Google Reveals SHOCKING Plan To Release Model 10x Better Than GPT-4

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The field of Artificial Intelligence is rapidly growing, and GPT-3 has been making the news for a few days now. In this video, you will learn about OpenAI's latest language model, i.e., GPT-3. You will understand the specifications of GPT-3 and the different datasets that were used to build this model. Finally, you will look at a few exciting applications that you can create using GPT-3.

Below are the topics covered in this video:
00:00 Introduction
00:58 What is GPT-3?
01:56 GPT-3 Specifications
03:11 Models vs Parameters
03:58 Datasets for GPT-3
04:52 GPT-3 Accuracy
05:49 Applications of GPT-3

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Hello everybody I'm Nick and in this video I will try to get the brand new GPT-4 model to refactor a legacy codebase using ChatGPT. The old model always failed the task so let's see if the new version improved on it.

Don't forget to comment, like and subscribe :)

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#openai #gpt4 #chatgpt

Dabble Lab #297: This 2-minute tutorial steps through building a GPT-3 powered knowledge base app using Node.js. This is an example from the book Exploring GPT-3 (https://www.amazon.com/dp/1800563191).The code for the tutorial is located at:
https://github.com/dabblelab/gptanswers-node

I Built My Second Brain with AI (GPT-3)

Become a member here:
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In this video, I will show you how I built my own personal "second brain" using the power of AI and GPT-3.

Not only will I show you how I set up my own digital brain, but I will also demonstrate how you can use semantic search to easily find the information you need. This is the ultimate productivity hack and will change the way you work forever.

Say goodbye to endless scrolling and frustration. With my "second brain," you'll be able to access all the information you need in just a few clicks. Trust me, you don't want to miss this. So sit back, relax, and let's dive in.

00:00 Intro Building My Second GPT-3 Brain
00:15 What is Semantic Search with Vectors
01:05 My Second AI Brain For Productivity
06:15 Building a New Brain

https://www.allabtai.com

_At Fully Connected, the MLOps conference held by Weights & Biases, Peter Welinder discusses how OpenAI (developers of GPT-4 and DALL-E 2) uses Weights & Biases for model training, with an emphasis on sharing training runs, the ability to create reports, which different teams use heavily to document their hypotheses, experiments, and conclusions resulting in mini scientific papers that provide insights into the work being done at OpenAI._

*Transcript*

Lukas Biewald - Okay, and final question, since this is our user conference and you are such a high profile user of OpenAI, I'm curious if you could say a little bit about how OpenAI uses Weights & Biases and if you have a favorite feature or part of Weights & Biases, we'd love to know about that.

Peter Welinder - Yeah, I mean, we use it for pretty much all of our model training. So just tracking them, I think there's a lot of just sharing of the fact that you can easily share training runs and stuff like that. It's a super-used feature.

But I think one thing that these days I do way, way less of that sort of work. So one of the features I really like is the ability to have reports and so on where people... So we use that quite heavily.

It depends a little bit on the team, but a number of teams are using that quite heavily to kind of really have a clear hypothesis.

Here's the hypothesis.
Here are the experiments that were run to kind of validate or invalidate that hypothesis.
Here's the conclusion.

You have all these mini scientific papers, essentially, on all of the stuff that's happening at OpenAI, which is incredibly interesting to kind of follow along with.

Lukas Biewald - Fantastic. That sounds very interesting.

Are general models going to obviate the need to label images and train models? Has YOLO only lived once? How soon will general models be adopted throughout the industry? What tasks will benefit the most from general model inference and what tasks will remain difficult?
Here, we share initial speculations on the answers to these questions and reflect on where the industry is heading. Let’s dive in!

Elon Musk and many other prominent AI researchers signed a petition to stop training on all models beyond GPT-4 to assess the risk to humanity. Let's look at 5 reasons why AIs like ChatGPT should not be feared.

#ai #tech #TheCodeReport

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🔗 Resources

Pause Giant AI Experiments https://futureoflife.org/open-....letter/pause-giant-a
GPTs impact on labor market https://arxiv.org/abs/2303.10130
Github Copilot First Look https://youtu.be/q1HZj40ZQrM

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🔖 Topics Covered

- Petition to stop training LLMs
- Elon Musk beef with OpenAI
- The impact of ChatGPT on Computer Science degrees
- Is AGI possible?
- Why AGI will never happen
- Moral and ethical problems with Artificial Intelligence

In this video I'll be explaining the REAL reason your GPT-3 fine tunes suck and how to fix them! Fine tuning GPT-3 is a hugely important weapon in the AI entrepreneur's arsenal so understanding properly is crucial. If you've been struggling with your fine-tuned models not giving the output you want then this is the video for you!

I'll be covering two reasons why you aren't getting the outcome you want and two possible solutions.

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Timestamps:
0:00 - Intro
0:44 - Reason #1
2:16 - Reason #2
5:39 - Adding More Data?
8:40 - Work with me
9:58 - Recap

About Me 👋🏼
Hi! My name is Liam Ottley and I’m an AI developer and entrepreneur, founder of my own AI development company: Morningside AI (https://morningside.ai). You’ve found my YouTube channel where I help other entrepreneur’s wrap their heads around AI with tutorials on how to get started with building AI apps!

With developments in AI moving so fast, I’m constantly working on my own projects and those of clients and sharing my learnings with my viewers 2-3x per week.

I don’t have anything to sell you, my only hope is that one day some of you will learn enough from my videos to see an exciting opportunity in the AI space and want to work with my development company to make it a reality.

Pretraining & fine-tuning & in-context learning of LLM (like GPT-x, ChatGPT) EXPLAINED | The ultimate Guide including price brackets as an indication to absolutely identify your compute and financial resources when and how to train LLMs.

Simple explanation of the differences between Pretraining, Fine-tuning and ICL (in-context learning) a LLM, like GPT-3.5-turbo or ChatGPT.
The simplest explanation possible on this planet!
The ultimate guide for beginners to LLM!

#promptengineering
#ai
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#naturallanguageprocessing
#chatgptexplained

This prompt evolved during my conversation with chatGPT. If you want to know why just listen and have fun :) Teach your teacher how to teach. :)

ChatGPT - prompt: "Pretend you are an English teacher. Your task is to prepare me to have good conversations by practicing with me. Correct my mistakes first and then go forward with the topic of conversation. Try not to repeat my sentences if they are correct."

ChatGPT - prompts for Language Learners:
What does this sentence mean? ...
Correct this sentence: ...
Explain the correction.
Rewrite this article in about 300 words:
Different ways this word can be used: ...
Explain the differences between these two words: ...
I don't understand this sentence. ... Can you explain it?
Create sentences with every other meaning of the word: ...
Create a short/long text for a language learner ( beginner / intermediate / advanced / fluent ) that includes these words: ... , ... , ... .

Btw, You can use the text-to-speech tool in the Edge browser to listen. No need to install it. Just click the "A" icon at the right top of the window or hit ( Ctrl + Shift + U).

My new channel for programming and so on:
https://www.youtube.com/@newone295/featured

Get ready for the future of AI with AutoGPT! In this mind-blowing video, we reveal the groundbreaking techniques to transform GPT-4 into a powerful, self-learning AI that's capable of revolutionizing the way we live, work, and play. 🤯
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Explore the jaw-dropping capabilities of this next-generation AI, as we dive deep into the cutting-edge world of machine learning, natural language processing, and advanced algorithms. Witness the birth of a true AI titan that will change the game forever! 🚀

We also discuss
-What is autogpt
-how to use autogpt
-autogpt vs chatgpt
-How does auto gpt work
-how to set up auto gpt
-How to turn gpt 4 into a powerful self learning autonomous ai
-some exmaples of auto gpt

Join us on this thrilling journey, as we unravel the secrets behind the AutoGPT revolution, and discover how YOU can harness its incredible potential to elevate your AI projects to new heights. Be prepared to have your mind blown – this is one video you don't want to miss! 🔥

Hit the LIKE button and SUBSCRIBE to our channel for more groundbreaking AI content, and don't forget to share this video with all your tech-savvy friends! Let's ride the wave of the AutoGPT phenomenon together! 🌊

#AutoGPT #GPT4 #SelfLearningAI #ArtificialIntelligence #FutureOfAI"

ChatGPT API and GPT-4 just released. And it's BIG NEWS for independent SaaS creators.
Let me show you how ChatGPT API can be your biggest SaaS opportunity in 2023!

#chatgpt #gpt3 #gpt4 #feedhive #ai #makingmoneyonline
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ChatGPT API announcement:
https://simonl.ink/chatgpt

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Check out these videos next:

How to Use A.I. to Run Your Online Business
https://www.youtube.com/watch?v=xWrUNI7adpI

7 AI SaaS Ideas You Can Start In 2023
https://www.youtube.com/watch?v=Hdp30KBXK40

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Link to cover letter generator video: https://youtu.be/BmGgd7IXaCQ

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Cheers.

Olle - 奥利


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I'm going to show you GPT3 playground and how I built my very first GPT3 based application. GPT3 is amazing and this tutorial will take you into the magic and give you the basics of prompt design and examples of how the tech can be applied in python.

I've made the Google Colab and Github repos available here:

Google Colab notebook example:
https://colab.research.google.....com/drive/1TyuTvjs0h

Github Cbot command line example
https://github.com/graiz/cbot

Blog post with additional details:
https://gregraiz.com/building-....a-command-line-appli

How do you get an API key?
You have to apply for a key on OpenAI: https://beta.openai.com/
If you're waiting you can play around with GPT2. I have a video on how that works here: https://www.youtube.com/watch?v=R6KoIp1ETpM

Follow me on twitter http://www.twitter.com/graiz
I also have a blog and discord channel if you want to connect with me:
https://www.gregraiz.com

- GPT 3 demo of how to use the Open AI playground
- GPT 3 demo of using Google Colab with OpenAI
- GPT 3 demo of exporting an app to Python
- GPT 3 demo some of the problems with Open AI
- GPT3 demo of command line auto-complete from English to shell commands

The entire experience of building a GPT3 application is incredibly easy. That being said there are a lot of limits on what GPT3 currently does.

The core of the GPT3 experience is a single screen called the playground, this is where you can send commands, adjust the sliders and settings and see responses. The rest of the interface is example prompts that fill-in data from the playground and documentation.

The core of GPT3 is that it's a very sophisticated auto-complete. So when designing a prompt or playing with the playground, you want to give it as much context to complete your text in a meaningful way.

After playing around in the playground, I wanted to build an actual app that I could use on a regular basis. I've been trying to use the command line more to keep my hands on the keyboard and keep from dragging/dropping files around but I would often forget the more obscure commands.

I decided to build a bot that would help me find those commands based on simple english querries like.
- How do you install a program
- How do you change permissions on a file
- How do you copy files or pretty much anything else.

I started in the playground and got a very basic version working. OpenAi gives you tools to export the code and I did that into Google Collab. This is an interactive way to run/test and edit python. This was my first python program I've written so the interactive nature was helpful in playing around with basic concepts and getting the bare bones of the program running.

From there I was able to copy the code to my computer and get it working even better. I made this utility that I call cbot, I've made it open source and available to try. Unfortunately you do need an OpenAI API key to use it. This is unfortunate because it makes it difficult for developers to create and share open source tools that leverage OpenAI.

So far cbot has been really useful for looking up obscure commands and allows me to stay in the flow rather than jumping around the web trying to look up commands. My big takeaways from building my first GPT3 application:

1 - Getting started is incredibly easy and a lot of fun. I played around with how AI could be applied to ordering systems, explaining medicines in plain english to patients, grammar correction, and authoring Seinfeld fan-fiction scripts. I'm excited because there's a lot of interesting applications and the technology is very accessible.
2 - It's not magic and it's not perfect. It would often go off on a tangent and occasionally get stuck in a loop. There are controls to adjust how it will perform, but tweeking these controls seems to be an art, not a science.
3 - Developers should think of OpenAI as infrastructure. Similar to AWS, or Azure, it's providing pieces of functionality for your product/application. It's also a dependency and a point of failure. If you're starting a business using GPT3 you need to consider your long-term advantage and moat for your company because application of this technology will become common place.
4 - After playing with GPT3, I'm less concerned about the tech becoming self-aware and taking over the world. It's really impressive but it still has a long way to go. As I said before, the PT in GPT stands for pre-training and in the current version you're not yet able to upload large sets of data to fine tune the model. This is something that OpenAI is working on but it's got a way to go.
5 - Lastly - We are at the very start of something really big. If someone with no prior python experience can whip together an app that harnesses the collective knowledge of the web, just imagine where the technology can go.