Difference between revisions of "Generative Pre-trained Transformer (GPT)"

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[https://www.youtube.com/results?search_query=Generative+Pre+trained+Transformer+GPT+GPT-3+GPT-4+GPT-5 YouTube]
 
[https://www.youtube.com/results?search_query=Generative+Pre+trained+Transformer+GPT+GPT-3+GPT-4+GPT-5 YouTube]

Revision as of 13:03, 30 June 2023

YouTube ... Quora ...Google search ...Google News ...Bing News


Generative Pre-trained Transformer 5 (GPT-5)

See page... GPT-5 | OpenAI ... what will the future bring?

Generative Pre-trained Transformer 4 (GPT-4)

See page... GPT-4 | OpenAI ... can accept prompts of both text and images1. This means that it can take images as well as text as input, giving it the ability to describe the humor in unusual images, summarize text from screenshots, and answer exam questions that contain diagrams. rumored to be more than 1 trillion parameters.

Generative Pre-trained Transformer 3 (GPT-3 & GPT 3.5)

YouTube ... Quora ...Google search ...Google News ...Bing News



Try...



Chat GPT 4 Was Just ANNOUNCED (Open AI GPT 4)
Get ready for the next generation of AI language technology with GPT-4! In this video, we'll be discussing what to expect from OpenAI's latest language model, including advancements in natural language processing, conversational AI, and language generation.

We'll also be looking at how GPT-4 is set to revolutionize industries such as customer service, content creation, and more. Stay tuned for an exciting look into the future of AI language technology with GPT-4!

What is Chat GPT 4 (Open AI) ? Parameters: GPT 4 vs GPT 3
Softreviewed

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

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

Generative Python Code with GPT
In a quest to teach neural networks via transformers to write Python code. Project name: Generative Python Transformers!

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 2 (GPT-2)

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:

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

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:

Supplementary links:

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:

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:

Clips playlist: https://www.youtube.com/playlist?list...

SOCIAL:

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