Generative Pre-trained Transformer (GPT)

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• Case Studies
  • Writing
  • Publishing
• ChatGPT
• Attention
• Text Transfer Learning
• Natural Language Generation (NLG)
• Natural Language Tools & Services
• Generated Image
• OpenAI Blog | OpenAI
• Language Models are Unsupervised Multitask Learners | Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever
• Neural Monkey | Jindřich Libovický, Jindřich Helcl, Tomáš Musil Byte Pair Encoding (BPE) enables NMT model translation on open-vocabulary by encoding rare and unknown words as sequences of subword units.
• Attention Mechanism/Transformer Model
• Bidirectional Encoder Representations from Transformers (BERT)
• ELMo
• SynthPub
• Language Models are Unsupervised Multitask Learners - GitHub
• Microsoft Releases DialogGPT AI Conversation Model | Anthony Alford - InfoQ - trained on over 147M dialogs
• minGPT | Andrej Karpathy - GitHub
• SambaNova Systems ... Dataflow-as-a-Service GPT
• Facebook-owner Meta opens access to AI large language model | Elizabeth Culliford - Reuters ... Facebook 175-billion-parameter language model - Open Pretrained Transformer (OPT-175B)

• GPT | Papers With Code

Let's build GPT: from scratch, in code, spelled out | Andrej Karpathy

Let's build GPT: from scratch, in code, spelled out. Andrej Karpathy ...We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video. Links: Google colab for the video GitHub repo for the video Playlist of the whole Zero to Hero series so far nanoGPT repo my website my twitter our Discord channel Supplementary links: Attention is All You Need paper OpenAI GPT-3 paper OpenAI ChatGPT blog post The GPU I'm training the model on is from Lambda GPU Cloud, I think the best and easiest way to spin up an on-demand GPU instance in the cloud that you can ssh to: https://lambdalabs.com . If you prefer to work in notebooks, I think the easiest path today is Google Colab. Suggested exercises: EX1: The n-dimensional tensor mastery challenge: Combine the `Head` and `MultiHeadAttention` into one class that processes all the heads in parallel, treating the heads as another batch dimension (answer is in nanoGPT). EX2: Train the GPT on your own dataset of choice! What other data could be fun to blabber on about? (A fun advanced suggestion if you like: train a GPT to do addition of two numbers, i.e. a+b=c. You may find it helpful to predict the digits of c in reverse order, as the typical addition algorithm (that you're hoping it learns) would proceed right to left too. You may want to modify the data loader to simply serve random problems and skip the generation of train.bin, val.bin. You may want to mask out the loss at the input positions of a+b that just specify the problem using y=-1 in the targets (see CrossEntropyLoss ignore_index). Does your Transformer learn to add? Once you have this, swole doge project: build a calculator clone in GPT, for all of +-*/. Not an easy problem. You may need Chain of Thought traces.) EX3: Find a dataset that is very large, so large that you can't see a gap between train and val loss. Pretrain the transformer on this data, then initialize with that model and finetune it on tiny shakespeare with a smaller number of steps and lower learning rate. Can you obtain a lower validation loss by the use of pretraining? EX4: Read some transformer papers and implement one additional feature or change that people seem to use. Does it improve the performance of your GPT? Chapters: 00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespeare baseline language modeling, code setup 00:07:52 reading and exploring the data 00:09:28 tokenization, train/val split 00:14:27 data loader: batches of chunks of data 00:22:11 simplest baseline: bigram language model, loss, generation 00:34:53 training the bigram model 00:38:00 port our code to a script Building the "self-attention" 00:42:13 version 1: averaging past context with for loops, the weakest form of aggregation 00:47:11 the trick in self-attention: matrix multiply as weighted aggregation 00:51:54 version 2: using matrix multiply 00:54:42 version 3: adding softmax 00:58:26 minor code cleanup 01:00:18 positional encoding 01:02:00 THE CRUX OF THE VIDEO: version 4: self-attention 01:11:38 note 1: attention as communication 01:12:46 note 2: attention has no notion of space, operates over sets 01:13:40 note 3: there is no communication across batch dimension 01:14:14 note 4: encoder blocks vs. decoder blocks 01:15:39 note 5: attention vs. self-attention vs. cross-attention 01:16:56 note 6: "scaled" self-attention. why divide by sqrt(head_size) Building the Transformer 01:19:11 inserting a single self-attention block to our network 01:21:59 multi-headed self-attention 01:24:25 feedforward layers of transformer block 01:26:48 residual connections 01:32:51 layernorm (and its relationship to our previous batchnorm) 01:37:49 scaling up the model! creating a few variables. adding dropout Notes on Transformer 01:42:39 encoder vs. decoder vs. both (?) Transformers 01:46:22 super quick walkthrough of nanoGPT, batched multi-headed self-attention 01:48:53 back to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF 01:54:32 conclusions Corrections: 00:57:00 Oops "tokens from the future cannot communicate", not "past". Sorry! :) 01:20:05 Oops I should be using the head_size for the normalization, not C

Transformers: The best idea in AI | Andrej Karpathy and Lex Fridman Lex Fridman Podcast full episode Please support this podcast by checking out our sponsors: Eight Sleep: to get special savings BetterHelp: to get 10% off Fundrise Athletic Greens: to get 1 month of fish oil GUEST BIO: Andrej Karpathy is a legendary AI researcher, engineer, and educator. He's the former director of AI at Tesla, a founding member of OpenAI, and an educator at Stanford. PODCAST INFO: Podcast website Apple Podcasts Spotify RSS Full episodes playlist Clips playlist: https://www.youtube.com/playlist?list... SOCIAL: Twitter LinkedIn Facebook Instagram Medium Reddit Support on Patreon

Generative Pre-trained Transformer (GPT-3)

• Language Models are Few-Shot Learners | T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei - arXiv.org
• GPT-3: Demos, Use-cases, Implications | Simon O'Regan - Towards Data Science
• OpenAI API ...today the API runs models with weights from the GPT-3 family with many speed and throughput improvements.
• GPT-3 by OpenAI – Outlook and Examples | Praveen Govindaraj | Medium
• GPT-3 Creative Fiction | R. Gwern

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Try...

• Sushant Kumar's micro-site - Replace your 'word' in the following URL to see what GPT-3 generates: https://thoughts.sushant-kumar.com/word

• Serendipity ...an AI powered recommendation engine for anything you want.

• Taglines.ai ... just about every business has a tagline — a short, catchy phrase designed to quickly communicate what it is that they do.

• Simplify.so ...simple, easy-to-understand explanations for everything

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What is GPT-3? Showcase, possibilities, and implications What is going on in AI research lately? GPT-3 crashed the party, let’s see what it is and what it can do. Hoping we do not forget how problematic it might also become. GPT-3 Paper : Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan et al. "Language models are few-shot learners." arXiv preprint arXiv:2005.14165 (2020). https://arxiv.org/pdf/2005.14165.pdf

14 Cool Apps Built on OpenAI's GPT-3 API 14 Cool applications just built on top of OpenAI's GPT-3 (generative predictive transformer) API (currently in private beta).

This text generation AI is INSANE (GPT-3) An overview of the gpt-3 machine learning model, why everyone should understand it, and why some (including its creator, open AI) think it's dangerous.

GPT-3 Demo Installation -Generative pretrained Transformer model (Third generation of OpenAI) Pythoncode.

How Artificial Intelligence Changed the Future of Publishing | OpenAI GPT-3 and the Future of Books Go from content chaos to clear, compelling writing that influences people to act without them realizing it: https://bit.ly/thebestwaytosayit As Ed Leon Klinger shows in his GPT 3 demo and GPT 3 examples thread

GPT-3: Language Models are Few-Shot Learners (Paper Explained) How far can you go with ONLY language modeling? Can a large enough language model perform Natural Language Processing (NLP) task out of the box? OpenAI take on these and other questions by training a transformer that is an order of magnitude larger than anything that has ever been built before and the results are astounding.

GPT3: An Even Bigger Language Model - Computerphile Basic mathematics from a language model? Rob Miles on GPT3, where it seems like size does matter! More from Rob Miles: https://bit.ly/Rob_Miles_YouTube This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

GPT-3 from OpenAI is here and it's a MONSTER! GPT-3 is the largest language model to date with 175 billion parameters. It is able to do various Natural Language Processing (NLP) tasks (translation, question answering) without additional finetuning.

GPT-3 vs Human Brain GPT-3 has 175 billion parameters/synapses. Human brain has 100 trillion synapses. How much will it cost to train a language model the size of the human brain? REFERENCES: [1] GPT-3 paper: Language Models are Few-Shot Learners [2] OpenAI's GPT-3 Language Model: A Technical Overview [3] Measuring the Algorithmic Efficiency of Neural Networks

Steve Omohundro on GPT-3 In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for Natural_Language_Processing_(NLP) and for Machine Intelligence / AGI.

GPT 3 Demo and Explanation - An AI revolution from OpenAI GPT 3 can write poetry, translate text, chat convincingly, and answer abstract questions. It's being used to code, design and much more. I'll give you a demo of some of the latest in this technology and some of how it works. GPT3 comes from a company called OpenAI. OpenAI was founded by Elon Musk and Sam Altman (former president of Y-combinator the startup accelerator). OpenAI was founded with over a Billion invested to collaborate and create human-level AI for the benefit of society. GPT 3 has been developed for a number of years. One of the early papers published was on Generative Pre-Training. The idea behind generative pre-training (GPT) is that while most AI's are trained on labeled data, there's a ton of data that isn't labeled. If you can evaluate the words and use them to train and tune the AI it can start to create predictions of future text on the unlabeled data. You repeat the process until predictions start to converge. The newest GPT is able to do a ton. Some of the demos include: - GPT 3 demo of how to design a user interface using AI - GPT 3 demo of how to code a react application using AI - GPT 3 demo of an excel plug-in to fill data using AI - GPT 3 demo of a search engine/answer engine using AI - GPT3 demo of command line auto-complete from English to shell commands

Panel discussion - GPT-3 and Artificial General Intelligence 27 Aug 2020 Is GPT-3 a step towards creating artificial general intelligence? Chair: Associate Professor Kate Devitt - Chief Scientist, Trusted Autonomous Systems Panel: • Professor David Chalmers (NYU) • Professor Susan Schneider (NASA and Florida Atlantic University) • Professor Marcus Hutter (ANU) A philosophical discussion on the development of artificial intelligence and specifically advances in Generative Pre-trained Transformer-3 (GPT-3). GPT-3 is an auto-complete algorithm created by OpenAI as part of their endeavour to develop artificial general intelligence. GPT-3 is the third in a series of autocomplete tools designed by OpenAI. (GPT stands for “generative pre-trained transformer.”). GPT-3 is fed on an unimaginatively large corpus of human knowledge including all of Wikipedia, millions of books, websites and other materials including philosophy texts. In fact, any type of information uploaded to the internet is possible food for GPT-3's artificial mind to dwell on. The result? Eerily coherent, complex and interesting thoughts about almost any topic. The sophisticated, nuanced text produced by GPT-3 seems to pass the Turing Test for many--including philosophers. Some of GPT-3's answers are shedding new light on enduring philosophical questions. Is GPT-3 the beginnings of an artificial general intelligence. Does it create ideas like a human mind, or even better than a human mind? Is human cognition similarly some sort of autocomplete program in our brains? Is it possible that GPT-3 one day becomes consciousness or is it already conscious?--How could we tell. If an AI passes our tests for consciousness, do we then have an obligation to accord it rights? If so, what sorts of rights might it deserve. Independently of rights, how should humans manage an AI that has access to everything that is posited and known and can trick humans into believing that another rational agent is communicating with them? The panel considers what GPT-3 tell us about the ambition to build an artificial general intelligence, consciousness, human thought and how we should treat AI in an increasingly digital and disembodied world rife with mis- and disinformation.

GPT Impact to Development

• Development
• https://analyticsindiamag.com/will-the-much-hyped-gpt-3-impact-the-coders/ Will The Much-Hyped GPT-3 Impact The Coders? | Analytics India Magazine]
• With GPT-3, I built a layout generator where you just describe any layout you want, and it generates the JSX code for you. | Sharif Shameem - debuild

OpenAI Model Generates Python Code Credits: Microsoft, OpenAI

OpenAI Model Generates Python Code This code completion engine can write an entire function from just the name! OpenAI demonstrates what happens when you learn a language model on thousands of GitHub Python repositories. Source Clip: https://youtu.be/fZSFNUT6iY8

OpenAI and Microsoft Can Generate Python Code OpenAI language model was trained on thousands of GitHub repositories using the same unsupervised learning as the GPT models. Build 2020

GPT 3 Explanation And Demo Reaction | Should You Be Scared ? Techies Reaction To GPT3 AI OpenAI In this video we will look at some of the demos and reactions across social media on GPT-3. The links to the tweets and demo you see in this video have been linked below so please do react out to them if you have any questions.

Code 10x Faster With This CRAZY New AI Tool (GPT-3) In this FREE LIVE training, Aaron and Naz will show you the new cutting edge machine learning AI, OpenAI's GPT-3.

Build 2 projects using GPT-3 in just a couple of minutes. Bare bones: branding generator, chat bot Co-founded by Elon Musk OpenAI wants to make AI safe and accessible. A year ago the startup released GPT-2. That language model was at that time deemed too powerful to release. Eventually OpenAI made the model available. This year they've trained a much more powerful model at least 1 magnitude larger than GPT-2. I was one of the lucky 750+ people granted access as Beta testers by OpenAI to see what can be built using the GPT-3 API. The model costs millions of dollars to train which makes it out of reach for most organizations. This video skims the surface of what you can get done with this amazing new model.

Generative Pre-trained Transformer (GPT-2)

• GitHub
  • (117M parameter) version of GPT-2
  • openai/gpt-2 GPT-2
• How to Get Started with OpenAIs GPT-2 for Text Generation | Amal Nair - Analytics India Magazine
• GPT-2: It learned on the Internet | Janelle Shane
• Too powerful NLP model (GPT-2): What is Generative Pre-Training | Edward Ma
• GPT-2 A nascent transfer learning method that could eliminate supervised learning some NLP tasks | Ajit Rajasekharan - Medium
• OpenAI Creates Platform for Generating Fake News. Wonderful | Nick Kolakowski - Dice
• InferKit | Adam D King- completes your text.

Coding Train Late Night 2

Coding Train Late Night 2: Fetch, GPT-2 and RunwayML The Coding Train 0:00 Live Stream Starts 3:51 Introduction With Dad Jokes 11:29 Coding Late At Night Projects and Notes 16:48 Scraping Dad Jokes With Fetch 50:10 Training a Model With Runway 57:52 Small Break 1:00:15 Controlling Hue Lights 1:20:00 Dad Joke Model 1:32:27 Skip: Audio Glitch (LOUD) 1:35:00 Dad Joke Model 1:49:25 Dad Joke Generator 1:54:25 Goodbyes and End of Stream Website: https://thecodingtrain.com/

Coding Train Late Night 3: GPT-2, Hue Lights, Discord Bot The Coding Train 0:00 Live Stream Starts 3:50 Introduction 9:50 AI Joke Generator 13:30 Live Stream Notes 19:50 Generative Text Training with GPT-2 29:40 Dad Joke Model Training 1:11:27 Using Hue Lights API 1:31:50 More Dad Joke Generator 1:37:33 Discord Bot 2:15:04 Goodbyes and End of Stream Website: https://thecodingtrain.com/

r/SubSimulator

Subreddit populated entirely by AI personifications of other subreddits -- all posts and comments are generated automatically using:

• Markov Chain Model
• GPT-2 Language

results in coherent and realistic simulated content.

GetBadNews

• Get Bad News game - Can you beat my score? Play the fake news game! Drop all pretense of ethics and choose the path that builds your persona as an unscrupulous media magnate. Your task is to get as many followers as you can while