<p>The post Custom Blender Hair To Metahuman Guide first appeared on Prompt Muse.</p>

<p>The post An Idiots guide to ComfyUI first appeared on Prompt Muse.</p>

Character Creator stands out for its ability to quickly generate base models that are ripe for customization. With its intuitive interface and versatile morph sliders, creators can easily shape the basic anatomy of their characters. This software shines in its ability to offer a base that includes rigs and morph targets, essential for both facial and body animation.

Once the basic form is created in Character Creator, ZBrush comes into play. Known for its robust sculpting tools, ZBrush allows artists to add intricate details and a unique personality to their characters. Its brushes are perfect for crafting stylized aesthetics, moving away from realism and embracing a more exaggerated, cartoon-like appearance.

One of the most significant advantages of using Character Creator and ZBrush is their seamless integration. With just a click, characters can be transferred between the two programs without losing any detail. This integration is a game-changer, ensuring that the creative flow isn’t interrupted.

Characters created with these tools are not just visually appealing but are also production-ready. They come with clean topology, rigs, and weights, making them perfect for animation projects. This readiness significantly cuts down the time from concept to production, a crucial factor in fast-paced project environments

For those who use Blender for composting, the good news is these characters are fully compatible. With the help of a simple plugin, characters can be imported into Blender, retaining all their rigging and morphing qualities. This flexibility opens up avenues for creators who operate in different software environments.

The combination of Character Creator and ZBrush is a testament to how technology is simplifying yet enhancing the art of 3D character creation. By reducing the technical barriers, these tools allow artists to focus more on the creative aspect of character design. As we continue to see advancements in these tools, the future of character creation looks more exciting than ever.

<p>The post Character Creator Workflow For Fast 3D Animation Production first appeared on Prompt Muse.</p>

Hello there, Prompt Muse here! In this comprehensive guide, I’ll walk you through the essentials of setting up ComfyUI and Animate Diff Evolve.

1. Introduction

• ComfyUI offers a node-based layout, allowing for a streamlined workspace tailored to your needs.mm
• Drag and drop features for images and workflows enhance ease of use.
• This tutorial aims to make you feel comfortable with ComfyUI, ensuring you make the most of its unique features.

2. System Requirements

• Nvidia RTX Graphics card is preferable (Works with AMD cards and Macs click here)
• Preferred: 12 GB of VRAM for processing videos.
• Allocate 50-80 GB of storage for outputs.

Tip: If your PC doesn’t meet these requirements, consider using Shadow Tech. It’s a remote PC service I personally use, providing an NVIDIA 1080 GPU, ample storage, and 12 GB VRAM for about $40/month.

3. Software Dependencies

• Git: Allows you to pull extensions from GitHub. Download here.
• FFmpeg: Essential for encoding videos. Download here.

4. Installing ComfyUI & comfyUIManager

1. Visit the ComfyUI GitHub page.
2. Under the “Installing” section, select the “Direct link to download” for the standalone portable version.
3. Once downloaded, extract the files to your chosen directory.
4. For ComfyUI models:
• Checkpoints: Download from civitai.com and place in the checkpoints folder.
• VAE: Download from Stability AI’s hugging face website and place in the vae folder.
5. Install ComfyUI Manager from the civitAI page for easy updates and add-on installations.

5. Basic Workflow Overview

• ComfyUI operates like a circuit board, with nodes representing each process.
• Start with the Load Checkpoints node, input your positive and negative prompts, and proceed to the K Sampler.
• The Latent Image Node determines your image dimensions and batch size.
• The VAE Decode node processes the final image.

Pro Tip: Images created in ComfyUI can be dragged and dropped back into the system to load their respective node layouts.

Here’s the continuation of the tutorial based on the provided transcript:

—

## **6. Installing Motion Models**

1. In ComfyUI Manager, go to **Install Models**.
2. Type “mm” in the search box, which stands for motion models.
3. Download and install **Motion Model version 14**, **version 15**, and **version 15 uncore 2**. The high and mid variants are also recommended.
4. After downloading, you should see a confirmation message.

## **7. Installing Control Net**

1. Still in the ComfyUI Manager, search for “control net”.
2. Locate and install **Stable Diffusion 1.5**. You might need to scroll down the list.
3. Install other control nets like **Line Artarts** and **Open Pose** as needed, ensuring you have enough storage.

**Note:** After installing, close all instances of the manager and ComfyUI. When reopening ComfyUI, it will automatically install the nodes and extensions you’ve selected.

## **8. Utilizing Pre-Made Templates**

1. Visit **cinka dink’s GitHub page**. He offers pre-made templates for Animate Evolve.
2. Download a workflow, drag, and drop it into ComfyUI to load the node layout.
3. If you encounter missing nodes (highlighted in red), simply go to the manager and click on **Install Missing Nodes**.

## **9. Running Workflows**

1. Use the **Load Checkpoints** node, followed by **Clip Setting**, **Positive Prompt**, and **Negative Prompt**.
2. The **K Sampler** determines sampling, while **Animate Diff Loader** adds motion.
3. Adjust settings such as image dimensions, batch size, and motion model as needed.
4. For advanced users, experiment with nodes like **Animate Diff Uniform Context Options** to enhance animations.

## **10. Adding Laura for Enhanced Details**

1. Double-click on an empty space in ComfyUI and search for **Laura**.
2. Select the **Laur Loader** and connect it as shown in the workflow.
3. Adjust the **Strength Model** to reduce the risk of artifacts.
4. Run the prompt and observe the enhanced details in the generated animation.

## **11. Video to Video Animations Workflow**

1. Use **Inner Reflections’** guide as a template for video-to-video animations.
2. For video inputs, use the **Video Load Node**.
3. Adjust settings like frame load cap, skip first frames, and select every M frame to customize the video output.
4. Use **Load Checkpoint** to select your desired model.
5. Implement control nets, like **Line Art**, to guide the animation style.
6. Experiment with different nodes and settings to achieve unique video outputs.

## **12. Exploring Other Templates**

1. Another great template to explore is **Prompt Scheduling**, which allows dynamic prompt changes over time.
2. This feature can be used to create evolving animations, adding depth to your projects.

—

**Conclusion**

Thank you for following along! With ComfyUI, the possibilities are vast, allowing for creative freedom and intricate animations. A written version of this tutorial is available on the Prompt Muse website. Feel free to reach out on social media @promptMuse for further assistance.

Remember: Art is a journey, and every masterpiece begins with the decision to try. Dive in and explore the world of ComfyUI and Animate Evolve!

<p>The post ComfyUI and Animate Diff Evolve Installation Guide first appeared on Prompt Muse.</p>

I was one of the early adopters of DALL·E 3.0, and I couldn’t wait to put this new AI image generator to the test. The initial results, I must say, blew me away. What set DALL·E 3.0 apart was its ability to understand not just single words or simple prompts but entire phrases and sentences. It felt like a significant leap forward in AI creativity.

The images it generated were a sight to behold – beautiful, bright, and full of life. It seemed like I could finally do away with the rigid, prompt-based language that had constrained previous AI models. DALL·E 3.0 was a breath of fresh air in the world of creative content generation.

Excitement filled my days as I used DALL·E 3.0 to create a wide array of images for a project I was passionately working on.  It quickly became an indispensable tool in my creative arsenal. However, all good things must come to an end, and in this case, it happened rather abruptly.

One fateful day, I logged onto the platform to create more AI-generated images, only to be met with a disheartening message: “Promptblocked.” I tried again, hoping it was just a minor glitch, but to my dismay, the same message appeared: “Promptblocked.”

It turned out that DALL·E 3.0 had been nerfed into existence. The issue lay in the specific prompt I had used – a celebrity’s name, Lana Del Rey, to be precise. Previously, DALL·E 3.0 had excelled in creating fantastic reference images for my project, but suddenly, it refused to cooperate. The reliable wellspring of creative inspiration had run dry.

As I reflected on my journey with DALL·E 3.0, I couldn’t help but acknowledge the rollercoaster of emotions it had taken me on. It had been an incredible tool, enabling boundless creativity with its brilliant images. But, it came with its limitations, and one had to tread carefully to avoid promptblocks.

In the end, my experience with DALL·E 3.0 was indeed remarkable, a brief and brilliant journey through the realm of AI-generated artistry. Though its limitations became evident, the potential of this technology remains undeniably promising. DALL·E 3.0 may have had its ups and downs, but it undeniably showcased the incredible power of AI in the world of creative content generation.

If you wish to try it out for yourself, a link is here: https://www.bing.com/create

It is still a fantastic tool, but not as good as it once was.

PromptMuse

<p>The post DALL·E 3.0: Is the Hype Justified? first appeared on Prompt Muse.</p>

We will be using a software called “Cascadeur,” which has been around for about 10 years and offers a free version with some powerful animation tools. While this method is not a replacement for professional animation software or mocap, it’s a viable alternative if you’re on a tight budget.

Note: Before you start, make sure you have Cascadeur and Accurig installed on your computer. You can download Cascadeur from the official website, and Accurig is a free auto-rigging tool that complements Cascadeur.

<iframe width=”560″ height=”315″ src=”https://www.youtube.com/embed/ScQTV2Xb–0?si=_4-LUd5vW3w7Nz64″ title=”YouTube video player” frameborder=”0″ allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” allowfullscreen></iframe>

Let’s get started!

Part 1: Rigging Your Character in Accurig

1. Open Accurig and click on “Choose File” to select your 3D character’s FBX file. You can use a sample character from Cascadeur, Mixamo, Sketchfab, or your own custom character.
2. After loading your character’s mesh, click on “Rig Body” to generate the joint structure for your character’s skeleton.
3. Accurig will display circles where joints should be placed. Ensure symmetry is checked to work on one side of the character.
4. Position the joint guides according to your character’s anatomy, following the on-screen guides for reference.
5. Use the tools in the bottom left corner to rotate and move around your character for precise joint placement.
6. Repeat the process for other body parts, such as arms and legs, ensuring correct joint placement.
7. Use the “Preview Motion” window to check the animation on various body parts, including fingers.
8. Ensure your character is in a neutral pose (A-pose or T-pose) before exporting.
9. Click “Upload to AccuRig” and then “Export” > “Export FBX.” Set the target application to “Maya” and check “Embed Texture.” Click “Export” to save the rig.
10. Export another FBX file of your character’s base mesh but set the target application to “Blender” for later use.

Part 2: Creating a Basic Idle Animation in Cascadeur

1. Open Cascadeur and start a new scene. Import the FBX file with Maya settings that you exported from Accurig.
2. Cascadeur will ask if you want to enter “Rig Mode.” Click “Yes.”
3. In the “Rig Mode Helper” dialog, click “Yes” and then “OK” on the next dialog.
4. Click “Add Rig Elements” at the bottom of the “Quick Rigging Tool” dialog.
5. Rotate your character by holding ALT and the left mouse button to navigate.
6. Select the “Auto Pose” tool to enable automatic control point positioning as you move your character.
7. Position your character into an initial pose for your idle animation by moving and rotating control points. Use ‘W’ to move and ‘E’ to rotate.
8. Add a keyframe at frame 10 by clicking the key icon.
9. Change the hand pose on frame 10 to create a hand open/close animation.
10. Duplicate the first frame to frame 20 and mirror the pose for variety.
11. Duplicate the second keyframe to frame 35 and mirror it to frame 45.
12. Extend the timeline to add more frames for smoother animation (e.g., 200 frames in total).
13. Copy the first frame to frame 145 and the second keyframe to frame 110.
14. Apply bezier curve interpolation for smoother animation between keyframes.
15. Review and refine your animation by adding subtle movements, such as chest and shoulder motion.
16. Create a seamless loop by ensuring the first and last frames are identical and adjust frame numbers accordingly.
17. 

Part 3: Exporting the Animation to Blender

1. Export the animation from Cascadeur to Blender by going to “File” > “Export” > “FBX.” Name the file and click “Save.”
2. In Blender, import the animation by going to “File” > “Import” > “FBX.” Use the default settings and click “Import FBX.”
3. Delete any existing objects in the Blender scene and select the imported Armature.
4. Adjust the Armature’s rotation to face the front and place it in the scene.
5. Create an animation track for the imported animation and rename it.
6. Copy the animation keyframes from the imported Armature and paste them onto your character’s Armature.
7. Delete the imported Armature to keep your scene clean.
8. Create an animation loop for your idle animation in Blender using the NLA (Non-Linear Animation) Editor.

9. 

Part 4: Exporting the Animation to Unreal Engine 5

1. In Unreal Engine 5, create a new project and organize your project folder.
2. Import your character’s 3D mesh into Unreal Engine by right-clicking in the “Content” folder, selecting “Import,” and choosing your FBX file. Ensure it’s assigned to the correct skeleton.
3. Add a Level Sequence to your project by right-clicking in the “Content” folder and selecting “Level Sequence.”
4. Drag your character’s skeletal mesh into the Level Sequence.
5. Add your idle animation to the Level Sequence by clicking the plus icon and selecting the animation.
6. Adjust the timeline as needed and press the spacebar to preview your animation.
7. Extend the timeline and blend your idle and walk animations for a seamless transition.

Part 5: Adding Free Mocap Data

1. Visit the ActorCore website and explore the free motion resources.
2. Download free motion data compatible with Cascadeur.
3. Import the downloaded motion data into Cascadeur, and apply it to your character as needed.
4. Refine and customize the imported motion data to suit your character and animation needs.

This tutorial should help you get started with animating and importing your game characters into Blender and Unreal Engine 5 using Cascadeur. Feel free to explore further features and animation possibilities in Cascadeur to enhance your character animations.

Remember, practice makes perfect, and with time, you’ll be creating stunning animations for your game characters. Enjoy animating!

<p>The post How to Animate Game Characters and Import Them into Blender and Unreal Engine 5 first appeared on Prompt Muse.</p>

In this tutorial, I will show you how to use the Headshot 2 plug-in for Character Creator to create a 3D character from a scanned head mesh. This new plugin from Real Illusion uses AI technology to turn any mesh into a retopgized 3D character, with UVW, rigged mesh and blend shapes for facial animation. There is a full length video of the tutorial here

What you will need

• A head mesh (you can purchase one online or scan your own head) I will be using my own scanned in head using my phone for this tutorial, I used the app Polycam (found on the Appstore) on my IOS phone to scan my head in.
• Headshot 2 plug-in for Character Creator
• Character Creator software
• If you intend to animate my preferable method is Iclone

Step 1: Import the head mesh into Character Creator

1. Open Character Creator software.
2. Go to File > Import > .obj.
3. Select the head .obj mesh file that you want to import.

Step 2: Headshot 2 Plugin 

1. Click on the Headshot 2 plug-in in the top toolbar. You need to install this prior from the Real Illusion website here.
2. The Headshot 2 dialog box will open.
3. Click on the Align Points button.
4. The starter pins will be automatically placed on the head mesh.
5. Move the pins so that they are aligned with the corresponding points on the Character Creator mesh. Delete pins by Ctrl & click the pin you want to remove.

6. 

Step 3: Refine the head mesh

1. Click on the Head Gen button from the horizontal toolbar.
2. A preview of the generated head mesh will be displayed. You can select the area of the mesh you wish to be generated onto the CC3+ model.
3. If you are happy with the preview, click on the Generate button, otherwise you can go back and adjust your points.

Step 4: Refine the head mesh

1. Use the brushes in the panel to refine the head mesh.
2. You can smooth the mesh, move the mesh, or project specific areas.

Step 5: Attach the head mesh to a body

1. Click on the Attach to Body button in the Headshot 2 dialog box.
2. Select the body type that you want to attach the head mesh to.
3. Click on the Attach button.

Step 6: Add skin textures and materials

1. Open the contents dialog.

2. Use the presets in the Skingen plugin to add realism to the skin texture, such as pores, muscle definition and freckles

3. You can also add make up and decals to the character.

4. In the Modify panel, go to the Morphs options and adjust the depth of the eyeballs and height of the teeth using the sliders.

Step 7: Add hair

1. I used the hair plugin (hair builder) and additional hairs assets from the Real Illusion marketplace.

Conclusion

This is just a basic tutorial on how to use the Headshot 2 plug-in for Character Creator. There are many more things that you can do with this plug-in, so I encourage you to experiment and explore.

I hope this tutorial is helpful. Please let me know if you have any questions.

<p>The post I Turned Myself Into A 3D Game character – Tutorial first appeared on Prompt Muse.</p>

In the rapidly advancing world of artificial intelligence (AI), content creators are continually seeking innovative ways to ensure their work bypasses AI detectors. Enter Pass AI Detection, a ground-breaking tool that not only refines your content for human readers but also ensures it’s tailored to bypass AI detectors increasingly utilised by search engines and other platforms.

What is Pass AI Detection?

Pass AI Detection is a sophisticated AI detection tool that analyses your text and modifies it to bypass both AI and human readers. It’s a game-changer for content creators, enabling them to create engaging content that meets the stringent requirements of AI detectors.

AI content detectors are utilised in a range of applications, from search engines to academic integrity tools like Turnitin. These AI detectors scrutinise the text to determine its relevance, quality, and originality. As a result, content creators need to ensure their content is tailored to bypass these AI content detection systems to improve visibility and reach.

Pass AI Detection leverages cutting-edge AI techniques to evaluate and modify your content. The process begins with the AI content detector, which scans your text and identifies areas for improvement. The AI detector looks at your distribution of keywords and topics and provides a suggested distribution that would optimise your content for AI content detection.

A Balanced Approach to Content Creation

Pass AI Detection centres on balance. It understands the negatives of keyword stuffing and the necessity for reader-friendly text. The tool doesn’t increase keyword frequency, but promotes balanced keyword distribution, crafting content that is both AI and reader-friendly.

As AI detection methodologies progress, Pass AI Detection adapts. The tool is continuously refining its algorithms to ensure your content remains optimised for the most recent AI detection techniques. This commitment to staying ahead of the curve positions Pass AI Detection as a leader in the AI content detection market.

Content creation is a critical component of successful marketing strategies. Pass AI Detection helps generate content that not only attracts your audience but also performs well with AI detectors, achieving an ideal balance between human readability and AI detectability. Experience cost-effective, innovative content creation with Pass AI Detection’s unique BYOK model. Join the revolution today.

<p>The post Meet Pass AI Detection: Your Free Tool to Bypass AI Content Detectors first appeared on Prompt Muse.</p>

Getting Started with Warp Fusion

Prerequisites

Before we dive into the tutorial, there are a few prerequisites you need to have:

• Google Colab Pro or Google Colab Pro Plus
• Access to Alex’s Patreon page where you can get the Warp Fusion notebook
• A model and a Lora from Civic AI

Setting Up Warp Fusion

The first step in using Warp Fusion is setting up the environment. This involves downloading the Warp Fusion notebook from Alex’s Patreon page and loading it into your Google Colab. Once you have the notebook ready, you need to connect it to a hosted runtime if you’re using Google Colab Pro. This ensures that you’re using your compute unit and the GPU.

Configuring Warp Fusion

Basic Settings

Once your environment is set up, you can start configuring Warp Fusion. The first thing you need to do is specify the name of the folder where your output files will be stored in your Google Drive. You also need to set the width and height of your output video to match your input video. A resolution of 720 by 1280 is a good starting point, but you can adjust this to suit your needs.

Video Input Settings

Next, you need to specify the path to your input video. This is the video that you want to animate using Warp Fusion. You can save your input video on your Google Drive for easy access. If your video is long and you want to reduce the diffusion time, you can set the ‘extra frame’ setting to two, which means Warp Fusion will diffuse every other frame.

Video Masking

Video masking is a useful feature that can help make the background of your animation consistent and stable. You can use your input video as the mask source and extract the background mask. If you want to add another video into the background, you can specify the path to that video in the ‘mask video path’ setting.

Defining SD and K Functions

The next step is to define the SD and K functions. You need to change the ‘load to’ setting to GPU and specify the path to the model you want to use. You can get models from Civic AI or Hugging Face. You also need to specify the directory where your control net models will be stored on your Google Drive.

Running Warp Fusion

Once you have all your settings configured, you can start running Warp Fusion. This involves running all the cells in the notebook up to the GUI section. This process can take about 5 to 10 minutes, and you will get a green tick for every cell that has successfully completed.

Customizing Your Animation

Using Prompts and Loras

Prompts and Loras are powerful features that can help you customize your animation. Prompts are instructions that guide the AI in creating the animation, while Loras are elements that you can add to your animation. You can specify your prompts and Loras in the GUI section of the notebook. You can also adjust the strength of the stylization and the prompt guidance to achieve the desired effect.

Using Control Nets

Control nets are models that you can use to control the animation. You can select the control net models you want to use and adjust their weights to influence their impact on the animation.

Using the Warp Tab

The warp taballows you to adjust the flow blend of your animation. This is the blending of your input video with the next frame of stylization. If you find that your animation is over-stylized, you can reduce the flow blend to achieve a more balanced effect.

Using the Mask Tab

The mask tab allows you to use a background mask for your animation. You can change the color or use an image or the original video as the background. This can help to create a more consistent and stable background for your animation.

Rendering Your Animation

Once you have customized your animation, you can start rendering it. This involves running the ‘diffuse’ cell in the notebook. As your animation progresses, you will see a preview frame that allows you to check for any errors. If there are errors or things you want to fix, you can stop the diffusion and adjust your settings.

Creating a Video from Your Animation

After your animation has been rendered, you can create a video from it by running the ‘Create Video’ cell. This will create a video from the frames that were diffused in the previous step. The video and all the frames will be saved to your Google Drive.

Post-Production

After you have created your video, you can bring the frames into a post-production software like DaVinci Resolve or After Effects for further editing. This can involve reducing the flickering of the animation or exporting the frames into a .mov file.

Upscaling Your Video

The final step in the process is upscaling your video. This can be done using a service like Topaz Labs or Pixel. These services use AI to increase the size of the video and make it sharper. Pixel is a browser-based service that is easy to use and offers a pay-as-you-go pricing model, making it a cost-effective alternative to Topaz Labs.

Warp Fusion is a powerful tool that allows you to create unique and eye-catching animations. With its wide range of features and customization options, you can create animations that truly stand out. Whether you’re a hobbyist or a professional, Warp Fusion offers a fun and innovative way to create animations.

Remember to share your creations on social media and tag Prompt Muse. We love to see what you create with Warp Fusion. Happy prompting!

<p>The post Warp Fusion: A Comprehensive Step-by-Step Tutorial first appeared on Prompt Muse.</p>

How to Use UseChatGPT.Ai

UseChatGPT.Ai is a browser extension that is compatible with Chrome, Firefox, and Safari. The application can be downloaded from the official website free of charge. Once installed, the extension allows you to use AI models such as GPT-4, OpenAI API, and Bing Bard on any text-based website or PDF with just one click. Here’s how to get started with UseChatGPT.Ai:

1. Go to the official website and download the extension for your browser.
2. Install the extension by following the instructions on the screen.
3. Once installed, click on the UseChatGPT.Ai icon in your browser toolbar.
4. Select your preferred AI model from the dropdown menu.
5. Copy and paste the text you wish to analyze into the text box.
6. Click on “Generate” and wait for the AI model to do its magic.
7. Review the generated text and use it as you wish.

Benefits of UseChatGPT.Ai

UseChatGPT.Ai offers a variety of benefits to its users, including:

Improved Productivity

With UseChatGPT.Ai, you can generate high-quality content in a matter of seconds, saving time and effort. The AI models used by the tool are capable of analyzing large amounts of data and providing accurate results, allowing you to focus on other tasks.

Customization

UseChatGPT.Ai allows you to choose from multiple AI models and customize your prompts as per your requirements. This feature ensures that you can generate content that is tailored to your specific needs.

Cost-Effective

UseChatGPT.Ai is completely free to use, making it a cost-effective alternative to other AI tools that may charge a significant amount of money.

User-Friendly Interface

UseChatGPT.Ai has a simple and intuitive interface that makes it easy for even non-technical users to navigate the tool. The interface is well-designed and organized, ensuring a seamless user experience.

Drawbacks of UseChatGPT.Ai

Like any tool, UseChatGPT.Ai has its limitations. Here are a few drawbacks to keep in mind:

Accuracy Issues

While the AI models used by UseChatGPT.Ai are highly advanced, they are not foolproof. There may be instances where the generated content is inaccurate or irrelevant, requiring manual editing.

Dependency on Internet Connectivity

UseChatGPT.Ai requires a stable internet connection to function correctly, which may be an issue for users with limited access to the internet.

Final Thoughts

Overall, UseChatGPT.Ai is a powerful AI tool that can significantly improve your productivity and content generation capabilities. The tool’s user-friendly interface, customization options, and cost-effectiveness make it an excellent choice for content creators and marketers seeking to streamline their workflow. However, like any tool, UseChatGPT.Ai may not be perfect, and users should be aware of its limitations before using it. We recommend trying out UseChatGPT.Ai for yourself and experiencing its benefits firsthand.

<p>The post Revolutionise Your Content Creation with UseChatGPT.AI – The FREE A.I content buddy first appeared on Prompt Muse.</p>

LINKS:

ChatBase LifeTime Deal

ChatBase Monthly Subscription

Prompts:

Base Prompt

you are a writer. you write content using the resources, as I request and always add links to references. your articles are well written, factual, and contain subheadings every 1-3 paragraphs. you can ONLY mention products from the resources I provided you. you article starts with an introduction and ends with a conclusion. Do not write ANYTHING except the requested article.

Main Prompt (in chat)

Instructions:  write an 800-word article 

Topic: Creating a Modern Nursery: Top Furniture and Decor Picks from Nubie 

Target Audience: Parents

Internal Linking: Always use hyperlinks for as many relevant links within the site as possible, Example: We have <a href=”https://www.thechildrensfurniturecompany.com/beds>Beds</a> for all ages

Writing Style: Informative, broken up with relevant subheadings | bold (with <strong> tags) hyperlinked words.

URLS: Do not add full URLs, it needs to be wrapped in <a href> tags.

Title: clickbaity / interesting

User Intent: deciding to make a purchase 

Language: British English

Subheadings & titles: Wrapped in relevant H tags eg <h1>title</h1>

Paragraph Length: Random between 200-600 words

What is Chatbase?

Chatbase is an application that is designed to help users create chatbots for various purposes. However, Alex has found a unique use for the application that he believes can help anyone who is involved in content creation. With Chatbase, you can analyze all the content on a website and use it to train an AI model to create high-quality content. The tool uses OpenAI’s GPT-3 model to generate content that is both engaging and relevant to your topic.

How to Use Chatbase for Content Creation

Alex has developed a systematic approach to using Chatbase for content creation. Let’s take a look at his method step-by-step:

Step 1: Purchase Chatbase
The first step is to purchase Chatbase and get an OpenAI API key. You can buy Chatbase for $29 from AppSumo but you will need to purchase an OpenAI API key which costs $159. The key is essential as it allows Chatbase to access OpenAI’s GPT-3 model, which is an integral part of the application.

Step 2: Analyze the Website
Once you have installed Chatbase and input your OpenAI API key, you can start analyzing the website you want to create content for. This is an important step as it allows Chatbase to learn all the content on the website. To analyze the website, simply input the URL and let Chatbase crawl the website. The process can take a while depending on the size of the site. The tool can crawl through up to six million characters of content. Alex advises that you make sure you have selected the correct website and region you would like to extract content from.

Step 3: Train the Model
Once the website has been fully analyzed, you can proceed to train the AI model. During this process, you can choose what type of data sources you want to teach the AI. For example, you can choose to teach the AI only specific pages, products, or searches. The Chatbot API can also hyperlink into pages found on the website, allowing it to generate content that is precise and specific to the user’s query.

Step 4: Customize the Content
After creating the Chatbot API, you can start generating content. The AI model produces the content quickly, but as Alex notes, it’s not always perfect. You can further customize the text generated by removing any parts you don’t like. The chatbot dynamically learns from your changes, so after making selections, the chatbot may produce better results the following time you use it.

Step 5: Use the Content for Articles, Blogs, Ads, Social Media Campaigns
Once you have fine-tuned the content to your preferences, you can export the content for use on your blog, social media channels, ads, or even content for a book. The Chatbase tool does not limit the user to specific content formats. Instead, you are free to use the generated content as you see fit. You can even store multiple versions of generated content until you decide which version to use, meaning more value for a single purchase.

Alex’s Tips and Tricks for Chatbase Content Creation

1. Test the AI
Before exporting the AI-generated content, Alex suggests testing the chatbot API’s accuracy. Perform some queries related to the keywords and phrases that are relevant to your brand, and see how well the Chatbot API responds. If any responses are inaccurate, make the necessary changes to enhance accuracy.

2. Use the Right Temperatures

When using GPT-3 formulas, Chatbase, GPT-4, or GPT 3.5, Play advises that the used temperature values will influence the output entirely. When creating articles, low temperatures like 0 must be used to achieve factual content while avoiding false statements.

Chatbase is a powerful tool that can help anyone who is involved in content creation. With Alex’s systematic approach, you can use the tool to analyze any website and generate high-quality content in just a matter of minutes. If you’re currently working on a content creation project and want to streamline your writing process, consider investing in Chatbase. This application will not only save you time but also produce content that is both engaging and relevant to your topic.

FAQ

1. What is Chatbase and how can it be used as a chatbot?
Answer: Chatbase is an application that can memorize and learn all content on a website to be used as a chatbot.
2. Can Chatbase be used in a different way?
Answer: Yes, it can be used in a different way, which will be explained in this episode.
3. How much does Chatbase cost?
Answer: Chatbase is available on AppSumo for $29.
4. Do you need an Open AI API key for Chatbase to work?
Answer: Yes, an Open AI API key is required to use Chatbase, and it costs $159.
5. What is Chatgpt and how is it used in the demo?
Answer: Chatgpt is a tool for creating chatbots, and Alex Player demonstrates how to use it in this episode.
6. Can you choose data sources to teach the chatbot using Chatgpt?
Answer: Yes, you can choose data sources to teach the chatbot using Chatgpt.
7. What is the maximum amount of characters you can use on the current tier of Chatgpt?
Answer: The maximum amount of characters on the current tier of Chatgpt is 6 million.
8. Can you remove things you don’t want with Chatgpt?
Answer: Yes, you can remove things you don’t want with Chatgpt.
9. Can Chatgpt read PDFs or certain URLs?
Answer: Yes, Chatgpt can read PDFs or certain URLs.
10. What kind of topics can Alex Player generate with the help of Chatbase?
Answer: Alex Player can generate article/topic ideas for a brand’s website with the help of Chatbase.

<p>The post Create GPT4 articles with automated internal linking first appeared on Prompt Muse.</p>

Artificial Intelligence (AI) has rapidly become one of the most versatile and powerful technological tools in recent years, providing users with a wealth of opportunities to explore, create, and express themselves in new and exciting ways.

In the world of music, AI has revolutionized the way musicians create, record, and market their art. From optimizing vocal tracks to generating entirely new compositions, AI is making music more accessible, dynamic, and personalized than ever before.
In this blog post, we will explore the many ways in which AI is transforming the world of music and the exciting possibilities it holds for the future.

The Rise of AI Vocals

One of the most fascinating developments in the music industry has been the rise of AI vocals, which has enabled musicians to create realistic vocal tracks without requiring the services of a professional singer. With AI vocals, musicians can fine-tune the pitch, timbre, and other vocal qualities to suit their creative vision, ensuring the final track sounds exactly as they imagined it.

AI vocals are also useful for people who might not have the self-confidence to sing themselves or who lack the resources to hire a professional singer. By providing a flexible and affordable solution, AI vocals allow artists to experiment with different styles, sounds, and arrangements without breaking the bank.

Collaborating with AI

The ability to collaborate with AI has also opened up new creative avenues for musicians, allowing them to create brand new vocals by combining different AI-generated voices. This makes it possible to create choirs without requiring a large number of singers, providing musicians with greater control over the final product.
In addition, AI can help improve the quality of music videos that independent artists produce, enabling them to create high-quality visuals without the need for expensive equipment or professional crews. This allows musicians to maintain their creative vision while still producing music videos that are visually stunning and engaging for their fans.

Learning from Others

The podcast team at Prompt Muse often discusses their creative process, which involves brainstorming ideas together for days or weeks, testing concepts, and then starting production in a relaxed and casual way. They also rely on hard work and spend hours researching information to learn new things, including the latest AI techniques.

To stay ahead of the curve, they often check Reddit for 10 minutes a day to keep up with the latest technology and advancements. Although some may find AI technology daunting, the team at Prompt Muse believes in adapting their workflow to new technology as it evolves. They remain open-minded and learn by experimenting and transforming assets to achieve their objectives quickly.

Misconceptions About AI

There are many misconceptions about AI, particularly its ability to create videos and music easily and independently. While AI can be used as a tool for creating quick visualizers, human input is still necessary for creating a good story and continuity. Music videos are seen as short films with a soundtrack and require a lot of time and effort to produce.

By suggesting that AI can replace the human touch, creators often find themselves feeling frustrated. Dismissing their work as “AI video” or “AI music” doesn’t recognize the human input and effort involved in the creative process.

AI-assisted Music Videos

However, AI can be a great ally for independent music artists in producing their own videos and music. In particular, Runway.ai is a popular tool used by the podcast team to edit backgrounds that are then added to the video edit on Adobe After Effects. While it is important to present the reality of what happened on the day of the shoot, it is equally important to bring the artist’s creativity and vision to life.
AI-generated music still requires the artist to record the song from start to finish – AI only changes the tone of their voice to match someone else – but previous tracks can be used to create a new track. Working in tandem with AI also allows the artist to produce something of which they are proud.

The Future of AI in Music

As AI technology continues to evolve, the future of music promises to be more exciting than ever before. With AI, artists will be able to explore new creative frontiers while also reaching new and diverse audiences around the world.

AI-generated music may not only be used by independent artists, but also by established stars to collaborate with other artists and explore new musical styles. This could help to expand the boundaries of music as we know it while also creating new and unique sounds that have never been heard before.

Innovation is the key to the future of music and Prompt Muse is committed to bringing our readers the latest developments in AI and music. The podcast and blog team encourages their audience to embrace the power of AI as a creative tool to develop their own unique vision.
As AI technology continues to evolve, it’s exciting to think about what the future will hold for music. With AI, musicians will be able to create more exciting, innovative, and personalized music than ever before, empowering them to take their creativity to new heights and explore new possibilities in the world of music.

FAQ

1. What are some benefits of AI vocals for artists and producers?
– AI vocals can be useful for people who are not self-confident enough to sing, and they can be merged to create brand new vocals and create choirs without needing many people.
2. How can AI assist independent music artists in creating high-quality music videos?
– AI can give power to music artists to create videos and music on their own, faster than before, and it can be a great ally for independent music artists in producing their own videos and music.
3. How do the guests on the podcast approach video production and what tools do they use?
– They start with walking in a Japanese environment, then edit the video to add a background frame, play with camera movements, and transform assets using Enringing; they use Warp fusion for primary tools and Deform for making scenes more interesting, and Erased backgrounds are edited on Runway.
4. Are there any misconceptions about AI and its ability to create videos and music?
– Yes, some people believe that AI can create videos and music with just one sentence or prompt, but human input is still necessary for creating a good story and continuity.
5. How do independent music artists feel about the use of AI in their work?
– There is frustration among creators when people dismiss their work as “AI video” or “AI music” without recognizing the human input and effort involved.
6. How has AI technology evolved and improved over time?
– When the speaker started with AI technology, there were limited resources available, but now tools like Dalle Fusion are available and powerful, allowing for greater creativity and faster problem-solving.
7. What tools and websites are available to bring creative ideas to life?
– There are many websites and tools available to create 3D visuals and animate drawings, and Runway can be used to achieve dream boosting and generating unique ideas.
8. What is Sagans, and what have they accomplished through their work with AI music videos?
– Sagans is an anonymous AI collective that has been producing music videos for popular artists since 2022, and they have produced videos for Lincoln Park Lawns Entropy and Die Antwood’s Age of Illusion in just one year.
9. How important is it to stay up to date with AI advancements as a creative?
– It is important to be aware of new technology for future use, but it is not necessary to know everything, and it is important to check casually without feeling overwhelmed.
10. How can creativity be enhanced through setting limits and restrictions?
– James Blake created a song by limiting himself to only four notes, and the N-word style and technique were developed mainly through experimentation and prompt.

<p>The post Sagans: The Anonymous AI Collective Taking Over the Music Video World first appeared on Prompt Muse.</p>

As OpenAI’s ChatGPT gained popularity as a powerful language model, concerns arose regarding the lack of privacy settings for users. Initially, the AI product did not provide options for users to safeguard their data, and OpenAI utilized data from conversations to train its language model further. With privacy watchdogs and regulators demanding better data protection, OpenAI introduced a new privacy setting. This guide will walk you through the process of opting out of ChatG

PT data sharing and explain the implications of doing so.

Step-by-Step Guide to Opting Out of ChatGPT Data Sharing

To stop ChatGPT from using your private data, follow these steps:

1. Log into your ChatGPT account using a browser.
2. Click on your account icon located in the bottom left corner.

3. Select “Settings.”

4. Find the “Data Controls” section and click on the “Show menu” next to it.

5. Toggle off “Chat History & Training.”

Note that OpenAI may have tried to obscure this privacy-enhancing feature. Within this menu, you can also export your ChatGPT data or delete your account if desired.

What Happens to Your ChatGPT Chat History After Turning Off Data Collection?

When you disable OpenAI’s ability to collect and use your data for training ChatGPT, your chats will only be stored for 30 days. After that period, your conversations will be permanently deleted. OpenAI states that while they will retain your data for 30 days, it will only be reviewed to monitor for abuse and not used for training ChatGPT.

In the future, OpenAI plans to launch a ChatGPT Business account that will allow users to keep their chat history even when disabling data collection.

What Happens to Your Existing ChatGPT Data?

Unfortunately, you cannot delete the data you previously shared with ChatGPT before the new privacy settings were implemented. However, you can opt to start sharing data again by re-enabling “Chat History & Training” within the settings.

OpenAI claims to use the shared data to improve their models, making them more helpful for users. They state that they do not use the data for advertising, selling services, or creating user profiles.

Future Improvements in ChatGPT Privacy Management

While the current privacy options for ChatGPT are limited, it is essential to keep an eye on potential improvements in the future. OpenAI may develop better ways to manage user privacy, ensuring a more secure and user-friendly experience.

In conclusion, the introduction of privacy settings in ChatGPT is a step forward in data protection. By following this guide, you can easily opt out of data sharing and safeguard your private data. However, there is still room for improvement in privacy management, and users should remain vigilant regarding their data security.

<p>The post Preserve Your Privacy: Take Action and Change ChatGPT Settings to Stop OpenAI from Training on Your Data first appeared on Prompt Muse.</p>

On Thursday, President Joe Biden held a meeting at the White House with CEOs of leading AI companies, including Google, Microsoft, OpenAI, and Anthropic. The meeting emphasized the importance of ensuring the safety of AI products before deployment and addressing the risks posed by AI. However, some AI experts criticized the exclusion of ethics researchers who have warned of AI’s dangers for years.
Over the past few months, generative AI models such as ChatGPT have quickly gained popularity, driving companies to develop similar products rapidly. However, concerns have been growing about potential privacy issues, employment bias, and the potential for using them to create misinformation campaigns.

AI Ethics Researchers Respond

Critics of the companies’ ethical track records were not impressed by the meeting. They questioned the choice of inviting people to the meeting who, they argue, represent companies that have created the issues with AI that the White House seeks to address.
On Twitter, AI researcher Dr. Timnit Gebru wrote, “It seems like we spend half our time talking to various legislators and agencies and STILL we have this… A room full of the dudes who gave us the issues & fired us for talking about the risks, being called on by the damn president to ‘protect people’s rights.'” In 2020, Google fired Gebru following a dispute over a research paper she co-authored that highlighted potential risks and biases in large-scale language models.

University of Oxford AI ethics researcher Elizabeth Renieris tweeted, “Unfortunately, and with all due respect POTUS, these are not the people who can tell us what is “most needed to protect society” when it comes to #AI.”

AI Safety and AI Ethics

The criticism highlights the divide between “AI safety” (a movement concerned primarily with hypothetical existential risk from AI) and “AI ethics” (a group of researchers concerned largely about misapplications and impacts of current AI systems, including bias and misinformation).
Author Dr. Brandeis Marshall suggested organizing a “counter-meeting” that would include a diverse group of AI ethicists, practitioners, and researchers to discuss the real-world implications of AI and propose more inclusive and responsible solutions.

Looking Forward

Although the White House meeting brought attention to the potential risks and challenges posed by AI, it remains to be seen whether the discussion will lead to concrete actions that address these issues. It is crucial for government, industry, and academia to collaborate and ensure that AI development is safe, responsible, and equitable.
To foster a more comprehensive dialogue on AI ethics and safety, including voices from marginalized communities and interdisciplinary perspectives, could be a critical step in building more resilient and fair AI systems. Engaging stakeholders from various backgrounds in the decision-making process will help address concerns around bias, privacy, and the potential misuse of AI technologies.

As AI continues to advance rapidly, it will become increasingly important for policymakers and industry leaders to work together and create robust regulatory frameworks that protect the public interest while enabling innovation. Only through a collaborative approach can we ensure that AI technologies are harnessed to benefit society as a whole, while mitigating potential risks and negative consequences.

<p>The post A New Era of AI Governance: The White House Leads the Way in Promoting AI Ethics and Inclusivity first appeared on Prompt Muse.</p>

In this article, we’ll explore how Tore Knabe created his groundbreaking VR games, and what makes them such a unique and exciting experience.

The Making of Tore Knabe’s VR Game

Tore Knabe’s VR games are the result of several different technologies working together. The game was built using the Whisper which is Open AI’s new Speech to Text (STT) api. In addition to Whisper, Knabe also used Eleven Labs, a company specialising in creating human-sounding artificial voices, and the GPT-3 API, which powers the AI characters in the game.

The game takes place in various settings, including a peaceful lakeside scene with a priest and an old bar tavern with a talkative bartender. In both scenarios, the player is able to have conversations with the AI characters using natural language. The AI characters are able to respond to the player’s questions and statements in real-time, thanks to the power of GPT-3.

What’s particularly impressive about Tore Knabe’s game is the level of detail that has gone into creating the AI characters. For example, in the bar tavern scenario, the AI bartender is able to keep track of the various patrons in the bar, and can discuss each one with the player as a separate topic. This adds a level of realism to the game that is rarely seen in other AI-powered experiences.

The Future of VR and AI

Tore Knabe’s VR game is just the beginning of what could be a whole new era of gaming. As VR technology continues to improve, and AI becomes even more advanced, we can expect to see more games that combine these two technologies in innovative ways.

Imagine a virtual reality world where every character you encounter is powered by AI, and is able to converse with you in a natural and engaging way. Or a game where the AI characters can learn from your interactions, and adapt their behaviour based on your choices. The possibilities are truly endless.

Tore Knabe’s VR game is an exciting glimpse into the future of gaming, where AI and VR technology are combined in new and innovative ways. By using GPT-3 to power the AI characters in the game, Knabe has created a truly immersive experience that feels like a real conversation.

As we continue to explore the possibilities of AI and VR, it’s important to consider the potential benefits and drawbacks of this technology. While there are certainly concerns about its impact on society, there’s no denying that the combination of AI and VR has the potential to revolutionize the way we play games, and even how we interact with the world around us.

<p>The post Virtual Reality meets Artificial Intelligence: The Fascinating World of Tore Knabe’s VR Game first appeared on Prompt Muse.</p>

Musk’s X.AI: The New Contender in the Generative AI Arena

Reports are swirling that Elon Musk has created a new company called X.AI to compete with OpenAI and join the generative AI race. X.AI is incorporated in Nevada and lists the Twitter-owner as the company’s director, with Musk’s personal wealth manager, Jared Birchall, listed as its secretary.

Rumours have been circulating for some time about Musk entering the race for the best generative AI system, particularly following the massive popularity of OpenAI’s ChatGPT chatbot. A recent Business Insider report revealed that Musk purchased 10,000 graphic processing units for use at one of Twitter’s two remaining data centres. A source told the outlet that this investment shows Musk is “committed” to the effort, as there’s little reason for Twitter to spend so much on the tech if it didn’t plan to use it for AI.

Musk’s AI Dream Team: SpaceX and Tesla Investors, Unite!

According to a Financial Times report, Musk has been assembling a team of AI researchers and engineers and is in talks with SpaceX and Tesla investors to pour money into his latest venture. “A bunch of people are investing in it… it’s real, and they are excited about it,” the outlet quoted a person saying.

This news comes just a week after it was revealed that Twitter Inc no longer exists because it has been merged into X Corp, another company owned by Elon Musk. The move is likely part of a broader plan to create an “everything app” in the style of China’s WeChat.

Musk’s AI Ambitions: Contradictions and Controversies

Interestingly, Musk’s AI plans come after he signed an open letter calling for a six-month pause on AI development due to safety concerns. Musk has also been an open critic of OpenAI, a company he invested in and co-founded in 2015, and eventually left after disagreements with colleagues.

Additionally, Musk recently revealed April 20 as the final date for removing the blue tick from legacy verified accounts in a bid to promote uptake of Twitter Blue, a plan that hasn’t been going too well.

As the AI race heats up, the tech world is eagerly watching Musk’s moves with X.AI. Will his latest venture disrupt the AI industry or simply add another layer of intrigue to Musk’s growing list of companies? Only time will tell, but one thing’s for sure: with Musk at the helm, it’s bound to be an exciting ride.

Stay tuned to Prompt Muse for the latest updates on this unfolding AI showdown, as well as your daily dose of essential tech news.

<p>The post Elon Musk Sparks AI Showdown with X.AI, Taking on OpenAI and Shaking Up Twitter first appeared on Prompt Muse.</p>

AI-generated animations are becoming increasingly popular in the creative world, and it’s no wonder why. These powerful tools allow artists to quickly and easily create stunning visuals that would otherwise take hours or even days to produce. In this comprehensive guide, we’ll walk you through the process of creating your own AI-generated animation using state-of-the-art techniques, tools, and workflows. With the help of this tutorial, you’ll be well on your way to producing professional-quality animations in no time.

Preparing Your Data for Training

Before diving into the animation process, it’s essential to prepare your data for training. Here’s a step-by-step guide to help you get started:

Gather a diverse dataset: To train your AI model effectively, you’ll need a large and varied dataset. This can include a mix of images, videos, and other visual content.
Preprocess your data: To ensure your AI model can effectively learn from your dataset, you’ll need to preprocess your data. This can involve resizing images, removing any unwanted artifacts, and normalizing the data.
Split your dataset: Divide your dataset into a training set and a validation set. This will allow you to train your model on one set of data while validating its performance on another.

How I Unleashed the Power of AI to Create Next-Level Animations
Once your data is ready, you can begin the process of training your AI model.

Training Your AI Model with Google Colab

Google Colab is an excellent platform for training your AI model, as it provides a powerful and user-friendly interface. Follow these steps to train your model using Google Colab:

Upload your dataset: Import your dataset into Google Colab, either by uploading it directly or connecting to your Google Drive.
Configure your training settings: Adjust the learning rate, optimizer, and other settings to optimize your model’s performance.
Train your model: Run the training cell to begin the training process. This may take anywhere from 30 to 40 minutes, depending on the size of your dataset and the complexity of your model.
Throughout the training process, be sure to monitor your model’s performance by checking its progress in the Google Colab interface.

Once your model has completed its training, you can export it for use in your animation project.

Creating and Animating Your 3D Character

Now that your AI model is trained, it’s time to create and animate your 3D character. Here’s a step-by-step guide to help you bring your character to life:

Generate an image of your character: Use a tool like Automatic 111, Run Pod, or CalicuristroC Lab UI to generate an image of your character using your trained AI model.
Create a 3D model of your character: Import your generated image into a 3D modeling program like Headshot or iClone, and use the built-in tools to create a 3D model of your character.
Add hair and other details: Use the libraries and tools provided by your 3D modeling program to add hair, clothing, and other details to your character.
Animate your character: Use a plugin like Motion Live to record your facial movements and apply them to your 3D model in real time. This will create a realistic, lifelike animation of your character.

Once your character is fully animated, you can export it for use in your final project.

Rendering and Finalizing Your AI-Generated Animation

After you have completed setting up your animation, it’s time to render and finalize your AI-generated creation. With the right settings and techniques, you’ll be able to produce smooth and realistic animations.

Batch Rendering Frames

To create a frame-by-frame animation, you need to batch render your frames. To do this, head over to the “Batch” tab in Automatic 111, select the input folder where your 3D files are stored, and the output folder where you want the rendered frames to be saved. Then, click on “Generate.” Once the rendering is complete, you will have a seamless animation that can be easily combined and edited in post-production software.

Post-Production and Final Touches

After rendering your animation frames, import them into a post-production software like Adobe After Effects or Da Vinci Resolve. Apply the appropriate effects, such as dirt removal and deflicker, to ensure a smooth and polished final result.

Remember that you can experiment with different checkpoint models in Automatic 111 to quickly change the render style of your animation. This flexibility allows you to create a variety of looks and styles with just a few clicks.

Sharing Your AI-Generated Animation and Continuing to Learn

Once your AI-generated animation is complete, be sure to share your work on social media and tag Prompt Muse so they can see and appreciate your creative projects. Connect with others in the community and continue learning from their techniques and ideas.

Join the Prompt Muse Community

Get involved with the AI creative community by following Prompt Muse on social media. You can find them on Instagram and Twitter as @PromptMuse. Additionally, join their Discord server to engage with other AI enthusiasts and share your projects.

Stay Up-to-Date with the Latest AI Tech News

To keep up with the latest developments in AI technology, subscribe to the Prompt Muse newsletter. You’ll receive regular updates on cutting-edge techniques and tools that can help you push the boundaries of your AI-generated animations and other creative projects.

Creating AI-generated animations is an exciting and ever-evolving field. By following the detailed steps and techniques shared by Prompt Muse, you can create stunning and realistic animations using AI technology. Experiment with different settings, checkpoints, and tools to discover new possibilities and unique styles for your projects.

Remember to share your creations with the AI community and tag Prompt Muse on social media, as they are always eager to see and celebrate the innovative work being done in this space. Stay connected, keep learning, and continue pushing the boundaries of AI-generated animation.

TRANSCRIPT:

And welcome back to another prompt muse tutorial. Today, I’m going to be showing you how to create a fully animated AI character. The beauty of this technique is that you don’t have to act out the scenes before. In fact, you don’t need to look like your actor or your actress. This method could potentially be used in the movie industry. If not just the concept artwork, it could be used for an entire movie itself. For this tutorial, I’m going to be training a realistic data set for my puppet. I’m going to be using myself so you can judge the likeness at the end for yourself. I gather up loads of images of myself. I then train it with Koya Laura. Then once I’ve done that, I use Character creator to create a puppet. You do not need to know any 3D. There is no learning curve to this software. You’re basically using sliders and dragging and dropping assets onto your 3D character. It is super simple to use. Obviously, you can go down the blender routeand the meta human route if you want to, but I just warn you, there is a learning curve, so it’s up to you what you want to do.

But my workflow is through character creator. This means I can then switch out to any character using the data set. So once I’ve got those rendered files of my puppet, I can then switch out those data sets from one character to another, and it’s a really easy and efficient way of working. So let’s dive in and get on with the tutorial. Step one, preparing your training images. I’ve prepared various images of myself. Put some images that are more like you, rather than photographs with a filter on, because trust me, it works ten times better. If you do that, you will get the likeness a lot better. It’s important that you choose your head, your body, some full bodies in there, some close up, some far away. You need to ensure that your hairstyles are quite different in each image, that you’re taking from different profiles, that your background’s changed, that your outfit’s changed. If you put too many headshots into your training data, you end up with things like this. Because the AI doesn’t know what the body is, so it gets confused. You need to put some body shots in there. I’m using a website called Burmy, which is absolutely free to use and it crops your images down to the size you want.

I’m going to be using 512 by 512 because that’s pretty much my resolution. You can go up to 768 by 768, but remember that’s going to use more VRAM and take longer to train. Once I’ve done that, I want to rename all my files. So click on Rename and just put X in the box and Save as Zip. And that will save all your files in numerical order. So 1. Png, 2. Png, because that’s how you want it. Head over to Google Drive and simply save your zip file there. Step two, training your data set. I’m using the Linquf Quia Laura Dream booth. We are going to make sure that we’re logged into our Google account here, and then we’re going to press Connect here. So once you’ve done both of those, we can start the training. Simply come down to step one. All we need to do is check the Mount Drive button and run that cell. And this is going to mount your Google Drive. I’m going to click Connect to Google Drive. It’s going to give you some warnings about connecting to an unGoogle offered notebook. Now that’s running. Once that’s completed, you’ll get a green check next to it.

So this downloads all the dependencies into your file structure over here. Now, remember, this is remote. This isn’t on your PC. So once you come out of here, you’ll lose everything. But this is where we connect the Google Drive so we can pull files across from our Google Drive. And then once we’re finished, we can take the train model and export that to our Google Drive. So we’re just going to come down here, the 1.2 Open Special Fire Explorer. Ignore that, that’s fine. We don’t need to do that. The default setting here is for anything version 3, which is more of an animation style model. And if you click here, there’s a few more preloaded links in there. If I click on Stable Diffusion 1.5, that’s good for me because I’m doing a realistic character. I just want to chip in and say the Stable Diffusion 1.5 base model is a good all rounder training file to use. Obviously, you can use whatever you want, but if you’re starting off, this is a good one to learn with. You can see in this bit below, you can actually load in your custom model, which means you can go to the hugging face link.

For example, a good model to use is Realistic Vision 2. So you get the Hugging Face link for your model and place it in there and run that cell. But we’re not doing that for this tutorial. So I’m just going to leave the space underneath, which is Stable Diffusion 2Model Blanks. We’re not going to be using that. And then just ignore 2.2. We’re not going to be running our own custom model. So 2.3 download available VAE. Sometimes you might notice when you switch models in automatic one on one or whatever program you’re using, that the images are really desaturated and lost their colours. That’s usually down to the VAE not the not being detected or being corrupted. So we are going to load in the Stable Diffusion 1.5 VAE, which is already there. So it’s a Stable Diffusion VAE, and that’s going to, again, just download it all into our file structure. And then we’re just going to hit on Run on there. And then we come down to 3.1 locating train data directory. So this is going to create some file path to where our train data file is. And that’s where all our input data set images, so my face images, will be going into that folder.

And don’t worry, you don’t have to do anything. It does it automatically for you. So I’m going to hit run on that cell once again, and it says your trained data directory. So if we go and have a look, so expand the Laura down here. By the way, if you don’t have this open, it’s this folder here to go into here. Now, go to Laura and you’ve got your regularisation data and your trained data. Do not drag and drop your images into here. Wait, we’re going to be doing that automatically. On to 3.2 unzip data set. So this zip file underscore URL. So this is why we zipped our file up and put it onto our Google Drive because we’re now going to grab it. So if you go to Drive, this is essentially your Google Drive, my Drive. And then I’m going to come down and find my zip file, which is 100 prompt muse images. Zip. I’m going to click on the three dots there and copy path. And then I’m just going to paste in the top zip file underscore URL. I’m going to leave the unzip, underscore, two blank. I’m just going to close this hierarchy here so we can see what we’re doing.

And you can see there it’s extracted all my files from that zip file into that new folder. So we’re going to come down to 3.3, which is the image scraper. Again, I’m not going to use this. This is based on more or less animation databases. So what it does is scrape regularisation images, which I don’t want to do because I’m not using animation. So I’m going to ignore this. But if you are using animation, you can do it here, ignore 3.3. Data clearing. This is to do with the cell above it. You’re scraping all these images, you might not be aware what they actually are. There will probably be some in there, but hopefully… No, don’t do that. And the convert transparency images. Well, it says what it does. So if the image has a transparent background, that’s very hard for machine learning. So you want to convert that and also random colours as well. So you check that if you’re doing the animation and scraping the images, which we’re not going to do. Okay, so let’s go down to 4.2, which is data annotation. We’re going to be using a blip captioning, which is tagging images with a description.

This is used for realistic imagery. The one below it, which is the way through diffusion, is used more for animation. So we’re just going to be using this blip captioning over here. here. So I’m not going to change any of these settings. I’m going to leave them as default and run that cell. What that will be doing is reading the input images that I put into the Google collab. It’s then going to be describing what it sees in the images. Everything it needs out of the description is what it’s going to train upon. So it’s going to describe my microphone and the fact that I’m wearing a necklace or potentially a brown top. This means it’s not going to train upon those things, which makes it so much easier to create a way more flexible model when I’m generating the images later on. So I’m not stuck in this room with this necklace and a brown top on. So to show you what I mean, I’m just going to show you the files it created. So if you come to your files, Laura, and then expand on train data, you can see it’s generated these caption files here.

So if we just pick any image here, 13, and you can see I’ve got a microphone and a necklace, so it should pick up on those attributes. So I’m going to click the caption that goes along with that image. And yeah, it said a woman sitting in a chair holding a microphone. So it’s actually that. Now I can actually add on to this and add necklace if I didn’t want it to train on me wearing a necklace, but I like my necklace and yeah, it’d be good to have that in the model as well. So you can edit these further if you want to. But for this tutorial, I’m not going to do that. I’m just going to leave it as is. I’m just going to close those images there and close that window. I’m going to ignore the way for your diffusion tagger. As I said, that’s for anime, and I’m going to ignore the custom caption tag. This creates you a text file caption, which again, I’m going to ignore that. Now we’re onto training model. So in 5.1 model config. So if you’ve used StableDiffusion version 2 to train your model, you need to check these two boxes here.

I haven’t. I’ve used Stable Diffusion 1.5, so I’m going to leave those unchecked. Under the project name, give your project name as something that you will remember. So I’m going to just call my imprompt tutorial. And then underneath it, you’ve got pre trained model name all. So I need to change this to my Stable Diffusion trained model. We downloaded all these dependencies in the first cells. This would have made you a pre trained underscore folder. So if you just expand that, and then within there sits your saved Hensers model. So if you go with the three dots, click on it, copy path, and simply just paste that in there. So we have the VAE, so the VAE file, which controls the color in your images. So we also need to grab that and that would have installed during the first sell as well. So that will be in the VAE folder. So repeat that process, copy that path and just simply paste it in there. Now, this is where it’s going to be saving your finalized models. And I say models because it’s going to create multiple versions, but we’ll get to that later. Once you’ve closed this Google notebook, this will all go.

All these files will disappear. Make sure you check output to drive and that will save it to your Google Drive and just run that cell. So you can see here, the output path is content drive, my drive, Laura output. So there’ll be a folder on your Google Drive called Laura, and it will be an output file. We’re getting to the most important settings here. So we want to keep the train repeats to 10. Got the instance token. I’m just going to keep mine at mksks. Now you will see random names sometimes like sks. This is because it’s not a name that Stable Diffusion associates with something, so it’s not going to call it up. So by associating M KSKS with my model, it knows it’s calling up my model, my image. I’m going to keep that as is. If you’re not sure, just keep it as M KSKS style. So we are not training a style. We are training a woman. Or you can put person. I actually prefer to put person. You can put woman. It’s up to your own discretion if you want to do that. Resolution, we’re doing 512 by 512 because we have input images that are 512 by 512.

If you’re doing 7 6 8, put 7 6 8 here, just change it up the slider. Just leaving all these settings here as default, and I’m just going to run that cell. So we come down to 5.3, Lauren Optimisation Config, but you really need to experiment with the settings yourself to see if you can get a better result because obviously you are training different images than I am. But however, I will give you my settings because I have done a lot of tests. Come down to the Convolution DIM, which I’m going to be setting quite low at eight, and the Convolution Alpha, I’m going to be setting at one. Then we come down to network dim, which I’m going to set at 16, and then I’m going to change the network alpha to eight. These settings actually have huge influence on your model. I used to do the settings at one, two, eight by one, two, eight, but I’ve played around and I quite like these settings for my realistic models. What settings might work for me might not work for you guys because of different training sets, different resolutions and all that. But I digress.

Okay, I’m going to leave the optimiser config as adding W 8 bit. So the first learning rate, I’m going to change to five, E 4. So the text encoder learning rate, I’m going to change to 1E4. And then the learning rate scheduler, I’m going to change to CoSine with restarts. The warm up steps, I’m going to do 0.05. And to be honest, I’m quite happy with that. So this is probably going to be a learning rate of about 950 steps. But we’ll see once we hit run. So we’re going to run that cell, and then we’re going to go to 5.4 training config. I’m going to leave low RAM on. I’m going to enable sample prompt. I’m going to leave the sampler as DTM. Noise offset, I’m going to leave at zero. Sometimes I go 0.01. I’m going to be creating 10 Epochs here, which will save a file at every learning stage, which means I can test the files out in my web UI at the end to see if it’s either undercooked or overcooked or just about right. I like to do about 10 because it gives me a nice, diverse range to pull from.

The trained batch sizes. Now, you can go quite low. You can go to one. I’m probably going to go to two and see how it goes from there. So the batch sizes is how many files it’s training together. If I’m training six, it’s going to be a lot quicker than it will be for two. If I went to one, I’d probably completely run out of RAM. So if you do have a RAM issue, try sticking to six or higher. But if you don’t have any RAM issues whatsoever, you can train on anything as low as one here. The mixed and saved precision, I’m both leaving those at F P 16. My Epoch, I’m going to save every Epox. So that’s 10 Epox I should have at the end. I’m saving the model as a saved tensers model, and I’m leaving this all as default here, so that’s pretty simple. I’m going to run that cell. Now we come to our final cell. You’ll be glad to hear, all you need to do, just run that cell and leave everything as default and let the training begin. This might take probably about 30 to 40 minutes. If I wanted it to be done quicker, I would actually increase the batch size.

Hopefully, all this makes sense. I wanted to describe what I’m doing as I do it, so you have at least a understanding of what’s going on, which hopefully, again, will allow you to make changes to suit your training data. Once the training is complete, you do not have to do any of the remaining cells in the notebook. Your files will now be saved automatically into your Google Drive. So head over to your Google Drive, you will have a Lo ra file in there, an output file, and in there lives your Lo ra files. And remember, I said it would save a file at every training step, and we said 10 in this demonstration, so it’s given us 10 files here. As you’re probably aware, I use automatic 111 on Run Pod, and the link for instructions are all here. So if you don’t want to use automatic 111 locally on your computer and you don’t want to have to set up a Run Pod like I’ve got, the developer of this Koya Lo ra notebook has just come out with a brand spanking new automatic 111 notebook with control net 1 and the brand new control net 2, as well as the ability to use your newly trained Lora files.

You can use the CalicuristroC lab UI, and it’s basically automatic 111 to generate your images. So I just thought I’d throw that in there as an additional option. Now, grab these files, download them and load them into your stable diffusion model LoRA file. Just whack them all in there. Step three, creating our puppet. Now, underneath the Generate button, you’ll see this sun icon here. Give that a click, and then this will open up this panel along here. Select Laura, and you should see your Laura files now in there. Now, if you don’t, you can always click the refresh button. You can test out all these files here by clicking in on each file. That will then load its tag up into the prompt like this. So you just test them all out, just use one at a time. You can also control the weights of these Laura files as well by adjusting this figure. So I’m just going to load in my LoRA file with a prompt as well as a negative prompt and just run it through and see what it looks like. So I’m quite happy with the overall state of my LoRA file.

It does look like me. So I’m just going to create an image of me bored. I’m going to be taking this bored image of myself and dragging and dropping that into the Headshot plugin, which then generates me a 3D model of my face. I can go in and sculpt this further if I want to, but I’m just using this as a puppet or a guide for the AI. It doesn’t have to look great. It just has to look similar to me. Once I’m happy with the face and the body shape, I then add some hair. Again, it’s super easy. I’m just dragging and dropping from a library. Now, once I’ve finished with my character, I’m now going to export it to iC loan. These programmes work together in a pipeline. So iC loan is more of an animation programme. So this is where I’m going to be adding the facial animation as well as the body idle. I use a plug in called Motion Live. I just activate Motion Live and I’ve downloaded the Motion Live app on my phone here. All links are below in the description. It’s super easy to use. All you’re doing is recording your facial movement and that’s being applied to your 3D model in real time.

I’ve just dragged and dropped a lighting situation in here and got some physics on the hair and I’m pretty much ready to render. Come over to the render panel, which is here and I’m going to I have selected PNG sequence. I actually went 7 6 8 by 7 6 8 in the end. Try and be visible by 16 if you can. And we come down here and I’m just doing 300 to 500 frames and I’m going to export those as PNG. So we’re going to jump back into automatic 111and I’m going to go through the settings I’m using. I’m using image to image and I’ve loaded in my Laura here. Remember the buttons over here. And then I’ve just added a simple prompt because I don’t want the prompt to fight the original image. That’s quite important. So you want to keep the prompt as brief as possible. Do not put too many details that are not in the image that you want to create. Then we have the negative prompt, which is as important as this prompt up here. So you can copy my one. I’ll put it down in the description below. So this is our rendered image added in here.

So the sampling method I have set to DPM then the SDE. You can use whatever you want. I’ve had good results with Euler A, I’ve had good results with KM Kuhares. Sampling steps, I’m keeping relatively low for this. Width and height, I’m going for 7.68×768, the same as my input. De noising strength, I’m keeping low. I’m keeping the de noising strength at 11. Actually, you’ll notice my CFG scale is down six as well. Again, we want to pull from this image as much as we can and apply the Laura over the top without the prompt compromising things too much. I’ve already set my seed because I’ve tested this out already. When you do this, just render a minus one seed until you get something you like and then lock it in with the Reuse button. So on Control net, I’ve enabled head and I have the model enabled as well. I haven’t changed anything from the default settings here. I’ve got a secondary model in as well, which I’ve enabled canny and enabled the Cany model. Again, I haven’t changed any of the default settings. So let’s render and see what it creates us. That looks like me on a really good day.

And it’s it’s following the mouth movements as well, which we need for lip syncing. I have seen so much AI generation where the mouth is just a blurry mess. Using my workflow, you can now get precise mouth lip syncing. This is why I made the model on me so you can see that it is working, okay? So we’re now going to batch render these frames, which will give us a frame by frame animation. And to do that, head over to batch and put your input, so where your 3D files are sitting on your drive, and then your output where you want them to render to. And then hit Generate. And congratulations, we have completed our animation. All that is rendering. I want to show you something really cool. And this is why I like this workflow. I literally can come over and switch my checkpoint file and run that again. And look at that. It’s created a semi animation style version with a click of a button. So you can literally change the render type by using your diffusion checkpoint. I think this is really cool. And this is a great starting point from where things are going to go from here.

Now, we’ve got Control net 2, which has just come out, which I’m not using in this video. So that’s going to take things to a whole new another level. So I’ve simply just thrown those frames into After Effects from Automatic 111. If you have Da Vinci Resolve, use the dirt removal and the deflicker times two in there because that will give you really nice smooth results to your animation. I’m going to hit space bar so you can have a look at the animation. I’ve slowed it down so you can see the frames are blending so well into each other. It’s probably not very good animation I did there, but you can see the technique. I switched the checkpoint model over. I didn’t change any of the settings. I’d probably go back in and change some settings to make the animation version smoother. But that shows you how quickly you can flick, essentially, the AI renderer to change. And then that took about two minutes to render those animation frames. I would love to see what you guys make with this workflow. So please tag me in your social media so I can have a look because I love looking through all the creative projects everybody’s doing with AI at the moment.

I put all my AI creative projects and workflows all on my social media as well. On Instagram, I’m @PromptMuse. On Twitter, I’m @PromptMuse. And on Discord, there is a link below. I’m really excited to see what you guys create with this. This tutorial will be written up step by step on the Prompt Muse website as well. And we have a fantastic weekly newsletter that surrounds the latest and greatest in AI tech. So please join that on the Prompt News website. Thank you for watching this video. I really appreciate it if you subscribe to this channel and give it a thumbs up. I do have a buy me a Coffee link down in the description, and that is just to buy me a coffee. I don’t put any content behind any paywalls. I like to make sure that all my content is free and accessible to everybody. And having that allows me to continue to do that. So I thank you very much. This video has been Days in the making because I have had to test out so much stuff and variations to get this to work. So I know you guys appreciate that. Please share this video on your social media and @ me as well when you do it because I like to see that.

That’s amazing and I can say thank you to you as well. So yeah, I think that will do it for today. Bye bye.

<p>The post How I Unleashed the Power of AI to Create Next-Level Animations first appeared on Prompt Muse.</p>

Ancient Times: Automata and Early Mechanical Devices

During ancient times, people had a fascination with creating machines that could perform tasks on their own. These early mechanical devices were often inspired by nature and the movements of animals. One of the most famous examples of these early machines is the Antikythera mechanism. Discovered in 1901 in a sunken ship off the coast of the Greek island of Antikythera, this device is thought to have been built around 200 BCE. It consisted of a complex system of gears and was used to predict the positions of the sun, moon, and planets, as well as lunar and solar eclipses.

The Antikythera mechanism was an incredible feat of engineering for its time and is often considered to be one of the first examples of a complex mechanical device. It was also a testament to the advanced knowledge of astronomy that existed in ancient Greece.

Other examples of ancient automata include the chessboard robot. This device was reportedly built in the 9th century and used a hidden human operator to move the pieces on the chessboard. The operator would sit inside the machine and use levers and pulleys to move the pieces, making it appear as though the machine was moving them on its own.

In addition to these early mechanical devices, there were also other types of automata that were created during ancient times. These included statues that could move and speak, as well as water clocks and other timekeeping devices.

Overall, the development of automata and early mechanical devices during ancient times was an important milestone in the history of technology. It paved the way for future innovations and helped to lay the foundation for the modern world we live in today.

Late 1700s – Early 1800s: The Industrial Revolution and Early Automata

The Industrial Revolution was a period of significant change that transformed the way goods were produced, and it had a profound impact on society. During this time, there were many advances in mechanical technology, which led to the development of early automata.

One of the most famous examples of early automata from this time is the Mechanical Turk. The Turk was a chess-playing automaton that was built in 1770 by Wolfgang von Kempelen, an engineer from Austria. The Turk was a life-size figure of a man sitting at a table, and it appeared to be capable of playing chess on its own, defeating many notable opponents throughout Europe and America.

However, the reality was that the Turk was not capable of playing chess on its own. Instead, it was operated by a human chess player who was hidden inside the machine. The player sat on a small platform inside the Turk and used a series of levers and pulleys to control the movements of the chess pieces on the board.

Despite the fact that the Mechanical Turk was not truly automated, it was an impressive feat of engineering for its time and became famous for its ability to defeat skilled chess players. It toured throughout Europe and America for over 80 years, attracting crowds of people who were amazed by its apparent ability to play chess on its own.

In addition to the Mechanical Turk, there were many other examples of early automata that were developed during the Industrial Revolution. These included machines that could perform simple tasks like weaving and spinning, as well as more complex devices like the Jacquard loom, which used punch cards to control the weaving of intricate patterns.

Overall, the Industrial Revolution was a critical period in the development of mechanical technology and automation. It laid the foundation for the modern era of manufacturing and set the stage for future advancements in automation and robotics.

1950s: The Birth of Artificial Intelligence

In 1956, John McCarthy, an American computer and cognitive scientist, coined the term “artificial intelligence” or “AI.” This marked the beginning of a new era in computing, where machines were no longer limited to performing basic arithmetic operations but were instead being developed to simulate human-like reasoning and decision-making.

At the time, computers were still in their infancy and were mainly used for scientific and military purposes. They were large, expensive, and required specialized knowledge to operate. However, McCarthy saw the potential for these machines to be used for more than just number-crunching.

In his proposal for the Dartmouth Conference, which was held in the summer of 1956, McCarthy outlined his vision for a machine that could reason and learn from past experiences. He envisioned a system that could simulate human intelligence by using a combination of logic, rules, and probability to make decisions.

This idea was revolutionary at the time, and it sparked a new wave of research and development in the field of AI. Over the next few decades, researchers made significant strides in developing algorithms and techniques that could simulate human-like intelligence.

One of the early breakthroughs in AI was the development of expert systems in the 1970s. These were programs that could replicate the decision-making abilities of human experts in specific domains such as medicine, finance, and engineering. Expert systems were widely used in industry, but they were limited in their ability to generalize to new situations.

In the 1980s and 1990s, there was a renewed focus on developing machine learning algorithms that could enable machines to learn from data and improve their performance over time. This led to the development of neural networks, which were inspired by the structure of the human brain.

Today, AI is a rapidly evolving field that is being used in a wide range of applications, from speech recognition and natural language processing to image and video analysis and autonomous vehicles. While the goal of creating machines that can match or surpass human intelligence is still far off, advances in AI are driving significant changes in industry, healthcare, and other fields, and the potential for future breakthroughs is immense.

1960s – 1970s: Rule-Based Expert Systems

In the 1960s and 1970s, rule-based expert systems were a significant area of research in the field of artificial intelligence. These systems were designed to solve complex problems by breaking them down into a set of rules that the computer could follow. The idea behind rule-based expert systems was to capture the knowledge and expertise of human experts and encode it into a set of rules that a computer could use to solve similar problems.

One of the earliest examples of a rule-based expert system was MYCIN, developed by Edward Shortliffe in 1976. MYCIN was a medical expert system designed to diagnose bacterial infections based on a set of symptoms and medical history. It was designed to replicate the decision-making process of a human expert, using a set of rules and heuristics to reach a diagnosis.

Another example of a rule-based expert system was DENDRAL, developed by Joshua Lederberg and his colleagues at Stanford University. DENDRAL was designed to help chemists identify the molecular structure of organic compounds based on their mass spectrometry data. It used a set of rules to generate hypotheses about the molecular structure and then used feedback from the user to refine and improve the accuracy of its predictions.

Rule-based expert systems were widely used in industry and government during the 1970s and 1980s. They were particularly useful in areas where there was a large amount of specialized knowledge that needed to be applied in a consistent and reliable manner. However, rule-based expert systems had some limitations, particularly when it came to dealing with uncertainty and ambiguity.

Despite their limitations, rule-based expert systems paved the way for further advances in the field of artificial intelligence. They demonstrated that it was possible to encode human expertise into a computer system and use it to solve complex problems. Today, the ideas and techniques behind rule-based expert systems continue to influence the development of more advanced AI systems, including machine learning algorithms and deep neural networks.

1969: The First AI Winter

In 1969, the US government cut funding for artificial intelligence (AI) research, marking the beginning of what is now known as the first AI winter. The term “AI winter” refers to a period of reduced funding and interest in AI research that occurred several times throughout the history of AI.

The first AI winter was caused by a combination of factors, including the lack of significant progress in AI research, the high cost of hardware and software needed for AI research, and the inability of AI researchers to demonstrate practical applications for their work. As a result, the US government, along with other organizations and institutions, began to reduce funding for AI research.

The first AI winter lasted from the late 1960s to the early 1970s and had a significant impact on the development of AI research. Many AI researchers were forced to abandon their work or move on to other areas of research, and funding for AI research remained low for several years.

The AI winter also had a profound impact on the perception of AI among the general public. Many people began to view AI as a pipe dream or a science fiction concept, rather than a realistic field of research with practical applications.

However, the first AI winter eventually came to an end, as new breakthroughs and innovations in AI research led to renewed interest and funding. In the 1980s, the development of expert systems and the rise of machine learning led to a resurgence of interest in AI research, which helped to drive significant progress in the field.

Today, AI is once again a rapidly growing field with significant investment and interest from governments, corporations, and individuals around the world. While the first AI winter was a challenging time for AI researchers and the field as a whole, it ultimately served as a reminder of the importance of perseverance and continued innovation in the pursuit of scientific advancement.

1980s – 1990s: Neural Networks and Machine Learning

In the 1980s and 1990s, researchers began exploring the use of neural networks and machine learning techniques in the field of artificial intelligence. These technologies represented a significant departure from the earlier rule-based expert systems and offered new possibilities for creating intelligent machines that could learn and adapt over time.

Neural networks are computer systems that are modeled after the structure and function of the human brain. They consist of interconnected nodes or “neurons” that can learn and adapt based on new information. Neural networks can be used for a wide range of tasks, from image and speech recognition to natural language processing and decision-making.

Machine learning involves creating algorithms that can learn from data and make predictions or decisions based on that data. These algorithms can be used to classify data, detect patterns, and make predictions. One of the key benefits of machine learning is its ability to improve over time as it receives more data, making it an ideal technique for tasks like image and speech recognition.

The development of neural networks and machine learning techniques in the 1980s and 1990s led to significant advances in AI research. Researchers were able to develop sophisticated algorithms that could learn and adapt to new data, opening up new possibilities for creating intelligent machines.

One of the most significant applications of neural networks and machine learning in the 1990s was in the field of computer vision. Researchers developed algorithms that could analyze and recognize images, opening up new possibilities for applications like facial recognition, object recognition, and autonomous vehicles.

Today, neural networks and machine learning continue to be a major focus of AI research. The development of deep neural networks and other advanced machine learning techniques has led to significant breakthroughs in areas like natural language processing, speech recognition, and computer vision. As these technologies continue to evolve, we can expect to see even more significant transformations in the field of artificial intelligence.

1997: Deep Blue Defeats Kasparov

The chess match between Deep Blue and Garry Kasparov in 1997 was a major turning point in the field of AI. Deep Blue was a specially designed computer system created by IBM, designed specifically to play chess at a professional level. The match was held in New York City and attracted a lot of media attention.

The match was played over six games, with Kasparov winning the first game, but then losing the second and third games. The fourth game ended in a draw, and Kasparov won the fifth game, leaving the match tied at 2.5 games each. In the final game, Deep Blue emerged victorious, defeating Kasparov and winning the match by a score of 3.5 to 2.5.

The victory of Deep Blue over Kasparov was a significant achievement in the field of AI, as it demonstrated that machines could be developed to compete at a high level in complex games like chess. It also showed that machines were capable of analyzing and evaluating vast amounts of data in a short amount of time, far beyond what a human could do.

After the match, there was some controversy over whether or not Deep Blue’s victory was a true test of AI. Some argued that Deep Blue’s victory was due more to its brute computational power than to any real intelligence. Others argued that the machine’s ability to adapt and learn from past games made it a true example of AI.

Regardless of the debate, the match between Deep Blue and Kasparov was a pivotal moment in the history of AI. It showed that machines were capable of performing complex tasks that were once thought to be the sole domain of human intelligence. This breakthrough paved the way for further advances in the field of AI, including the development of machine learning algorithms and deep neural networks, which have led to even more significant breakthroughs in recent years.

2000s: Big Data and Deep Learning

In the 2000s, the advent of the internet and the explosion of data led to a renewed interest in artificial intelligence. Big data analytics became an essential part of AI research, with the ability to analyze vast amounts of data to find patterns and insights. Deep learning, a subset of machine learning, also emerged during this time and became an area of intense research and development.

Big data analytics involves the use of advanced algorithms and tools to analyze and make sense of large and complex data sets. The explosion of data in the 2000s, including social media, digital devices, and other sources, meant that big data analytics became increasingly important for businesses and organizations looking to gain insights and improve decision-making.

Deep learning, a subset of machine learning, involves the use of artificial neural networks with multiple layers. These networks are designed to learn from data and make predictions based on that data. Deep learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, and decision-making.

One of the most significant breakthroughs in deep learning came in 2012 when a deep neural network called AlexNet won the ImageNet Large Scale Visual Recognition Challenge, a competition for computer vision systems. AlexNet’s success demonstrated the potential of deep learning to revolutionize computer vision and image recognition, opening up new possibilities for applications like self-driving cars and facial recognition.

Overall, the 2000s saw significant progress in the development of AI, driven by the explosion of data and the emergence of big data analytics and deep learning. These technologies have had a significant impact on many industries, including healthcare, finance, and manufacturing, and have paved the way for further advances in AI research and development.

2010s – Present: AI Goes Mainstream

The 2010s saw a significant surge in the mainstream adoption of AI applications in various industries. This period marked the beginning of the fourth industrial revolution or Industry 4.0, which involved the convergence of technology, data, and physical systems.

One of the key drivers of this AI revolution was the growth of big data and cloud computing. The rise of the internet and digital technologies led to the collection of vast amounts of data, which could be used to train machine learning algorithms and develop sophisticated AI models. With cloud computing, businesses could access these resources on demand, without the need for significant upfront investment in hardware and software.

This period saw the emergence of virtual assistants like Siri and Alexa, which became ubiquitous in many households around the world. These assistants used natural language processing and machine learning algorithms to understand user queries and provide personalized responses.

The use of AI also expanded into various industries, including healthcare, finance, and manufacturing. In healthcare, AI is being used for early disease detection, personalized treatment recommendations, and drug discovery. In finance, AI is used for fraud detection, trading algorithms, and risk management. In manufacturing, AI is used for predictive maintenance, quality control, and supply chain optimization.

The development of self-driving cars also gained significant attention in this period, with major tech companies like Google, Tesla, and Uber investing heavily in autonomous vehicle technology. Self-driving cars use a combination of machine learning algorithms, computer vision, and sensor technologies to navigate and make decisions on the road.

Overall, the 2010s saw a massive expansion of AI applications in everyday life and across various industries. With continued advances in AI technology, we can expect to see even more significant transformations in the way we live and work in the coming years.

2011: Watson Wins Jeopardy!

In 2011, IBM’s Watson computer made history by winning a Jeopardy! match against two former champions, Ken Jennings and Brad Rutter. The match was broadcast on national television and attracted a lot of attention from the media and the public.

Watson was a highly advanced computer system designed by IBM to understand and respond to natural language clues. It was named after Thomas J. Watson, the founder of IBM. The system was built using a combination of advanced algorithms, machine learning, and natural language processing techniques.

The Jeopardy! match was a significant breakthrough in the field of natural language processing. Jeopardy! is a game show that involves answering questions in the form of answers, and the questions can be quite complex and require a deep understanding of language and culture. Watson’s ability to understand and respond to these questions in real-time was a major achievement for the field of natural language processing.

Watson’s success in the Jeopardy! match was due to its ability to analyze vast amounts of data and make connections between seemingly unrelated pieces of information. It used a combination of statistical analysis and natural language processing to understand the questions and generate responses.

The victory of Watson over human champions was a significant moment in the history of AI. It demonstrated that machines were capable of understanding and responding to natural language, a task that was once thought to be the exclusive domain of human intelligence. It also showed that machine learning algorithms and natural language processing techniques were becoming increasingly sophisticated and capable of performing complex tasks.

2016: AlphaGo Defeats Lee Sedol

In 2016, Google’s AlphaGo computer made history by defeating world champion Lee Sedol in a five-game match of the ancient Chinese game of Go. Go is considered one of the most complex games in the world, with more possible moves than there are atoms in the universe. AlphaGo’s victory was a significant achievement for the field of artificial intelligence and demonstrated the potential of deep learning and AI to solve complex problems.

AlphaGo was developed by DeepMind, a British AI research company acquired by Google in 2015. The system used a combination of deep neural networks and reinforcement learning to learn the game of Go and improve its gameplay over time. Reinforcement learning involves training a computer system by rewarding it for positive behavior and punishing it for negative behavior, allowing the system to learn from its mistakes and improve its performance.

The match between AlphaGo and Lee Sedol attracted a lot of attention from the media and the public, as it pitted human intelligence against artificial intelligence in a highly competitive and complex game. The victory of AlphaGo over Lee Sedol was a significant milestone in the development of AI, demonstrating the potential of AI to perform complex tasks that were once thought to be the exclusive domain of human intelligence.

AlphaGo’s success in the game of Go had significant implications for the future of AI research and development. It showed that deep learning and reinforcement learning techniques could be used to solve complex problems and learn new tasks, paving the way for further advances in AI technology. The victory of AlphaGo also sparked renewed interest and investment in AI research, leading to significant progress in areas like natural language processing, computer vision, and robotics.

Overall, the victory of AlphaGo over Lee Sedol was a significant moment in the history of artificial intelligence. It demonstrated the potential of deep learning and AI to solve complex problems and perform tasks that were once thought to be the exclusive domain of human intelligence. As AI technology continues to evolve, we can expect to see even more significant transformations in the way we live and work in the coming years.

2021: GPT-3 and Advanced Language Models

In 2021, OpenAI released GPT-3, a state-of-the-art natural language processing model that has been hailed as a breakthrough in AI research. GPT-3 stands for “Generative Pre-trained Transformer 3,” and it is the third iteration of a series of language models developed by OpenAI.

GPT-3 is a massive deep learning model that was trained on a vast amount of data from the internet, including books, articles, and websites. It has over 175 billion parameters, making it one of the largest and most complex language models ever created.

One of the most significant advances in GPT-3 is its ability to generate human-like text. It can write essays, stories, and even computer code with remarkable fluency and accuracy. GPT-3’s language generation capabilities have been used in a wide range of applications, from chatbots and virtual assistants to content creation and language translation.

GPT-3’s language generation capabilities are made possible by its deep learning architecture, which allows it to learn from large amounts of data and generate responses based on that learning. It also has the ability to understand context and generate responses that are appropriate to the situation.

GPT-3’s release has sparked a lot of excitement in the AI community, as it represents a significant step towards creating more advanced AI systems that can understand and interact with humans more effectively. It has the potential to revolutionize the way we interact with machines, making them more human-like and easier to use.

Final Thoughs

As we’ve seen, the history of AI is a long and fascinating one, filled with many breakthroughs and setbacks. From ancient automata to advanced deep learning models, AI has come a long way over the centuries. But where is it headed next? What new breakthroughs and innovations lie ahead?

As AI continues to evolve and develop, it raises many questions and challenges. Will machines eventually surpass human intelligence, and if so, what will that mean for our society? How can we ensure that AI is used ethically and responsibly? And what role will humans play in a world dominated by intelligent machines?

In the words of Stephen Hawking, “The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which.” But by continuing to push the boundaries of AI research and development, and by engaging in thoughtful and ethical discussions about its implications, we can work towards creating a future where AI is a force for good, and where humans and machines can coexist in harmony.

<p>The post A Brief History of Artificial Intelligence: From Its Humble Beginnings to Its Future Possibilities first appeared on Prompt Muse.</p>

The creators of the wildly popular AI chatbot, ChatGPT, have come to its defense in light of recent concerns about the potential dangers it poses to society. In a blog post published on Wednesday, OpenAI acknowledged the “real risks” associated with their technology but emphasized that their artificial intelligence systems undergo “rigorous safety evaluations” to mitigate potential issues.

The AI giant revealed that their latest GPT-4 model underwent six months of safety testing before it was released to the public, showcasing their commitment to responsible AI development.

The Call for Regulation: An Industry-Wide Effort

OpenAI also took the opportunity to call for greater regulation within the AI industry to ensure safety standards are followed by all players. The company recognizes that despite tighter regulations and safety testing, predicting all the ways people may misuse the technology remains a challenge. OpenAI stated in their blog post, “We will be increasingly cautious with the creation and deployment of more capable models, and will continue to enhance safety precautions as our AI systems evolve.”

Experts Demand a Pause: Can AI Development Wait?

The blog post comes a week after over 1,000 experts called for AI development to be paused until the full risks can be properly understood. They urged that if such a pause cannot be enacted quickly, governments should step in and institute a moratorium. OpenAI expressed its willingness to contribute to these discussions and is “actively engaging with governments on the best form such regulation could take.”

Data Privacy Concerns: The European Response

OpenAI also addressed recent accusations surrounding its collection of user data. Italy became the first Western country last week to ban ChatGPT due to privacy concerns, and other European countries, including Germany and Ireland, are reportedly considering similar bans. These nations are currently in conversation with Italy’s data protection agency.

The company stated in their blog post, “While some of our training data includes personal information that is available on the public internet, we want our models to learn about the world, not private individuals.” OpenAI further explained that they work to remove personal information from their training dataset where feasible, fine-tune models to reject requests for personal information of private individuals, and respond to requests from individuals to delete their personal information from their systems.

The Future of ChatGPT: A Story Still Unfolding

OpenAI did not respond to a request for comment from The Independent about the prospect of further bans. However, their proactive response to the concerns surrounding ChatGPT indicates that the company is committed to engaging with the ongoing debate and finding a responsible path forward for AI technology.

As the controversy surrounding ChatGPT continues to unfold, it raises important questions about the role of AI in society, the need for regulation, and the potential impact of these technologies on our everyday lives.

<p>The post The AI Dilemma: OpenAI Defends ChatGPT Amid Controversy and Potential Bans first appeared on Prompt Muse.</p>

What is Fine Tuning?

Fine tuning is the process of taking an existing AI model and training it further on a specific task or dataset. This allows the model to specialize in a particular area and improve its accuracy. Fine tuning is essential for many applications, including chatbots, language models, and image recognition.

Two Parts of Uploading Fine Tuning Data

The first step in fine tuning is getting your data into a specific format that Open AI likes. This is where the JSONL Converter comes in. The JSONL Converter takes a list of prompts and their corresponding completions and converts them into a JSONL file, which can be uploaded to Open AI. The prompts and completions can be anything from simple questions and answers to more complex conversations.

Once you have your JSONL file, you can upload it to Open AI using the Auto AI Fine Tuner. This tool allows you to fine tune your model using the uploaded data. It takes care of all the details, such as setting the number of epochs and batch size, so you don’t have to worry about them.

Using the JSONL Converter

The JSONL Converter is a simple web application that allows you to create a JSONL file from a list of prompts and completions. It’s easy to use and doesn’t require any coding experience. Here’s how to use it:

1. Go to the JSONL Converter page.
2. Click on the “Add Prompt” button to add a prompt and its completion.
3. Repeat step 2 for each prompt and completion you want to include in your dataset.
4. Click on the “Format for Upload” button to generate a JSONL file.
5. Download the JSONL file and save it to your computer.

You can also upload your prompts and completions directly from Google Sheets. Just create a spreadsheet with two columns: one for prompts and one for completions. Then, go to the “Import from Google Sheets” tab on the JSONL Converter website and follow the instructions.

Using the Auto AI Fine Tuner

The Auto AI Fine Tuner is another web application that allows you to fine tune your Open AI model using the data you uploaded with the JSONL Converter. It’s designed to be easy to use and doesn’t require any coding experience. Here’s how to use it:

1. Go to the Auto AI Fine Tuner page.
2. Enter your Open AI API key and click on the “Connect” button.
3. Go to the “Uploaded Files” tab and click on the “Select File” button to upload your JSONL file.
4. Copy the ID number of the uploaded file and go to the “Fine Tuned” tab.
5. Enter a name for your fine-tuned model and the ID number of the uploaded file.
6. Select the AI model you want to fine tune (e.g., Da Vinci).
7. Set the number of epochs and batch size.
8. Click on the “Create Fine Tune” button.
9. Wait for the fine tuning process to complete (usually takes a few hours).
10. Once the fine tuning is complete, go to the Open AI Playground and start using your fine-tuned model.

Conclusion

Alex’s no-code fine tuning method for Open AI is a game-changer for those who have been struggling with the process. By breaking it down into two simple steps and providing easy-to-use web applications, anyone can fine tune an AI model without any coding experience. The JSONL Converter and Auto AI Fine Tuner are both free to use and open source, making them accessible to everyone.

However, it’s important to note that fine tuning requires a good dataset to work effectively. Garbage in, garbage out, as they say. It’s not enough to just throw any data at your model and expect it to magically improve. You need to have a well-curated dataset that reflects the specific task or domain you’re trying to fine tune your model for.

Therefore, Alex plans to create a series of tutorials on how to create a good fine tuning dataset. He’s also working on a Google Sheets automation that will make it easy to create the simple prompts and completions needed for fine tuning. With these tools and resources, anyone can fine tune their AI models and achieve better results.

In conclusion, Alex’s no-code fine tuning method is a much-needed solution for those who have been struggling with the process. It’s easy to use, accessible, and free. However, it’s important to have a good dataset and follow best practices for fine tuning to achieve the best results. We look forward to seeing what other tools and resources Alex will create to make AI more accessible to everyone.

<p>The post Introducing the First True No Code Fine Tuning Method for Open AI first appeared on Prompt Muse.</p>