Unleash the Power of AutoGPT: Create Your Own Video Masterpieces

Table of Contents

1. Introduction
2. What is Artificial General Intelligence?
3. The Rise of Self-Prompting Tools
4. Efforts Towards True AGI
5. AI Agents and Simulated Worlds
6. Building Auto GPT from Scratch
7. Creating Assets for Video Generation
8. Narrating the Video with Cloned Voice
9. Editing the Video with Fluent ffmpeg
10. The Final Result: "Rust Explained in 100 Seconds"
11. Conclusion

Introduction

Artificial General Intelligence (AGI) has been a topic of fascination and speculation for many years. This type of intelligence goes beyond narrow AI systems and aims to create autonomous machines that surpass human capabilities. In recent times, there has been a lot of excitement around self-prompting tools with long-term memory, such as Auto GPT and baby AGI. These tools utilize open AI's GPT models and vector databases to prompt themselves and achieve specific objectives. While they are impressive, they are far from achieving true AGI. In this article, we will explore the current state of AGI development and various approaches taken to push the boundaries of AI capabilities.

What is Artificial General Intelligence?

Before diving into the advancements in AGI, it is essential to understand its definition. Artificial General Intelligence refers to an autonomous system that surpasses human capabilities in a wide range of cognitive tasks. Unlike narrow AI systems, which are designed for specific tasks, AGI aims to possess human-like understanding, reasoning, and adaptability. It can independently learn new tasks, acquire knowledge, and generalize information across various domains. Achieving AGI has been a long-standing goal in the field of artificial intelligence.

The Rise of Self-Prompting Tools

In recent times, there has been significant buzz surrounding self-prompting tools with long-term memory. These tools, such as Auto GPT and baby AGI, utilize open AI's GPT models and vector databases like Pinecone DB to prompt themselves and accomplish specific objectives. The process starts with a single objective provided by a human, like "Build me a million-dollar business." The tool then generates a task list and recursively prompts itself to figure out how to achieve the objective. While these tools are fascinating and show promising results, they are not true AGI.

Efforts Towards True AGI

While self-prompting tools have gained attention, other efforts are underway to push the boundaries of AI capabilities. Companies like Microsoft have developed tools like Hugging GPT and Jarvis, which utilize language models as controllers to interact with other AI models. This approach aims to create more general-purpose AI tools. Additionally, a recent paper by Stanford and Google Research showcased the use of 25 AI agents in a simulated world, producing believable human behavior. Despite these advancements, true AGI remains an elusive goal.

AI Agents and Simulated Worlds

The use of AI agents in simulated worlds has shown promising results in replicating human behavior. In a groundbreaking study, researchers placed 25 AI agents in a simulated world and observed their interactions. The AI agents were able to exhibit believable human behavior, portraying the potential of AI in creating complex virtual environments. This research opens up possibilities for developing intelligent systems that can coexist and interact with humans in a realistic manner.

Building Auto GPT from Scratch

Taking matters into his own hands, the author decided to build his own version of Auto GPT from scratch. Using a programming language called JavaScript, the author developed an Auto GPT system that takes a video idea as input and uses gpt4 to generate a video script. However, the uniqueness lies in the secondary prompt that turns the script into an actual video. To accomplish this, the system requires assets like images, code snippets, voice narration, and video rendering capabilities.

Creating Assets for Video Generation

To generate a visually engaging video, the Auto GPT system utilizes several tools. For code snippets in the script, a headless browser called Puppeteer is used to generate PNG images by scraping a website called rey.so. Additionally, the system identifies any people or logos mentioned in the script and retrieves corresponding images from the internet. The Giphy API is utilized to search for and incorporate animated gifs. These assets are then downloaded and stored for video editing.

Narrating the Video with Cloned Voice

A crucial aspect of video generation is the narration. The author leverages a program that clones the voice of any YouTuber by linking their video. This program utilizes an API called 11 Labs and the author's voice was cloned using recordings. While the quality is decent, improvements can be made. The Auto GPT system makes API calls to 11 Labs to generate the cloned voice, which is saved as a WAV file. This ensures that the video has a captivating and personalized voiceover.

Editing the Video with Fluent ffmpeg

To piece together all the assets and narrations, the Auto GPT system utilizes fluent ffmpeg, a library for video editing. The system loops over each script file, makes an API call to generate the voice narration, and saves it as a WAV file. Finally, the assets, including the code snippets, images, and gifs, are combined using fluent ffmpeg into a single video. This seamless integration of assets and narration creates a visually pleasing and informative video.

The Final Result: "Rust Explained in 100 Seconds"

Using the Auto GPT system, the author demonstrates the capabilities by creating a video titled "Rust Explained in 100 Seconds." Rust is a programming language known for its safety, concurrency, and speed. The video generated by AI took approximately 30 seconds to create and showcased the potential of intelligent video generation. The author acknowledges that without mentioning it was made by AI, viewers would never know the difference. This highlights the remarkable progress made in AI-driven content creation.

Conclusion

The development of Artificial General Intelligence remains an ongoing and challenging pursuit. While self-prompting tools and AI agents in simulated worlds show promise, true AGI is still far from realization. The author's experience in building Auto GPT from scratch demonstrates the power and limitations of current AI capabilities. Generating video content using AI opens up exciting possibilities but also raises ethical concerns. It is crucial to tread carefully and ensure responsible and ethical use of AI technology moving forward.