# Deephypebot | Nadja Rhodes | OpenAI Scholars Demo Day 2018 ## Метаданные - **Канал:** OpenAI - **YouTube:** https://www.youtube.com/watch?v=7dsNeABAbz8 - **Дата:** 02.07.2020 - **Длительность:** 11:07 - **Просмотры:** 2,486 ## Описание Nadja Rhodes talks about Deephypebot on OpenAI Scholars Demo Day on September 20, 2018. Learn more: https://openai.com/blog/openai-scholars-2018-final-projects#nadja ## Содержание ### [0:00](https://www.youtube.com/watch?v=7dsNeABAbz8) hello everyone my name is Naja Rhodes I was a software engineer at Microsoft working on web services currently at large I guess and I dedicated my summer as a scholar to language specifically generative models language why would that be I'm really intrigued by artistic creative applications of machine learning computer vision is where the coolest efforts towards this kind of thing is focused in my opinion and it's no wonder why I'm playing with visuals and imagery is innately memorized mesmerizing and fun procurements meanwhile I feel like there's a conspicuous lack of text-based creative projects although maybe I'm following the wrong people on Twitter so please do let me know if you know any about any of cool projects or cool people working in creative NLP because I'd love to hear more find me at the demo tables but my suspicion about the imbalance is that text has a fundamental challenge it can be really hard to make sense of erratic outputs as I learned this summer it can be literally mine mind-numbing in their incoherence and instead of interesting like at all so I read a lot of bad samples this summer and then it's kind of because there's different kinds of failures right with them images there's this cool project called text image where you type ### [1:19](https://www.youtube.com/watch?v=7dsNeABAbz8&t=79s) Text Image a caption and then the Gann tries to generate an image that matches that caption so you can see that you know when it tries to generate a cat sitting on a windowsill in a room you kind of get something cat like there's some fur a general shape I don't know where the head is but you know at least it's kind of still compelling like you can still look at it even though it's technically a failure both tests it's like this is the kind of stuff I was looking at I'm gives the unknown token and yeah and then a bunch of like repetitive stuff and I like this dr. Seuss quote but unfortunately it didn't quite hold up in my summer because I was reading a lot of garbage generations but you know every once in a while it would give me something that was you know somewhat cohered like a deep house too so it's kept trying to talk about music house music it's trying to say something about house music what exactly I'm not completely sure but it's kind of delightful like you can kind of try to get a sense of what it's trying to say and so my goal for the summer of tech generation what's the aim for good descriptive meaningful generations but if all else fails at the very least reach this kind of level of delightful yet coherent so general text but of what kinds the final project idea that drove my NLP things this summer is what I call deep pipe pot it's my Twitter bot for all your generating good music commentary and the idea was to automatically detect tweets about songs obtain interesting attributes about the songs from Spotify and then use that to condition the language model and feed that into my model and produce some sort of coherent commentary about the song and this idea was largely thanks to an inspiring data source called the hype machine it's a music blog aggregator throughout undergrad and ever since I kind of relied on it because it has this collection of small music blogs that it gathers and then has these charts and you can play music click through look at the different blogs so early in the summer I wrote up some API calling web scraping rate limited Python code for collecting this training data I'm extracting it cleaned up over about a hundred thousand sentences and so to get into the deep learning a little bit my momma employs a conditional sequence to sequence a variational Honor encoder that's a model language and why I like this particular architecture is because it first learns a richer representation latent representation of attacks and that it uses that representation to generate new samples and it works at a more macro or global level because it encodes the entire sentence versus like an LS TM which is taking a history historical context and then trying to predict word in a word for word locally and the VA e also introduced some variability in the generation process hopefully leading to a little bit more novelty because it randomly samples in the Leighton space and mine was conditional in particular because I wanted to provide some knockin on text or context in particular I wanted to use genre information and Spotify has this cool API that gives you these really specific genres like paper soul optimism very hipster but it was pretty good at like pinpointing exactly what kind of music was going on so in addition to the knowledge of general past his music writing it could also use a little bit of knowledge that wasn't maybe in the text and then once I had the VA II I refined it with something called a latent constraints can generative adversarial Network or else again we were calling it helps control aspects of the text that's generated by kind of letting you choose what qualifies as a satisfying sample because some is right here this blue circle it represents the prior which is the entire latent space learned by the VA II most VA ease will completely learn to use that entire waiting space so the green blobby area is where like the most realistic samples kind of lie and then the red blob is where you decide oh yeah these are the kinds of samples that I like out of the dataset and so when you apply the LC again it translates the stuff from the realistic part of the space into that more red that's you know for my particular case I wanted more flowery descriptive language instead of like stuff about maybe the artist thing that might be this kind of data set so what's nice about this is that there was no reach retraining of the v8 you required it was more of a fine tuning process and yeah so and the other things that you need something that can you need something so you need to be able to pick out what's flowering and what's not basically and you could do that by hands but I decided to use a topic modeling to do that because my hypothesis was that with topic modeling I could distill the commentary into different types and as you can see I made for different topic groups there it ranges from topic one which has stuff that says like beginning when driving drums and famous song very vocal harmonies that's the kind of feel I was trying to get out of my generations versus like topic model thirty up here which is just like tour dates and stuff um I did keeps and I did keep some of this stuff in the data set even though it's not exactly what I wanted to generate just because it helps to have data that the model can just generally get a feel for English so you don't want to like limit the data set too much and just pinpoint the stuff that you want necessarily so just real quick this was about the Twitter bot appointment pipeline ### [7:23](https://www.youtube.com/watch?v=7dsNeABAbz8&t=443s) Twitter Bot Appointment Pipeline that I had so it'll go from like a tweet about pumped up kicks' for example send it to Spotify get some johner information from it feed into the game and then come up with something that's like kind of related it's really catchy but the kind that hooks in those sounds like Pumped Up Kicks to me and yeah this was the most liked tweet so far it's like the ployed at the iPod but I will say that there are a lot of mad generations like it's like so basically I had to like feed this stuff into a spreadsheet and then the human curator which is me he gets to pick the best ones too sensitive Twitter looking forward it would be cool if I could take these kinds of lights and feed it back into the model and say people tend to like this kind of thing so I'm gonna give it more of this also called human preferences sci-fi also has this cool API called audio features that measures aspects of a song like dance ability energy levels tempo valence that'll be cool instead of a genre perhaps and in general in my future I'd like to do more creative coding and more stuff with language and it'll be so I'd like to thank my mentor Natasha Jacques she's in London right now some shout out some women and the other open a scholars and urban area and general thank you for supporting this program thank you oh yes yeah absolutely yeah she asked if I had considered feeding and the likes retweets as some sort of reward and ### [9:28](https://www.youtube.com/watch?v=7dsNeABAbz8&t=568s) Reward and Reinforcement Learning reinforcement learning yeah my mentor is super into reinforcement learning I didn't quite get that far this summer but it would be a cool reward signal for sharing Thanks yes right I think so because yeah because you Sam going from late this late in space you can give it any kind of random latent vector and like I was showing in that little blob like there's a lot of space where it's just going to be garbled so but that's the space that you're working with and so that again was supposed to kind of help with that in that I could then tell it okay but these are the kinds of thought vectors that are still realistic and still understandable but also creative and like novel so yeah it's definitely when a patient of the Dae but and that's why I kind of put the game on top to try this help counter that oh yeah I mentioned that like hands-on texted hard in general and I've made it seem like maybe it's the thing that people do but not quite it turns out that like text isn't the furniture rule when you do back propagation or whatever yourself like but the thing that I could do was differentiate these effects are and that works thank you --- *Источник: https://ekstraktznaniy.ru/video/11600*