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Artificial intelligence continues to evolve at an accelerating pace.
From GPT-3 to GPT-4, we saw significant improvements with regard to advanced reasoning, input settings, fine-tuning behavior, and understanding longer contexts.
Learn more about GPT-4 vs. GPT-3.
However, the AI-human interaction remained the same. You need to carefully craft AI prompts and fine-tune their behavior to get your desired results.
Well, what if you simply state your goals to the AI, and it does everything for you? Like how you enter your destination in a Tesla car, and it takes you there without requiring your active involvement.
Yes, we are talking about autonomous AI agents. And guess what? They’re already here!
AutoGPT is the latest application out of GPT-4 that has taken the internet by storm. Developers worldwide are building new applications using AutoGPT across industries. And some are calling AutoGPT an AGI!
In this blog, let’s look past the hype and understand:
- What is AutoGPT
- How AutoGPT works
- How AutoGPT compares to ChatGPT
- How to use AutoGPT
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What is AutoGPT?
AutoGPT is an open-source AI application leveraging OpenAI’s GPT-4 language model to create fully autonomous and customizable AI agents. It was released on 30th March 2023, by Toran Bruce Richards. Toran is a game developer by trade and founded a gaming company called Significant Gravitas.
AutoGPT is unique compared to other AI tools because it operates independently, meaning you no longer have to steer the model to meet your needs. Instead, you write your objectives, and the AI does the rest for you. So AutoGPT is fundamentally changing the interaction between AI and humans, where humans no longer have to play an active role while still maintaining the same or better quality of results as other AI applications like ChatGPT.
Check out 31 ChatGPT alternatives that you should know about.
How Does AutoGPT Work?
AutoGPT works based on an autonomous AI mechanism where the AI system creates different AI agents to meet specific tasks, which include:
- Task Creation Agent: When you enter your goals on AutoGPT, the first AI agent to interact with the task creation agent. Based on your goals, it will create a list of tasks with steps to achieve them and send it to the prioritization agent.
- Task Prioritization Agent: After receiving the list of tasks, the prioritization AI agent ensures the sequence is correct and makes logical sense before sending it to the execution agent.
- Task Execution Agent: Once prioritization is done, the execution agent completes one task after another. This involves tapping into GPT-4, the Internet, and other resources to get results.
The above agents also communicate with each other. So when the execution agent completes all tasks, and the results are unsatisfactory, it can communicate with the task creation agent to create a new list of tasks. This becomes an iterative loop between the three agents until all user-defined goals are completed.
The actions of the AI agents are also shown on the user interface by categorizing them into four groups: thoughts, reasoning, plan, and criticism. Firstly, the AI agent shares thoughts after completing a task. Then comes reasoning, which explains why the AI agent is doing what it’s doing. After that, the system provides a plan to complete the task. Finally, the system also gives criticism to allow the AI agent to rectify its mistakes and overcome any limitations.
By sharing this computational flow, AutoGPT gives insights into how it approaches a particular problem and overcomes them without any user intervention.
How Does AutoGPT Compare with ChatGPT?
While the underlying LLM model is the same, we can draw quite a few distinctions between AutoGPT and ChatGPT. Some of them are given below.
The latest GPT-4 model that ChatGPT uses is trained on the same data as GPT-3.5, which is only till September 2021. So you can’t get real-time insights using ChatGPT as you can’t access websites and online platforms to extract information.
On the other hand, AutoGPT has access to the internet. It cannot only surf the Web but can also validate whether a source is legitimate. Further, AutoGPT can access any platform to perform a task. For example, if you ask the AI to research prospects for selling a product and send an outreach email, it will draft and send emails directly using your Gmail account.
Context window is very important for language models to give accurate answers. But in LLMs like GPT-4, the window has a limit of anywhere from 4000 to 8000 tokens. So, if the requirement is beyond the limit, the model may not follow all the instructions properly or may go off on a tangent and give unreliable outputs.
In contrast, AutoGPT is good at short-term and long-term memory management. By using vector databases, AutoGPT can store the context or previous experiences to allow the model to make better decisions.
AutoGPT is capable of image generating as it uses DALL-E. If you want to enable image generation functionality for your AI agent, you need API access to DALL-E. This feature is currently unavailable in ChatGPT-4 despite being a multi-modal input-wise.
Text to Speech
You can enable text-to-speech on AutoGPT by typing python -m autogpt --speak in the command line. But you have to enter the command every time you interact with AutoGPT. You can also add different voices to the speech by connecting AutoGPT to Eleven Labs, a versatile AI speech software.
Limitations of AutoGPT
There is no doubt that autonomous nature adds a new dimension to AI systems. At the same time, we cannot ignore the limitations and risks of AutoGPT. Some of the key limitations you must know are given below.
Too Expensive to Use
While the functionality is amazing, AutoGPT’s practicality will likely disappoint you. As AutoGPT uses an expensive GPT-4 model, the cost per task completion can be high, even for a small task. This is mainly because AutoGPT can use GPT-4 numerous times at the step of a particular task.
Furthermore, it is not practical because it can’t replicate outputs in a different scenario when the input is the same. For example, if you ask the model to find top running shoes on the road and turf, it won’t create a function to replicate the process for the turf category; instead, it will start from scratch.
Gets Stuck in a Loop Too Often
The most common issue users are facing with AutoGPT is it gets stuck in a loop. If this goes on for more than a couple of minutes, it likely means you have to restart the process. This happens because AutoGPT relies on GPT-4 to properly define and decompose tasks. So the results are inadequate for AutoGPT to take any action.
Data Breaches are Possible
When an AI model operates autonomously with access to your system and the internet, your data may be leaked. As there are no security agents, it is a concern, and you have to be careful when using AutoGPT. You can’t leave the model running without giving proper instructions and safety guidelines.
How to Install AutoGPT?
Unlike other AI tools, AutoGPT has no simple sign-up procedure to access its platform and features. You must download various softwares to meet requirements before you start using AutoGPT. So, to make things easy for you, we have shared a step-by-step process for installing AutoGPT.
Step 1: Download Prerequisite Software
You have three main software requirements to install AutoGPT: Git, Python, and Visual Code Studio. You can use the quick links notion doc of AutoGPT here to download all three software.
Step 2: Create OpenAI Account for API Keys
If you don’t have one already, create an OpenAI account. Once you open an account, head over to the API Keys tab. You will see an option (highlighted below) to create a secret key. Click on it, and copy the secret key.
Step 3: Copy AutoGPT Repository to Hard Drive
You have to do two main things to copy the AutoGPT GitHub repository to the hard drive. First, copy the link of the project from GitHub.
Second, open the command prompt and clone the AutoGPT GitHub repository by pasting the link as shown in the image below.
As you have VCS installed, you can also type code in the command prompt to access AutoGPT from the VCS editor.
Step 4: Install Python Modules
Once you open VCS, you will see many files on your left hand side. One of the files will be requirements.txt. In this file, you will see the required modules for AutoGPT to run.
To install these modules, type pip install -r requirements.txt and press enter. Note: You have to ensure the directory is pointed to the location where you copied the repository.
Step 5: Rename .env.template file
Find the .env.template file on your VCS and remove the “.” and “template.”
Step 6: Enter OpenAI API Keys
The final step is pasting your API keys in the .env file. After you entered the key and saved the file, head over to your command prompt and type python -m autogpt. That’s it. You have now installed AutoGPT and can start using it.
While I think the possibilities of AutoGPT will be exciting to watch, it’s important to set realistic expectations when it comes to emerging technologies. It hasn’t even been a full month since AutoGPT launched, so we still haven’t seen what this new AI application can and can’t do in different use-cases. Further, it comes with many personal privacy and data security issues that need immediate attention.
That said, I believe AutoGPT is changing how we interact with LLM models like GPT-4. We can now see how an AI approaches a problem, learns from mistakes, and delivers good results, allowing us to optimize our prompts better. So AI agents aren’t going anywhere, but they will take time to mature and drive meaningful user adoption.