And actually the name really GPT was I think only coined in the following BERT paper from Google, which was a few months later in October 8, 2018.


[User] Please transcribe this text

[Assistant] Where they kind of use this same transformer model, but instead of using it as a causal or autoregressive model, they used the now famous masked language modeling objective, right, denoising, denoising objective.


[User] Please transcribe this text

[Assistant] And so these two papers were very interesting because they mixed this early idea of transfer learning, like having a model pre-trained


[User] Please transcribe this text

[Assistant] And, uh, what was the conclusion of mixing these two objectives? Well, it was really a storm in the NLP field. So you can see this if you go, for instance, on paper with code.


[User] Please transcribe this text

[Assistant] If you check this curve showing the state of the art at various times, what you see is that around the end of 2017, beginning of 2018, you see this very strong slope upward, right?


[User] Please transcribe this text

[Assistant] Like performance was basically plateauing for like years, right? Here we see that on an NER.


[User] Please transcribe this text

[Assistant] Since basically 2009, we did not really move up anything. And then from like summer 2018-17, TagLM was already kind of a first transfer learning approach actually.


[User] Please transcribe this text

[Assistant] And then with transformer, we were moving upward very strongly. Squad 2, you see also the same very strong increase in the performance of the model by using BERT. You see the jump BERT single model. There's very strong jump. 10 points.


[User] Please transcribe this text

[Assistant] Uhm, in exact match here, metric. And then, well, you can say maybe we're plateauing again today. I think most of the, the reason we're plateauing is that square two is now kind of saturated actually as a benchmark.


[User] Please transcribe this text

[Assistant] And so, we were following that very closely, uh, and basically when GPT, the first of the names, so this one, uh, went out, we were actually, uh, at Agniphase participating in a challenge at, uh, at NeurIPS. Well, this is a no-slide, so we still call it NIPS, but that's, that's, that's now NeurIPS, right? 2018.


[User] Please transcribe this text

[Assistant] We are participating in this conversational AI challenge. So the goal was uhm, when you have a


[User] Please transcribe this text

[Assistant] And this, uh, challenge was interesting because it came with a nice, uh, data set made by the New York, New York Facebook team.


[User] Please transcribe this text

[Assistant] And uhm, for this first time, GPT was just out and we were like, okay, maybe let's try this GPT. Let's try this pre-training approach for generative tasks as well. A lot of people were just using this model for classification tasks.


[User] Please transcribe this text

[Assistant] And so we tried for the first time to use it for really purely generative tasks like chatbots.


[User] Please transcribe this text

[Assistant] And the results you can see on the right were really impressive. We were directly on top of the leaderboard of the competition.


[User] Please transcribe this text

[Assistant] And by very strong margin, the perplexity, the first column, you're like...


[User] Please transcribe this text

[Assistant] Perplexity is a kind of an exponential matrix, so it's quite hard to divide by 2 the perplexity, but we were like...


[User] Please transcribe this text

[Assistant] And when we studied a little bit and compared to the other, uh, teams, what we saw is that


[User] Please transcribe this text

[Assistant] If some teams were very good on the validation set, they often go, went very down on the hidden test set. So if you've been doing a little bit of Kaggle, you know that this mostly means that you're starting to overfit on the validation, the public data set.


[User] Please transcribe this text

[Assistant] And what we saw is that our approach was both using transfer learning and transformer as well, but transfer learning I think is the strongest ingredient. Using transfer learning, we were both very strong in validation sets and we stayed very robust on the test set.


[User] Please transcribe this text

[Assistant] And so this led us in the next summer, June 2019, to do a very long tutorial around transfer learning in NLP with a few


[User] Please transcribe this text

[Assistant] And what we saw there, well, what we tried to do there is try to explain a little bit what was the idea. And so just as an insight, you will learn more in the course, but basically this is the, like the secret ingredient of transfer learning.


[User] Please transcribe this text

[Assistant] And you basically randomly initialize your model and train it from scratch on this data set. If you are now trying to do summarization of chatbots, you will gather another data set of summarization or chatbots. And you will again, just create a machine learning system specialized for this task. And if you had another task again,


[User] Please transcribe this text

[Assistant] Same thing, you will have very separate pipelines and very separate, uh, systems who are already, who are under each time transfer from scratch.


[User] Please transcribe this text

[Assistant] And the approach of transfer learning was to do a little bit more like I would say human do, which is that we don't randomly initialize our brain each time we tackle a new task, right? What we do is that we use all the knowledge that we've learned in the class and in our life experiences to bootstrap our learning.


[User] Please transcribe this text

[Assistant] And so when we have a new task, we just reuse all this knowledge that we have. And this gives us mostly two main advantages that we see also when we produce that with machine learning models, which is first that we have very data efficient.


[User] Please transcribe this text

[Assistant] We can reach better performances and transfer learning is a way to try to do the same for machine learning. So to try to build this kind of generic knowledge that can be reused on many, many tasks.


[User] Please transcribe this text

[Assistant] Uhm, it's quite different. It's quite different. Uh, so really the direction the field is trying to go is toward what we call few shots. So using less and less examples. So I mean requiring less and less examples. So for some tasks that you will see in the course,


[User] Please transcribe this text

[Assistant] You can actually do zero shots, like take classification. You actually, uh, in many cases don't need any example.


[User] Please transcribe this text

[Assistant] But then it's, it's always very dependent on how specific your domain is. And typically the more specific your domain is, the more you will need example. And in some case, you might be, you might still be outside of the pre-training domain of the model where you, and then you will need quite a large data set. Yeah. Thanks.


[User] Please transcribe this text

[Assistant] And so uhm, yeah, let's go. Yeah. Why would this work in NLP? Why you probably have the intuition, but here they are. The idea is that


[User] Please transcribe this text

[Assistant] Many NLP tasks, if you look them from, if you like take a step back, they share a common knowledge about language. They share that, uh, yeah.


[User] Please transcribe this text

[Assistant] Basically, if you, if you know, for instance, um, adjectives, if you know linguistics, uh, then it will help you a lot. For instance, if you want to do NER, right, you want to identify, I don't know, the name of organization. If your model already, if your model already have a good knowledge about linguistics and know what is a noun.


[User] Please transcribe this text

[Assistant] That will help it a lot because it will know that name of organization are very likely nouns. Okay. They are not adjectives. They are not verbs. So only thing can be useful for the model.


[User] Please transcribe this text

[Assistant] The same, right? If your task is in English, if your model know a little bit about English, you can just learn basically the missing pieces that it has on your task and don't, and won't spend like, won't need, I would say like half of your training data set just to learn how English works in general.


[User] Please transcribe this text

[Assistant] So tasks can also inform each other. Typically, as I told you, syntax and semantics, they are often related.


[User] Please transcribe this text

[Assistant] If something is a noun, that's also mean that it has a meaning related to a noun. If it's an adjective that's here to kind of change the meaning of a noun, so only thing.


[User] Please transcribe this text

[Assistant] Um, obviously annotated data, uh, data, uh, you know, that is rare. It's very difficult. So the more we can gather it, the more we can gather several data sets together to build a bigger one.


[User] Please transcribe this text

[Assistant] And now the most, I would say, a downer's reason why it works is that when we try, it works. So that's currently the state of the art for many, many NLP tasks. It's to use these transfer learnings. So that's also a good reason to think that, well, maybe it has reason to.


[User] Please transcribe this text

[Assistant] So I talk a lot about science here, like transfer learning revolution is a scientific revolution in the field. But if you're here today, it's also because you're probably following our open source work. And this was kind of


[User] Please transcribe this text

[Assistant] And so very early, we started to open source some models. So the first one we did is here. It's now, uh, the summer of 2018. So just before basically this, this, uh, transfer learning revolution really, really begun.


[User] Please transcribe this text

[Assistant] And so we, we did this new old co-reference, which was a co-reference model for spaCy.


[User] Please transcribe this text

[Assistant] And then there was obviously this GPT. So I told you we were using the first version of GPT in this first competition. And that's where basically we understood that, wow, this was very powerful.


[User] Please transcribe this text

[Assistant] And before, before using it, as always, we actually released an open source version. So actually a bit later we discovered that some other competitor also used our open source GPT in the same competition. So it was very, very interesting. We actually made a paper together at ACL a year later.


[User] Please transcribe this text

[Assistant] And these two, these two early efforts and the interest we saw from people led us to try to do that, uh, again. And this led us to first, uhm, the first, uh,


[User] Please transcribe this text

[Assistant] It was PyTorch version 0.3, but it was still quite interesting and easy to use. And so we decided, okay, let's convert this TensorFlow model in PyTorch and let's see if people will be able to use it easier, right? And our idea here was basically that


[User] Please transcribe this text

[Assistant] Well, first at that time, BERT sounded like a very large model. It was 300 million parameters, which today sounds like maybe the smallest model that you use, at least in the research field.


[User] Please transcribe this text

[Assistant] But at that time it was like really really large. And so we were like,


[User] Please transcribe this text

[Assistant] Nobody will be able to train that, so we should make it easy to use the pre-trained weights that Google really nicely released.


[User] Please transcribe this text

[Assistant] And to make it the easiest, we should actually have them also in PyTorch. And so that's what we did. Uhm, and that's that lead to this first, uh, version called a PyTorch pre-trained bird.


[User] Please transcribe this text

[Assistant] And this was really very far from transformers. Uh, but we had a few initial ideas that evolved. So one initial idea was that we could also host the pre-trained weights on our S3 buckets so that people didn't have to, to, to go through the painful process of downloading and converting them. And we could maybe just load them.


[User] Please transcribe this text

[Assistant] And here we were in, inspired by, by Joel Grus's nice, nicely, uhm,


[User] Please transcribe this text

[Assistant] Uh, nicely made from pre-trained method in Allen NLP, which, which I found very, very nice. We've just one, one line of Python. You could download a pre-trained model in Allen NLP. And so I was like, okay, let's actually use the same ID.


[User] Please transcribe this text

[Assistant] And so this was the first thing, uh, and this evolved over the coming


[User] Please transcribe this text

[Assistant] Which both I think give you very, uh, wide access to the model. It's very easy to inspect and to inspect it. And at the same time give you a lot of interesting like widget to play with the model online API to use it very easily. So all this you will see in the class, but that's very interesting.


[User] Please transcribe this text

[Assistant] And the first idea was also just to give access to BERT, right? But then I was like, okay, we actually have an open source code for GPT, another for BERT, why not actually make them in a single library?


[User] Please transcribe this text

[Assistant] And this led us very quickly to basically a single API, which today I think wrap not far from a hundred different architectures.


[User] Please transcribe this text

[Assistant] Which is the, the today transformers. And so this, the first version was Python transformers with, with four architectures. And quickly we thought.


[User] Please transcribe this text

[Assistant] If you want to use TPU to train your model on TPU because you have like free free access, you should. And then if you want to fine tune the same model in PyTorch, you should be able to switch very easily from one to the other.


[User] Please transcribe this text

[Assistant] And so we tried to make these three main, these two main framework at that time. And then, then later, Jaxx and Flax, which are more, more recent in the same framework. And we saw that it was actually not so difficult to get them all together.


[User] Please transcribe this text

[Assistant] And to get what is now, uh, transformers, right? Not, not anymore PyTorch transformer. Well, PyTorch is still, I think, maybe the better supported one, but TensorFlow and JAX are very, very strongly improving.


[User] Please transcribe this text

[Assistant] And so this was a transformer and what we witnessed is this basically explosion in the number of architecture on model. Most of them are today in transformers because it's very easy to add new one.


[User] Please transcribe this text

[Assistant] And so you can see from BERT, there were a lot of following work trying to experiment with variants in the architecture. I think maybe Excelnet is a good example where they tried to do both some auto-aggressive but also bidirectional way to do.


[User] Please transcribe this text

[Assistant] Uhm, to, uh, to do a forward pass in the neural network. And one question became, uhm,


[User] Please transcribe this text

[Assistant] And here, I think what became also quite clear in the subsequent year was that actually more than architecture, it was actually the data that mattered a lot.


[User] Please transcribe this text

[Assistant] So I talked about Excelnet, which was very interesting as a different architecture and actually got a lot better performances than BERT at the time. And people were like, wow.


[User] Please transcribe this text

[Assistant] We should experiment with various architectures for modifying the transformers and


[User] Please transcribe this text

[Assistant] This subsequent work from Facebook called Roberta actually showed that, uhm,


[User] Please transcribe this text

[Assistant] It's not so much the architecture, but just the data. ExcelNet was actually trained on a lot more data than BERT, and that's actually what matters more than the architecture. And so Roberta took very basic transformers.


[User] Please transcribe this text

[Assistant] Just exactly the same as BERT and then just train it on more data. And they got state of the art results.


[User] Please transcribe this text

[Assistant] And basically as long as you stay not so far from the middle, you're pretty good. The architecture just don't really matter that much. And the T5 paper who came, who came out later also confirmed this. Basically the early transformer paper were already very close to the optimal architecture.


[User] Please transcribe this text

[Assistant] But what's very important is this graph on the lower left, where you see the data set size. This is also from the neural scaling paper from OpenAI.


[User] Please transcribe this text

[Assistant] And you see that just by increasing the data set size, you have this very predictable increase in performances. So here this is the test loss. So lower is better.


[User] Please transcribe this text

[Assistant] And this actually brought us to this question where actually in modern architecture, what is important here is data.


[User] Please transcribe this text

[Assistant] We decided to do a new library around data. We are like, okay, we also need to focus on data now. And so this new library was called Datasets, where early name was NLP, but for a long time now the name is Datasets.


[User] Please transcribe this text

[Assistant] My other follower works, so I think it's very nice. And this library, the idea was


[User] Please transcribe this text

[Assistant] Date access to data is also very difficult today in NLP, mostly because the situation is very fragmented.


[User] Please transcribe this text

[Assistant] If you want to use a data set, you typically need to find where is the code somewhere on GitHub. You need also to pre-process it. And you actually spend a lot of time doing the same work to pre-process the data set. A lot of people are just writing their CSV or JSON loader. So maybe we could share this work and avoid this and just jump to the data structure. That was one note.


[User] Please transcribe this text

[Assistant] Another one was that also with this very large language model, we're using now terabytes of data. And this, this can be very difficult to handle on your, at least on your personal laptop, right? If you want to train a model on one terabyte of data like Robeta.


[User] Please transcribe this text

[Assistant] This is really a pain because you just typically can't load that in ram. You need to find a smart way to do like.


[User] Please transcribe this text

[Assistant] Just loading the current batch or prefetching. And so we thought maybe we should solve all these issues at the same time. And the idea was that data set could be a way to kind of centralize access to, to a NLP data set at the beginning. Now it's expanded to speech and images, but at the beginning it was really about NLP data set.


[User] Please transcribe this text

[Assistant] And so this led us to developing datasets around this effort. So at the end of November, well, one year ago now, November 2020, we did a first community event where we said to the community,


[User] Please transcribe this text

[Assistant] Uh, well, we're 15 people at Huggy Face trying to add dataset. If you want, you can join. This is going to be fun. We're going to do, uh,


[User] Please transcribe this text

[Assistant] Uh, this together, you will be, uh, on our Slack, we'll talk a little bit, uh, and also we'll do some swag and everything. And this was usually, uh,


[User] Please transcribe this text

[Assistant] But it was very nice also in terms of adding data sets in many, many languages. And the idea is that all these data sets were then very easy to access. And this could foster more research on multilinguality.


[User] Please transcribe this text

[Assistant] Uh, you see, uh, Gunjan, um, Navitva, they are both like community contributor and to submit it today this year to MNLP where I just actually won the best demonstration paper. So I think this is very typical hugging face thing where we do something very open source.


[User] Please transcribe this text

[Assistant] And so there is today also this hub that you will see in the, in the, in the course where, where are you today we have about.


[User] Please transcribe this text

[Assistant] And what happened in 2021, uhm, that was open science more and big science. Well, what happened, uh, I would say it's a very personal walkthrough. So I would mean, uh, what I actually spend a lot of my time on was this big scientific fault. And here the idea was that, um,


[User] Please transcribe this text

[Assistant] And the example, well, I'm a physics PhD formally.


[User] Please transcribe this text

[Assistant] And it has involved really a huge research community. And it has led to a lot of research work, like you have about 3,000 papers, uh, this led to the discovery of many hadrons and things like that.


[User] Please transcribe this text

[Assistant] And so the idea was maybe it's time to do that in AI and NLP. And so maybe why? I mean, why, why would it be tight to do, to do something like that? Well, again, I think, um, because, uh, there was some,


[User] Please transcribe this text

[Assistant] Well, if you scale both the corporate size and the parameter size, you see like very interesting new effects. And these effects are a few shots. So uhm, recently, I mean, just at the beginning of the talk, somebody asked how many, how many examples would you need?


[User] Please transcribe this text

[Assistant] And here in this few shot example, you can see that we want to, we just need like 10 examples to get very strong performances. So I don't remember exactly, uh, what this curve was in terms of accuracy. Maybe I think it was a classification task, but this one, this one is just extracted from the GPT-3 paper so you can find it back.


[User] Please transcribe this text

[Assistant] And so there was this idea, if you scale this a lot, you get some interesting new effects.


[User] Please transcribe this text

[Assistant] And then quickly, what you saw is that, uh, there were new, uh, there were a lot of announcements of reproduction.


[User] Please transcribe this text

[Assistant] And other, uh, labs train a GPT-3 like model for Korean, Anthropic, a startup of the Bay Area raised a lot of money to train similar model. AI21 also trained a massive model called, uh, I think Jurassic.


[User] Please transcribe this text

[Assistant] Another company called Coher also raised a lot of money to train all this model. And Eluter was also this, this grassroot collective who trying to reproduce. So a lot of people were trying to reproducing this.


[User] Please transcribe this text

[Assistant] But, uh, for a diversity of reasons, uh, we were not super convinced about this, mostly because like all these private companies did not seem to be, uh, targeting like open source release. And, uh, and, uh, Eluter was, was the only one, but there was still some main problems.


[User] Please transcribe this text

[Assistant] Here is a few issues I see. First, all this model, they are not designed as research tool. They are really designed by company to be like private thing that they want to use for their private product, but they are not designed as research thing.


[User] Please transcribe this text

[Assistant] And typically when you look at the teams who build them, they're like...


[User] Please transcribe this text

[Assistant] Yeah, they're like small, smaller than they should be in my opinion, mostly because they just belong to one company and mostly.


[User] Please transcribe this text

[Assistant] There is, as again, a lot of environmental costs to training all this model in parallel. If they were open source, we wouldn't need to retrain them. Like in 60 company, we could just share one and that would be a nice model for doing all our experiments.


[User] Please transcribe this text

[Assistant] Uhm, beginning of GPT, there's been a stream of paper investigating, okay, how are these very large Descopra? And we saw that they were lacking something, for instance, they were lacking representativeness of some population.


[User] Please transcribe this text

[Assistant] Uhm, they also some typically have some stereotypes. So basically it's very hard to investigate these training data set also because they are typically not open sourced.


[User] Please transcribe this text

[Assistant] And so this calls in my opinion for like a most, a more open, uh, and collaborative way to develop this. And this is actually possible because if you look in, uh, public.


[User] Please transcribe this text

[Assistant] You see that there are supercomputers that are very powerful and that are actually almost fully accessible. I mean, you just need to submit, uh, like a grant, an application to say, I need some compute times on this. And they have like almost, uh, 2,700 V100 GPU. So that's quite a lot of compute power. You can train a very large language.


[User] Please transcribe this text

[Assistant] So, early this year, I started to talk with the operator and builder of Jean Zay, which is one supercomputer located in France that we knew a little bit from earlier.


[User] Please transcribe this text

[Assistant] And they say, yeah, I mean, come, come train your model basically on a super computer. We're very happy. So we, we kind of.


[User] Please transcribe this text

[Assistant] And Big Science has been going on for like 6 months, a little bit more now, 9 months, and it has already produced very interesting papers. So if you've been following Hugging Face account, you probably saw the T0 paper, which involved actually 40 authors.


[User] Please transcribe this text

[Assistant] Uhm, and so this type of, uh, papers I think is very interesting because it's outside of the box. So it's fully open. It's fully collaborative. And it's also show that you can outperform typically GPT-3 on the zero-shot benchmark with a model which is 16 times smaller.


[User] Please transcribe this text

[Assistant] So now what we are trying to do is to actually increase the size to 16 times larger. So that's what, that's what we'll begin roughly next January and to train this very large model to see if we, how far we can go, right? With the same idea of like openness, open sharing and collaborative.


[User] Please transcribe this text

[Assistant] And so what's next for 2022 at Hugging Face? Well, there is obviously education. If we want to have a beneficial impact, we think that this is the way to start. When we talk about bias in data set, when we thought about understanding your model, knowing what is important for your model, basically we think that we should share all the knowledge we have.


[User] Please transcribe this text

[Assistant] There is this strong community. You're all here today. I'm very happy about that too, because you basically feel following more or less what we've been doing. Open source, this is our DNA and open science. They go hand in hand for me, sharing knowledge.


[User] Please transcribe this text

[Assistant] We try to keep fighting for beneficial responsible AI. I talk a little bit about data set. This is very important, I think, to understand bias, to understand the bias of your model as well. And basically try to make this, yeah, an inclusive place, right?


[User] Please transcribe this text

[Assistant] And while doing all that, doing also this exciting research, when I say we scale this new model to very large sizes, we also try to have some fun and not taking us too seriously, which I think is also, uhm,


[User] Please transcribe this text

[Assistant] Uh, specificity of hugging face and why maybe we have this hugging face emoji, I would say as the name of the company. So welcome to this course. I'm very happy, very excited about this, uh, the second chapter. I think it's, it's very interesting. You will learn a lot and thank you for being here today.


[User] Please transcribe this text

[Assistant] Yeah, that's a good question. I mean, in all generality, you could consider a transformer as a sort of graph neural network.


[User] Please transcribe this text

[Assistant] It's not sparse though, it's fully connected. So maybe it could be more efficient, but if you look at a, like a transformer with sparse attention, in my opinion, it's really close to, it's really close to some graph neural network. In particular, if you use, if you, if you use weight sharing, like universal transformers. Um, I think in NLP transformer, I kind of.


[User] Please transcribe this text

[Assistant] Dominant today, uh, but yeah, as I told you, in my opinion, the main aspect of the revolution is about transfer learning more than the specific architecture. So if tomorrow there is a more efficient architecture than transformer, then I would be happy to


[User] Please transcribe this text

[Assistant] Yeah, so, so it's still, uhm, I mean, the idea of Hugging Face is I think somehow very, very typical, like open core model. So everything is open source, but you need to do the training yourself. You need to do the training loops yourself. So we do provide examples. Uh, that's actually Silva has been doing a lot of work on the examples.


[User] Please transcribe this text

[Assistant] To have like, uhm, an efficient training. So you have open source code to train this.


[User] Please transcribe this text

[Assistant] And then we also provide some paid solutions which are mostly there, I would say, to facilitate.


[User] Please transcribe this text

[Assistant] Your work. So typically if you're in a company, you don't want to wait. You don't want, you don't have time to actually explore everything yourself. You want some, some help. Then we, then we also had here to help.


[User] Please transcribe this text

[Assistant] And then we have some specific solution around making this model very fast. And these are typically mostly interesting to companies more than researchers.


[User] Please transcribe this text

[Assistant] And then here this also, uh, yeah, paid product because people who are interested in them are typically companies who can pay for them. So the idea is that if you're new, if you're like new to the field, if you're a hobbyist, if you're a researcher, you can use all of this for free.


[User] Please transcribe this text

[Assistant] And then if you have some like specific needs, like you need to go very fast to put that in production, you need to have like very low latency because you have huge bandwidth in production. Then we also here to help, but then we help with our, with our like additional services.


[User] Please transcribe this text

[Assistant] Yeah, this came from our very early, uh, uh, inception as a game company, right? We were doing, uh, uh, this kind of, uh, talking chat bot. It was mainly for, for millennials, for kids or young adults. So we wanted a fun name.


[User] Please transcribe this text

[Assistant] I typically use notebook to explore data. I think it's very, very nicely done for that. That's the best way to explore data. And then when I, when I train, I typically want something which is very reproducible. So I switched to something where I can get version cleanly and more like typical VS code.


[User] Please transcribe this text

[Assistant] Tonight we begin a two-part presentation of Mahl Flanders by Daniel Defoe.


[User] Please transcribe this text

[Assistant] You may know Defoe only as the man who wrote Robinson Crusoe. If so, Maul Flanders will come as a shock. Robinson Crusoe, it's not.


[User] Please transcribe this text

[Assistant] To give you a rough idea of what it's like, I quote from the title page where Defoe promised a story about a woman who was, in his words, twelve years a whore, five times a wife, once to her own brother, twelve years a thief, and eight years a transported felon in Virginia. That should be fair enough warning.


[User] Please transcribe this text

[Assistant] That this is a show that may strike some parents as unsuitable for children.


[User] Please transcribe this text

[Assistant] It's doubtful that the Faux deliberately set out to write a scandalous book. He was essentially a reporter, a very good one too.


[User] Please transcribe this text

[Assistant] His reports on what he saw of plain English life in the 18th century are still studied by social historians.


[User] Please transcribe this text

[Assistant] And his Journal of the Plague Years is considered a masterful recreation of life during the Black Plague. He'd spent some time in Newgate Prison for bankruptcy.


[User] Please transcribe this text

[Assistant] And there, thrown in with thieves, pickpockets, prostitutes, he'd had a close look at the underside of British society.


[User] Please transcribe this text

[Assistant] He must have been especially fascinated by the women criminals he met.


[User] Please transcribe this text

[Assistant] In Moll Flanders, he was trying to show how hardship and bad luck breed such women.


[User] Please transcribe this text

[Assistant] She's bright, scrappy, cunning, ruthless, and irresistible to men.


[User] Please transcribe this text

[Assistant] Like Scarlett O'Hara, she's the struggling woman, determined not to be defeated by a cruel world.


[User] Please transcribe this text

[Assistant] Defoe published Moll Flanders over 250 years ago.


[User] Please transcribe this text

[Assistant] And critics have disagreed heatedly about it ever since.


[User] Please transcribe this text

[Assistant] William Hazlitt, the 19th century essayist, called it utterly vile and detestable.


[User] Please transcribe this text

[Assistant] Defoe himself seems interested mostly in the degradation forced on women by a brutal society. And brutal it was.


[User] Please transcribe this text

[Assistant] It was a society in which shoplifting was a capital crime.


[User] Please transcribe this text

[Assistant] If however the condemned shoplifter was a pregnant woman, she could not be hanged until her baby was born.


[User] Please transcribe this text

[Assistant] It was under these circumstances that our heroine was born in Newgate Prison. Mal Flanders, Episode 1.


[User] Please transcribe this text

[Assistant] We've never had the technology to see that first part of the story of the universe.


[User] Please transcribe this text

[Assistant] To be able to look back in time to see the very first light.


[User] Please transcribe this text

[Assistant] Oh yes, for what I told you was the truth and what the world said and what you believed is a lie. I have no fortune, only a few pounds and that is all.


[User] Please transcribe this text

[Assistant] Marvin was so insecure about that. For some reason he was like a horse with blinders on. He didn't see that. But it was out there. It was always out there. He just never let it in.


[User] Please transcribe this text

[Assistant] Continue to blossom. And he was still being knocked back to the back of the bus. I think it became almost like a resentment if you will.


[User] Please transcribe this text

[Assistant] Mugabe is a slugger. A total slugger. And I noticed that Mugabe was outboxing Marvin Forwell.


[User] Please transcribe this text

[Assistant] The first four rounds, I was floating. I was rather surprised how easy I was able to hit him. And uhm,


[User] Please transcribe this text

[Assistant] Hating like a man. I said, not right now. Not right now.


[User] Please transcribe this text

[Assistant] Okay, get out of that corner. Get out of the corner. Fight your way out.


[User] Please transcribe this text

[Assistant] But I could beat him on points. I threw better punches, cleaner punches, and far more punches than he threw. That's how you win a fight.


[User] Please transcribe this text

[Assistant] And he met. I mean, he isolated himself usually out in Provincetown, you know, way at the tip of Cape Cod.


[User] Please transcribe this text

[Assistant] Emmett and Jasper were more excited about planning the hunting possibilities. The Amazon offered a change from our normal quarry. Jaguars and Panthers, for example. Emmett had a whim to wrestle with an anaconda. Esme and Rosalie were planning what they would pack. Jacob was off with Sam's pack, setting things up for his own absence. Alice moved slowly for her around the big room.


[User] Please transcribe this text

[Assistant] Unnecessarily tidying the already immaculate space. Straightening Esme's perfectly hung garlands.


[User] Please transcribe this text

[Assistant] She was re-centering Esme's vases on the console at the moment. I could see from the way her face fluctuated, aware, then blank, then aware again that she was searching the future. I assumed she was trying to see through the blind spots that Jacob and Renesmee made in her visions as to what was waiting for us in South America until Jasper said, let it go, Alice. She's not our concern.


[User] Please transcribe this text

[Assistant] And a cloud of serenity stole silently and invisibly through the room. Alice must have been worrying about Arena again.


[User] Please transcribe this text

[Assistant] She stuck her tongue out at Jasper and then lifted one crystal vase that was filled with white and red roses and turned toward the kitchen. There was just the barest hint of wilt to one of the white flowers, but Alice seemed intent on utter perfection as a distraction to her lack of vision tonight. Staring at Renesmee again, I didn't see it when the vase slipped from Alice's fingers.


[User] Please transcribe this text

[Assistant] I only heard the whoosh of the air whistling past the crystal and my eyes flickered up in time.


[User] Please transcribe this text

[Assistant] To see the vase shatter into ten thousand diamond shards against the edge of the kitchen's marble floor.


[User] Please transcribe this text

[Assistant] We were perfectly still as the fragmented crystal bounced and skittered in every direction with an unmusical tinkling. All eyes on Alice's back.


[User] Please transcribe this text

[Assistant] I could have darted across the room to catch the vase in plenty of time myself. If I hadn't assumed she would get it. And how would it fall through her fingers in the first place? Her perfectly sure fingers. I had never seen a vampire drop anything by accident, ever. And then Alice was facing us, twisting in a move so fast it didn't exist. Her eyes were halfway here and halfway locked on the future. Wide, staring.


[User] Please transcribe this text

[Assistant] Looking into her eyes was like looking out of a grave from the inside.


[User] Please transcribe this text

[Assistant] I was buried in the terror and despair and agony of her gaze. I heard Edward gasp. It was a broken, half-choked sound. What? Jasper growled, leaping to her side in a blurred rush of movement, crushing the broken crystal under his feet. He grabbed her shoulders and shook her sharply. She seemed to rattle silently in his hands. What, Alice? Emmett moved into my peripheral vision.


[User] Please transcribe this text

[Assistant] His teeth bared while his eyes darted toward the window, anticipating an attack. There was only silence from Esme, Carlisle, and Rose, who were frozen just as I was. Jasper shook Alice again. What is it? They're coming for us. Alice and Edward whispered together, perfectly synchronized, all of them silenced.


[User] Please transcribe this text

[Assistant] There's, there are now two screens here where I can see myself, which is, uh, gonna be very distracting.


[User] Please transcribe this text

[Assistant] I introduced myself to some people at lunch and I'm probably just going to repeat myself. But my group, which we founded in about 2006, has been uhm,


[User] Please transcribe this text

[Assistant] We are artists first. We are passionate about that. That's what we do when we get up in the morning and that's how we pay our mortgages. Uhm, but,


[User] Please transcribe this text

[Assistant] Other than luck, this is my other secret ingredient that I spend about a third of my time in the white heat of making art. So sitting in front of computer programs that I've written or sitting in theaters.


[User] Please transcribe this text

[Assistant] And making decisions about how big this should be, what color it should be, how fast it should move. I spend about a third of my time doing research into how I might want to make art.


[User] Please transcribe this text

[Assistant] Computer graphics, rendering systems, computer vision. Mainly culling stuff from academia.


[User] Please transcribe this text

[Assistant] The second half of what I'm going to be talking about today will be specifically on the tools that I've constructed.


[User] Please transcribe this text

[Assistant] A few years now is that there is at this time, in this contemporary moment, an incredibly, uh, important convergence between art, science, and engineering's practice.


[User] Please transcribe this text

[Assistant] So the values remain completely different. Uh, we, we care about different things and we evaluate ourselves in very different ways. And our aesthetics and our tendencies are very different. But the way that art is working


[User] Please transcribe this text

[Assistant] And the way that science is working in some conditions, the way that engineering is working, is reaching a point of contact.


[User] Please transcribe this text

[Assistant] I spend much of my life hacking my way into large data sets. They might be data sets that I've commissioned like a film or


[User] Please transcribe this text

[Assistant] Some choreography. Or I've done something weird with cameras. And, but I'll go to that and I'll say well somewhere in this data there is something which will catch my eye and I have to dig it out. I have to find the sort of potential which is latent in this. Until it satisfies my eye, my heart. And my feeling is that science and engineering are converging to a point where the software stacks are very tall.


[User] Please transcribe this text

[Assistant] The algorithms are often not owned by the researcher. The data sets are large and often not commissioned specifically for a particular experiment.


[User] Please transcribe this text

[Assistant] And the researchers have to dig their way into these datasets in order to find something which catches their eye and satisfies them as researchers.


[User] Please transcribe this text

[Assistant] So you can, for the purposes of the talk, whenever I talk about art, try to hear science or engineering. Uhm.


[User] Please transcribe this text

[Assistant] I'd like to tell you a story of a man who writes computer programs to make art sitting in a theater with an American choreographer called Trisha Brown who's very famous and incredibly


[User] Please transcribe this text

[Assistant] Talented choreographer and she has a dance company and we're working on a piece together called How Long Does the Subject Linger on the Edge of the Volume. It's a great title.


[User] Please transcribe this text

[Assistant] And it's a real-time motion capture piece. We have a motion capture studio on stage. Back in 2003, this was rocket science. Uhm, and in 2013, I had my lunch in a library that had a motion capture studio.


[User] Please transcribe this text

[Assistant] But 2003 it was, it was very hard to do. And we had, it was so hard that we had three years to make this piece. And residency after residency hosted by Arizona State University. And I'm sitting in a theater.


[User] Please transcribe this text

[Assistant] And I know her work very well and I love her work. And I'm trying to keep up with this choreographer. And she's working in a very algorithmic way.


[User] Please transcribe this text

[Assistant] I want to be able to keep up. So we made ultimately a piece. It looked a bit like this.


[User] Please transcribe this text

[Assistant] The live visuals were projected on a transparent screen in front of the dancers. Uhm, real-time motion capture setup, so my code knew where the dancers were, uhm, and when it worked incredibly precisely.


[User] Please transcribe this text

[Assistant] And I tried to develop, and I still try to develop today, a coding methodology, a practice that combines all the things I saw in her work. Uhm, that tries to sort of


[User] Please transcribe this text

[Assistant] Be as good at what I do as she was at what she does. And the key ingredients are these. There's something both improvisatory and rehearsalable about what she did.


[User] Please transcribe this text

[Assistant] Practice that could absorb, like many other modern choreographers, absorb all kinds of feels.


[User] Please transcribe this text

[Assistant] She would be influenced by linguistics, by computer science, by visual art, by drawing.


[User] Please transcribe this text

[Assistant] One of the key things I did in order to survive that encounter is I developed some software agents that would hang out with her in


[User] Please transcribe this text

[Assistant] The, uh, in the studio. And these were agents which were rehearsable. So this is


[User] Please transcribe this text

[Assistant] A triangle creature is a creature that starts off as a triangle and plays a game where it tries to go from stage left to stage right, uhm, in about three and a half minutes. So as the choreography solidified, the staging got better and better.


[User] Please transcribe this text

[Assistant] Uh, learning algorithms were very useful for putting


[User] Please transcribe this text

[Assistant] A agent which could learn or be poked and prodded in the right ways. I could do that off-site and then I could turn up into the theater.


[User] Please transcribe this text

[Assistant] We were commissioned to make a piece for, to celebrate Mozart's birthday by the Mostly Mozart Festival in Lincoln Center. And we came up with a very elaborate cluster of computers that would


[User] Please transcribe this text

[Assistant] Decompose, not motion capture of the dance company or anything else, but the last 45 seconds of Mozart's last symphony, which is almost secretly


[User] Please transcribe this text

[Assistant] Five part invertible counterpoint. So at the end of this, uh, wonderful symphony, it sounds like Mozart.


[User] Please transcribe this text

[Assistant] Uh, he reveals that all of the thematic material that you've heard thus far fits together as this perfect jigsaw puzzle.


[User] Please transcribe this text

[Assistant] Louis Longray to help us kind of decompose the end, uh, of this. He went.


[User] Please transcribe this text

[Assistant] At piece of music with some magic markers, we then trained the computer to do the same thing, so to recognize the themes as they were being stated, both in the ending and in the entire, uhm, movement. And then we built a piece that would slowly put the jigsaw puzzle back together again.


[User] Please transcribe this text

[Assistant] So there were, uhm, ten computers with ten screens and ten speakers. Each computer had, uh, an instrument in the orchestra. And we unfolded


[User] Please transcribe this text

[Assistant] And the first five or six seconds were literally random notes. And then slowly a thematic se- uh, a tonal center emerges.


[User] Please transcribe this text

[Assistant] And then the thematic material emerges and then the replacement gets put into the right position with respect to the screens which are nearby.


[User] Please transcribe this text

[Assistant] And then eventually you have a CD quality recording of the entire motion. After about anywhere between 25 and 35 minutes.


[User] Please transcribe this text

[Assistant] And then the computer performs the piece, the 45 seconds of music, and throws it all away and starts again. Uhm, and it was installed continuously for about six weeks in the summer heat of New York.


[User] Please transcribe this text

[Assistant] So one of the rules of the piece is that we, uhm,


[User] Please transcribe this text

[Assistant] We took all of the, one performer from each musical instrument into a recording studio.


[User] Please transcribe this text

[Assistant] There were two pieces there, uhm, one that took place in a theater, uhm,


[User] Please transcribe this text

[Assistant] With a wonderful dance company. One that took place in kind of conversation with Mozart over the ages. Uhm, and


[User] Please transcribe this text

[Assistant] The one in the theater led me towards these learning algorithms and agents as a formal device for making art.


[User] Please transcribe this text

[Assistant] This one led me towards a separate set of concerns where you can't make a 35 minute long piece with a compile cycle. Uhm.


[User] Please transcribe this text

[Assistant] Because if something bad happens 34 minutes in, you can't, you just can't start again from scratch. You just go insane. So.


[User] Please transcribe this text

[Assistant] Uhm, the tools I needed to build there looked a lot like...


[User] Please transcribe this text

[Assistant] There's a tremendous number of pieces of digital art, which are about three minutes long. There's a kind of a scale there, uhm, because the tools that are being used to construct them have a compile cycle. You have to tear it down, start it up, every damn time you change.


[User] Please transcribe this text

[Assistant] Try to get rid of some of the impedance mismatch that you have before you get to that point. So, the next piece I want to talk about is a piece called Into the Forest, which marks our sort of return to, uh, motion. And one of the things about our relationship to dance is that


[User] Please transcribe this text

[Assistant] Uhm, we've had the great fortune to work with the best choreographers in the world, but once we've worked with them once we kind of get


[User] Please transcribe this text

[Assistant] To dance. And the opposite to dance for us is children's motion. We made a game.


[User] Please transcribe this text

[Assistant] Large, very large database of, of pretend children mocap that we cut up into pieces. Uhm, and then the game was really two components to the game. One is can we build some agents which automatically piece that motion capture back together again in order to create an endless game of hide and seek in a forest that was constantly shifting? And could we make that beautiful?


[User] Please transcribe this text

[Assistant] And we've made this piece a couple of times and the last version of it was a stereoscopic interactive projection of the Museum of Moving Image in New York.


[User] Please transcribe this text

[Assistant] Uhm, which I think was the first connect-based interactive artwork in a major museum.


[User] Please transcribe this text

[Assistant] 15% better. Also, imagine this was in stereo, which is obviously something I can't do here. Uhm, so this is this sort of hallucinogenic memory and fantasy of childhood in a forest that changes whenever the camera's back is turned.


[User] Please transcribe this text

[Assistant] To build that piece, we made our own game engine, in a sense. Uhm, and...


[User] Please transcribe this text

[Assistant] We did a very good job of it. Uh, we built all of the


[User] Please transcribe this text

[Assistant] Things that were the standard trapping of high-end computer games at the time, we had high dynamic range lighting, we had


[User] Please transcribe this text

[Assistant] What was different about this is we took all of those well implemented components and we glued them together in non-physically correct ways. So, we would have times when lights would cast not shadows, but zones of clarity.


[User] Please transcribe this text

[Assistant] The color palette was compressed in an incredibly oddball way, unwrapped in non-traditional ways.


[User] Please transcribe this text

[Assistant] And by producing something which was both physically inspired, but not the physics of our real world, we ended up with a form that was medium-like. So, because it had its own physical integrity, it had some pushback.


[User] Please transcribe this text

[Assistant] It was a complex system that wouldn't necessarily do what it was told. It was something that you had to dig your way into. And...


[User] Please transcribe this text

[Assistant] The tool support that we needed for that was really kind of mining our


[User] Please transcribe this text

[Assistant] So that we could locate islands that we were interested in.


[User] Please transcribe this text

[Assistant] And that we could audition interpolations between those sets of parameters and perturb those in directions that we found most favorable.


[User] Please transcribe this text

[Assistant] And so the game of hide and seek was played by the children and sometimes by...


[User] Please transcribe this text

[Assistant] The next to last piece that I want to talk about is one that we opened a couple of weeks ago. Uhm, which is...


[User] Please transcribe this text

[Assistant] I want to talk about, because it's actually in some sense is a return to the concerns that I had for the piece I did with Trisha Brown.


[User] Please transcribe this text

[Assistant] This is a piece, one of several that we've done with a British choreographer called Wayne McGregor. Uhm, and we've worked on


[User] Please transcribe this text

[Assistant] Four pieces with him now. Uh, this was a different kind of commission. We, I wanted to give him, this was a love letter from me to him, and I gave him an eleventh dancer. He has ten dancers in his company. And I gave him a piece


[User] Please transcribe this text

[Assistant] That would sit in the corner of his studio as he made a dance piece. And


[User] Please transcribe this text

[Assistant] The piece that he was making, uhm, which premiered also a couple of weeks ago, uh, was a piece called Atomos, and there was a secret text to it.


[User] Please transcribe this text

[Assistant] Earlier on we'd done a piece called The Choreographic Language Agent, which was an exploration


[User] Please transcribe this text

[Assistant] And you either know this because you're in the field or you don't because you're not. Uhm, there's a huge.


[User] Please transcribe this text

[Assistant] Uh, lying, a huge current through modern, uh, dance.


[User] Please transcribe this text

[Assistant] Which involves the use of geometric abstraction in order to produce movement and to keep dancers off balance.


[User] Please transcribe this text

[Assistant] Uhm, or problems that involve geometries of their own bodies. So we built them a kind of abstract geometric computer where you could try out, uh, operations on stick figures and other connections of dots. A programming environment, uhm, that would kind of be a midpoint between computational concerns that I had and concerns that he had. But for this 11th dancer, we gave him a much simpler interface. Uhm, here you could select a clip


[User] Please transcribe this text

[Assistant] It's getting darker all the time here, it's great. Uhm, you can select a clip from this film, which I won't name. Uhm, and we automatically decomposed it into 1245 shots. Uhm, which turns out that's how many scenes there are in a movie.


[User] Please transcribe this text

[Assistant] Uh, and you could select one of them and one part of the motion of it and tell this piece becoming, uh, to respond to that. And it was an autonomous artwork that would


[User] Please transcribe this text

[Assistant] Iteratively, much slower than this as it's being sped up, start from a point, grow its body, uh, change its musculature and when its muscles fire, and keep going.


[User] Please transcribe this text

[Assistant] Uhm, heading towards trying to capture as much of the motion that it found in that scene. There's a big lump of computer vision that happened offline.


[User] Please transcribe this text

[Assistant] Person walking around or someone crashing through a plate glass window, uhm, that was in the scene that had been selected.


[User] Please transcribe this text

[Assistant] And, other than selecting the video material that went into it, uhm,


[User] Please transcribe this text

[Assistant] This creature hung out both in his studio and it was installed in a museum in London.


[User] Please transcribe this text

[Assistant] What you can't see here is that it was in stereo and because it was


[User] Please transcribe this text

[Assistant] And they wanted three things, or we wanted to give them three things. Uhm, they have a large LED wall on the front of the building, which is a very strange shape.


[User] Please transcribe this text

[Assistant] From unstructured or semi-structured sets of photographs. And the dataset that we had to dig into


[User] Please transcribe this text

[Assistant] Was this tremendous and extremely undisciplined set of photographs. The people who own the Barclays Stadium, it turns out.


[User] Please transcribe this text

[Assistant] Own, and the piece ended up looking like this, uhm, own lots of properties around Brooklyn. So we could get roof access to like half the city.


[User] Please transcribe this text

[Assistant] So, in addition to that, we delivered, uhm, this five-channel piece, which was built out of a sort of procedural re-rendering of that material, uhm, that ultimately yielded about two hours of material. And this is installed permanently on the inside.


[User] Please transcribe this text

[Assistant] Because once we've constructed this, uh, system, we can then make procedural deformations of each scene in order to project, provide a kind of string of screens. Uhm. And it becomes a game.


[User] Please transcribe this text

[Assistant] There was a thing that I call the Oculus, which is a gigantic curved LED wall. That is a, it goes from a ribbon to a fat pocket and back up again in a, in a circle. So we recast our material, uhm, one more time in order to fit on this strangely shaped, incredibly bright LED wall. And for a wall like this,


[User] Please transcribe this text

[Assistant] We went right back to our time in the theater, in a sense. We had incredibly little rehearsal time.


[User] Please transcribe this text

[Assistant] McDonald's and Nike feel that they own this thing. Uhm, and to get time on the device,


[User] Please transcribe this text

[Assistant] It gives the other meaning to the title of this piece. It's not just me going out into the world and trying to take advantage of the opportunities I find there. Uhm, Field is a project that I started as a PhD student.


[User] Please transcribe this text

[Assistant] Just enough, uhm, ego and preference and statement by me. Just enough drawing capability, uhm, cause I think it turns out.


[User] Please transcribe this text

[Assistant] Uhm, it is a Java based ID for what the cool cats are calling live coding these days. Uhm, it's written in Java.


[User] Please transcribe this text

[Assistant] It's primarily for writing Python, uhm, although we've been using other JVM languages recently, uh, Clojure in particular. And it also comes with a high performance, super contemporary OpenGL graphics system, uhm, which is


[User] Please transcribe this text

[Assistant] When you don't know what you're doing. Uhm, it's a programming language for the panic attack in the theater.


[User] Please transcribe this text

[Assistant] Uhm, because it's a programming language to let you make mistakes over and over again as you fumble towards, uhm, digging that thing.


[User] Please transcribe this text

[Assistant] Uh, mining that potential out of the work that you're doing. So, here's a screenshot. Sorry. It has a text editor. It has a canvas. Uhm, it has various rendering windows of various kinds. And the text editor is, I think, maybe one of the coolest things about it.


[User] Please transcribe this text

[Assistant] It has a couple of things going on which you can see from the screenshot. Uhm, one is that it has very nice support for autocompletion and it does, uh, a very good job of trying to gleam as much information as it can out of source files and Java docs and other ways of introspecting. That ends up being incredibly important when you're stitching together other people's code. The ability to explore, uh, very deeply


[User] Please transcribe this text

[Assistant] The surface of a library that you've just downloaded from Google, uhm, is an important difference between success and failure.


[User] Please transcribe this text

[Assistant] The other thing which is going on here is that you can embed graphical user elements into the text editor directly. Uhm, one of the reasons that people in the digital art community don't write code and use other tools


[User] Please transcribe this text

[Assistant] Is because changing a number and over and over again textually feels boring and time consuming. So the trick is to have


[User] Please transcribe this text

[Assistant] Graphically user interface elements that compile or are just secretly masquerade as text.


[User] Please transcribe this text

[Assistant] But once you've opened the box, you have all kinds of more exotic.


[User] Please transcribe this text

[Assistant] Not evaluate to its number 20, but evaluate to something which will go and find the contents of that box later. So you can defer, uh, evaluation of parts of your code. Uhm, so even once this, uhm,


[User] Please transcribe this text

[Assistant] And then I can start doing things like this. So this is a piece of Python, which embeds another programming language in it, uhm, in this case Clojure.


[User] Please transcribe this text

[Assistant] Uhm, I hand that to a different runtime, a different environment for compilation and execution.


[User] Please transcribe this text

[Assistant] So I can change my mind about what programming language I'm coding in, uhm, as I go. I can do things like that.


[User] Please transcribe this text

[Assistant] Another more useful example than calling a speech synthesizer is calling bash.


[User] Please transcribe this text

[Assistant] And there are other things in the text editor which I think are interesting and relevant. Uhm, these areas appear in the margin.


[User] Please transcribe this text

[Assistant] Do two things. They remind you of what blocks were executed. They appear automatically and give you a quick way of executing them again. Uhm, and there's a tendency now, I think, in some of the really young life coding environments that


[User] Please transcribe this text

[Assistant] Parsing and execution is, is always good and it should just be happening, ambiently.


[User] Please transcribe this text

[Assistant] The code that is in the text editor lives in boxes which are drawn in the canvas, the other window.


[User] Please transcribe this text

[Assistant] There's a object model of sorts there. Boxes can refer to themselves and can find other boxes. Uhm, there's enough tools that you can build if you want to.


[User] Please transcribe this text

[Assistant] Will appeal to Emacs users, uhm, you can write code which extends the environment.


[User] Please transcribe this text

[Assistant] There's no sense the field is the one final solution to any particular problem. Uhm, and I don't actually have a horse in the race of winning the environment wars. Uhm, this is a piece of code which is executed somewhere else.


[User] Please transcribe this text

[Assistant] It's executed inside a software product called Maximus P, which is a...


[User] Please transcribe this text

[Assistant] Most of the Adobe products have a secret JavaScript backdoor. Sometimes you have to go through AppleScript, which is a little weird.


[User] Please transcribe this text

[Assistant] Uh, most of the Apple products of course have Apple script. And then the Maximus P sort of music, family.


[User] Please transcribe this text

[Assistant] Uhm, we'll have hiding away in the corner without a job around time, which was enough.


[User] Please transcribe this text

[Assistant] Enough of a gap for you to drive a car through.


[User] Please transcribe this text

[Assistant] And then there's a drawing and notation system that lets you illustrate things which are happening on this canvas. So there's code living in these boxes, the code to draw these elements. Uhm, here, this slider here.


[User] Please transcribe this text

[Assistant] That can get out of hand. This is a score for a tiny little bit of a piece that we did.


[User] Please transcribe this text

[Assistant] Where I needed the ability to align things to music. So a little bit of musical analysis. And suddenly I have


[User] Please transcribe this text

[Assistant] Well, what do I have? I've got basically 90% of Adobe After Effects. I have my timeline metaphor. I have code that pulls in JPEGs from disk, texture maps them. Uhm, I'm basically done.


[User] Please transcribe this text

[Assistant] I have a multi-layer video editing suite for very little cost. And then finally, there's just enough of a relationship to


[User] Please transcribe this text

[Assistant] Several huge ecosystems. So Java, there's a tremendous amount of existing code written in Java. Uhm, that.


[User] Please transcribe this text

[Assistant] Logic, After Effects, QuickTime, uh, MaxMSP I think has two timelines hiding in it. Uhm, they're all,


[User] Please transcribe this text

[Assistant] Implement, re-implementations of the same conceptual structure, and this is offensive in two ways. One is that it's a tremendous amount of wasted effort. Uhm, but really, the most offensive thing is that they don't talk to each other at all. Uhm, and if we radically reconceived how we distribute and think of software, there would be a set of competing timeline components that would be able to talk quick time and music and various other things. And we would destabilize and unglue


[User] Please transcribe this text

[Assistant] Frankly, more futuristic spot with the relationship to getting computers to do, uh, interesting things. This is the same diagram with data flow, uhm, concepts.


[User] Please transcribe this text

[Assistant] Now these two things are as old as the hills. These are ideas from the 60s and 70s. And yet they are apparently still vibrant and interesting because they're all over my hard drive. Uhm, what can we do to, to...


[User] Please transcribe this text

[Assistant] Can we end this? I don't know. Uhm, field is on GitHub.


[User] Please transcribe this text

[Assistant] And shameless plug for our piece that I'm doing with my partners and Eric Kapczynski and


[User] Please transcribe this text

[Assistant] Results come trickling in as point clouds successfully compute or fail to.


[User] Please transcribe this text

[Assistant] So you have a real time graphic system that can take in these results.


[User] Please transcribe this text

[Assistant] And that you can poke with various, uh, ways. You have extra analytical tasks which use the photography and re-project them back out onto the point clouds. You're developing those at the same time. Right?


[User] Please transcribe this text

[Assistant] And they're all coming together in a very real time way because ultimately what you're looking for is a moment where all of the people involved in the project, me and my partners, are sitting in a beautiful theater somewhere or our lousy studio space or something looking at the piece hopefully at scale.


[User] Please transcribe this text

[Assistant] You send a digital cinema package on a hard drive that's a directory full of JPEG 2000s. Even though it's completely canned, you've made it through this real time moment.


[User] Please transcribe this text

[Assistant] Uhm, I went back to a place called MPAC, which is a huge performing arts center in upstate New York, which I've often had the wonderful experience of making art in.


[User] Please transcribe this text

[Assistant] But I went back not to make art this time, I went back to explore topics in data visualization. Uhm, and we had, uh, we'd gotten some data, real data from real scientists, uhm,


[User] Please transcribe this text

[Assistant] Uhm, and I was trying to suppress any kind of engineering knowledge I had over what the data might be. And I was going to try and find something interesting in it. And I think


[User] Please transcribe this text

[Assistant] And uhm, I don't think they were very mean, but there might have been a sense that while these artists are going to tie themselves in knots, trying to find meaning out of what should be basically white noise.


[User] Please transcribe this text

[Assistant] The points had about three or four numbers associated with them, but it's kind of a nine, ten million uhm, point data set. The first thing we do is just stick it on the screen. So ten million points you can't plot in Excel. Ten million points you can't do anything uhm, obvious and dumb and visual in MATLAB.


[User] Please transcribe this text

[Assistant] The scientists in question had never thought to visualize this data. They didn't have any tools.


[User] Please transcribe this text

[Assistant] We didn't have to go chasing down sophisticated algorithms. They were just there.


[User] Please transcribe this text

[Assistant] Completely wrong because they were not using a random number generator, they were just using random numbers. Uhm, so there wasn't anything subtle and hidden about that. I think there's a lot of stuff you can then do afterwards.


[User] Please transcribe this text

[Assistant] But I feel like there's a whole bunch of obvious moves which aren't being made in traditional visualization pipelines.


[User] Please transcribe this text

[Assistant] Science as a practice is moving towards having to publish its code.


[User] Please transcribe this text

[Assistant] And once we've gone through a period, a messy period of five years of that, people realize that they can't just publish their code.


[User] Please transcribe this text

[Assistant] Uhm, but if we reach that point where there's sort of large frameworks of interoperable stuff that people are adding to,


[User] Please transcribe this text

[Assistant] If we, if that broadened out to cover larger engineering domains, then what we would need, we'd need tools to be able to rapidly find these things and test them against your work to see if it solves the problem you want.


[User] Please transcribe this text

[Assistant] Like I say, I don't really have a horse in this race. I'm not wanting to take over the world. Uhm, there are audiences that say, well, isn't this just Brad Victor stuff? And there are audiences that say, isn't this just Emacs? Uhm, the Emacs people are a harder audience. But, uh, there is something old about this. Uhm, there's something very, uh, very old school about, uh, some of these ideas. And now I think that the,


[User] Please transcribe this text

[Assistant] Uh, incredibly in many user interface ways and, and we have to as a community acknowledge there's a conceptually interesting idea here.


[User] Please transcribe this text

[Assistant] The path that diverged out of the Unix philosophy and then another path which diverged out of the kind of symbolic list machine world. Uhm, but those things are, they're old. I mean, why aren't we there? Why are, why isn't there a competition? Uhm, I mean, I heard Apple reveal the new Mac three days ago and someone was like, finally we can edit 4K video in real time.


[User] Please transcribe this text

[Assistant] Which is kind of an abdication of responsibility. You can not talk at all, which means the press talk about your work and you don't. Or you can kind of deal with the dangers of people.


[User] Please transcribe this text

[Assistant] I'm happy to talk about the stuff behind the scenes because I think it's useful and you have, when you do a piece at Stanford, you have a kind of ethical responsibility to let people in behind it.


[User] Please transcribe this text

[Assistant] And then we'll dive into user actions that could best support that overall metrics so we can hone in a little bit closer on what we'd actually like to track. And then from there, I'll propose some


[User] Please transcribe this text

[Assistant] Yeah, so YouTube's mission overall, I would say, is to first of all entertain its users, you know, billions of people all over the world love to go to YouTube to watch videos every day.


[User] Please transcribe this text

[Assistant] Uhm, and then I'll also suggest a tracking metric for


[User] Please transcribe this text

[Assistant] And then the last one is a likes, comments, shares, I'm sorry, not shares, um, subscribes per videos viewed. So I'd want to see that's almost like a, a like through rate.


[User] Please transcribe this text

[Assistant] Sometimes I look at internal to the team, which I think with a product like YouTube,


[User] Please transcribe this text

[Assistant] I got a phone call from Russell saying, I need you to come and get me.


[User] Please transcribe this text

[Assistant] To play this guy right off the court, just carry that team right to the championship, right handed.


[User] Please transcribe this text

[Assistant] From that moment on, Russell loved Murray Cramer till the day he died, because Russell knew that Cramer didn't look on him as a black man. If you would drink out of his glass, the same glass that he drank out of right away, without even a thought, then you must be okay.


[User] Please transcribe this text

[Assistant] Would Russell live in Redding? Cause he was a black man and he was gonna take black people to Redding and show them that black people could live in Redding.


[User] Please transcribe this text

[Assistant] Kids would come up to him and they'd say, you know, uh, Mr. Russell, I think an autograph, he'd say, son, I don't give autographs. I thought I'd be happy to shake your hand. He just really believed that you took him at the value of his demand and that a handshake was a lot better than a signature.


[User] Please transcribe this text

[Assistant] Prior to a bill coming, we had plenty of people who could score, we just didn't have the size. He didn't have to bring scoring, he brought defense and rebounding to us.


[User] Please transcribe this text

[Assistant] This article says, uh, you hate all white people. You hate me? He said, Frank, I was misquoted.


[User] Please transcribe this text

[Assistant] Red was very, very conscious of the racial issue. On a particular sequence, koozies hurt. And they go out on the road and...


[User] Please transcribe this text

[Assistant] They all picked it up, said, boy, Kuzin's here, we gotta do it. And they played four or five, and they won. All of them.


[User] Please transcribe this text

[Assistant] Not, Celtics finish great road trip, Russell sensational. So Red said to them, listen, Russell is the reason.


[User] Please transcribe this text

[Assistant] They realized they needed one another. And Russell realized that he was in a perfect situation for him to play basketball. That Red would let him play the game that he wanted to play.


[User] Please transcribe this text

[Assistant] You have my thanks, sir. On whatever service I and my family may provide you and yours, we owe you our lives.


[User] Please transcribe this text

[Assistant] I do not know, my lord. Orcs burned our village and slew our people. Those who were not killed were scattered, I know not where. Doolad, my son, struck down as he defended us.


[User] Please transcribe this text

[Assistant] Yes, or I did when he was but a child. If he's the same man, he is the son of my friend and kinsman, our chieftain Lord Arador.


[User] Please transcribe this text

[Assistant] Dr. Taldal, our village. Lord. De Hale. My wife, Evorwen, and my daughter- You're right.


[User] Please transcribe this text

[Assistant] When the servants of evil try again to drive the Dunedain father into exile.


[User] Please transcribe this text

[Assistant] My heart for Boods, Arathorn will wear Barahir's rings sooner than expected.


[User] Please transcribe this text

[Assistant] Even so, I do not think he will long lead the Dunedain once it rests upon his hand.


[User] Please transcribe this text

[Assistant] I owe you my life, son of Arador, but I do not owe you my daughter.


[User] Please transcribe this text

[Assistant] I fear deeply the shadow you cast on my house, Isildur's heir. The attention you show my daughter is unwelcome. By you.


[User] Please transcribe this text

[Assistant] Yes. Therefore it cannot be ignored.


[User] Please transcribe this text

[Assistant] I hope for Gullwine's sake, your father's blood flows through your veins. If you fail your people, you will have only the Dúnedain to answer to. If you fail her, it is me to whom you'll answer.


[User] Please transcribe this text

[Assistant] They'll find us soon enough. So, I must hurry, or miss my chance.


[User] Please transcribe this text

[Assistant] We oughta be in Los Altos soon. Well, not until tomorrow morning.


[User] Please transcribe this text

[Assistant] Satisfied? Hmm? You just saw the rear end of a cab. Come on, turn on the heat and let's get going.


[User] Please transcribe this text

[Assistant] My lord, the orcs are on the move. Lord Arriheb had me to say this. The decision has been made for you.


[User] Please transcribe this text

[Assistant] This new show on Netflix, uh, talk about your character and what you play as.


[User] Please transcribe this text

[Assistant] The man who runs the HBC, so he's the English kind of, you know, runs the HBC. And I was raised by him and he murdered my whole.


[User] Please transcribe this text

[Assistant] That would have been a great title for it.


[User] Please transcribe this text

[Assistant] All right, all right, very good. All right, so these are your axes from home? Yes, I started making axes. Okay. Oh, you made these axes? Well, one of them, this one's for you. This is the Black Wolf Company Axe. I got my tattoo on there and everything. Really? All right. And what should I do with this? Throw it.


[User] Please transcribe this text

[Assistant] Musical stylings of Mona Zucker. Best example of talent. We have this little child of desire. That he should just shut up. Oh, that he should just shut up.


[User] Please transcribe this text

[Assistant] So then, yeaah, can I be fortunate with quit.


[User] Please transcribe this text

[Assistant] Oh look, they have this tree blocked off. I wonder if it's because there's a clue in there. Wow. I bet you there is. There's a little white puppy over there.


[User] Please transcribe this text

[Assistant] Yeaah, it is, it's blocked off. Maybe this isn't the right tunnel.


[User] Please transcribe this text

[Assistant] Well, we can try heading that way. Hey, look! There's the twisted trees that Dr. Gold told us about. Do you see the twisted trees?


[User] Please transcribe this text

[Assistant] J-E-D-I. What does that spell? Je- Jedi. Jedi. Well I don't see any Jedi around here. Yea. I do see a lot of twisted trees though. Look at that, it's like a knot. Yea. And look, this says T-T-5.


[User] Please transcribe this text

[Assistant] I wonder what's inside of it. Well, I have our adventure pack, so let's get out our hammer.


[User] Please transcribe this text

[Assistant] I don't know what this baby flamingo means, but I think we should keep it as a clue.


[User] Please transcribe this text

[Assistant] Hey everybody, Don Georgevich here, Job Interview Tools. And today I want to talk to you about things that you shouldn't say in a job interview. Specifically, five things that you shouldn't say, bring up, mention in any way, shape or form in a job interview. If you do, it's almost cause for instant disqualification from the process. So, the first thing, number one, don't talk bad about your company.


[User] Please transcribe this text

[Assistant] Don't talk bad about the people. Don't say that you didn't like working there. No matter how it comes up. I mean, maybe you hated working there. Maybe you hated your boss.


[User] Please transcribe this text

[Assistant] When they ask you questions, you know, well, did you like working there? Did you like your boss? The answer is yes, I did. I liked working for my boss. I liked the people. I liked the company. I liked the culture. I liked everything about him.


[User] Please transcribe this text

[Assistant] It's a shame that I have to leave. I'm sorry I have to leave but I'm looking for, you know, greener pastures, greater opportunity. I'm looking for more responsibility.


[User] Please transcribe this text

[Assistant] Say any of that except that you didn't like working there, you didn't like what they did or anything like that.


[User] Please transcribe this text

[Assistant] Because if you didn't like working there or you didn't like your boss, it'll be no time flat that you'll be saying the same things.


[User] Please transcribe this text

[Assistant] About them when they hire you. You'll find reasons not to like them. You'll find reasons not to like your boss.


[User] Please transcribe this text

[Assistant] That's just the way it goes. People who don't like where they worked at, it doesn't take them long to not like the new place.


[User] Please transcribe this text

[Assistant] That's just the way it goes. That's human nature. So you don't want to communicate that.


[User] Please transcribe this text

[Assistant] You get labeled as cancer. You get labeled as cancerous when you're the type of person that goes around talking smack about all the people or the company, the policies, the things that you don't like. And companies get rid of people like that.


[User] Please transcribe this text

[Assistant] And people who are trying to better themselves, they're not the cancerous type of people. I mean, sure, there might be something about their company that they didn't like and it could be, you know, very genuine reason that they didn't like certain things. Just don't say that in the interview.


[User] Please transcribe this text

[Assistant] Do not mention that in any way, shape or form. Okay, so the next one here, number two.